Marvell paid $3.25 billion for Celestial AI in February, with the company generating no revenue at close. First meaningful revenue is not expected until fiscal 2028. A well-funded competitor could build a similar design in a year. What they cannot buy is four years of product history.

The Fifth Filter called this structural irreplaceability, a premium that has nothing to do with revenue and everything to do with what a competitor cannot replicate on the same timeline. Marvell wasn't buying a chip. It was buying four years nobody else had.

Chasing the next SpaceX is one way to build a deep tech portfolio. It isn’t the only one. There’s a second path: finding companies positioned to become acquisition-ready long before the public markets or later-stage investors value them that way.

Some get bought at a premium. Some grow into the generational company themselves. Either outcome can work. The harder question is when the first outcome becomes predictable enough to underwrite.

M&A already happens in deep tech. The question is how early you can see the conditions that make an acquisition predictable.

If you're investing in, advising, or working with companies in physical systems, this is the framework we're using to think about acquisition timing.

What this essay argues

• There is no universal M&A clock. Acquisition timing depends on what forces the buyer’s hand.

• Semiconductors have a tested qualification clock. Robotics has a plausible mechanism but no tested interval yet.

• Defense follows a different pattern. Supply constraints, program needs, and regulation can set the clock.

• The underwriting risk is borrowing a timeline from one vertical before a transaction proves it applies to another.

Semiconductors: Qualification Moat

In semiconductors, a qualification record, not a working chip, is what a hyperscaler is actually paying for. Photonic integrated circuits shift wavelength under heat, roughly 0.1 nanometers per degree Celsius, and a failed optical engine inside a co-packaged switch can take down the entire board, not just one swappable module. Proving a design holds up under sustained, high-heat compute loads over years, not a lab demo, is what that qualification record actually verifies, and no amount of capital shortens the cycle.

That cycle runs two to three years in current practice, a range confirmed independently of the Celestial AI transaction itself. Industry practitioners tracking hardware development timelines report the same window. That convergence is what gives semiconductors a tested clock. We have a real transaction and a separate industry estimate, and both land in the same range.

Celestial AI’s asset was Photonic Fabric, an optical interconnect platform that transmits data as light rather than electrical signals over copper. The scarce asset wasn't the technology. It was four years of production history proving the design stayed thermally stable. That stability mattered as data center racks cycled between hot and cool operating conditions. That was the failure point that stalled competing designs before they reached a customer. That stability record took four years to accumulate, one qualification cycle at a time.

At PaperJet, we use the same progression when we underwrite companies. Not every company clears all three gates in the same way or in the same order.

Some of our strongest positions came from our own underwriting, research, portfolio-company pattern matching, and subject-matter experts before any outside validator was in the picture.

Celestial AI's own record maps cleanly onto this filter, and it cleared all three. Imec, the Belgian research institute that validates physics at the earliest stages, invested in the seed round rather than just providing services, and cleared the co-investor bar years before Marvell bought the company. Foundry qualification cleared the factory gate. AWS's endorsement of the finished product was the fleet signal, the one that moved Celestial from a validated design toward an acquisition target. Not every company we back will have that first checkpoint. When it exists, it's a real signal, but the factory and fleet signals can still carry the underwriting.

Robotics: The Untested Trigger

In robotics, a lab demo does not make a hardware-software startup acquisition-ready.

Two conditions must be met first. The system has to run across more than one company's hardware, including industrial robot arms from manufacturers like FANUC and ABB, as well as the factory systems layered on top. It also needs a sustained field record, with multi-platform qualification and at least 12 months of active deployment data. That data has to show the system holds up outside a controlled lab, under inconsistent lighting, temperature fluctuations, and objects that differ slightly from anything it trained on.

A competitor entering from outside that dataset cannot replicate it on any acquisition-relevant timeline.

Simulation tools like NVIDIA Isaac have closed much of the motion-planning gap, but a simulated robot never actually meets a factory floor. Deployed hardware accumulates wear, drift, and failure modes that only surface at volume.

The safety rulebook is still catching up to the hardware, too. Industrial robot safety standards were just overhauled in 2025, and even that new version assumes a robot bolted to the floor, not a humanoid walking around one; the standard covering legged robots specifically is still being written. A single unhandled incident during a pilot doesn’t just delay the program. It resets the one thing the buyer is actually paying for: the confidence that the qualification record holds.

No transaction exists yet to test whether this timing holds in robotics the way the Marvell-Celestial AI deal does in semiconductors. One recent deal looks like a counterexample and isn’t. Mobileye’s $900 million purchase of Mentee Robotics in January 2026 was priced between related parties; Mobileye’s own CEO co-founded Mentee. It closed before Mentee had cleared either qualification test.

The other live case worth naming is even murkier. Anthropic and Physical Intelligence held acquisition talks this spring, as reported by The Information, though no deal has emerged and Physical Intelligence’s response stopped short of a firm denial. Even that attempt sits inside a complication unique to this vertical: OpenAI is already an investor in Physical Intelligence, which may give it the stronger claim to any future sale than a rival trying to buy in from outside.

We’re watching three signals.

• Teleoperation data is the first. It’s still an early, unmatched category with no commercial product built around it.

• Actuator integration is the second. Schaeffler has secured preferred supplier positions across multiple humanoid platforms, with named agreements spanning Neura Robotics, Humanoid, and Leju Robotics.

• Tactile sensing is the third. Several suppliers are competing on sensor integration, but none has established a dominant position across OEMs yet.

These are our own read on the market, not a confirmed pattern.

Defense: Deadline Clock

Anduril’s acquisition of Adranos followed a specific defense pattern: a small supplier demonstrates technology readiness with non-dilutive government funding, then a prime buys the finished capability rather than building internally. Adranos demonstrated ALITEC, an aluminum-lithium alloy solid rocket propellant, in a live Army-backed flight test in 2019, proving roughly a 40 percent increase in range over conventional propellant.

The flight test mattered, but it didn't start the acquisition clock. It showed technical progress, not a reason to expect a deal.

What actually set the clock running was the structural shift happening around it. Solid rocket motor supply had narrowed to two dominant primes, Northrop Grumman and Aerojet Rocketdyne. That left Anduril facing the same two suppliers as every competitor, with no independent source of its own.

The deal closed in June 2023, roughly 43 months after the flight test, longer than the 24 to 36-month window that governs semiconductors. That’s not a claim about defense’s average time to exit. One transaction isn’t an average. It’s the one tested comparable we have, and the 43-month figure only makes sense once you understand why: the duopoly risk above, not the propellant test.

The Army’s 2025 decision to have Anduril build a new 4.75-inch rocket motor is the actual proof the bet worked. It handed Anduril production work on the exact capability it bought Adranos to secure.

The same logic shows up elsewhere, at an earlier stage. Anduril’s acquisition of Blue Force Technologies priced IP, facility footprint, and pipeline position months before Fury had cleared wind-tunnel testing, let alone a completed qualification record. Anduril was buying position before proof, the same sequence Adranos followed, just earlier in the company's life.

The clearest forcing event here is regulatory. Starting January 1, 2027, US defense contracts will require neodymium and samarium-cobalt magnets used in motors and actuators to have a documented mine-to-magnet supply chain with no material sourced through China. Almost no supplier can prove that chain today.

Expect the same pattern that played out with Adranos. Primes have an incentive to acquire small, compliant suppliers ahead of the deadline, not because those suppliers have years of field data, but because after January 2027, being compliant is the only way to keep selling at all.

Three different forcing events produce the same acquisition behavior: a shrinking supplier base, a prime’s roadmap need, or a regulatory cliff. Defense doesn’t run on the qualification clock semiconductors run on. It runs on whichever deadline arrives first.

Why the Buyer Class Tells You Which Clock, If Any, Applies

For industrial automation incumbents including Fanuc, ABB, Schaeffler, Bosch, and Siemens, the trigger isn’t robot capability. It’s where an acquisition ranks against every other use of the same capital.

Hyundai’s Georgia Metaplant is the clearest example. From the outside, it looked like a bet on American manufacturing. Inside the boardroom, it was a reset of the depreciation clock, an IRA eligibility fix, and an escape from a Korean labor environment that made automation uneconomic rather than merely difficult.

Hyundai didn’t automate Korea harder. They removed the constraints that made automation uneconomic in the first place.

That's the comparison every robotics startup is competing against: every other way the buyer could deploy capital to reset the system. The acquisition only makes sense once it beats that alternative at a price justified by four to seven years of internal replication.

Humanoid OEMs, Figure AI, Apptronik, Boston Dynamics, Agility Robotics: the trigger is becoming a critical, multi-platform supplier before a competitor locks up the same position.

Schaeffler secured preferred supplier status across Neura Robotics, Humanoid, and Leju Robotics within 18 months, engaging roughly 45 developers globally. That’s the kind of position an OEM can’t risk letting a rival own first.

Defense primes, with Anduril as the reference acquirer: The trigger can be a program deadline, a supply constraint, or a regulatory cliff. The forcing logic is the same, but the clock is set by the defense environment rather than a qualification record.

Semiconductor contract manufacturers, Amkor, Samsung, Lumentum, Coherent: the trigger is customer qualification and production readiness. It’s the same three-gate progression Celestial AI already proved, but here the buyer is acquiring the capability to compress its own timeline.

The Regulatory Archetype Worth Watching Now

Regulatory deadlines create different acquisition pressures depending on who has to respond. We see three patterns.

• Eligibility Cliff - A regulation that names specific companies as banned, with compliance required or the contract lost outright.

• Margin Cost - A tariff adds to an ongoing cost. Comply or keep paying a penalty on every unit, indefinitely.

• Credential Value - A qualification list. Comply or spend years earning access another company already has.

All three are active in defense procurement, and they’re not limited to one component category. Magnets, as covered above, fall in the eligibility category. Batteries below sit in the same category through a different regulation entirely.

Compliant Battery Supply

FY24 NDAA Section 154 cuts off Department of Defense purchasing power for six named Chinese battery makers: CATL, BYD, Envision Energy, EVE Energy, Gotion, and Hithium, starting October 1, 2027. After that date, a battery is prohibited if any of those entities assembled or manufactured the final product, or supplied a majority of its components.

Every defense contractor running a battery-dependent program needs to know where its cells actually came from well before the deadline hits. A prime with battery-dependent programs has a clean incentive to acquire a compliant supplier now, ahead of the date, rather than scramble for one once the window closes. Amprius, a battery supplier to AeroVironment, is the live example. It's working toward NDAA compliance, but hasn't cleared that bar yet.

Tariffs on Imported Drones and Key Hardware Components

Section 232 UAS tariffs, signed August 13, 2026, hit imported drones and components with layered timelines and rates, not a single number.

A prime facing a 25 to 100 percent cost overhang on imported components has an incentive to acquire a domestic or already-compliant supplier, not for eligibility, but to protect margin on every unit shipped.

Blue UAS Cleared List

A separate mechanism worth tracking: the Blue UAS Cleared List, the Department of Defense’s own roster of drone platforms and components already cleared for military and federal purchase without further review. Getting listed independently takes time, including cybersecurity audits and supply-chain verification. Most companies don't clear that process quickly. The current Blue UAS Cleared List includes dozens of platforms from AeroVironment, Anduril, Shield AI, Skydio, Teal, and others.

That gives larger companies a reason to acquire a company already on the list rather than wait out the process themselves, buying instant market access instead of building eligibility from scratch. The logic mirrors the semiconductor qualification pattern from earlier in this essay: buying a credential that took years to earn is often cheaper and faster than earning it directly.

Closing Note

The bigger risk is pricing a position against the wrong category, then expecting liquidity on a timeline the position was never actually running on. A defense deal priced against the semiconductor window, for example, would imply a 24 to 36-month exit. The tested Adranos transaction says otherwise.

So which of these four categories does the timeline actually fall into?

• A tested interval, backed by real outcome data.

• A specified but unconfirmed mechanism.

• A borrowed number nobody’s checked.

• A tested number that overturns the assumption everyone defaults to.

The semiconductor vertical is the only one that sits in the first category right now. Robotics sits in the second: a real mechanism, but no transaction old enough to test it. Defense sits in the fourth. Adranos ran for 43 months, longer than the 24- to 36-month semiconductor window. That tested result rules out borrowing that window for defense. The third category isn't occupied by any vertical in this essay yet. It's the mistake waiting to happen: borrowing the semiconductor window for robotics before a transaction confirms that it holds there too.

We don't have enough outcome data to call the framework proven yet. What we have is an opportunity most later-stage capital can't take. A fund waiting for category-one certainty across every vertical is waiting for the comp to already exist, and by the time it does, the price has moved.

That’s the opportunity at pre-seed: pricing a mechanism before the transaction exists to test it, or a number before anyone has confirmed whether it holds.

The first mover in a Physical AI deployment layer does not earn a revenue premium. It earns a structural position that competitors cannot replicate from behind.

Most pre-seed investors are not screening for that. They are running four filters that get you to the right pond. This essay is about the fifth one. The filter that tells you which company becomes the foundation everyone else builds on.

The Setup

Much of pre-seed investment in Physical AI does not run a systematic framework. The most common entry points are founder pedigree (second-time founder with a prior exit, Palantir or SpaceX alumni, defense program office background), network proximity (lab spinout with a known Principal Investigator, warm introduction from a prior portfolio company), or narrow vertical conviction (manufacturing only, defense only) without a cross-vertical deployment thesis. Early commercial traction is the preferred signal because it removes the need to make a judgment call before consensus exists.

Logos impress venture. Roadmaps decide industry.

None of these is wrong as a pattern recognition tool. They are efficient shortcuts for reducing selection risk. The problem is that in Physical AI specifically, the companies that become structural winners rarely look like consensus picks at the moment of entry.

PaperJet screens for something different:

1. Tech Stack Layer: deployment infrastructure, not intelligence

2. Timing: pre-seed, before generalist capital reaches consensus

3. Geopolitical Resilience: no critical dependency on single-source markets or supply chains

4. Demand Wedge: defense or government anchor demand before the commercial market matures

Product quality explains who wins a market. The Fifth Filter asks who sets the terms everyone else has to win by.

These four filters are PaperJet’s differentiated lens, not the field standard.

Even with these four filters applied, every company that passes is competing within the same deployment layer. Product quality itself is replicable. Given enough capital, competitors usually catch up within two to four years.

The four filters identify the right pond. The fifth filter identifies which company sets the standard inside it.

Four Verticals, One Pattern

Before constructing the framework, four concrete examples show how the standard layer forms in practice.

Semiconductor: the qualification record is the acquisition asset.

In February 2026, Marvell acquired Celestial AI for $3.25 billion. The unusual part: Celestial AI had no revenue at closing. Marvell itself guided that the first meaningful revenue would not arrive until the second half of fiscal 2028, more than two years after the deal closed, as seen in the transaction summary below:

So what did Marvell pay $3.25 billion for?

Celestial AI had solved a problem every major cloud computing company will eventually face. Data center equipment cycles between hot and cool as computing workloads fluctuate. Most optical components drift out of tolerance as equipment cycles between hot and cool operating conditions. Celestial AI built a design that stayed stable. More importantly, they had years of production history proving it worked at scale under real operating conditions.

That production record was the asset, not the product or the revenue. Any team with enough money can build a competing design. No team can go back in time and accumulate years of production history proving it works.

The standard layer premium is not a thesis. It is a closed transaction - $ 3.25 billion for a pre-revenue company, priced in this market, in the last twelve months.

The question at pre-seed is whether you can identify the company making that kind of manufacturing decision before Marvell writes the check.

Robotics: the supply position before the commercial window closes

In Q1 2026, Schaeffler CEO Klaus Rosenfeld confirmed the company is engaged with approximately 45 humanoid robotics developers globally. The goal is to become the world’s leading component supplier for humanoid robots, covering strain wave gears, linear and rotary actuators, sensors, and bearings. Actuators are the most expensive subsystem in a humanoid robot, around 56 percent of the total component cost. Schaeffler is targeting an order book in the hundreds of millions of euros by 2030.

The deals announced so far show the breadth of that position. Humanoid, formerly SKL Robotics, signed an agreement to deploy thousands of wheeled units inside Schaeffler’s own factories.

That makes Schaeffler both the preferred component supplier and the anchor customer operating the robots. Schaeffler’s own investor materials list its ecosystem partners across the humanoid landscape, including Boston Dynamics, Figure AI, Agility Robotics, Unitree, and Fourier.

Schaeffler’s automotive-grade manufacturing infrastructure is the kind of qualification barrier a new entrant cannot replicate with capital alone. Precision tolerances, high-volume tooling, certified quality systems. Those take decades, not funding rounds.

Moving from zero humanoid exposure to preferred supplier across 45 active developers happened in under two years, before any OEM had committed to a production platform.

Once those production platform commitments are signed, Schaeffler is already inside the design process. Any new supplier trying to displace that position would require the robot maker to restart the qualification process, a rebuild that takes four to seven years. As of Q2 2026, no US company holds an equivalent preferred supplier position across the humanoid-robot ecosystem.

The asset is access to the design process. The contract is confirmation that the window has already closed.

Defense: the command and control environment is the standard, not the drone

In March 2026, the Army awarded Anduril Industries an $87 million task order to deploy its Lattice software as the command and control backbone for the Pentagon’s Joint Interagency Task Force 401 (“JIATF 401”). That task order sits under a ten-year enterprise agreement worth up to $20 billion, allowing federal agencies to purchase Anduril’s commercially available products directly.

The significance is not the dollar figure. It is what the agreement did to the buying process around it. More than one hundred previously separate procurement pathways were consolidated into a single vehicle running through 2036. Any company selling sensors, weapons, or software into the JIATF 401 ecosystem now operates within the environment that Lattice defines. Not because a contract requires it. Because the market is organized around it.

Anduril did not win by building the best drone or the best counter-drone system. It won by becoming the operating environment that every platform after it must account for. The drone is a product. The command and control layer is the standard.

The investor signal worth noting: Lattice adoption was already spreading across the ecosystem before the enterprise agreement was signed. The institutional mandate confirmed what the market had already decided. The sequence to watch is simple: integration before mandate. The contract announcement is confirmation, not an opportunity. By the time it is public, the window has already closed.

Energy: the licensing framework is the market creation event

For the last decade, nuclear energy has been one of the most discussed and least deployed solutions to the power demands facing AI data centers, defense installations, and remote infrastructure. The technology works. The bottleneck has always been the regulatory pathway. Getting a new nuclear design approved in the United States has historically taken ten to fifteen years and cost hundreds of millions of dollars in licensing fees alone. That timeline made commercial microreactor deployment functionally impossible at the scale the market needs.

In May 2026, the Nuclear Regulatory Commission (“NRC”) changed that. It published Part 57, a new licensing framework specifically designed for microreactors, under Federal Register document 2026-08550. The final rule is mandated by November 2026. The first commercial applications are possible in January 2027.

The company that files the first complete application and achieves approval on its design becomes the reference standard for the entire microreactor category. Every competing design that is not deemed essentially the same must run a full, independent NRC review at full cost. Every design that is deemed essentially the same must reference the first company’s approved license, placing that company structurally inside the approval chain for every subsequent entrant.

For investors, the pricing implication is direct. Before a manufacturing license, a microreactor company is priced as a project developer. After one, it is priced as a product platform. Those are different multiples entirely. The filing date determines the structural position. Everything else follows from it.

The question for the rest of 2026 is simple: who files first?

What a standard layer actually looks like

A standard layer company is one whose production record, licensing position, or platform architecture becomes the reference point every subsequent entrant must either match or work around. Three mechanisms explain how that position forms.

The Qualification Anchor

Think of how the FAA certifies aircraft. Once a design achieves certification, every competing design that wants to carry passengers has to clear the same bar or explain why its approach is safer. The first company to certify does not just have a product. It has set the test that every competitor takes.

In Physical AI, replicating a production record of that kind requires four to seven years of manufacturing history. That is not a problem money can solve. You cannot buy back time. For an acquirer, the asset is not the revenue. It is the irreplaceable history. Celestial AI had none of the former and all of the latter.

The Regulatory Floor Anchor

The nuclear microreactor example is the clearest version of this. The regulatory milestone is not a compliance burden for the first mover. It is the barrier that every company behind them has to clear.

Platform Integration

When Apple launched the App Store in July 2008, it did not require apps to exist. But once developers built on iOS, the cost of switching grew with every new app. Not because of a contract. Because the market is organized around the platform. The standard was set before anyone formally declared it.

The same thing happens in Physical AI. Lattice adoption spread across the defense ecosystem before the JIATF 401 enterprise agreement was signed. The mandate confirmed what the market had already decided. The advantage does not require a contractual lock. It requires the market to organize around you before alternatives are formalized.

One distinction matters. A supply relationship available to every participant in a category is shared infrastructure, not a standard layer position. BWX Technologies, Inc. (“BWXT”) supplies fuel and reactor components across four simultaneous microreactor programs. Access to BWXT is a cost of participation. The Fifth Filter applies only to positions that gate competitors out. If every company in the category has the same access, no one holds the standard layer.

Robotics does not fit cleanly into any single mechanism. Schaeffler’s position combines elements of all three: a production qualification barrier new entrants cannot replicate, a supply position secured before the selection window closed, and a presence across enough platforms that displacing it requires the robot maker to absorb the cost of starting over. That combination is what makes the actuator supply position the most instructive example for founders. The window does not announce itself. It closes when production platform commitments are signed.

China sees this more clearly than US capital does

To understand where the Physical AI landscape is heading over the next decade, it helps to look at how China is funding industrial standards rather than individual products.

China’s Ministry of Industry and Information Technology (“MIIT”) published its 2025 National Key R&D Program pilot industrialization list in January 2026. It is a state-level commitment to move specific research outcomes from laboratory to production scale within two years, with co-investment from regional governments and designated industrial parks. Four items map directly onto the deployment gap thesis.

The magnets that power every robot joint

Every major Western humanoid design depends on a material that China has just made harder to get. That is not an accident.

The motors inside humanoid robot joints run on permanent magnets. The dominant type is NdFeB, short for neodymium iron boron. It is what gives motors the power density required to move a human-scale limb with precision. Without it, the performance standard of every major Western and Japanese humanoid design collapses.

Current NdFeB production requires two additional rare earth elements, dysprosium and terbium, to prevent magnets from losing their magnetic strength when motors heat up under load. In April 2025, China introduced export controls on both elements. Western humanoid manufacturers depend on these elements, and China controls the overwhelming majority of global processing capacity.

MIIT Item 22 is China’s program to build a new NdFeB magnet formulation that achieves the same performance without dysprosium or terbium, with a target production scale by 2027. Within months of the MIIT list’s publication, Japan’s Proterial, the dominant global NdFeB patent licensor formerly known as Hitachi Metals, scheduled its own heavy rare earth-free NdFeB pre-production samples for April 2026.

Two national programs reaching the same milestone in the same period confirm that the technology has crossed from laboratory to production readiness. As of June 2026, no equivalent domestic production program exists in the United States.

That is the first vector. China removes its own dependency on the constrained elements while export controls remain active against Western manufacturers. Western precision actuator designs stay exposed to the supply restriction. Chinese designs will not.

The second vector is architectural. Unitree, the highest-volume Chinese humanoid currently deploying at scale, uses a different motor and gearbox design called Quasi-Direct Drive that reduces rare earth requirements at the system level by eliminating strain wave gearboxes. This was a deliberate design decision to make the platform less dependent on the highest-grade magnets while remaining cost-competitive.

China is not responding to a rare earth constraint. It is using one. The formulation program removes the dependency. The export controls keep it active against everyone else.

Two simultaneous strategies. One at the materials level, one at the architecture level. Western manufacturers face pressure from both directions. Chinese manufacturers are being designed to avoid it. The runway is two years. The playbook is the same in the next section.

The component that makes AI data centers run faster

Every major AI data center upgrade underway right now depends on a technology called co-packaged optics. The basic idea: instead of sending data between chips through copper wires, which generate heat and slow down at scale, co-packaged optics sends data as pulses of light through fiber. The result is dramatically lower power consumption and higher data throughput across the same rack of equipment.

The component that makes this work is the laser modulator. It takes a steady beam of laser light and switches it on and off billions of times per second, turning computer data into light pulses that travel through the system. Without a reliable laser modulator, co-packaged optics does not work at the data center scale.

The key material inside that modulator is lithium niobate. China controls more than 90 percent of the global lithium niobate crystal supply.

MIIT Item 20 is China’s program to build the manufacturing capability for the finished optical device that sits directly above that crystal supply, completing vertical integration from raw crystal through finished component. The executing institution is the Fujian Institute of Research on the Structure of Matter, the Chinese Academy of Sciences’ principal crystal research center. The institution that controls the upstream material is now building the downstream device manufacturing.

The pressure from the other direction is arriving at the same time. In April 2026, Tower Semiconductor secured supply agreements guaranteeing approximately $1.3 billion in contracted revenue for 2027, including $290 million in prepayments from its largest customers. That level of contracted backlog means available manufacturing capacity for producing these optical components is already spoken for well in advance. A Western startup that has not secured access to manufacturing is not facing a pricing problem. It is facing an availability problem.

Quantum and fusion sensors for GPS-denied defense applications

GPS can be jammed or simply unavailable in contested environments. When that happens, autonomous systems fall back on measuring physical forces directly: magnetic fields, acceleration, and orientation.

Diamond NV sensing measures magnetic fields at the atomic scale. Unlike earlier quantum sensing approaches, it works at room temperature and normal atmospheric pressure, in the ocean, in orbit, anywhere in between. MIIT Item 46 commits the University of Science and Technology of China to move it from lab to production for defense applications. The target applications are GPS-denied navigation, submarine detection, and autonomous system orientation without satellite dependency.

At the integration layer, MIIT Item 36 commits Hebei Meitai Electronic Technology to mass produce GPS and inertial measurement fusion sensors at automotive-grade precision and Chinese cost curves. An inertial measurement unit tracks acceleration and rotation directly, measuring position and movement without any external signal. When GPS is unavailable, the inertial system takes over. The architecture is the same sensor combination powering Chinese autonomous vehicles, which have accumulated more real-world deployment data than any Western equivalent. That civilian production base now provides the infrastructure for autonomous military systems, including drones, at commercial cost.

China is not funding a GPS-denied navigation product. It is funding the entire navigation stack, from the quantum sensing component through the system integration layer, simultaneously, through two separate institutions, on the same two-year timeline.

As of June 2026, no named US company is commercializing diamond quantum magnetic sensors for defense applications. The entry window is measured in months, not years.

The pattern across all four

Each of these four programs funds a standard-setting institution, not an individual company. The output in each case is a qualification floor that the entire domestic Chinese industry builds toward. Competing designs reference it, build to it, or explain why they diverge from it.

US capital funds individual companies competing on product specifications and hopes that a leading one sets the standard on its own. The MIIT list is not evidence that China has better technology. It is evidence of a fundamentally different theory of how standards get built.

That difference is what the Fifth Filter is designed to surface. US capital waits for a product winner to emerge. The MIIT list funds the floor the winner has to build on.

How to identify the standard layer company before the standard exists

The standard layer is only visible after the window has closed. By the time the acquisition is announced, the application is filed, or the enterprise agreement is signed, the structural position was already established two to four years earlier.

The data is visible years before the capital follows.

The signal is not the standard itself. It is whether the company is in the room where the standard gets written.

Four observable signals indicate a company is building toward standard layer access before the standard exists.

Procurement Anchor

The first signal is a company that is part of a major government or industry program and generates the first qualification record in the category. The company is not applying to the program from outside. It is inside the program, producing the data.

Project Pele, the Department of Defense mobile microreactor demonstration built by BWXT, is the clearest current example. Any company that generates operational safety data from a deployed military microreactor is structurally ahead of every subsequent entrant that has to produce that data from scratch. The qualification record is the asset. The program is where it gets built.

Regulatory Access

The second signal is a company named in the regulatory development process as a reference design. Not filing public comments. Being cited by the regulatory body itself as evidence that the approach works.

The nuclear example shows how this works across both defense and commercial markets. A company with existing government authorization can reference that authorization in a commercial NRC application under Part 57. The regulatory body wrote that provision because prior government authorizations are the strongest available evidence that a design is safe. A company that has generated that evidence through a defense program compresses the commercial review for any related design. That is a structural head start that cannot be replicated through lobbying or policy engagement alone.

Platform Integration Adoption

The third signal is that other companies are designing their products to work with a platform’s architecture before anyone requires them to. The sequence matters: adoption comes before the mandate. By the time the mandate is formalized, the standard layer window has already closed.

The Lattice example in defense shows this directly. Partners were already building in the Lattice environment before the enterprise agreement consolidated the procurement pathways. Investors who waited for the contract announcement were reading yesterday’s news.

Supply Position before Commitment

The fourth signal is a company that has secured preferred supplier access across multiple platforms before any manufacturer has committed to production. The Schaeffler example makes this concrete: the opportunity is not the supply agreement itself. It is the period when the manufacturer is still deciding what goes inside the product. A company already in the design process across multiple platforms before production commitments are signed becomes the default choice. The commitment announcement confirms the window has closed, not that it has opened.

The investment question at the pre-seed stage is not whether the product works. It is whether this company is structurally positioned to become the reference point before the market formally recognizes it as one.

The Cost of Running Only Four Filters

Two types of companies pass the first four filters. Only one passes the fifth.

Companies that pass four filters build real products with real customers. They compete on specifications. Margins compress over time. The acquisition multiples reflect revenue and growth. These are good companies. They are just not the standard.

Companies that pass all five write the production record that their acquirer cannot replicate. They set the regulatory parameters that competitors must design to. They become the operating environment their ecosystem depends on. The acquisition multiple reflects structural irreplaceability, not current revenue.

The Celestial AI transaction makes the difference concrete. $3.25 billion for zero current revenue, with a two-year lag to first meaningful contribution. The standard layer premium is not a thesis. It is a closed transaction, priced in this market, in the last twelve months.

Every fund says it wants to find the company that becomes the platform. Most are funding companies that build for someone else’s platform. The Fifth Filter is the framework that separates those two outcomes before the difference is visible.

The companies that pass the fifth filter are not recognizable as standard-setters at the moment of entry. They look like good products in the right layer. The standard layer status becomes visible later. The entry window is only open earlier.

That is the cost of the framework. It requires conviction before consensus. The moment the consensus arrives, the window has already closed.

Close: Three Clocks, One Tradeoff

Three verticals in this essay are at three different points on the same clock.

Lattice: the window is closed. The $20 billion enterprise agreement is signed. More than one hundred previously separate procurement pathways are now consolidated into a single vehicle running through 2036. Any company building into the JIATF 401 ecosystem is building into Anduril’s operating environment. The standard is set.

Part 57: The clock starts in January 2027. The final rule is mandated by November 2026. The first company to file a complete application and achieve approval sets the reference design for the entire microreactor category. Every competitor after that either designs to those parameters or pays for a full independent review. The filing date determines who holds the structural position, and the clock is now running.

Actuator supply: the window is in progress. Schaeffler has five customer contracts and active engagement across 45 developers. The selection window closes when the remaining mid-tier Western manufacturers commit to production platforms, approximately 12 to 24 months away, based on the platform commitment timelines we track. No US company holds an equivalent position across the humanoid robot ecosystem as of mid-2026.

The Fifth Filter is only actionable before the standard is written. One window is closed. One starts in January. One closes in 12 to 24 months. The only question is which of those clocks you are watching.

Introduction: When Infrastructure Becomes the Constraint

In recent months, the idea of data centers in orbit has resurfaced in public discussion. At first glance, it reads as speculative.

The more important context is less dramatic. AI scaling is no longer constrained primarily by model design. It is increasingly constrained by physical infrastructure.

Rack densities continue to climb. Campus-scale load requests are becoming routine. Interconnection queues are extending. Transformer lead times are stretching. Permitting cycles are slowing deployment.

When the bottleneck shifts from compute procurement to electricity delivery and activation timelines, geography becomes part of system architecture.

That shift does not make orbital data centers inevitable. It makes alternative architectures worth examining.

The question is not whether GPUs can operate in orbit. It is whether constraint alignment makes it economically rational.

This is not a forecast of hyperscale cloud relocating to orbit next year.

It is a conditional infrastructure thesis.

If terrestrial power delivery, permitting friction, and activation timelines continue tightening over the next five to ten years, a narrow segment of latency-tolerant, energy-intensive inference workloads could shift off-planet.

The design space excludes training clusters, robotics, and real-time control systems. It focuses on workloads where activation speed and energy availability outweigh fiber proximity.

The investable exposure is not orbital megastructures. It is the enabling infrastructure that would make such deployment viable.

Launch cadence, communication efficiency, silicon roadmap adaptation, and procurement anchors determine whether that segment expands or remains specialized.

This essay evaluates whether those variables are beginning to align.

Why This Matters for Capital

AI scaling is increasingly constrained by physical infrastructure rather than model design. As rack densities rise and data center loads approach utility-scale demand, interconnection queues, transformer lead times, and permitting cycles have become gating variables. When infrastructure becomes the bottleneck, capital searches for alternatives.

This is neither a near-term migration of hyperscale cloud to orbit nor a distant science-fiction scenario. The relevant horizon is the next five to ten years, when terrestrial grid bottlenecks, rising inference energy demand, and improving launch economics may intersect. Elements of this shift are already visible in adjacent infrastructure investments.

Orbital compute does not need to replace hyperscale to matter. It only needs to capture a subset of energy-dominant, latency-tolerant workloads to justify specialized deployment. A modest reallocation of inference demand toward energy-constrained use cases would constitute a meaningful market.

It will not train frontier models or support millisecond control systems. It does not compete with fiber-bound terrestrial architectures. The viable design space is narrower: latency-tolerant workloads where power density is the binding constraint and bandwidth requirements remain bounded.

The investment exposure is not ownership of monolithic “space data centers.” It sits earlier and lower in the stack:

• Radiation-tolerant compute packaging

• Vacuum-optimized thermal systems

• High-gain communications hardware

• Inter-satellite networking layers

• Orchestration software

• Replacement logistics tied to launch cadence

Many of these layers are investable before orbital compute reaches scale. They also participate in broader space infrastructure expansion regardless of whether orbital compute becomes hyperscale.

A natural objection is that terrestrial innovation may relieve the constraint first. Additional generation capacity, improved cooling, and grid reform will expand supply. Yet energy abundance does not eliminate delivery friction. Transmission buildout, interconnection approvals, and local siting remain political and geographic constraints. Activation timelines may continue to lag compute procurement cycles.

The principal risk is coordination failure. Without sustained launch cadence, communication efficiency, silicon adaptation, and procurement anchors, the wedge remains specialized rather than systemic.

With that frame established, the practical question becomes: what is actually breaking on Earth?

The Ceiling: What Is Actually Breaking on Earth?

For most of the past decade, AI scaling was constrained by silicon. More GPUs, higher-bandwidth memory, advanced packaging, and greater parallelism defined the frontier.

That constraint is shifting. The emerging bottlenecks are physical.

Power Density

AI rack density is rising rapidly.

Hopper-era racks operated in the 40–80 kilowatt range. Blackwell-class systems are approaching 120–140 kilowatts per rack. Roadmaps for Rubin and subsequent architectures project configurations exceeding 200 kilowatts, with some projections approaching 600 kilowatts later this decade.

At these densities, individual campuses request hundreds of megawatts. Utilities are now evaluating load applications comparable to those of small cities.

The constraint is not national generation capacity in aggregate. It is localized delivery. Substations, transformers, and distribution networks were not designed for concentrated demand at this scale.

The U.S. generation interconnection queue now ranges between roughly 2,300 and 2,600 gigawatts. In PJM, the core region of the U.S. data center market, projected load growth through 2040 exceeds 600 terawatt-hours, with a significant share attributed to data center expansion.

Median interconnection timelines approach five years. Transformer supply deficits near 30 percent have extended lead times for large units to two to four years.

AI clusters can be procured in months. Grid upgrades require multi-year planning cycles. That mismatch introduces deployment risk that capital must price.

This is less a shortage of energy than a misalignment between compute procurement cycles and infrastructure activation speed.

Cooling Limits

Higher rack density increases thermal density.

Air cooling approaches practical limits at these power levels. Direct-to-chip liquid cooling and advanced thermal systems are becoming standard in high-performance facilities.

These systems add dependencies. Water supply, treatment capacity, discharge regulation, and environmental review processes become part of the infrastructure stack.

A 100-megawatt facility can consume on the order of two million liters of water per day. At the gigawatt campus scale, usage approaches that of a mid-sized municipality.

In water-constrained regions such as parts of Arizona and Virginia, water rights are politically sensitive and often contested. Thermal management becomes a siting and permitting variable, not just an engineering decision.

Permitting and Political Risk

Large AI data centers are industrial assets measured in hundreds of megawatts.

They compete with residential and commercial customers for grid allocation. They trigger debate over land use, water consumption, noise, and long-term grid stability.

As of early 2026, more than 300 state-level bills referencing data centers have been introduced across over 30 states. Several jurisdictions have imposed temporary moratoriums, revised rate classes, or added grid-impact review requirements. In some regions, large new loads face curtailment during peak stress unless paired with dedicated generation.

These measures do not halt expansion. They increase activation uncertainty and timeline variability.

Ireland’s 2025 decision requiring large data centers to provide 100 percent on-site or proximate generation illustrates how constrained jurisdictions may respond.

Even when approvals are granted, extended review cycles add delay. Delay raises financing costs and reduces deployment predictability.

The issue is not whether AI demand exists. It is whether existing infrastructure can absorb that demand at the pace deployment cycles require.

As the constraint shifts from chip design to electricity delivery and heat removal, location becomes part of system architecture.

That is the ceiling orbital compute attempts to arbitrage.

The Orbital Wedge

If terrestrial infrastructure constraints create pressure, the relevant question is not whether compute can operate in orbit. It is under what conditions that deployment becomes rational.

Orbital compute does not compete on the lowest cost per megawatt. It competes on activation speed under constraint. When the cost of delay in terrestrial deployment exceeds the capital and replacement premium required in orbit, the tradeoff changes.

Most workloads do not meet that threshold. The framework below identifies the narrow subset that might.

Three variables determine plausibility: latency sensitivity, bandwidth required, and power intensity.

The chart maps the first two. Power acts as a gating filter.

Axis A: Latency Sensitivity

Some AI systems operate in feedback loops measured in milliseconds. Autonomous driving, robotics, and industrial control require sub-50 millisecond response times. Interactive applications typically target sub-second responsiveness.

Orbital compute introduces an unavoidable transmission delay. Once routing and processing are included, response times exceed the tolerances required for real-time control.

Millisecond systems belong near the physical processes they govern. Orbit is structurally misaligned with real-time physical AI.

Axis B: Bandwidth Required

Many workloads are data-movement dominant rather than compute-dominant. Continuous high-resolution video, LiDAR pipelines, and sensor-heavy robotics generate large volumes of raw data.

Transmitting terabytes of telemetry between Earth and orbit erodes any advantage.

Orbital bandwidth is constrained by spectrum allocation, ground station density, and link capacity. Workloads requiring sustained high-throughput exchange are poor candidates.

Workloads that process data locally and transmit compressed outputs are more compatible.

Axis C: Power Intensity

Power intensity acts as the first filter.

Certain workloads consume substantial compute energy while moving modest data volumes and tolerating delay. These systems are compute-dominant rather than bandwidth-dominant.

Only when power availability becomes the binding constraint does the latency–bandwidth tradeoff become relevant.

The framework, therefore, isolates power-intensive workloads first. Within that subset, latency and bandwidth determine plausibility.

Defining the Wedge

Orbital deployment becomes plausible only when three conditions align:

• Power demand dominates system cost

• Latency tolerance extends into seconds or minutes

• Data transfer volumes remain bounded relative to compute intensity

Under those conditions, model weights can be uploaded periodically, inference can execute under continuous solar power, and compact outputs can be transmitted back to Earth.

The economic center of gravity shifts from fiber density and interconnect speed to energy availability and replacement cadence.

Workloads Inside the Wedge

Two illustrative categories:

In both cases, raw data is processed near the energy source, and only higher-value outputs are transmitted.

Share

Where It Does Not Work

Orbital compute is poorly suited for:

• Autonomous driving

• Robotics and physical AI

• Industrial closed-loop control

• Real-time edge inference

• Continuous raw data streaming

These systems require low latency and sustained high bandwidth. Their economics favor proximity.

From Cloud Logic to Triage Logic

Terrestrial cloud assumes abundant bandwidth and persistent connectivity. Orbital systems operate with scheduled transmission windows and constrained downlink capacity.

As a result, orbital architectures must decide what matters before transmission. Selection precedes delivery.

This is not traditional cloud logic. It is triage logic.

The segment is narrow by design. Its viability depends on workload discipline, not technological enthusiasm.

Where It Breaks

Orbital compute may relieve certain terrestrial constraints while introducing others. The wedge is narrow for structural reasons.

1. Lifecycle Becomes Infrastructure

In orbit, hardware degradation is structural, not incidental. Radiation exposure, thermal cycling, and material fatigue impose finite lifetimes. Shielding extends survivability but does not remove decay.

Terrestrial facilities rely on on-site maintenance. Orbital systems rely on replacement launches.

Maintenance becomes cadence. Launch availability becomes an operating rhythm rather than a contingency.

The capital model shifts accordingly. Depreciation aligns more closely with silicon cycles. Asset turnover becomes planned. If launch cadence tightens or pricing rises, reliability and margins are directly affected.

2. Congestion and Risk Pricing

Low Earth orbit is increasingly dense. Additional infrastructure increases aggregate mass and cross-sectional exposure to debris.

Collision probability, regulatory scrutiny, and insurance pricing scale with orbital congestion. Insurance is no longer marginal for high-value, compute-heavy missions.

These variables do not prohibit deployment. They introduce recurring cost components that must be embedded in operating assumptions.

3. Politics Does Not Disappear

Relocating infrastructure to orbit does not remove governance. It changes jurisdiction.

Terrestrial facilities face land use and water debates. Orbital systems face spectrum allocation, brightness concerns, and national security implications.

Large constellations have already drawn objections from astronomical communities. Additional reflective infrastructure increases scrutiny.

Jurisdictional friction may also emerge. Nations may resist reliance on foreign-controlled orbital processing layers, particularly where defense or sensitive data is involved.

Infrastructure does not escape politics by leaving Earth. It enters a different regulatory regime.

4. Fragmentation and Coordination Risk

Satellite systems today remain largely proprietary. Interoperability across constellations is limited. There is no shared orbital compute layer.

Without common standards for routing, task orchestration, and ground integration, orbital compute fragments into parallel systems.

Fragmentation erodes capital efficiency. Redundant infrastructure multiplies cost. Coordination failure constrains scale even if deployment is technically feasible.

The Structural Risk

The primary risk is not physical impossibility. It is institutional misalignment.

If terrestrial constraints tighten while coordination remains fragmented, orbital compute remains specialized rather than systemic.

Its scale depends less on physics than on sustained alignment across launch providers, silicon vendors, network operators, and regulators.

Why Inference is Different From Training

AI systems are often discussed as a single category, but training and inference impose different architectural demands.

Training

Training constructs the model. It requires massive datasets, continuous data exchange, tightly synchronized accelerator clusters, high-bandwidth interconnect, and extremely low internal latency.

During training, thousands of GPUs exchange gradients continuously. Communication dominates system behavior. Fiber density, cluster topology, and synchronization speed define the frontier.

This architecture is deeply terrestrial. It depends on dense fiber networks, stable grid infrastructure, and tightly coupled hardware environments.

Orbit is poorly aligned with this workload profile.

Inference

Inference uses the trained model.

Once model weights are produced, they can be deployed across many independent tasks. Communication intensity declines relative to training. Requests are often loosely coupled and can execute asynchronously.

Inference typically involves independent or lightly coupled queries, smaller data exchanges, reduced reliance on synchronized interconnect, and greater tolerance for batch execution.

The dominant constraint shifts. Training scales with communication efficiency. Inference increasingly scales with available compute energy.

Why This Matters For Orbits

An orbital node is unlikely to function as a training cluster. Synchronization demands alone make it inefficient relative to fiber-bound terrestrial systems.

Inference, particularly delay-tolerant and compute-intensive workloads, aligns more closely with orbital constraints. Model weights can be uploaded periodically. Queries can execute without continuous inter-node synchronization. Outputs can be compressed and transmitted during scheduled windows.

This does not make inference universally suitable for orbit. It sharpens the wedge defined earlier: workloads that tolerate delay, are energy-dominant, and require bounded bandwidth.

If inference begins to operate in orbit for triage or background processing, a secondary question follows. What portion remains there as deployment scales?

The answer depends on cost structure, launch cadence, and coordination across the stack.

The Physics of Orbital Compute

Once the wedge is defined economically, the next issue is practical: can orbital infrastructure operate reliably at scale, and what does it cost?

An orbital compute node is capital-intensive at deployment. Shielding mass, radiator surface area, power systems, structural reinforcement, and launch logistics all increase upfront cost relative to terrestrial buildout. This is infrastructure construction, not incremental expansion.

Deployment is not inexpensive. The relevant question is whether constraint relief offsets structural overhead.

Power and Orbit Geometry

In low Earth orbit, solar arrays receive the full solar constant of roughly 1,361 W/m². In dawn–dusk sun-synchronous orbits, exposure can exceed 95% annually depending on altitude and inclination.

The distinction is not only irradiance but capacity factor. Terrestrial solar farms cycle through night and weather variability. Orbital arrays, when positioned appropriately, experience more predictable exposure. These dynamics have revived interest in space-based solar power concepts, though those remain experimental and capital-intensive.

Predictability reduces reliance on deep battery cycling and stabilizes power availability for compute-heavy workloads. It does not eliminate mass. Panels, trusses, and power management systems add to launch weight.

Continuity improves. Structural mass increases.

Cooling In Vacuum

Power density creates thermal constraints.

Terrestrial facilities rely on convection and liquid cooling systems that ultimately dissipate heat into air or water. In orbit, heat rejection occurs through radiation alone.

Radiative cooling requires surface area. As compute density rises, radiator size scales with it. Megawatt-class systems require large deployable radiators, adding mass and structural complexity.

Cooling in orbit is therefore a geometry and launch-economics problem rather than a fluid logistics problem. Radiator mass directly affects cost per deployed megawatt.

Radiation and Hardware Lifecycle

At altitudes between roughly 1,200 and 1,600 kilometers, trapped proton flux increases radiation exposure relative to Earth’s surface.

Commercial silicon degrades under sustained radiation unless shielded or qualified for tolerance. Shielding reduces dose rates but increases launch mass.

Operational lifetimes are therefore likely shorter than terrestrial facilities. Replacement becomes scheduled.

Depreciation aligns more closely with silicon innovation cycles. Nodes must be designed for turnover.

Modular Deployment

A plausible architecture is modular rather than monolithic. Independent nodes can be launched incrementally, networked, and replaced on cadence.

This reduces single-point failure exposure and allows scaling through repetition rather than singular megastructures.

The tradeoff is operational complexity. Replacement cadence becomes part of baseline operations.

Engineering Does Not Remove Risk

Orbital systems must account for debris exposure, radiation degradation, thermal cycling, launch delays, and insurance pricing. These variables shape operating margins and capital structure.

Physics does not prevent deployment. It imposes mass, cost, and lifecycle discipline.

Viability depends on whether the constraint relief provided justifies that discipline.

The Hidden Constraint: Communications

Even if orbital compute relieves power constraints, its value depends on communication. Processing in orbit has limited economic relevance unless outputs integrate efficiently into terrestrial networks.

Power enables computation. Communication determines utility.

Throughput and Economic Viability

The orbital inference model assumes large data volumes are processed locally, and only compact outputs are transmitted to Earth. That architecture works only if the downlink capacity scales with output demand.

Several variables shape this capacity:

• Spectrum allocation limits usable RF bandwidth

• Optical links offer higher throughput but require precise alignment and depend on atmospheric conditions and ground infrastructure

• Ground station density determines transmission windows and sustained rates

Communication becomes a design constraint, not an afterthought. If output transmission cannot scale relative to compute capacity, the energy advantage in orbit becomes irrelevant.

Aperture, Mass, and Launch Cost

High-bandwidth transmission depends on antenna gain. Gain increases with aperture size. Larger apertures require structural support to maintain surface geometry.

In orbit, structural reinforcement translates directly into mass. Mass translates into launch cost.

As compute capacity increases, communication hardware can become a meaningful share of total system mass. Larger apertures improve throughput but increase deployment cost. Smaller apertures reduce mass but constrain output rate.

Economic viability depends on maintaining a favorable mass-to-throughput ratio.

Lightweight Deployable Structures

Recent research into lightweight deployable reflectors, including kirigami-inspired thin-film geometries, seeks to increase aperture efficiency without proportionally increasing structural mass.

The technical detail matters less than the economic implication. Improving aperture-to-mass efficiency reduces cost per transmitted bit.

Communication efficiency influences total delivered inference cost, not just system design.

Network Architecture and Standards

Hardware does not determine scalability alone. Orbital networks remain largely proprietary. Interoperability across constellations is limited.

Without shared standards for routing, task orchestration, and data formatting, compute nodes risk functioning as isolated assets rather than as an integrated layer.

Terrestrial cloud scaled in part because common abstractions reduced coordination friction. Orbital compute will require similar alignment to achieve capital efficiency.

The communications constraint is both physical and architectural. Power availability is insufficient if integration into terrestrial data flows remains fragmented.

The Convergence Thesis

Orbital compute is technically feasible. Its investability depends on scale. Scale depends on institutional coordination.

Three constraint curves and three coordinating forces determine whether the wedge expands.

Constraint Curve 1: Terrestrial Infrastructure Friction

AI load growth is accelerating while infrastructure activation cycles lengthen.

Interconnection queues stretch into multiple years. Transformer procurement remains constrained. In major data center hubs, large-load approvals face delay.

The issue is not generation capacity in aggregate. It is localized activation friction. When procurement cycles shorten, and grid upgrades require multi-year planning, deployment risk rises.

If this friction persists, alternative architectures draw attention.

Constraint Curve 2: Launch Economics

Orbital deployment becomes plausible only if launch cost and cadence evolve together.

Falcon 9 pricing through 2026 (as shown below) implies roughly $74 million for a dedicated launch. Starship’s widely discussed sub-$200/kg economics remain contingent on full reusability and sustained cadence.

At the heavy-lift scale, several variables change:

• Multi-megawatt clusters can fit within a single launch envelope

• Additional shielding and radiator mass become tolerable

• Replacement cadence becomes schedulable

Cost compression does not make orbit inexpensive. It reduces structural infeasibility.

Without predictable cadence and pricing, orbital compute remains experimental.

Constraint Curve 3: Workload Composition

Training remains fiber-bound and communication-intensive. Inference workloads, particularly background and batch processes, continue to expand.

As inference proliferates across agents, embeddings, and summarization pipelines, energy consumption becomes a larger share of total compute cost relative to synchronized interconnect.

This shift is conditional. If AI usage remains dominated by tightly coupled training clusters, orbital compute remains marginal. If delay-tolerant inference expands, the wedge strengthens.

Coordinating Forces

Constraint curves create pressure. Coordination determines whether pressure converts to scale.

Vertical Integration

An actor controlling launch, satellite networking, and internal AI demand can internalize early inefficiencies. Vertical integration allows deployment before broad market validation.

This does not guarantee scale. It creates a proving ground.

Silicon Roadmap and Standards

Orbital constraints are entering semiconductor planning cycles.

At GTC 2026, NVIDIA introduced the Vera Rubin Space-1 module, engineered for size-, weight-, and power-constrained environments, with partnerships including Axiom Space, Starcloud, and Planet.

The engineering challenges are structural. Radiation tolerance, launch survivability, and radiative thermal management require different packaging and system architecture than terrestrial deployments.

This does not signal imminent orbital hyperscale clusters. It indicates that orbital operating conditions are being incorporated into roadmap discussions.

Standardization will determine scalability. Terrestrial AI benefited from shared abstractions that reduced fragmentation and improved capital efficiency. Without comparable coordination across hardware, networking, and orchestration layers, orbital deployments remain isolated.

Vendor attention reflects evaluation, not inevitability.

Procurement Anchors

Large-scale public procurement has historically catalyzed infrastructure markets. Current spending allocations for distributed space systems and resilient architectures suggest sustained public-sector interest in orbital capabilities.

Procurement anchors can absorb early inefficiencies, define interoperability standards, and support replacement cadence economics.

Commercial markets often follow established infrastructure layers.

The Conditional Equation

Orbital compute scales only if:

• Terrestrial infrastructure friction persists

• Heavy-lift cadence sustains cost compression

• AI workload composition broadens toward delay-tolerant inference

• Silicon vendors define radiation-aware standards

• Procurement anchors support early deployment

Remove one variable and the design space narrows.

Scale depends on alignment rather than novelty.

Strategic Implication

The opportunity is not orbital data centers as a headline concept. It is the enabling infrastructure that accrues value if the architecture expands.

Orbital compute does not replace terrestrial hyperscale. It does not train frontier models or support millisecond control loops. Its relevance is limited to workloads where terrestrial activation friction becomes binding.

The investable exposure sits below the application layer.

Where Capital Enters

Relevant categories include:

• Radiation-tolerant compute packaging

• Vacuum-optimized thermal systems

• High-gain, mass-efficient communications hardware

• Inter-satellite networking middleware

• Orchestration layers for distributed nodes

• Replacement logistics integrated with launch cadence

These resemble telecom and semiconductor supply chains more than venture software platforms. They also participate in broader space infrastructure growth, independent of orbital compute reaching hyperscale.

Diversification across end markets reduces dependence on a single outcome.

The Economic Asymmetry

Orbital compute is unlikely to be cheaper per watt than terrestrial facilities. Launch, shielding, and structural mass impose capital intensity.

The asymmetry emerges when delay carries economic cost.

If interconnection timelines extend, permitting uncertainty increases financing risk, or local siting constraints slow deployment, capacity that operates outside terrestrial delivery bottlenecks gains strategic value.

The premium reflects insulation from activation friction, not inherent efficiency.

Insulation matters only when constraints tighten.

The Risk

The primary risk is misalignment rather than engineering failure.

Scale falters if:

• No shared abstraction layer emerges

• Interoperability remains fragmented

• Replacement cadence overwhelms operating economics

• Spectrum allocation or regulatory friction delays integration

Any one constraint narrows the segment. In combination, they confine the market to specialized use cases.

Capital Discipline

Orbital compute should be treated as a conditional infrastructure thesis.

If terrestrial constraints ease, the wedge contracts. If launch cadence stalls, margins compress. If workload composition does not shift toward inference-heavy models, the addressable segment remains limited.

Capital allocation belongs in segments with optionality beyond orbital compute alone.

The thesis is alignment under constraint, not inevitability.

Current Signals

Activity across adjacent layers suggests the architectural components described above are moving beyond theory.

On the compute side, startups such as Sophia Space are developing orbital edge processing systems focused on in-space data analysis and AI acceleration. Starcloud’s plan to deploy AWS Outposts hardware in orbit reflects early experimentation with extending terrestrial compute infrastructure beyond Earth.

The communications layer is further along. Companies such as Kepler Communications are deploying optical relay constellations designed to support higher-throughput inter-satellite links.

Modular spacecraft platforms are also attracting capital. Firms like Apex are productizing satellite buses capable of hosting repeatable payloads, enabling incremental deployment and scheduled replacement consistent with distributed architectures.

These developments do not confirm large-scale orbital compute. They indicate that capital and engineering effort are already flowing into enabling layers — compute, communications, modular platforms, and standards — that would be required if deployment scales.

Closing Frame

Orbital compute is a narrow design space within a broader infrastructure transition. It is capital-intensive, technically constrained, and dependent on coordination across launch, silicon, communications, and procurement.

Its relevance grows only if the terrestrial infrastructure lag persists while launch economics and inference workloads evolve in parallel.

If infrastructure constraints ease, the design space contracts. Coordination failure produces fragmentation. Sustained alignment across the stack turns it into infrastructure.

The central issue is not whether GPUs can operate in orbit. It is whether energy delivery, activation timelines, and workload composition shift enough to justify alternative architectures.

Any durable scale would emerge from alignment under constraint, not novelty alone.

Best,
Chris Kong
Founder & GP, PaperJet Ventures

P.S. Thank you to Jen Liao, Bola Adegbulu, and Jaireh Tecarro for the perspective and feedback that sharpened this piece. Thanks also to Jonathan Stock, Ellen Chang, and Igor Bargatin for industry and research insight.

The Illusion of Robotics Leadership

America builds some of the most recognizable robots in the world.
Optimus.
Figure.
Atlas.

They dominate headlines, conference stages, and keynote reels. If you follow robotics through press coverage, it’s easy to assume the U.S. is far ahead.

Spend a week inside real factories, and that assumption starts to fall apart.

The United States runs just under 300 robots for every 10,000 manufacturing workers. South Korea runs more than 1,000. China sits near 470. Even smaller industrial economies deploy automation more deeply than we do.

Density alone doesn’t explain the gap.

China now consumes roughly a third of the world’s industrial robots each year. The U.S. accounts for less than ten percent. The difference isn’t ambition. It is absorption capacity, shaped by who can actually deploy systems, keep them running, and justify the next install after the first one drifts.

The more revealing signal isn’t the ranking. It’s where the robots actually end up once you stop looking at press releases.

Roughly 40 percent of U.S. industrial robots are deployed inside automotive plants. Another 20 percent are tied up in electronics and heavy machinery. Most of American manufacturing barely touches them.

As Formic’s founder, Saman Farid, puts it:

“Most robots in the U.S. are deployed in automotive and logistics, which make up less than 2 percent of the country’s manufacturing labor. That means 98 percent of U.S. factories still operate with little to no robotics support.”

The technology works. The demos usually do too.

What breaks is everything around deployment. How robots are sold. How factories underwrite them. Who owns performance after the install team leaves.

When something drifts at 3 a.m., learning doesn’t compound. It resets. Downtime stops being a technical issue and starts showing up as a balance-sheet problem.

That’s the gap most U.S. factories live in. Approval happens upstairs. Failure shows up on the floor.

The Overlooked Majority Becomes the Binding Constraint

When people talk about U.S. manufacturing, they usually picture defense primes, automakers, or semiconductor fabs. Those companies dominate headlines, site visits, and policy decks.

They are not the industrial base.

The U.S. has more than 239,000 manufacturing firms. Fewer than 4,200 companies employ over 500 people (source: National Association of Manufacturers). The rest are small and mid-sized shops making capital decisions under very different constraints.

Walk into a typical mid-market factory, and you won’t find clean, repeatable cells. You’ll see one-off lines built around whatever equipment was affordable at the time, often run by a small team that also keeps the place from breaking.

This is where most American manufacturing actually happens. It’s also where robotics stops looking plug-and-play.

These factories don’t have automation teams. They don’t buy custom integrations. And they don’t have room for six-figure experiments that only work when an expert is on site.

As Formic’s founder, Saman Farid, put it:

“American robotics delivery has relied on expensive system integrators incentivized toward customization, cost-plus work, and lock-in. Factories get overengineered options that don’t scale.”

That complexity is manageable for large enterprises. For mid-market factories, it blocks adoption before a robot ever reaches the floor.

Less than 2 percent of U.S. manufacturing labor is meaningfully served by robotics. Not because these factories lack interest, but because the capital bar they operate under is different.

The Diagnosis Is Converging

Policymakers are beginning to describe the same problem that factories have faced for years.

∙ The Congressional Robotics Caucus was relaunched after six years

∙ 157,000 welders are nearing retirement, while most welding remains manual

∙ Most U.S. manufacturers are SMBs with no viable robotics delivery pathway

∙ Only one in ten ML engineers works on physical automation

The Real Bottleneck Is How Capital Gets Underwritten

Robotics doesn’t fail because the technology breaks.

It stalls when what gets sold never lines up with what capital actually underwrites.

Most U.S. robotics is still built for a narrow buyer profile. Big factories. Big budgets. Long timelines. Teams that can absorb months of custom integration.

From the outside, robotics still looks like it’s being sold as a project. Inside manufacturing organizations, it isn’t.

Executives aren’t approving experiments. They’re underwriting production systems that have to survive depreciation cycles, labor constraints, and downtime risk.

That framing changes everything.

In large enterprises like automotive OEMs or mega-warehouses, robotics isn’t judged on demo performance. It’s judged on whether a system can fit into an existing capital plan without forcing a rewrite of everything around it.

Projects don’t survive in that environment. Systems do.

That’s why robotics tends to stall everywhere outside the top tier of buyers.

In the mid-market, automation still shows up as one-off projects. Cost-plus system integrators. Custom installs that never quite repeat. Each deployment restarts the learning curve. Lessons stay locked inside a single plant. The economics don’t get a chance to compound.

Meanwhile, the hardware keeps improving. Autonomy does too.

Deployment still stalls.

Not because the technology fails, but because the delivery model gives factories no reason to standardize, no anchor demand to plan around, and no way for learning to carry from one site to the next.

More grants won’t fix this. Bigger capex incentives won’t either.

What robotics is missing isn’t capital. It’s an operating model that lets factories underwrite automation as infrastructure, instead of relearning the same lesson on every install.

That gap is what keeps robotics from spreading across most of American manufacturing.

Where Capital Actually Decides the Fate of Robotics

When a factory leader looks at automation, they’re not asking whether the robot performs.

They’re ranking it against every other way the company can deploy capital. Build a new plant. Expand a line. Shift production geography. Or wait another year.

Robotics isn’t competing with labor. It’s competing with relocation, write-downs, and the option to wait.

That’s why the Hyundai Georgia “Metaplant” is a useful example.

From the outside, it looks like a bet on American manufacturing. Inside the boardroom, it was a reset of the depreciation clock.

South Korea had become a high-cost, low-flexibility environment. Labor agreements constrained how automation could be deployed. Legacy lines were still on the books. Any meaningful upgrade meant fighting labor and the balance sheet at the same time.

Georgia offered a different starting point. A clean asset base. IRA tax credits. A non-union workforce. And the ability to design for automation before the first bolt was tightened.

Hyundai didn’t automate Korea harder. They removed the constraints that made automation uneconomic.

That’s the comparison every robotics startup is implicitly competing against. Not another robot vendor, but alternative ways capital can reset the system altogether.

Labor Savings Is the Wrong ROI Lens

Most robotics pitches still start with headcount. Reduce X workers. Payback in Y months.

That’s not how factories underwrite capital.

A CFO isn’t modeling labor in isolation. They’re modeling throughput, depreciation schedules, utilization, and how a system competes with every other project for capital.

A robot that technically works but sits idle isn’t a small miss. It breaks the math that the rest of the plan depends on.

One factory owner put it plainly:

“I treat robotics like any other piece of equipment. We eat the loss. But robotics is different. It depends on the software. When it underperforms, it just sits there.”

Idle automation still depreciates. It still occupies floor space. It still drags utilization below plan.

Miss a quarter, and the system stops being debated. It gets classified. A good idea. The wrong year to try again.

Once that label sticks, it rarely gets another chance in the next budget cycle.

The Balance Sheet Gravity Well

From the outside, automation looks like an engineering decision. Inside a factory, it behaves like a capital gravity well.

Every robotics sale competes with forces that rarely show up in pitch decks, but dominate internal capital reviews.

None of these forces show up in demos. All of them determine whether a robot ever earns a second purchase order (“PO”).

Why Robotics Dies After Install

We spoke with factory owners, operators, and engineers, including a Tesla engineer. The pattern wasn’t a technical failure. It was an ownership failure.

Responsibility evaporates once the install team leaves

When we asked who actually feels responsible six months after install, the answer was blunt.

“Vendors ship trial units cheap or free. If it underperforms, they eat the loss.”

When recovery has no owner, systems don’t fail loudly. They drift. Then they sit. Still depreciating.

What appears to be a technical stall is often a capital decision made quietly.

Learning stops once things look stable

“Once everything is working, nobody cares until things break.”

Iteration halts the moment a line runs. ROI is modeled once, then locked. There’s no budget or incentive to improve a system that already looks “good enough.”

Even basic operator tooling breaks down here. Without clean documentation and system context, AI assistants and remote support never take hold. There’s nothing durable for them to attach to.

Tribal knowledge strangles scale

“Learning breaks when documentation is weak. People protect what keeps them employed.”

“Nobody is incentivized to help integrate a robot. It feels like automating your own job.”

The second deployment doesn’t fail technically. It fails socially. Knowledge stays attached to individuals instead of moving across sites.

Repurchase is governed by trust, not specs

“If it doesn’t work on time, nobody will work with you again.”

“If I need a consultant, it’s too complex. It has to feel like buying a laptop.”

Miss a delivery window once, and the next plant goes to someone else.

Factories buy outcomes, not robots

“Companies that scale compete on cost and relationship. They support customers even when it hurts.”

“I’d pay $150k for a robot if I could trust the software like a laptop.”

This is where robotics ROI collapses. Not at install. After.

A Useful Contrast: How Failure Gets Underwritten in Mining

One contrast kept coming up in conversations with mining operators.

When an autonomous system goes down six months after deployment, someone is responsible for it.

As one autonomous mining founder put it:

“Failure underwriting depends on the business model. Operators usually take the losses. They’re money printers with tight quotas and risk-averse stakeholders. But commercial models can shift that liability by baking full-time servicing into pricing.”

That ownership changes everything.

Re-buy decisions don’t hinge on whether a system looked good at install. They hinge on how problems were handled afterward.

“Customers don’t rebuy based on what goes right,” he said. “They rebuy based on how problems are handled when things go wrong.”

Who showed up. How fast was it fixed. Whether responsibility was owned or deflected.

This is why autonomy can scale in the mining industry. Not because the technology is inherently better, but because failure is expected, priced, and dealt with.

“There’s a hidden assumption that all autonomous systems are the same,” he added. “New deployments introduce variance. The question isn’t whether variance exists. It’s whether anyone is set up to absorb it.”

Factories work differently.

Fragmentation doesn’t stop at procurement. It persists after deployment, across vendors, integrators, internal teams, and budget cycles. When something drifts, ownership of recovery stays ambiguous. Capital notices. Then it moves on.

Same machines. Similar pressure. Different outcomes. The difference isn’t capability. It’s how downside gets absorbed when things break.

What Startups Optimize vs What Factories Underwrite

This mismatch quietly kills most robotics deployments.

Founders are rewarded for closing a sale. Factories get stuck with systems that degrade halfway through their asset life.

From the startup’s side, success is a signed PO and a demo that works. From the factory’s side, success is whether the system still delivers throughput three years later, after the integrators leave, the internal champion moves on, and the line shifts to a different product mix.

Sales teams optimize for approval. Capital committees optimize for avoiding assets that create downside.

When a pilot underperforms, the startup loses margin. When a production system underperforms, the factory eats depreciation, downtime, and internal fallout.

Those risks don’t balance. And until robotics companies design for the side that actually carries them, adoption will keep breaking at the same place: after the first install.

The Real Test of Product-Market Fit

The real question isn’t whether a startup can ship a robot. It’s whether the same factory would buy the system again without reopening the entire deal.

That second purchase is where most robotics companies disappear.

Factories don’t measure success by pilots or demos. They measure it by whether a system survives its depreciation cycle.

Does it still deliver throughput after the integration team leaves?

Does it hold up when the product mix changes?

Does it stay in use when something small breaks and no one feels responsible?

This is why robotics can look impressive on press tours and fragile on balance sheets.

A system that requires renegotiation, reintegration, or retraining every time it’s deployed isn’t a product. It’s a project. And projects don’t move smoothly through capital committees.

Until robotics is built for repeat underwriting instead of first-time approval, adoption will keep stalling. Not because the technology fails, but because it can’t clear the capital bar a second time.

The Workforce Constraint Is a Second-Order Effect

When capital stops flowing, skills stop forming.

Robotics talent doesn’t scale through certification programs or hiring pushes. It scales through repetition. Install after install. Failure after failure. Small fixes that turn into muscle memory.

When deployments don’t repeat, neither do skills.

In today’s model, most robotics projects stall after the first install. Technicians never specialize. Operators never build confidence. Documentation stays thin. Each plant becomes dependent on a handful of people who learned the system the hard way.

That’s why workforce shortages show up in nearly every robotics conversation. Not because people can’t be trained. Because there’s nowhere for training to attach once deployments stop compounding.

You can see the same pattern across industrial infrastructure. After major funding arrived, transformer lead times stretched from roughly 20 months to as long as five years, as Heather Carroll, Chief Revenue Officer at Path Robotics, observed. Capital was available. Deployable expertise wasn’t yet formed.

Roughly 157,000 skilled tradespeople are nearing retirement, while much of the critical industrial work remains manual. That knowledge isn’t disappearing because no one wants the jobs. It’s disappearing because systems don’t repeat long enough for skills to transfer.

Robotics skills form inside live deployments. During installs. During failures. During rework. Classroom training can’t substitute for that. Certifications don’t create it either.

Until robotics clears the capital bar for repeat deployment, workforce development can’t compound. Skills form inside capital-backed systems, not ahead of them.

This is why the operating model matters more than the talent pipeline. Fix delivery, and the workforce follows. Keep deployments bespoke, and every factory stays a one-off.

Where the Next Robotics Wave Actually Comes From

The next wave of robotics won’t come from humanoids or headline demos. It will come from places where capacity expansion is already underwritten.

These environments look different on the surface. What they share is pressure.

Uptime matters. Utilization is high. Downtime is visible.

Robotics scales when it behaves like infrastructure, not an experiment.

Automotive isn’t doubling down. Mega-warehouses are nearing saturation.

Growth is emerging in environments where uptime, throughput, and yield matter more than novelty. That includes:

• Mid-market factories in metal fabrication, machining, plastics, and food processing, where labor gaps are structural

• Semiconductor fabs, where downtime is existential

• Shipyards and heavy industry, where assets run for decades, and labor is scarce

In December 2025, the U.S. Navy committed $448 million to modernize shipyards using robotics. Not experimental autonomy. Task-level systems. Robotic welding. Automated panel handling. Inspection.

The mandate wasn’t innovation. It was throughput at scale.

Across these environments, the mechanics repeat. Autonomy stays constrained. Utilization stays high. The economic case doesn’t rely on hope.

Take welding. A human welder might complete a few dozen meters per shift. A robotic cell can run continuously, supervised by a small team, producing several times that output. The gains only appear when parts are designed for automation. When they do, productivity compounds.

Welding. Material handling. Inspection. Surface prep. Different industries. Same economics.

This is how robotics actually scales. Quietly.

As we argued in the manufacturing essay, incumbents are often trapped by depreciation cycles. Retrofitting old plant strands capital. Real value appears when products and production systems are designed together, from the ground up.

That’s why new robotics demand clusters around greenfield capacity, constrained facilities, and long-lived assets. Not consumer demos.

You can see the pattern clearly:

The next robotics cycle won’t be led by the most impressive machines. It will be led by systems that clear capital underwriting, survive depreciation cycles, and get bought again.

Why This Moment Is Different

Robotics didn’t stall because the technology was missing. It stalled because the pressure was optional. In most factories, waiting still worked.

That option is disappearing.

Labor scarcity has replaced labor arbitrage. Absenteeism, safety, burnout, and reliability now surface in board meetings, not HR reports. In many operations, work simply doesn’t happen without automation holding the line.

At the same time, robots finally inherit a real software control layer. Telemetry persists. Updates ship remotely. Failures can be captured, replayed, and corrected instead of rediscovered months later at the next site. Learning no longer has to reset with each install.

Demand is shifting as well. Industrial buyers and infrastructure operators are pulling automation into production. Not for demos. For throughput, uptime, and readiness.

Policy pressure is catching up. In 2025, the bipartisan Congressional Robotics Caucus was relaunched around a blunt diagnosis: reshoring won’t work without robotics and workforce alignment.

None of this excitement is new. What’s new is the cost of waiting.

Capital has fewer places to hide now. Waiting shows up as lost output. Patching strands assets. Pilots give competitors time to scale first.

Inside capital committees, the question has changed. Not whether automation is ready, but what breaks if systems aren’t redesigned around it.

Where PaperJet Plays

We’re not chasing robotics. We pay attention to where capital actually sticks.

The question for us isn’t whether a robot looks impressive. It’s whether a system survives its first real factory, then shows up somewhere else without being rebuilt from scratch or renegotiated from zero.

The teams we spend time with don’t start with autonomy slides. They start with what breaks on day three of an install. Uneven operators. Messy sites. Drift at the edges. They assume fragmentation because that’s how most factories actually run.

They build modular systems because nothing else repeats. Not because it’s elegant, but because it’s the only way learning carries forward.

Deployment isn’t treated as a handoff. It’s part of the product. If learning resets after install, the system hasn’t really shipped.

Just as importantly, these teams think early about who underwrites the first few cycles. Not as a logo, but as an operating environment. A place where failures are tolerated long enough to be fixed, documented, and reused instead of buried.

Those choices don’t show up in a demo. They show up later, when a second factory decides whether to buy the system without reopening the entire deal.

That’s usually where robotics companies either start to compound or quietly stall.

Quiet Compounding Beats Loud Demos

Demos are useful. They just don’t tell you who’s still around after five installs.

Robotics doesn’t arrive all at once. It shows up in small moments. A cell that finally holds calibration. A night shift that runs without someone hovering nearby. A factory manager who stops bracing when a fault pops up.

None of that looks impressive from the outside. Inside the plant, it changes how risk gets priced.

Each of those moments lowers friction on the balance sheet. Not because someone declared success, but because the system stopped creating surprises.

By the time anyone outside the factory notices, the decision has already been made. The system wasn’t exciting. It was reliable enough to get another budget cycle.

The winners won’t be the loudest machines on stage. They’ll look like the teams that kept showing up to dirty floors and boring problems long enough for the economics to start repeating.

The Question That Matters

If this essay resonated, it’s usually because you’ve seen some version of this failure up close.

On a board. In a plant. Inside an investment committee.

The question isn’t whether robotics works.

It’s how systems get underwritten, so learning compounds instead of resetting.

That’s the conversation worth having.

Thanks for reading.

Best,
Chris Kong
Founder & GP, PaperJet Ventures

P.S. A sincere thank you to Jen Liao, Lawrence Wong, Bola Adegbulu, Ellen Chang, Johann Dias, Jisoo Chung, and Jaireh Tecarro for the thoughtful feedback and perspective shifts that helped sharpen this piece.

That framing changes how factories get built. AI requirements now show up in plant layouts, supplier contracts, and provincial funding targets before a single machine is ordered.

A few years ago, the debate was whether AI belonged on the factory floor. Today, the real problem is that factories struggle without it. Across multiple provinces, “AI+” is not a software push. It shows up as specific mandates to replace legacy machines, standardize data interfaces, and align suppliers around shared production targets.

On paper, it is one slogan. On the ground, it turns into very different execution paths.

So here’s the real question for the U.S.:

If China is building robotics as infrastructure, why does the U.S. still treat it like a pilot?

This isn’t a standoff. It’s a race between two manufacturing systems built on different assumptions.

A recent Stanford US–China dialogue framed it as strategic competition without escalation.

China designs programs around deployment from the start. In the U.S., most funding still stops at pilots that never become production systems.

Even in Washington, this framing is starting to surface. After a six-year pause, the Congressional Robotics Caucus was recently relaunched around a blunt premise: reshoring without robotics and workforce alignment is a dead end.

It’s not a solution. But it’s a signal that the problem is no longer invisible.

People inside factories have been living with this disconnect for years. The policy world is only now catching up.

We’ve spent years debating reshoring and industrial policy. Agencies like the Department of War (“DoW”) and Department of Energy (“DoE”) are trying to lead the charge.

But we may still be missing the point:

What if advanced manufacturing is not a support function at all, but something closer to power generation or national security? Something you plan around, not bolt on later.

Below, I’ll walk through the core breakdowns holding back U.S. Industrial AI, then outline a deployable playbook for capital, policy, and talent to realign.

Without production systems that can actually absorb new technology, the U.S. will continue to invent things it struggles to manufacture at scale.

The Strategic Gap: Innovation Without Alignment

The U.S. leads the world in AI software.

That success came from decades of compounding advantages.

• Deep university research

• A global magnet for talent

• Open-source culture

• And relentless private investment from NVIDIA, Meta, Amazon, and Google

But in Industrial AI, where intelligence is embedded into manufacturing, robotics, and energy systems, we lag.

As one factory operator recently put it:

“World-class robots, paper workflows, siloed data, and still no way for factories to learn from each other.”

Not because we lack innovation, but because we lack alignment.

These failures show up on factory floors every week. Here is what they look like in practice.

1) Fragmented Funding

Research funding lurches every time the federal budget shifts.

The standard 10-year venture clock is built for software, not for factories that take years to stand up.

Only a few companies ever receive compounding capital. Most industrial startups stall long before scale is even in sight.

Tesla looks like a miracle story until you trace the inputs. Years of government support, aligned policy, and patient capital gave it time to learn how to build before anyone demanded scale. Most industrial startups are pushed to prove economics long before their systems are ready.

2) Pilots Instead of Deployments

Pilots create demos, not ecosystems. Without deployment density:

• Suppliers don’t learn

• Cost curves don’t fall

• ROI never materializes

• Systems never compound

In practice, this phase breaks down because each deployment becomes a bespoke rebuild. Knowledge, telemetry, and lessons don’t carry forward.

That’s where most pilots die.

We’ve already seen this failure mode at scale.

After the bipartisan infrastructure bill unlocked funding, transformer lead times stretched from roughly 20 months to as long as five years, not because capital was missing, but because skilled labor and deployment capacity could not scale fast enough, as noted by Heather Carroll of Path Robotics.

The checks showed up after the bottlenecks were already baked in.

The factories were not ready for the money.

You can see the fallout on factory floors today.

Across U.S. manufacturing plants, world-class robots coexist with paper checklists and whiteboards. ERP systems are decades old, data is siloed, and Excel remains the default interface. AI is discussed everywhere, but deployed almost nowhere. Even factories owned by the same company operate like separate worlds.

These observations reflect firsthand reporting by Harmony’s founder, George Mungui, who visited more than a dozen U.S. factories and documented the same pattern: advanced hardware constrained by fragmented software, aging systems, and missing integration.

3) No Anchor Demand

Anchor demand is when a buyer shows up for five years, not five pilots. That is when suppliers stop hedging and start building real capacity.

Without predictable, multi-year demand:

• Suppliers won’t expand capacity

• Investors won’t underwrite early infrastructure

• Startups optimize for survival, not scale

4) Broken Talent Ladder

The U.S. celebrates two extremes: elite software roles and traditional trades.

Industrial AI requires the ladder in between:
operators → technicians → systems engineers → Industrial AI specialists.

The missing link is the middle of the ladder: technicians and systems engineers who can operate, maintain, and improve intelligent machines in real environments.

Only about one in ten machine learning engineers work on robotics, even though robotics is where AI turns into physical capability, as Ashish Kapoor, co-founder of General Robotics, has noted.

The hardware is here. The people who know how to run it, fix it, and improve it are not.

Fragmented Funding: Why Inception Capital Is Missing

A few specialist firms will write the first check. Most of the venture model is not built for the first two years of Industrial AI.

Before any factory can scale, founders run into the same four problems.

• Technical feasibility. Can this system actually hit real cycle times under real constraints?

• Supplier reality. Who will build the first parts, and at what minimum order size?

• Deployment economics. How does it perform in the field, not in a lab?

• Dual-use paths. Can early DoW or DoE demand coexist with commercial buyers?

Most funds still evaluate Industrial AI as if it were software. That shows up in three ways:

• small exploratory checks

• traction-first expectations

• wait-and-see posture

Industrial AI is not SaaS.

It needs investors who fund proof in the real world, not prettier pitch decks.

This is where the system breaks.

Without inception capital, policy floats in the abstract, and suppliers stay on the sidelines.

Everyone waits. Nothing compounds.

What Inception Capital Actually Changes

Manufacturing Needs an Anchor Customer

If the U.S. wants manufacturing to compound the way software did in Silicon Valley or hardware did in Shenzhen, one problem has to be solved first.

Industrial AI does not grow from “fund and walk away” pilots. It requires sustained ownership of deployment so systems can learn and improve in real production environments.

China’s advantage is not cheap labor. It is long-cycle demand.

Ten- to twenty-year roadmaps cascade from Beijing to the provinces.

Each deployment teaches suppliers, builds workforce capability, lowers costs, and makes the next rollout easier.

Meanwhile, the U.S. runs pilots that never scale.

We stop right when the learning curve should begin.

Each pilot uses different interfaces, data formats, and operating assumptions.

Suppliers learn once, then relearn the same lessons again.

Provisions in the latest National Defense Authorization Act (“NDAA”) point toward multi-year procurement and production capacity, not just R&D or one-off prototypes. The direction is right, but the approach is still fragmented.

It does not have to work this way.

If the U.S. is serious about reindustrialization, the public sector has to act as an anchor customer.

China does not rely on a single anchor customer in the way the U.S. might. Instead, central plans set direction, and provinces, state-owned enterprises, and large industrial buyers execute against shared multi-year targets. The result is coordinated demand at scale, even without a single buyer signing every contract.

DoW and DoE already have the budgets and the mandate. If they show up with predictable demand instead of one-off grants, suppliers will scale. Cost curves fall. The same batteries, motors, sensors, and chips then serve both government and commercial buyers.

Capability Compounds Over Deployment Cycles

A practical starting point is simple. Long-term procurement. Small factory clusters. Clear dual-use targets.

Anchor customers are not just buyers. They tell suppliers, founders, and investors when it is safe to commit.

Some of this thinking is starting to show up in policy. The 2026 NDAA includes multi-year purchase commitments and direct funding for production capacity. That is what lets suppliers invest before the next contract arrives.

DoW will never be the main buyer for most of this tech. But its early commitments can carry suppliers through the hardest years.

Without predictable demand, suppliers stay on the sidelines, founders burn out, and every pilot turns into a reset.

The Microfactory Strategy

The U.S. keeps announcing megafactory and gigafactory plans. EVs. Defense. The list keeps growing.

But megafactories drag a lot with them.

• Capital up front.

• Years of construction.

• Politics, unions, permitting.

• Supply chains that snap when anything goes wrong.

Only a handful of companies can actually make this work.

The real constraint is not engineering. It is depreciation. Factories built twenty years ago are still on the books, and nobody wants to torch them before they are written down.

Incumbents are locked into factories designed decades ago. Rebuilding production systems before existing assets are fully written down destroys capital, so boards resist even when better automation exists. The result is structural inertia. The biggest winners won’t be retrofitting old plants. They’ll be the ones designing new products and production systems from scratch.

This is the moment where paths split.

China can pull capital, labor, and suppliers into a few mega-sites and plan ten years out.

The U.S. cannot. Talent sits in universities and labs. Demand is scattered across defense, energy, and commercial buyers.

That fragmentation is not accidental. It is the system we inherited.

Microfactories exist because the U.S. does not build like China. They are what happens when you design around fragmentation instead of trying to fight it.

So what does this look like on a real factory floor?

You can already see early versions of this. Fewer people on the floor. More machines that need supervision instead of babysitting.

The closest thing we have today is China’s “dark factories.” They run 24/7 on AI, robotics, and dense sensing, inside a system the U.S. does not share.

It works in pockets today. What is missing is the ability to copy it, site to site, without starting from zero.

From Concept to Execution: Software-Coordinated Microfactories

America does not win with giant plants. It wins by placing small, fast factories near:

• university and technical talent

• simulation ecosystems

• supplier, and OEM clusters

• DoW and DoE infrastructure

Think of microfactories as production nodes you can spin up quickly and reconfigure in software.

The model we want to see:

The model starts with DoW or DoE as the anchor, paired with at least one commercial buyer. That combination gives suppliers confidence to commit. Once that happens, deployments stop being isolated, and demand stops resetting.

What Microfactories Enable

1. Design, build, test.
   Then do it again next week, not next year. Short cycles turn into more shots on goal.
   This is the closest the U.S. gets to Shenzhen-style compounding today.
   

2. Local supply chains
   Post-COVID fragility + tariff uncertainty expose long-haul suppliers.
   Microfactories + local suppliers solve “wait 14 weeks for a part.”
   

3. Reinforcement learning for factories
   With sensing and OTA updates, production starts to look like a feedback system instead of a fixed process.

Microfactory’s Data Flywheel

Factories as a Platform

To build a “Shenzhen of the West,” microfactories must be modular, extensible, and open:

• plug-in modules (“factories in a box”)

• shared testing + metrology

• shared supplier networks

• standardized automation cells

If factories cannot update, adapt, and share improvements, they freeze in place while China keeps pulling ahead.

Manufacturing as Dual-Use Capability (Energy + Defense)

When DoW commits to the same system for five years, suppliers stop guessing. They start hiring, buying tooling, and locking in vendors instead of waiting for the next Request for Proposal (RFP).

To make this real, three shifts must happen:

1) Fund Industrial AI + Digital-Twin Simulation

Industrial AI depends on simulation, not static design files, but continuously updated operational models.

A digital twin is a living model of a factory that changes every time the real line changes.

If a robot drifts out of tolerance, the model drifts with it.

If a weld fails at 2 a.m., the twin records the conditions that caused it.

Most factories only learn that something is broken after bad parts are already on a truck.

Teams use digital twins to test changes without stopping production, catch failures before they become scrap, and train systems before expensive mistakes happen.

This simulation layer supports modeling, quality assurance, autonomous systems testing, and process optimization.

Today, these layers are underfunded, even though they are required for Industrial AI to scale.

2) Build Shared Digital-Twin Data Architecture

Today’s fragmentation prevents learning.
No shared telemetry means no shared improvement curve.

Policy already recognizes part of this problem on the qualification side. The NDAA emphasizes standardized specifications, expedited qualification, and third-party certification to reduce friction across programs.

Qualification reduces friction, but it does not create learning loops across deployments.

Funding helps. Procurement helps. But the people running the machines still cannot move what they learn from one site to the next.

In the field, teams report the same pattern. Every deployment becomes a bespoke rebuild.

Telemetry does not transfer. Interfaces do not align. Benchmarks do not persist.

Without shared data, factories cannot learn from themselves or from each other.

A shared digital-twin and telemetry layer would do a few very unglamorous things that almost nobody is funding today:

• Align vendors around common interfaces

• Accelerate dual-use startups

• Enable benchmarking across factories and systems

• Turn every deployment into a learning loop

Standardization is what lets one good idea show up in the next factory instead of dying inside the last one.

3) Treat Manufacturing as Infrastructure

Procurement stops when the contract is signed. Infrastructure only begins when the system survives its first year in the field.

Manufacturing needs to move past one-off contracts and into systems that get better every time they run, including:

• continuous improvement, not one-time awards

• predictable, multi-year demand

• shared supplier networks

• platform-style deployment cycles

The language exists in Washington. On the factory floor, almost nothing feels different.

The NDAA frames manufacturing across funding, qualification, workforce, networks, and policy. This is a clear signal that capacity is no longer viewed as a one-off procurement problem, but as an ecosystem that must be sustained and upgraded over time.

What nobody has figured out is how to make this survive beyond the pilot.

Think F1, Not Waterfall

When simulation, data, and infrastructure align, industrial AI starts to look like an F1 pit crew:

Speed does not matter if every plant is rediscovering the same failure in isolation.

Telemetry, role clarity, and repeatable execution turn each cycle into usable learning.

This is the operating model the U.S. needs across energy, defense, and manufacturing. One system that gets better every lap.

Talent: AI Should Augment, Not Replace

Automation is usually framed as job loss.

The real pressure is demographic.

Roughly 157,000 skilled tradespeople, including welders, are approaching retirement, while nearly 80% of critical industrial work remains manual. If that knowledge isn’t captured and scaled through real deployments, it disappears. Automation isn’t about replacing workers. It is the only way to compound expertise before it’s lost.

The problem isn’t unemployment. It’s that there are no clear paths into the jobs that already exist.

Three realities we must accept:

1. As experienced workers retire, their knowledge is lost instead of being passed on and compounded.

2. Without shared systems, every shift change becomes a reset. Teams relearn the same fixes on different lines, in different buildings, every week.

3. Much of the world’s tacit manufacturing expertise lives in Japan, Korea, Taiwan, and other advanced manufacturing economies. Rebuilding U.S. capability will require deep partnership, not isolation.

The Real Shift: Old Jobs Disappear, New Jobs Don’t Have Pathways

High-value roles emerging now include:

• Teleoperation: remotely monitoring and intervening in automated systems.

• Simulation and digital-twin supervision: testing and improving systems in software before deploying changes.

• Maintenance and AI diagnostics: combining hands-on repair with predictive software tools.

• Systems engineering for Industrial AI: integrating machines and software into reliable production systems.

Learning While Producing: Paid upskilling through real factory systems

These roles aren’t abstract titles. They are the natural progression when training is embedded in live factory systems.

The U.S. Needs a New Workforce Blueprint

Today’s workforce programs live far from the factory floor. Skills are taught in classrooms. Production happens somewhere else.

That separation is why nothing sticks.

Training has to live inside real deployments:

• microfactories

• robotics rollouts

• dual-use hubs

• simulation centers

Here is the part most people miss. The funding model already exists.

A technician who keeps a robotic cell running is cheaper than downtime. That is why operators pay.

Every trained worker shortens rollout cycles across a supplier network. Anchor customers feel that pain first, so they co-fund.

Public programs only work when training lives on live systems, not in empty labs.

This is how production gets financed. Training has to follow the same logic.

A Modern Night-School Model

This is not a new idea. During past industrial transitions, many engineers retrained at night while working full-time jobs to support their families, including immigrant engineers during the semiconductor boom.

Most recently, workers from nontraditional backgrounds have done the same, attending night and weekend programs to move into software roles while continuing to earn a living.

We need:

• night-school programs inside community colleges

• colocated with deployment sites

• co-designed with DoW, DoE, OEMs, and startups

• focused on high-value Industrial AI roles

Training must follow deployments, not precede them.

Bottom Line

Jobs rarely vanish in a single layoff. What changes first is who understands the machines.

When only a handful of people know how a line actually behaves, production slows the moment one of them walks out.

Align training with deployment, and that risk compounds in your favor.

Summary: Policy and Venture Have to Move Together

If Industrial AI is going to scale in the U.S., policy and capital have to operate on the same clock.

Today, they move on different clocks. That’s why the table below feels more like a repair manual than a strategy slide.

Questions That Actually Matter

• What would a U.S. microfactory cluster look like if it were designed on purpose?

• Should advanced manufacturing be treated as national infrastructure, the way we treat energy or broadband?

• Which agencies can act as anchor customers today? DoW, DoE, NASA, or some combination?

• How do we co-fund early Industrial AI platforms so government demand and venture capital reinforce each other instead of drifting apart?

These decisions determine whether factories learn across cycles or whether every new program resets back to zero.

Closing

Thanks for reading this far.

The future of industry won’t be built inside a single factory.

When capital, policy, data, deployment, and talent move on different timelines, nothing compounds. Systems reset instead of improving.

When those layers finally line up, Industrial AI stops feeling like a project and starts behaving like a team that learns every lap. The question is how many cycles we burn before that happens.

Best,
Chris Kong
Founder & GP, PaperJet Ventures

P.S. A heartfelt thank-you to Bola Adegbulu, Ellen Chang, Jen Liao, and Jaireh Tecarro for their invaluable feedback, thoughtful tips, and unwavering support in shaping this piece.

The next industrial revolution won’t just be about automation—it will be about intelligence.

After years of backing visionary deep tech founders, I’ve seen one thing hold back even the best ideas: the lack of infrastructure to scale.

The most common question I get from seasoned investors is simple:

“Can these founders actually build at scale?”

We’ve never lacked technical talent in the U.S. What’s missing is the pathway to take deep tech beyond the prototype stage—a commercialization gap that’s kept many breakthroughs from ever reaching the market.

That gap matters more than ever.

National priorities, supply chain shocks, climate innovation, and a push for re-industrialization have made it urgent to rebuild smarter, more resilient industrial capacity here at home.

The good news?

AI and robotics are already converging with advanced manufacturing, enabling software-defined, adaptive factories that don’t just automate—they think. This shift is here now, not in some distant future.

With tariffs and supply chain disruptions dominating headlines, the momentum to rebuild our industrial base has never been stronger, making Industrial AI not just timely, but strategically essential.

What’s Missing: Why Startups Struggle to Scale

We all use AI tools like ChatGPT and Perplexity every day. But what’s next?

AI that meets the physical world.

AI is moving beyond software. It’s transforming power grids, vehicles, factories, and entire supply chains—creating multi-trillion-dollar market opportunities.

One of the best examples is Anduril’s Arsenal OS—an AI-native factory system where software runs the entire production stack, from design to deployment. Think of it like upgrading from static assembly lines to self-driving factories. Factories that optimize themselves, just like your phone updates overnight.

This is the leapfrog moment—one that positions the U.S. and its allies to compete globally in manufacturing, robotics, and industrial AI.

We’re already seeing early signals:

• Humanoid robots like Tesla’s Optimus and Figure are showing real-world demos.
  Morgan Stanley forecasts a $5 trillion market by 2050, with nearly a billion robots moving from industrial to everyday life.

• Massive tailwinds are driven by labor shortages, energy demands, and automation.

But the hard truth remains:

Most early-stage deep tech founders get stuck. They have great demos but struggle to cross the chasm to scalable manufacturing. After decades of offshoring, America’s industrial muscle is weak. Startups get trapped in fundraising loops instead of building real customer traction.

Historically, VCs have avoided deep tech for exactly this reason—hardware was seen as too capital-intensive, too slow to scale, and too risky compared to software.

But the landscape is shifting. AI-native manufacturing platforms, like Arsenal OS, show that the economics are changing. What once looked impossible to scale now looks inevitable to win. And the window to lead is wide open.

Industrial AI Isn’t About Replacing People—It’s About Empowering Them

Industrial AI isn’t about eliminating jobs. It’s about creating new, good-paying opportunities here in the U.S.—from systems engineering and advanced manufacturing to AI operations and industrial design.

As intelligent machines take on the repetitive and dangerous work, people can focus on higher-value roles that drive innovation, safety, and efficiency across the industrial economy.

While there’s renewed momentum to bring manufacturing back onshore, we believe the bigger opportunity is to rethink what manufacturing excellence looks like in the AI era—building modular, AI-first manufacturing systems from scratch, designed to scale intelligently, not incrementally.

How We Solve It: PaperJet’s Model to Bridge the Gap

At PaperJet, our mission is to close the commercialization gap first, helping founders build real companies, not just pitch decks.

We’ve built what we call the PaperJet Commercialization Engine—a hands-on system that guides founders from lab demo to industrial scale.

How It Works:

• University Labs - We mentor PhD teams at MIT, Stanford, and Berkeley to shape commercialization pathways early.

• Early-Stage Founders - We invest and embed operational support, helping founders de-risk their go-to-market and pilot programs.

• Industrial Partners - We broker real-world pilots, customer deals, and manufacturing scale-up pathways with industry leaders and corporate VCs.

Scaling these platforms takes more than just venture equity.

It requires a layered capital strategy—combining venture capital, flexible debt, non-dilutive government funding, strategic partnerships, and later-stage private equity. Our network is built to help founders navigate these capital stacks.

Why Industrial AI Is Personal to Us

We first sharpened this model in electrification and space, backing Tesla and SpaceX when they were still contrarian bets. Those lessons shaped how we invest today—read more about that journey here.

• H3X – Electric motors for aviation

• Auriga Space – Reusable, electromagnetic launch platforms

We applied the same playbook to robotics:

• Polymath Robotics – Plug-and-play autonomy for tractors and mining trucks

• UCR – Humanoids for hazardous industrial work

And we’ve extended it to university spinouts—helping PhD teams in semiconductors, aerospace, and AI build companies like:

• Hexaspec – Advanced chip cooling for AI data centers

• Stealth Cornell startup – Autonomous drone simulation

But we don’t stop with capital alone.

We connect founders with customers, co-investors, operators, and government partners to build the next generation of industrial platforms.

Why This Is a Global Opportunity

But Industrial AI isn’t a U.S.-only play. It’s a global team sport.

From Japan’s robotics leadership, to Korea’s manufacturing depth, to Taiwan’s semiconductor dominance, we’re building cross-border partnerships to unlock scale.

We’ve seen this model work:

• Tesla + Panasonic building Gigafactory Reno

• TSMC + Amkor creating Arizona’s semiconductor hub

We actively build bridges with global partners to help founders scale faster, smarter, and more defensibly.

Why I’m All In

This is a once-in-a-generation opening.

AI is moving off the screen and into the real world—powering factories, energy systems, and infrastructure. The upside is measured in trillions.

But ideas alone won’t get us there.

Founders need infrastructure, capital, and hands-on partners to close the commercialization gap. That’s why PaperJet exists.

We’re all-in on Industrial AI because we believe the next iconic companies will be built at the intersection of intelligence and industry.

Let’s build the future of industry—together.

And like the best live music, the magic happens before the rest of the world catches on.
Read more about why the thrill of early discovery drives everything we do at PaperJet.

Best,
Chris Kong
Founder & GP, PaperJet Ventures

P.S. A heartfelt thank-you to my friend Jen Liao and Jaireh Tecarro for their invaluable feedback, thoughtful tips, and unwavering support in shaping this piece.

I’ll never forget seeing Coldplay perform at Irving Plaza, just steps away from my old apartment in Union Square. At the time, the band was virtually unknown in the U.S., playing to a crowd of fewer than 500 people. But that night, you could feel something special—a shared moment we could all look back on, knowing we had witnessed something extraordinary.

Investing, for me, carries that same thrill—the joy of identifying something extraordinary, whether it’s a band on the brink of fame or a company poised to transform its industry.

My first early experience in investing came when my aunt showed me how to look up companies’ stock prices at the age of 12. From that point on, I was hooked—fascinated by the idea of discovering hidden potential and watching it grow.

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Public Market: The Early Aha Moments

In my sophomore year of college, I opened my first brokerage account and started investing. Investing felt like discovering bands before they hit the mainstream—a mix of curiosity, intuition, and excitement.

Inspired by Peter Lynch’s “buy what you know” philosophy, my first aha moment came with Apple. As an early adopter of the iPod, I could see its transformative potential to reshape digital music. That insight led me to invest, long before Apple became the tech juggernaut it is today.

Chipotle was another early win. My first visit to its restaurant in the Financial District of Lower Manhattan was purely by chance, but I quickly saw how fast-casual dining could disrupt traditional restaurants. Backed by financial insights from my Motley Fool subscription, I recognized the strength of their unit economics and scalable model just before their IPO.

Tesla’s Model S launch was a pivotal moment. It wasn’t just an EV—it was, in my view at the time, the first electric car that could genuinely compete with luxury incumbents. My high-conviction, concentrated investments followed after a deep dive into Elon Musk’s Master Plan, an influential post by Wait But Why, and my in-depth financial analysis.

Nvidia’s early GPU innovations gave me another glimpse into the future. Recognizing the immense potential for AI applications and Jensen Huang’s long-term vision for the company solidified my belief in Nvidia’s trajectory.

Each of these investments wasn’t just about financial returns—it was about understanding innovation and identifying transformative potential before the world caught on.

Of course, not every investment worked out as planned. There were moments when I mistimed a macro trend or was too slow to adapt to a changing investment thesis, but these experiences sharpened my ability to assess long-term potential and adapt more effectively.

Transitioning to Private Markets: Backing the Jockey

As my journey took me into the tech industry, with roles ranging from startup founder to product manager at early-stage companies, I came to a key realization: the success of a startup is more about the people behind it than the product itself. This shift in perspective became even more apparent as I moved my investment focus from public companies to early-stage startups. Backing visionary founders often matters more than betting on the idea or product alone.

Take H3X, my first direct deal. While I worked closely with a former Tesla engineer to assess the electric motor startup’s technology, my ultimate conviction came from the founding team—a small group of raw talent with the right mix of experience, adaptability, and execution-driven mindset.

Similarly, investing in Auriga Space was a challenging decision. A space launch company's high-risk, nonconsensus nature meant leaning heavily on subject matter experts, including PhDs, to validate the technical feasibility and market opportunity.

Unlike public markets, where data and financials are readily available, early-stage investing requires a different approach: deep collaboration, trust, and a 360-degree view of both the founder and the opportunity.

In private markets, the stakes are higher, and some bets inevitably fall short. Each experience, whether a success or a misstep, has taught me invaluable lessons that now guide PaperJet’s approach to identifying and supporting transformative founders.

These lessons—combined with my firsthand experience as a founder and operator—shaped how I evaluate founders, assess customer validation, and collaborate with experts. Early-stage investing requires a level of trust and partnership, unlike anything I’ve encountered in the public markets.

Building PaperJet: Scaling the ‘Aha Moments’

The joy of discovery—the thrill of finding the next transformative opportunity—is what inspired me to start PaperJet Ventures. My aha moments in public markets laid the groundwork for spotting transformative opportunities in private markets, where the focus shifted from great ideas to great founders.

At PaperJet, our mission is informed not just by success stories but also by the lessons learned from challenges. These insights help us prioritize visionary founders, strategic timing, and strong execution—ensuring that every aha moment is rooted in a deeper understanding of what it takes to build transformative companies.

This realization became the foundation for PaperJet's mission: to consistently back visionary founders building transformative companies.

We believe visionary founders are the key to unlocking transformative impact. That’s why we prioritize founder-first support—whether through strategic partnerships, technical expertise, or placing advisors to guide startups in critical areas like customer validation and navigating complex industries.

PaperJet bridges the gap between research labs and commercialization, uncovering non-consensus opportunities in Deep Tech and AI. We partner with top schools and accelerators to develop a robust talent pipeline, tapping into innovation ecosystems at places like MIT, Stanford, and Berkeley. Through platforms like Substack, we share transparent and actionable investment theses on emerging sectors, such as In-Space Manufacturing and Robotics, showcasing PaperJet’s approach and building trust within the Deep Tech community.

By maintaining an active presence in the tech ecosystem, we’ve developed a strong framework to identify founders who align with our vision. For example, we look for founders who exhibit an obsession with the problem space, deep hardware expertise, and a willingness to master AI. These qualities, combined with execution ability, create the foundation for success in the most ambitious markets.

PaperJet is more than an investment firm—it’s my attempt to scale the magic of those early discovery moments into a platform that empowers both founders and investors to shape a transformative future. As PaperJet evolves, I’m constantly learning from the founders we back and the ecosystems we engage with, refining our approach to create an even greater impact.

Closing: The Love of Discovery

Much like discovering Coldplay in a small venue, early-stage investing is about spotting greatness before it becomes obvious. It’s the thrill of uncovering something extraordinary—whether in a concert hall or a pitch meeting—that never gets old.

Every discovery—whether a breakthrough or a setback—refines the systems we’re building at PaperJet to uncover the extraordinary and consistently back the next wave of visionary founders. The lessons learned along the way refine not just my process, but the systems we’re building at PaperJet to consistently back the next wave of visionary founders. It’s a process rooted in curiosity and conviction—working alongside experts, founders, and networks to uncover potential that others might overlook.

What drives me isn’t just the discovery itself but the opportunity to help visionary founders turn bold ideas into reality. Recognizing transformative potential is only the first step; the real work begins in building the support system they need to thrive.

This philosophy is at the heart of PaperJet's mission:

At PaperJet, we combine a deep technical network with a founder-first approach, helping turn bold ideas into transformative realities. By prioritizing founder-first support—strategic partnerships, technical expertise, and impactful advisory networks—we unlock visionary founders' potential to create lasting change.

If you’ve ever experienced that ‘aha moment’—whether in business, art, or life—you know its power to inspire and transform. At PaperJet, we’re building a platform to bring those moments to life. Let’s connect, collaborate, and shape the future together—one idea, one founder, one transformative journey at a time.

Best,
Chris Kong
Founder & GP, PaperJet Ventures

P.S. A heartfelt thank-you to my friend Jen Liao for her invaluable feedback, thoughtful tips, and unwavering support in shaping this piece.

In recent years, we’ve seen the emergence of general-purpose humanoid robots like Figure 02 and Tesla’s Optimus. I had the privilege of seeing Optimus in action during Tesla’s recent Robotaxi event, and the potential is immense. According to Goldman Sachs, the global market for humanoid robotics could exceed $150 billion by 2035, driven by opportunities to solve labor shortages and support aging populations with smarter, adaptive solutions.

Building on the momentum of our previous post on In-Space Manufacturing, I’m excited to explore another core investment theme for PaperJet: Robotics. This deep dive is shaped by insights from industry leaders like Daniel Theobald, founder of Vecna Robotics and MassRobotics, conversations with founders, and perspectives from fellow investors.

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Introduction

Robotics is one of the most exciting frontiers of innovation, where science, engineering, and technology converge to create machines capable of operating autonomously or alongside humans. Robots are already transforming our world:

• Industrial robots handle complex assembly tasks on factory floors.

• Service robots assist in healthcare, hospitality, and logistics.

• Mobile robots navigate dynamic environments with precision.

• Collaborative robots (cobots) work seamlessly with humans to increase efficiency.

But the real opportunity lies beyond automation. Robotics today isn’t just about building machines—it’s about redefining systems, solving meaningful problems, and enabling solutions at scale.

Why Now?

According to robotics expert, Daniel Theobald, robotics today is where self-driving technology was a decade ago—poised for exponential growth.

Key Drivers of Robotics Innovation

• Breakthroughs in AI & ML: Robots are now smarter, and capable of real-time learning and decision-making. These advancements enable robots not just to perform tasks but to serve as the foundation for reimagining workflows, systems, and entire industries.

• The Industry 4.0 Revolution: Automation, interconnectivity, and real-time process optimization are creating opportunities for robotics to solve critical challenges in under-innovated industries like agriculture, logistics, and construction.

Goldman Sachs forecasts the humanoid robotics market could exceed $150 billion by 2035, rivaling today’s EV market. This explosive growth could address critical labor shortages while supporting aging populations—provided hurdles like affordability, product design, and public acceptance are overcome.

Key Hurdles Toward Robotics’ iPhone/ChatGPT Moment

What’s holding robotics back from its “iPhone” moment? According to Freda Duan from Altimeter Capital, two critical challenges are data scarcity and hardware costs:

“Data: We have 15T text tokens, but robotics data is still limited. A diverse and large-scale dataset is crucial. Progress has been made in teleoperation, AR data, simulation, and video learning, but real-world data remains invaluable yet scarce. Google, for example, spends $300/hour to collect it.

Hardware: Creating dexterous robotic hands requires advanced planning algorithms and the challenge of replicating human-like flexibility and strength. Low-level control and physical interaction are delicate processes.”

Data Challenges

While simulated environments and AR provide useful training grounds, they cannot replicate real-world complexity and variability. High-quality, real-world data—essential for improving decision-making and adaptability—remains costly and logistically challenging to acquire.

Startups can tackle this by partnering with forward-thinking companies in high-risk industries like mining or oil & gas, where collaboration unlocks data and validates solutions.

Hardware Challenges

Hardware remains a formidable bottleneck. Achieving dexterity, human-like strength, and cost-effectiveness requires planning algorithms, low-level control, and scalable production breakthroughs. A glimpse of Tesla’s Optimus hand from the Robotaxi event (see GIF below) highlights progress in robotic dexterity while underscoring the immense complexity of replicating human-like movement and strength.

For robotics to achieve mass adoption, hardware costs must drop by an order of magnitude (10x) while ensuring reliability and scalability. The market often favors established players like Tesla or experienced founders like Brett Adcock (Figure), who have the capital and execution track record to tackle these hurdles head-on.

Hardware innovation isn’t just about cost—it’s about creating reliable, scalable systems that can transform entire industries.

Early Observations: Rethinking the ‘Right’ Approach

Many robotics solutions focus on automating existing processes to reduce costs. While practical, this mindset risks solving the cheapest problems with the most expensive solutions.

Take Wei Ding from Under Control Robotics (UCR), a startup I’m invested in. After embedding himself in warehouse operations, Wei recognized the disconnect between cost and value in automating logistics. This insight led him to pivot to higher-impact opportunities like robotics for oil & gas construction fields.

The lesson? Instead of optimizing a single process, founders should rethink entire systems where robotics can deliver exponential ROI, not incremental savings.

The Right Approach: Robotics as the Catalyst for Full-Stack Transformation

Robotics founders must think bigger—rethinking entire systems rather than incrementally automating single processes. Robotics shouldn’t just optimize workflows—it should enable a full-stack transformation where robots power safer, faster, and more intelligent systems at scale.

As Malcolm McLean revolutionized logistics with container shipping by reimagining the entire supply chain, robotics has the potential to redefine industries from the ground up:

• Instead of selling robots to optimize warehouses, build end-to-end robotic warehouses where automation isn’t an add-on but the backbone—driving 24/7 workflows, eliminating inefficiencies, and setting new performance benchmarks.

• Rather than integrating robotics into legacy operations, create next-generation businesses where robotics is foundational—redesigning processes, systems, and business models with automation at their core.

This first-principles approach positions robotics as more than a tool—it becomes the cornerstone of scalable, transformative systems that deliver exponential, not incremental, value.

Founder Qualities: What We Look For

At PaperJet, we look for founders who:

• Immerse Themselves in the Problem Space:

  • Founders like Daniel, who bought an entire farm to master agricultural operations, or Wei, who embedded himself in warehouses before pivoting to oil & gas robotics, exemplify the importance of immersing deeply in the problem space.

• Solve Critical Challenges:

  • Address data scarcity by acquiring and leveraging real-world datasets.

  • Innovate on hardware to enable scalable, modular, and cost-effective robotics solutions.

• Reimagine Under-Innovated Markets:

  • Founders who take a first-principles approach to build systems that unlock transformative ROI, not just automate tasks.

Final Thoughts

Like The Wild Robot discovering its purpose in an unfamiliar world, robotics today is on the cusp of redefining how we live, work, and care for our planet. This isn’t about replacing humans—it’s about building systems that adapt, solve meaningful problems, and make our world safer, smarter, and more efficient.

At PaperJet, we invest in visionary founders who tackle robotics’ toughest challenges and reimagine entire industries with scalable, transformative solutions. As robotics evolves, the opportunity to solve humanity’s most pressing challenges—from safety and labor shortages to sustainability and innovation—has never been greater. The future is being built, one system at a time.

I appreciate you taking the time to explore robotics with me. If you have thoughts, questions, or insights, I’d love to hear from you.

Best,
Chris Kong
Founder & GP, PaperJet Ventures

P.S. Massive thanks to Daniel Theobald (MassRobotics), Wei Deng (Under Control Robotics), Stefan Seltz-Axmacher (Polymath Robotics), David Li (BHP Ventures), Lawrence Wong (Tesla), and the members of Tesla’s Optimus team I had the pleasure of meeting at the Robotaxi event for sharing their insights in past conversations. Special shoutout to my friends Jaireh Tecarro and Jen Liao for their invaluable feedback and support. Don’t forget to hit the sign-up button to stay updated on the latest trends and insights!

But what if I told you that this is closer to reality than you think—and rather than building planet-destroying weapons, it’s leading us toward solutions that can sustain our world?

Imagine creating rare drugs, advanced materials, and technologies that benefit Earth and push our ambitions for a space economy.

In this post, I’ll dive into one of PaperJet’s core investment themes: in-space manufacturing (“ISM”). This journey is shaped by insights from our friend at Seraphim Space and conversations with industry experts.

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In-Space Manufacturing (ISM) & Microgravity

ISM leverages the unique properties of space—microgravity, near-vacuum conditions, and higher radiation—to create products that can’t be made on Earth.

Microgravity, in particular, is the game changer. In this near-weightless environment, materials mix evenly, bond precisely, and form complex structures that gravity simply doesn't allow.

However, the combination of microgravity, vacuum, and radiation can present unique challenges that must be navigated carefully, and ISM is still in the early phase of experimental validation.

Despite these challenges, microgravity opens up opportunities in key industries:

• Pharmaceuticals: Faster drug discovery and more efficient development.

• Semiconductors: Enhanced materials and improved efficiency for next-gen chips.

ISM’s applications are rapidly expanding to fields like defense, IT, and communications. For now, the near-term focus is on validating these applications through rigorous experimentation. The industries that stand to benefit the most are those that can leverage the unique properties of space to achieve results impossible on Earth. For those interested in diving deeper, McKinsey’s latest report offers extensive insights into this growing opportunity.

Why Now?

There’s never been a better time to capitalize on ISM. Recent advances are making space more accessible and creating unprecedented opportunities:

Affordable Access to Space: Fully reusable rockets, like SpaceX's Starship, have significantly lowered the cost of space launches. Launch costs could fall to as little as $2-10 million, with the estimated cost per kilogram dropping to $150, making space more affordable than ever for manufacturing and experimentation.

Commercial Space Stations & Expanding Infrastructure: NASA's Commercial LEO Destinations (CLD) program is backing private companies to build space stations—following the playbook that led to Falcon 9's success. This program is laying the foundation for a new private space ecosystem—from research facilities to manufacturing platforms—that will fuel industries like pharmaceuticals, materials science, and space tourism. This expanding infrastructure is crucial, enabling consistent access to microgravity for experimentation and production.

Robotic Manufacturing & 3D Printing: Advances in robotic and additive manufacturing are enabling the production of components directly in orbit, overcoming the size limitations imposed by rocket launches. Importantly, current 3D printing technology will need to evolve significantly to meet the unique challenges of manufacturing in space, where both the absence of gravity and the need to overcome it create new design considerations. This evolution will allow us to create larger, more complex structures, minimizing the need for costly resupply missions from Earth.

Broad Manufacturing Techniques: ISM includes diverse technologies—such as 3D printing and electronics manufacturing—that allow for groundbreaking products, from next-gen semiconductors to advanced medical devices. These capabilities will serve needs both on Earth and in future space missions, laying the foundation for sustainable space exploration.

Building for Space Missions: While ISM can revolutionize industries on Earth, a compelling near-term application might be for in-space needs—such as construction and life support systems. With the renewed focus on manned missions, ISM could play a key role in supporting the infrastructure needed to sustain human life in space. This makes ISM a cornerstone of sustainable space exploration, as well as a driver for terrestrial (earth) industries.

The convergence of declining costs, new space infrastructure, and evolving technologies make this the moment for ISM to thrive. Building products in space will support industries on Earth and propel humanity further into the cosmos.

The AWS Moment: Key Inflection Points for ISM

To find the “AWS moment” for ISM—the point when it becomes undeniably transformative—certain conditions need to be met. Here are the key factors:

Proven Commercial Traction

ISM must demonstrate more than theoretical potential; it needs to show real, measurable value to legacy industries. Space-made products can’t just be marginally better—they need to be revolutionary. The startups that will succeed are those that articulate how ISM solutions deliver transformative value, fitting seamlessly into existing value chains and compelling established players to adopt or partner. Importantly, ISM must be seen as the only practical solution for achieving certain revolutionary outcomes.

We’ve seen a mixed response from legacy players regarding ISM—some may prefer partnering with startups rather than making major in-house investments. Founders must make a strong case for why these companies should invest, addressing how ISM fits into corporate innovation budgets and showcasing validated customer demand. Industries like semiconductors, bolstered by initiatives like the CHIPS Act, show that traction is possible, but founders must communicate the unique benefits ISM offers, balancing scalability with space-enabled value.

Technological Milestones

For ISM to become mainstream, key technologies must be validated. We look for founders who can demonstrate proof-of-concept success—whether on the International Space Station or through suborbital experiments.

But it’s about more than proof; it’s about scalability. Can the founders move from a successful experiment to a large-scale production capability, essentially creating a “Gigafactory” in orbit? This leap from testing to scaling is a critical indicator of ISM’s viability.

Founders must also showcase breakthroughs in manufacturing techniques that leverage microgravity effectively. It’s not just about experimentation; it's about transitioning these experiments into repeatable, scalable production—turning potential into commercial value.

Infrastructure Progress

Robust infrastructure is essential for ISM's growth. Affordable and frequent access to space is becoming a reality, thanks to reusable rockets like those from SpaceX. However, ISM requires more than just launches—it needs persistent, on-orbit facilities for regular manufacturing activities. Players like Axiom Space and Sierra Space are leading the charge in building commercial space stations that will support these activities.

Reentry Capabilities: Bringing products safely from orbit to Earth is another critical logistical piece of the puzzle. While SpaceX’s Dragon capsule has demonstrated this capability, we’re also keeping an eye on emerging startups that can provide complete end-to-end space logistics, including reentry and product retrieval.

The economic viability of ISM depends heavily on keeping reentry costs manageable, with the price per kilogram of manufactured products playing a crucial role. High-value, low-mass goods, such as pharmaceuticals, are likely the best candidates for early ISM success, especially as launch costs approach thresholds—like $200/kg to low Earth orbit—where space-based manufacturing starts to become economically feasible.

Supporting technologies, like in-space communications, data processing, and energy systems, will further enhance ISM’s growth, making the case for its feasibility at scale.

Regulatory Landscape

The regulatory environment is key to ISM’s commercial future. We rely on industry experts to navigate the evolving regulatory landscape, particularly regarding bringing space-manufactured products back to Earth. Agencies like the FAA, NASA’s Commercial Cargo and Crew Program Office, and other international space authorities need to provide clear guidelines to support safe commercialization. Supportive regulations will be vital for bringing ISM products to market.

The Right Timing and Founder Fit for ISM Investment

Given these considerations, I believe the optimal timeline for ISM investments is a 10-15-year horizon—beyond the typical fund model. This is a long game requiring extraordinary patience, but one that promises immense rewards.

As a pre-seed investor, I’m prepared to invest time in finding the right founder. Conversations with Lewis from Seraphim Space highlighted a critical point: the ideal ISM founder might not come from the space industry but from industries, that ISM aims to serve, such as pharmaceuticals or semiconductors. The right founder will not only understand the technical opportunities but also possess deep industry knowledge—knowing the market inside and out, and why ISM can transform it.

While sci-fi often imagines futuristic factories on Mars or the Moon, my focus is on the immediate market potential here on Earth. How can materials manufactured in space solve problems for us today? That’s where the value lies, and that’s the market I want to support as the foundation of our investments.

Investing in space takes patient capital and a commitment to the long term. Despite the extended timeline, I’m confident about staying close to this sector because of its potential to change humanity itself. When ISM hits its inflection point, I believe it will be as disruptive as cloud computing was in its early days—transforming industries and reshaping our future.

I appreciate you taking the time to explore ISM with me. Feel free to reach out if you have any questions or thoughts—I'd love to hear from you.

Best,
Chris

P.S. Massive thanks to Lewis Alun Jones (Seraphim Space), Mark Peterson, Ray Owen (BMNT), and Jisoo Chung for their insights and feedback. And don’t forget to hit the sign-up button to stay updated on the latest trends and insights!

Energy Transition & Data Centers

According to a recent presentation by Apollo, the capital demand to support energy transition projects is projected to reach $30-50+ trillion, particularly in renewable energy and digital infrastructure.

The rise of AI is further accelerating data center growth, positioning it as a critical sector for investment.

A single AI query is estimated to use 10x the energy of a typical Google search, and by 2030, conventional data centers could account for up to 15% of the nation’s total power consumption.

At PaperJet, we’re focused on investing in new material technologies that enable AI and data centers to scale sustainably and operate more energy efficiently. One example is a team I mentored at the MIT Climate & Energy Prize, which is developing innovative solutions to reduce energy consumption in data centers.

This trend is a key pillar in shaping PaperJet’s investment thesis.

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Robotics: The Next Frontier?

Goldman Sachs recently reignited interest in humanoid robotics with Tesla’s announcement of its Optimus robot prototype. The global market for humanoid robotics could exceed $150 billion by 2035, driven by demand to address labor shortages in manufacturing and elderly care.

But for this vision to materialize, two critical challenges must be addressed:

Data: We have 15T text tokens, but robotics data is still limited. A diverse and large-scale data set is crucial. Progress has been made in teleoperation, AR data, simulation, and video learning, but real-world data remains invaluable yet scarce. Google, for example, spends $300/hour to collect it.

Hardware: Creating dexterous robotic hands requires advanced planning algorithms and the challenge of replicating human-like flexibility and strength. Low-level control and physical interaction are delicate processes. This should be where global collaboration adds value - some Chinese robotics companies are getting crazy good at hardware (but lag in AI), while manufacturing remains a challenge for U.S. players (but is far more advanced in AI). However, geopolitical concerns force U.S. companies to design and manufacture their hardware.
— Freda Duan, Altimeter Capital

PaperJet is actively exploring startups that can address these challenges and deliver solutions to unlock the full potential of the robotics market.

PS: I’ll also be attending the Tesla Robotics event on October 10—let me know if you’ll be there!

Sourcing Early-Stage Unicorns

A recent study by Defiance Capital, analyzing 845 unicorns, found that early-stage investment is highly fragmented. Here’s a key insight:

Outside of SV Angel (6.4%) and YC (10%), no other VC fund got into more than 2.8% (Sequoia) of unicorns.

— Source: TechCrunch

This fragmentation presents an opportunity for emerging funds like PaperJet to identify and back outlier startups before they gain broader attention.

Additionally, over half of unicorn CEOs have degrees from the top 10 global universities, with a strong concentration in STEM fields. This supports our strategy of partnering with university labs from top-tier schools and focusing on founders with deep technical backgrounds.

Our Investment Strategy: Execution & Transparency

Through years of investing, we’ve observed a positive correlation between founders who prioritize execution and provide consistent updates and their long-term success. These founders tend to navigate challenges more effectively, which is why we consider these attributes critical in our investment decision-making process.

Opportunities on the Horizon

We remain engaged in discussions with various startups in these sectors and are leveraging our relationships with university labs and corporate VCs to build a robust deal pipeline. If you have any connections or insights in these areas, I’d love to explore potential collaborations.

Thanks for reading! Feel free to reach out with any questions or feedback.

Best,
Chris

P.S. Intrigued? There’s a shiny sign-up button below to stay updated on the latest trends and insights!

These 'computers on wheels' have redefined our driving experience, setting the stage for an even greater leap into the electric age: the skies above us.

Pause for a moment and envision a flight with your family from LA to Hawaii, where the familiar roar of engines is replaced by the soft hum of electric propulsion. Imagine the skies—clear of noise and pollution, as pristine as the beaches awaiting your arrival.

Free of old constraints, this vision of electric-powered air travel is not a distant dream but an approaching reality. 

Advances in energy storage, electric motors, and renewable energy sources are joining forces to reshape aviation. They're reinvigorating an industry known for its conservatism, driving economic growth through renewed infrastructure and pioneering manufacturing.

We're not just on the cusp of changing how we reach our destinations; we're at the dawn of a new connection with the world. 

Are you ready to join this journey into the future?"

Towards a Greener Horizon: The Environmental Imperative

As we chart a course for the future of flight, grasping the environmental stakes is imperative. EVs have already illuminated the path, showcasing the superior efficiency of electric propulsion—a beacon we now aim to follow in the aviation sector.

Traditional aircraft engines—pistons and turbines alike—often squander fuel, with energy losses reaching up to two-thirds during operation. Enter the electric motor: a beacon of efficiency that converts over 90% of its input into propulsive force.

Yet, the real game-changer lies in the environmental dividends. Aviation's current share of the US transportation emissions stands at 11%, a figure projected to balloon to 20-30% by 2050 as per International Air Transport Association (“IATA”)’s forecasts. What is the route to subduing this escalating trend? Electrification. 

By adopting electric planes, we can slash greenhouse gas emissions dramatically, steering the aviation industry towards a trajectory that complements our planet's ecological boundaries.

The transition to electric aviation emerges not merely as a technological upgrade but as an environmental imperative—a cornerstone for constructing a sustainable future above and beyond.

The Spark of Innovation: Technological Breakthrough

Perched on the brink of an aerial revolution, the future of flight is being shaped by three pivotal innovations: advanced batteries for energy storage, efficient electric motors, and the generation of sustainable energy.

Energy Storage - Enduring Power

The cornerstone of electric aviation lies in batteries that store more energy per pound, extending flight times and bolstering performance. 

Emerging technologies like lithium-metal and solid-state batteries, alongside hydrogen fuel cells, are outpacing older models, thanks in part to the electric car industry's push, which has led to more affordable battery materials.

Electric Motor - The Pulse of Aviation

In aviation, where weight is money, electric motors offer a lucrative trade: more power for less weight. The Department of Energy's benchmark for efficient flight—motors with a power density above 12 kW/kg—is becoming a reality, paving the way for long-haul electric flights that redefine our travel boundaries.

Energy Generation - Embracing Eco-Friendly Power

The shift towards green energy is in motion, yet the potential for growth remains vast. 

Airports, akin to self-sustaining eco-villages, could harness solar and wind power, with innovations like Tesla's Megapacks capturing and storing renewable energy, ready to fuel our journeys.

These aren't just incremental changes; they represent a seismic shift in aviation and energy use. Imagine a world where every flight you take contributes to a cleaner planet. This is the promise of electric aviation—a future where the technology not only takes us to new heights but also preserves the world beneath our wings.

Enhancing the Passenger Flying Experience 

The advent of electric aircraft is poised to transform air travel into an experience characterized by tranquility and cleanliness—a stark departure from the noisy, fuel-dependent flights of the past. 

Envision boarding a plane where the gentle whir of electric motors signals the start of a calm and unpolluted journey to your chosen haven.

Leading this charge is the NASA X-57 Maxwell, a beacon of innovation signaling the dawning of a quieter, more efficient mode of aviation. This isn't just about reducing noise; it's about an elevation in overall flight efficiency, granting passengers rapid ascents to cruising altitudes without the traditional clatter and tremors.

Global consultancy Roland Berger acknowledges the burgeoning sector, noting over 100 electric aviation initiatives mirroring Tesla Model S's pioneering days. These ventures extend beyond engineering feats—they are redefining the very essence of travel, promising a seamless and enjoyable passage from departure to arrival.

Imagine a future where in-flight experiences similar to companies like JSX offer, complemented by Starlink's connectivity, offer passengers continuous internet access. Picture live streaming, uninterrupted gaming, and smooth productivity, all while gliding through the skies. 

This vision is the essence of the new air travel era—where the journey is as enriching as the destination. How do you envision your next flight in this electrified, elevated future?

Energizing the Stagnant Airline Industry

The airline industry, traditionally viewed as conservative and struggling with slim profit margins—as described by Willie Walsh of IATA—is poised for a significant shift. Although passenger traffic is rebounding to near pre-pandemic levels, the financial challenges remain daunting.

Electric aircraft present a transformative solution. They offer a fresh approach to revitalizing regional markets, particularly in the United States where a vast majority of the 5,000 public-use airports remain underused. 

These airports, constrained by consolidation trends and regulatory challenges, are ripe for the introduction of electric aircraft, which can adhere to noise and environmental regulations more effectively. The versatility of electric aircraft in carrying both passengers and cargo offers a competitive edge, potentially reducing travel costs and presenting an attractive alternative to long road journeys.

Operational Cost Savings

The transition to electric motors significantly reduces dependence on fluctuating fossil fuel prices. This, combined with the advancements in renewable energy and energy storage technologies, points to growing financial benefits as electric aircraft become more prevalent.

Maintenance Efficiency

Electric motors also bring a notable reduction in long-term maintenance needs, enhancing the environmental and cost efficiency of airline operations—a vital factor for airlines aiming to boost operational efficiency and sustainability.

Autonomous Advancements

Innovations in autonomous and semi-autonomous operations, particularly for cargo and non-passenger flights, promise further cost reductions and operational flexibility. This technology can help address pilot shortages and reduce expenses associated with pilot scheduling, thereby decreasing per-passenger costs.

Revitalizing Brand Image

For airlines, adopting electric aircraft is not just an operational upgrade—it's an opportunity to redefine their brand. This shift resonates strongly with the growing segment of environmentally conscious travelers, enabling airlines to project an image of innovation and commitment to sustainability.

As the industry stands on the cusp of transformation, electric aviation emerges as the potential catalyst for a new era—one characterized by profitability, sustainability, and enhanced customer experience.

Propelling Our Economy Forward: Industrial and Manufacturing Revitalization

Revamping Airport Infrastructure

Consider the expansive landscapes of regional airports, ripe for sustainable transformation. Envision these areas bustling with solar farms and equipped with advanced energy storage systems like Tesla's Megapacks. Such a transformation could power electric aircraft and foster self-sustaining energy ecosystems, turning airports into autonomous energy hubs that benefit ground transportation and other airport operations.

Enhancing the End-to-End Travel Experience

The travel experience, from the moment passengers leave for the airport to their final destination, should be as seamless as their flight. The integration of electric aircraft, particularly suited for short-haul routes, could simplify the travel process, making it more intuitive and accessible. This seamless journey, with minimal need for in-flight personnel, presents an opportunity to standardize processes across the industry, enhancing efficiency and passenger satisfaction with minimal investment.

Stimulating Manufacturing and Economic Growth

Beyond regulatory support, government involvement in revitalizing aviation manufacturing can spark a surge in innovation and job creation. This new era in aviation, echoing Tesla's impact in the automotive industry, poses an intriguing question: who will rise as the Tesla of the aircraft manufacturing world?

The advent of electric aviation promises more than just quieter, greener skies. It represents an opportunity to reshape our infrastructure, enhance travel experiences, and reignite the manufacturing sector—a sector that has historically driven economic growth. 

As we stand at the brink of this new era, one can't help but wonder: how will electric aviation reshape our local economies and global travel experiences?"

Closing Thoughts: Embracing the Future Together

As we navigate the exciting transition to electric aviation, we're part of a greater movement toward a sustainable and technologically advanced future. This evolution extends beyond the skies, touching the realms of AI in Enterprise, the burgeoning space industry, and the electrification of our daily lives. Each advancement opens a door to new possibilities, fostering a world where technology enhances our lives in harmony with the environment.

But the journey doesn't end here. Join me on Substack and follow me on Twitter for continued insights and discussions on these pivotal changes. Let's explore these dynamic fields together, sharing ideas and perspectives that shape our collective future. 

For those interested in being more than observers, I invite you to join my syndicate, where we delve deeper and take an active role in shaping these innovations.

Stay informed, engaged, and part of the conversation. Let's embrace the future, one breakthrough at a time.

Massive kudos to Jaireh Tecarro and Ravi Raghuraman for sharing their unparalleled wisdom and feedback.

Best wishes,

Chris

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Bootstrapping demystified: Shopify describes it as,

“Launching and scaling a business using personal funds or operational revenues, rather than external investments or loans.” 

Opting for this route means embracing amplified personal financial risks. It's common for founders to tap into personal savings, accrue credit card debt, or secure personal loans to keep their business ventures afloat.

A familiar milestone for bootstrappers is reaching "Ramen Profitability." Coined by Paul Graham of Y Combinator, it signifies a startup's ability to generate just enough revenue to cover the living expenses of its founders—akin to living on a diet of instant noodles. A special nod to Jaireh Tecarro, an experienced designer and angel investor, for suggesting this idea.

In this state, a founder's grit and perseverance become the lifeline of the startup, propelling it toward self-reliance and financial independence.

By sidestepping external funding, bootstrappers retain undiluted control and ownership, navigating the intricacies of business growth within the bounds of stringent financial discipline.

The Bootstrapped Founder's Persona

What makes a bootstrapped founder stand out?

• Autonomy in Ownership: They cherish complete control over their venture, valuing freedom without external investor involvement.

• Steady Progression: Limited resources mean bootstrapped founders typically nurture their business's growth organically, contrasting with the rapid scale that venture capital can fuel.

• Early Profit Focus: Essential to their ethos is a drive toward profitability from the outset, ensuring their venture can stand on its own financially.

• Calculated Financial Stewardship: Every penny counts. Bootstrapped founders are known for their judicious financial management, ensuring their limited funds are strategically allocated.

By weaving in these characteristics, bootstrapped founders chart a unique path that defies the norms of venture-backed growth, often leading to a venture that is as resilient as it is independent.

Spotlight on Bootstrapped Triumphs

Imagine embarking on a business venture armed only with your wits, skills, and slender resources. It's a journey many have navigated, and a few have emerged as benchmarks of bootstrapped success.

Mailchimp's Humble Beginnings

What started as a modest side project for Ben Chestnut and Dan Kurzius morphed into Mailchimp, a behemoth in email marketing. Their journey underscores the power of leveraging in-house talent and minimal funding to cultivate a brand synonymous with innovation and customer engagement.

Braintree's Determined Path 

Bryan Johnson's vision for Braintree wasn't swayed by early investor interest. Instead, he bolstered the company's foundations with personal investments for four solid years. His strategy paid off when well-known VCs like Accel, NEA, and Greycroft finally courted him, affirming the potent mix of patience and self-reliance.

GitHub's Organic Ascension

GitHub's story is a testament to organic growth. Bootstrapping its way through the tech jungle, GitHub has become an indispensable tool for developers globally, proving that strategic ingenuity can prevail even without the initial infusion of external capital.

These narratives aren't just success stories; they inspire the bootstrapper in every entrepreneur. They demonstrate that while the path may be steep and strewn with challenges, the summit is attainable and well worth the climb.

Key Bootstrapping Considerations for Founders

Embarking on the bootstrapping journey requires thoughtful deliberation, especially amidst the current market's unpredictability. Founders must introspect on several fronts:

1. Capability for Self-funding

Ask yourself, can you fuel your startup's journey? Nicholas Woodman's GoPro origin, built on personal savings and a modest family loan, showcases the quintessential bootstrap spirit.

2. Ownership and Empowerment

Bootstrapping preserves your steering wheel. Reflect on Bryan Johnson's Braintree experience, where his financial autonomy translated into a profound sense of control and satisfaction, underlining the value of being the sole architect of your venture's fate.

3. Assessing Business Viability

Understand the economics of your business model and its constraints. Are your customers willing to pay upfront? Is your operation lean and scalable? Can you secure Product Market Fit swiftly? A startup's longevity hinges on these critical questions.

4. Leveraging Networks and Expertise

Location and connections matter, but so does expertise. Founders with a track record in product development and sales can often offset the absence of a broad investor network, using their skills to build early momentum.

In essence, the bootstrapping route demands a mix of self-reliance, strategic planning, and the ability to turn constraints into creative fuel for your startup's engine.

Success Stories: Calendly and Zapier

In bootstrapping, Calendly and Zapier stand out as pinnacles of success. Each company showcases a unique trajectory from modest investments to commanding valuations, underscoring the effectiveness of its strategic growth and value propositions.

Calendly's Product-Led Ascension

From an initial $550K pre-seed round, Tope Awotona's Calendly transformed personal adversity into a scheduling powerhouse. Without initial venture backing, it achieved $70 million in subscription revenue and a $3 billion valuation. Calendly’s ascent is a testament to product-led growth, viral marketing, and a shrewd freemium pricing strategy.

Zapier's Bootstrap to Billions

Zapier began its journey with a $1.4 million seed round, steadfastly adhering to bootstrapped principles en route to a valuation of over $5 billion and $140 million in annual recurring revenue (ARR). The company's success narrative is defined by clear value propositions, a savvy blend of Salesforce integration with a self-serve approach, and a commitment to securing paying customers from inception.

The graphics above echo each company's remarkable strategic milestones, charting their course from startup to multi-billion-dollar valuations.

For a deeper dive into Wade Foster’s strategic mindset and Zapier's bootstrapped rise to a $5 billion valuation, check out this insightful video for firsthand entrepreneurial insights.

In Conclusion: Is Bootstrapping the Voyage for You?

Bootstrapping stands as a bold declaration of a founder's independence and resolve. It's a challenging yet potentially rewarding endeavor where strategic ingenuity is the key to unlocking success without substantial financial backing. The inspiring achievements of companies like Calendly and Zapier are not outliers but emblematic of what's possible.

As you weigh the merits of bootstrapping, take a moment to reflect. Does this path align with your vision and circumstances? It demands unwavering commitment and the savvy to navigate the entrepreneurial landscape wisely.

If bootstrapping piques your interest, dive into the collective wisdom of the startup community. Engage with fellow entrepreneurs on forums like Indie Hackers, search for financial planning tools and resources tailored for startups, and seek inspiration from reads such as Seth Godin's "The Bootstrapper's Bible."

Are you prepared to embark on a bootstrap journey to success? Start evaluating your options and plot your startup's course toward self-reliant triumph.

Up Next: The Y Combinator Effect – How Accelerators Propel Startups

Continuing our Comprehensive Playbook on Startup Fundraising, we conclude the series by turning to accelerators, focusing on the celebrated Y Combinator (“YC”).

Snapshot: Accelerators as Startup Catalysts

• Exclusive Access: YC distinguishes itself with a rigorous selection process, granting more than just funds—it’s a gateway to prestige and a strategic advantage.

• Capital Kickoff: YC’s $500K initial funding significantly extends the runway for early-stage companies.

• Beyond Capital: Admission into YC opens doors to an extensive network and specialized mentorship, crucial for scaling efforts.

• Customer Acquisition: YC participants often secure early customers from within their cohort, paving the way for swift expansion.

• Demo Day Strategy: Startups with solid traction and persuasive storytelling can leverage YC’s Demo Day for better valuation deals.

• Global Impact: For startups outside the U.S., YC’s backing provides a more extended runway due to currency advantages.

Stay Informed: To delve deeper into how YC and other accelerators can energize your startup’s journey, subscribe.

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Closing Thoughts

I hope this piece has been a valuable addition to your entrepreneurial path. If it resonates with you, please share it across your networks, like LinkedIn. Your support is invaluable to this newsletter. I’m always open to questions and feedback, so don't hesitate to contact me via Twitter (X) or email.

Special thanks to Jen Liao and Jaireh Tecarro for their invaluable insights and contributions.

Best,

Chris

P.S. Intrigued? Click the sign-up button below! And if there’s a topic you’d like me to explore, drop me an email.

If you missed it, you can read Part 1 here. It gives important context and essential advice.

Now, let's dive into the next steps for a successful startup launch:

• Building Your Team: Attract and keep people who believe in your mission.

• Strategic Team Building - Advisors: Choose advisors who match your startup's goals.

• Strategic Team Building: Don't give out top titles too early. Use clear goals to decide on promotions.

• Finding Your Product-Market Fit (“PMF”): Listen to your customers. Aim for a product that they can't live without.

Building Your Team: Attract and Keep People Who Believe in Your Mission

• Start with Trust: The journey will be long. Make sure you're with people you trust.

• Diverse Skill Sets: Team up with people who have different skills. This helps cover everything you need.

• Resilience is Key: Find people who stay positive and can handle tough times.

• Flexibility Over Experience: While experience is valuable, in the rapidly changing landscape of startups, adaptability and a willingness to learn can often be more beneficial.

• Shared Sacrifice: Everyone should know the early effort and sacrifices needed.

• Feedback-Ready: Fast feedback is common. Get used to it.

• Cultural Fit: It's important to have shared values and ways of communicating.

• Passion First: Make sure your team cares deeply about the problem you're solving.

In conclusion, while the startup journey, as depicted in the Startup Curve diagram below, is fraught with challenges, having the right team by your side can not only make the ride smoother but also significantly increase your chances of reaching the desired 'Scale' phase. Choose wisely, and remember – it's a marathon, not a sprint.

Mission-Oriented Company: More Than Just Words

A company's mission is not merely a tagline or a lofty declaration; it's the core essence that drives every decision, action, and strategy. It’s true that as a startup, your target customer might evolve, which might also influence your mission. However, the core principles generally remain consistent.

Think of it this way: if your startup is a ship sailing through uncertain waters, the mission is its compass, guiding every move. While the destination (i.e., the specific target customer) might shift, the compass ensures you're always oriented in the right direction.

Real-World Examples:

1. Airbnb: Their mission is to "help create a world where you can belong anywhere." Even as their services expanded and diversified, the core idea of fostering belonging and community stayed central.

2. Tesla: Tesla started with a mission to "accelerate the advent of sustainable transport." As they evolved, so did their mission, but the core theme of sustainability remained. Now, it's "to accelerate the world's transition to sustainable energy."

3. Slack: Originally developed as a communication tool for a gaming company, Slack pivoted to become a standalone product. Their initial focus was on improving internal team communication. However, the broader mission was always about 'making work simpler, more pleasant, and more productive.

When crafting or refining your mission, focus on the larger impact you want to create. Ask yourself: Even if our products or services pivot, what core value do we always want to deliver? This exercise ensures that even as you iterate on specifics, your startup's foundational ethos remains unshakeable.

Defining and Finding "Startup" Personalities

Startups indeed require adaptability and versatility. However, it's a myth that those with extensive corporate experience lack these traits. The real metric is not their work history but their mindset and how they tackle challenges.

Beyond Traditional Structures: It’s not just about “corporate” vs. “startup” employees. What you need are people who can work efficiently without the constraints of traditional structures or procedures. Look for those ready to delve into strategic planning and hands-on tasks. A special nod to Jaireh Tecarro, an experienced designer and angel investor, for shedding light on this perspective.

Sales in Today's Business World: Drawing inspiration from the supply chain logistics platform Flexport, led by founder Ryan Petersen, it becomes clear that sales teams should prioritize genuine client relationships and adeptly navigate their unique challenges. It's no longer solely about chasing "qualified leads" (those potential customers deemed most likely to convert). Instead, the modern sales playbook calls for agility, responsiveness, and a client-first mentality. The goal? To truly understand clients and ask, “How can we tailor our product to meet their specific needs?” My gratitude goes out to Arti Villa, an experienced builder/operator, and angel investor, for her invaluable feedback, which encouraged me to research this topic further.

Traits of a Startup Personality:

• Curiosity: A never-ending thirst for knowledge and an embrace of the unfamiliar.

• Versatility: The ability to take on various roles as required by the startup's growth.

• Problem Solving: They view obstacles as challenges, ready to be conquered.

• Beyond the Job Description: They view their role as a base, always willing to do more for the company's growth.

When hiring, hunt for these characteristics. And realize: they can be found in individuals from all professional backgrounds, whether they've been with an industry leader or a new startup. It's the mindset that matters, not the CV. In the unpredictable startup world, harnessing this attitude is crucial for sustained and meaningful growth.

Missionaries v Mercenaries

"It is about pushing the train, not jumping on the running train." - Jürgen Klopp, a renowned figure in football and the manager of Liverpool FC, my lifelong team.

The essence of this sentiment? Founders should prioritize team members and backers genuinely invested in the mission over those just eyeing the potential for profit. Why? Because those truly aligned with the purpose are your startup's strong supporters. They're the ones driving you forward, especially during the inevitable tough times.

Look at companies like Tesla or SpaceX, where intense work cultures aren't deterrents but magnets for those with a deep commitment to the mission. But it's not all about the big names. Many emerging startups have used the power of mission-driven talent to experience tremendous growth.

Remember, the right people can push your startup from one milestone to the next, even when the journey gets challenging. They're not just there for the easy times.

Strategic Team Building: Leveraging Advisors

Building a startup isn’t just about creating products or services—it's about forging the right partnerships. Often, these partnerships come in the form of advisors. These are individuals who provide invaluable expertise, networking, or mentorship, crucial in guiding a startup through its challenges.

1. Assess Your Strengths and Weaknesses: Before seeking advisors, reflect on your current team's strengths and the gaps you need to fill. This self-awareness helps identify advisors who can offer tangible benefits, like finding the missing puzzle pieces.

2. Conduct Careful Examination: After pinpointing your ideal advisor, it's essential to thoroughly examine their background. Check out their past roles, successes, and lessons learned. Talk to startups they've guided before. This ensures their experience aligns with your needs.

3. Align Expectations: The relationship between you and your advisor requires clear communication. It's not just about their skills but how they deliver them. Are they available for in-depth sessions, regular updates, or on-the-fly guidance?

   For example, Y Combinator startups typically spend 1-2 hours monthly with an advisor. These sessions, though short, provide invaluable insights that can pivot a startup's direction. When discussing roles, consider both the depth and regularity of interactions. By being clear from the get-go, you set the stage for a productive partnership.

4. Document Agreements: Trust is vital, but it's equally important to have everything in writing. This ensures clarity and ensures everyone's aligned, especially in the whirlwind startup environment.

   Regarding compensation, advisory roles and their practices are constantly evolving. Check the attached chart on typical equity offers for advisors. Use these figures as a reference, but remember, every startup is different, and an advisor's value can be more than just equity.

Platforms like Holloway offer more on this topic. For an in-depth exploration, check out Peter Walker’s (of Carta) detailed post on advisor equity nuances.

Strategic Team Building: A Delicate Balance

For startups, crafting the right team structure is vital. An integral part of this is smartly assigning job titles. This isn't just about creating a pecking order; it's about clarity, motivation, and alignment. Inspired by the wisdom of Gokul Rajaram, a notable figure in the tech industry, there's a subtle balancing act at play here.

CEOs, often in a rush to instill urgency and draw talent, might rush title assignments, particularly for senior roles. This "title inflation"—giving impressive-sounding titles to perhaps less-than-impressive roles—can seem enticing, especially for small startups. Imagine having a "VP of Engineering" for just a team of four. It sounds great, right? However, it's wise to tread carefully here. While this might work in the short term, it can later cause confusion and misalignment. A more prudent strategy could be starting with general roles and then offering promotions based on clear milestones. The risks of going overboard with titles often overshadow the benefits. Setting clear lines of authority from the get-go and giving team members more responsibility and freedom often proves a better way to attract and keep talent without resorting to fancy titles.

Titles do have their merits. They clarify roles, give direction, and foster a sense of inclusion. On the flip side, they can lead to entitlement and a jumbled array of titles that confuse the startup's inner workings. That's why some champion a culture without titles early on, where roles evolve based on what people actually do, not just what they're called.

In my own experiences, I've witnessed the pitfalls of mishandling titles. Senior figures from big corporations might struggle when a nimble startup demands a different pace, particularly in the initial growth phases.

Furthermore, as startups come close to finding their PMF, the focus often moves from making a product to growing the business. In these key stages, thoughtfully designating titles and promoting a flexible ethos can guide the team, encouraging adaptability and teamwork.

The Long Path Towards Finding Your PMF

Navigating the journey to PMF often feels like wandering through a complex maze. Amid the whirlwind of contrasting ideas, feedback, and potential routes, founders face the daunting task of identifying the right direction. Reaching PMF goes beyond just building a product—it's about designing an impeccable solution that deeply connects with a large market segment, propelling your startup to new heights.

To shed some light on this intricate terrain, Arti Villa, an experienced builder/operator and angel investor, aptly states:

“Building a product for a single customer is a consulting gig. Building a product for the masses with lukewarm solutions is a recipe for low adoption that blocks the high-velocity feedback loop you need to refine the idea. What you want to build is the most elegant solution, often just one, to a tricky problem so useful that your customers and fans will stick through it until you satisfy all their needs.”

Arti’s insights emphasize the fine line between catering to specific requirements and addressing wider market needs. Achieving PMF requires an unwavering commitment to understanding your target audience, ensuring your solution goes beyond the 'decent' mark to truly excel in alleviating their concerns.

While PMF is deeply connected to product evolution and comprehending customer needs, it's also intertwined with fundraising endeavors. I've touched on aligning fundraising strategies with broader business goals in past writings. Additionally, for those battling through this pivotal stage, Lenny's newsletter dedicated to the nuances of PMF provides invaluable advice and tactics—a must-read for any startup enthusiast.

Following Post Preview: Bootstrapping

In the next installment of our Comprehensive Playbook on Startup Fundraising, we'll delve into the captivating realm of bootstrapping. While it's often perceived as navigating financial tight spots, it's so much more than that. Bootstrapping is about retaining freedom, charting your own course, and savoring the sweet fruits of complete ownership. Let's explore the riveting journeys of bootstrapped behemoths like Mailchimp, Braintree, and Github. Together, we'll uncover the gems from their success stories and ponder whether bootstrapping might be the secret sauce for your startup's meteoric rise.

Closing Thoughts

I genuinely hope this piece added value to your entrepreneurial journey. If it struck a chord with you, I'd be immensely grateful if you'd spread the word via LinkedIn and other social channels. Your support is the lifeblood of this newsletter. Please, never hesitate to ping me with questions, insights, or just a friendly chat.

For the curious souls craving more or those with burning questions, ping me on Twitter (X) or shoot over an email. Massive kudos to Jen Liao, Jaireh Tecarro, Arti Villa, and Ryan Endacott for sharing their unparalleled wisdom and feedback.

Best wishes,

Chris

P.S. Intrigued? There's a shiny sign-up button waiting just below! And if a topic is simmering in your mind that you'd love my take on, drop me a line anytime.

Recently, I had the privilege of meeting with an aspiring founder poised to launch a startup after completing the National Science Foundation's Innovation Corps ("I-Corps") program.

She has an impressive background, character, technical expertise, and experience to lead a startup, making her a promising candidate.

As a founder, operator, and investor, I aim to provide actionable guidance to budding founders like her to ensure they lay the foundation for a successful startup launch.

• Build a Solid Foundation: Learn how to establish a robust founder team.

• Founder Dynamics: Find the right co-founder by asking pertinent questions. Fairly distribute equity to incentivize long-term thinking.

• Customer-Centric Approach: Prioritize customer interactions for product success.

Part 2 will dive into the following:

• Building Your Team: Recruit a mission-aligned team for enduring success.

• Strategic Team Building - Advisors: Strategically bring advisors on board to meet your startup's specific needs.

• Strategic Team Building: Delay assigning high-level titles to maintain flexibility in your startup's early days and encourage contributions to define roles.

• The Long Path Towards Finding Your PMF: Create solutions that customers find valuable for continuous feedback.

Build a Solid Foundation

Successful startups start with a robust foundation, primarily anchored by the founder team.

The founder team is pivotal, often serving as the most critical factor in early-stage investment decisions.

Given that many startup failures stem from founder disputes and a lack of market demand, it is imperative to assemble the right team from the outset.

Founder Dynamics

Finding the Right Co-Founder

Drawing from my experience as a former operator and now an investor, I've witnessed and encountered various instances of founders' misalignment. The significance of founder dynamics for achieving long-term startup success cannot be overstated. The foundation for this lies in trust, unwavering commitment, and the compatibility of co-founders – all of which play a pivotal role.

As Ryan Endacott, a Y Combinator (“YC”) Founder and Angel Squad member, aptly puts it:

“Trust is most important. Make sure they're as committed as you are to building a startup - it's a longer journey than you think.” 

Here are some actionable tips for finding the ideal co-founder:

• Ask the Right Questions: Discuss critical topics such as roles, equity, financials, and work arrangements.

• Get Personal: Delve into each other's strengths, weaknesses, interests, and life stories.

• Set Ground Rules: Arrange in-person meetings, collaborate on a trial project if you’ve never worked together, and conduct reference checks.

• Prioritize Communication: Establish a task prioritization system and regularly hold 1-on-1 meetings.

For those looking to explore this subject more comprehensively, you can find valuable resources in the following links:

• How to Find the Right Co-founder, YC

• 10 Questions to Discuss with a Potential Co-Founder, YC

• 50 Questions to Explore with a Potential Co-Founder, First Round

Equity Split Distribution

Properly distributing equity among co-founders is of paramount importance. YC Combinator provides an excellent guide on how to apportion equity among co-founders.

Here are the actionable key takeaways from YC’s guide:

1. Keep It Simple: Equal equity splits often best preserve co-founder relationships.

2. Avoid Complex Calculations: Elaborate contribution-based models can lead to problems.

3. Consider 51/49: It can indicate commitment in case of team separation while still maintaining essential equality.

4. Minimum 10% Equity: YC defines a co-founder as someone holding at least this equity level, underscoring its significance.

5. Early Discussions: Address equity distribution in initial conversations to prevent future conflicts.

6. Document Agreements: Promptly record the agreed-upon equity split to prevent ambiguity.

7. Stability Matters: Altering equity post-agreement can disrupt co-founder relationships.

8. Implement Vesting: Establish standard vesting, typically four years with a one-year cliff, for seamless transitions.

However, from my experience as a founder, I can’t entirely agree with the notion of equal equity splits. It's vital to consider roles, responsibilities, and expectations for effective company building rather than adhering to an arbitrary equal split for the sake of harmony.

The key takeaway is the importance from day one, establishing a solid agreement, as many founders encounter conflicts related to equity distribution.

For those interested in how other startups have divided equity based on founding team size and industry, check out Carta's Co-founder Equity report.

Customer-Centric Approach

The core of startup success revolves around satisfying customer needs. Early engagement with customers is pivotal to shaping the right product.

As highlighted in the post Finding Next-Gen Founders and Ideas from Research Labs, the focus should be on the following:

1. Identifying Vital User Needs: Understand your potential users' fundamental requirements and pain points.

2. Assessing Improvement Over Existing Solutions: Determine if your product significantly enhances existing market solutions.

3. Evaluating Value Proposition: Define the value your solution offers and its significance to potential users.

4. Understanding Requirements: Comprehend essential requirements, spanning product form factors to regulatory considerations.

Emphasize customer interactions during the early stages to prioritize learning and insights over immediate fundraising.

Following Post Preview: Launching Your Startup Successfully (Part 2)

In Part 2 of Launching Your Startup Successfully, I will cover the other actionable guide for aspiring founders, including:

• Building Your Team: Attract and keep people who believe in your mission.

• Strategic Team Building - Advisors: Choose advisors who match your startup's goals.

• Strategic Team Building: Don't give out top titles too early. Use clear goals to decide on promotions.

• Finding Your Product-Market Fit (“PMF”): Listen to your customers. Aim for a product that they can't live without.

Closing Thoughts

If you found this post valuable, please share it on professional networks like LinkedIn and social media platforms. Your support means a lot to this newsletter. Feel free to ask questions or feedback; I'm eager to hear from you.

If you're eager to learn more or have questions, contact me on Twitter (X) or by email.

A special thank you to Jen Liao, Jaireh Tecarro, Arti Villa, and Ryan Endacott for their invaluable input.

Best,

Chris

PS: Sign up ☟ if you find this interesting, and email me if you have excellent topics you'd like me to explore further!

While our discussion covered market trends, I also shared valuable fundraising advice tailored especially for founders. Navigating the intricacies of the fundraising landscape can be challenging, and in this post, I aim to provide practical insights to support founders in this journey.

In this post, we'll delve deeper into tactical advice, including:

1. Time Constraints: As we approach the holiday season, discover why time is of the essence for founders aiming at substantial fundraising rounds.

2. Venture Market Decline: Explore the continuous downturn in deal volume and capital raised, as outlined in Carta's latest venture market report.

3. Adjusting Your Narrative: Learn why adjusting your fundraising story is crucial in the current market conditions.

4. Investor Relations Strategies: Gain practical insights into managing investor relations during challenging times.

5. Proactive Fundraising Practices: Explore savvy moves beyond pitch meetings.

6. Decision Dilemmas: Founders contemplating funding during the downturn face crucial decisions and need contingency plans.

Time Is Not On Your Side

As you gear up for your fundraising efforts, here's a critical heads-up (as of the publication date, October 17th).

With the holiday season approaching, time is of the essence. You have just one month to wrap up your fundraising initiatives, primarily if you aim for a substantial round beyond the pre-seed or friends & family stage.

In addition, investors may find it challenging to process transactions due to uncertainties tied to broader economic conditions. Hence, thoughtful planning is critical for founders navigating this time-sensitive landscape.

Continued Decline in the Venture Market

Adding to the complexity is the prevailing decline in the venture market, a trend outlined in Carta's latest venture market report. Quarter after quarter this year, deal volume and capital raised have witnessed a continuous downturn.

According to Carta, one reason behind the fundraising slowdown is the differing expectations between investors and companies. Investors are eager for a reset in private valuations to mirror the decline seen in public valuations. 

However, most companies are cautious about opting for a down round. As investors await more clarity, companies are cutting back on expenses to reduce burn rates and are patiently waiting for a more favorable fundraising environment.

In addition, this downturn is now trickling down to impact fundraising at the early stage, as illustrated in the chart below:

For more in-depth insights, you can explore the complete market report here. I highly recommend following Peter Walker, Carta’s Head of Insights, to stay abreast of regular market updates.

In the face of these market dynamics, I want to provide some actionable insights to navigate these challenges successfully.

Adjust Your Narrative To Embrace the New Reality

Once founders recognize the urgency and current market conditions, adjusting the fundraising story is crucial.

This adaptation aims to build momentum, expedite round closures, and ideally position your startup to command premium pricing with heightened investor interest.

Let's explore the two main narrative categories.

Capital Efficiency

In the Zero Interest Rate Policy (“ZIRP”) era, the venture market saw an influx of capital that fueled investor enthusiasm for startup investments. This enthusiasm translated into a surge in deals and premium valuations for founders.

Amid this era of abundant capital, there was a prevailing mindset favoring founders to prioritize rapid growth at any cost, leading to overhiring.

However, as the market dynamics shifted, so did investors' sentiments toward prioritizing efficient growth.

The key takeaway for your narrative crafting should include:

• Demonstrating that your company operates with a leaner structure and enhanced cost control.

• Outlining the path toward achieving cash flow positivity or self-sufficiency.

• Presenting realistic milestones and expectations with a transparent view of the burn rate and runway.

Show, Don’t Just Tell

Continuing our discussion on adjusting your fundraising story, let's discuss what's happening in the startup world. In the recent YC Summer 2023 group, startups were nudged to speed up their sales game and prove they're gaining traction in the market.

“Many startups wait quite a while to build a product, test, and get into the market, but I was struck by the speed at which this cohort gained commercial traction. In previous years, companies would spend more time ideating, but the changing market conditions seem to have lit a fire under founders to make their business revenue at the outset.” - Eli Blee-Goldman’s LinkedIn post.

Chatting with investors is not just about pitching cool ideas, especially in a more challenging market. Unless you're one of those super cool AI startups, getting funding for your ideas might not fly.

What's even more important today is to show real progress. It's not only about saying you're making money but proving it's sustainable and of good quality. Back then, you could get away with fancy numbers that didn't mean much, but now investors want more substance.

That means:

• Winning big deals with enterprise companies.

• Showing off important ways your product or service is being used.

• Proving your contracts are getting bigger.

• Make it clear that people who use your stuff keep coming back, and they're not leaving.

In this world where every dollar counts, investors are wary of founders relying on small deals that might not stick around. So, when talking about your startup, ensure you're not just telling a story but showing the real, solid progress you're making. It's not just what you say; it's what you can prove!

Take Charge of Your Fundraising Journey

Now that you've got some insights into shaping your pitch deck and impressing investors in meetings let's not overlook the opportunities to manage your fundraising process for quicker success actively.

Do Your Homework on Potential Investors

Smart founders who've been around the fundraising block know that doing some homework on potential investors pays off big time.

Different types of angel investors exist, especially in well-established startup hubs like the Bay Area. Many of these angels are tech-savvy operators or former founders themselves. They invest for various reasons, such as:

• Hunting for potential big winners to build a track record for a future venture capital career.

• Wanting to support founders in their specific area of expertise, whether it's B2B sales or developer tools.

• Backing underrepresented groups, like minorities and women, who might face challenges in attracting early-stage capital.

Knowing the type of investor you're dealing with helps you tailor your story to match what they care about.

For founders eyeing VC funding, I often advise them to dig into the backgrounds of key partners in the VC firm and understand the fund's overall mission. This way, you can explain why your startup is the perfect fit for their support.

Consider the Current Climate

In today's financial climate, it's essential to recognize how the fundraising landscape is impacting venture capitalists (VCs). Many VC funds find it challenging to gather capital from their investors (LPs) because their money is tied up in previous funds. LPs have too much invested in risky assets, making things even trickier.

Here's where founders need to be savvy. Knowing which VCs are actively fundraising and which ones might be a bit cash-strapped can guide your strategy. Prioritize your outreach to those with the means to invest, making your fundraising journey more targeted and effective.

Other Best Practices

Let's dive into some additional savvy moves that can make your fundraising journey even more effective.

Smart startups I've come across don't just stop at pitch meetings—they share Notion documents before and after. Why? It's a twofold win: First, it answers those Frequently Asked Questions (FAQs), and second, it allows founders to address the critical risks of their companies proactively.

This proactive approach streamlines the process and nudges investors toward quicker decisions.

Another gem is creating a handy "Read Me" guide in your data room. This guide acts like a compass for investors, helping them navigate critical data such as customer contracts, financials, and patents. Making vital information accessible positively reflects on your startup and significantly speeds up decision-making.

For founders tackling the challenge of building a financial model for a price round such as Series A, check out Roy Bahat's Making a Financial Model for your early-stage startup post and watch Kirsty Nathoo's Managing Startup Finances video. These resources provide invaluable insights into structuring your financial plans for success.

Given my deep finance background, I often counsel founders to invest extra time discussing financial modeling assumptions. While predicting the next five years for an early-stage startup might seem like a crystal ball exercise, focusing on the assumptions section is vital. A robust assumptions section should tackle critical questions:

• What are the key drivers?

• Where do the unit economics improve over time or with scale?

• What are the key milestones?

• What are the founders' long-term plans to achieve their goals?

In summary, supplemental documents like Notion files, FAQs, Read Me guides, and Financial Modeling Assumptions should seamlessly tie back to the crucial points we explored—Capital Efficiency and Show, Don't Just Tell. These practices enhance the efficiency of your fundraising process and reinforce the narrative you're building.

Closing Thoughts

For those founders fortunate enough to secure funding during the ZIRP era at record-high valuations, a crucial decision looms: whether to raise during a downturn to execute expansion plans or await a market upswing.

As explored in the Considerations In Fundraising and Financing Trends recording, the market's resurgence hinges on robust IPO deals bringing liquidity back to LPs and increased macro certainty, often tied to interest rate changes.

Startups that have secured at least one funding round but are yet to attain Product Market Fit face a pivotal question. By delving into Paul Graham's essay, determine whether your startup is in a "default alive" or "default dead" status. If fundraising is on your radar, brace yourself for an extended process and heightened competition for limited investor capital. Have contingency plans (B and C) ready based on your startup's default status.

Feel free to share your thoughts below or contact me for more insights.

For those seeking detailed data on the current venture market by sector or geography, delve into Carta’s latest Q3 report.

Following Post Preview: Launching Your Startup Right

As we navigate the world of fundraising, it's essential to lay a strong foundation. In the next post, I'll share crucial advice for budding founders considering the launch of their startup, including:

• Build a Solid Foundation: Explore the vital elements for a successful startup launch.

• Founder Dynamics: Discover actionable insights with '50 Questions to Explore with a Potential Co-Founder,' ensuring a solid foundation for enduring partnerships.

• Customer-Centric Approach: Learn why talking to customers is critical to creating the right product for the right audience.

• Building Your Team: Understand the importance of finding the right people for the long journey ahead.

Last but not least, if you've found posts like these valuable, subscribe to Ante Up.

Subscribe now

My prior guide is a must-read for founders searching for a comprehensive playbook on fundraising.

If you're eager to learn more or have questions, contact me on Twitter (X).

Thanks always to Jen Liao and Jaireh Tecarro for the support and feedback.

Best,

Chris

PS: Sign up ☟ if you find this interesting. Email me if you have excellent topics you'd like me to explore further!

In my few years as an angel investor, I've learned that building a robust support network for founders sets me apart.

Another differentiating point is that I find extraordinary founders before their companies are obvious opportunities.

As a reminder, PaperJet Ventures invests in founders with bold and disruptive ideas at the earliest stages.

This summer, I mentored a promising team out of MIT through the National Science Foundation's Innovation Corps ("I-Corps") program.

Before I forget, here is a quick shoutout to Databento's founder & CEO, Christina Qi, for the intros that led to this opportunity.

While mentoring a research team is a new experience for me, it undoubtedly aligns well with PaperJet Ventures' goal:

• Invest deeply in building relationships with future founders before they start their entrepreneurial journeys. (As a joke with my fellow angel investor friends, I refer to this as my "pre-pre-seed strategy").

• Get a glimpse of the future with technological innovations from the research labs (great examples include GPS and computer vision) that could shape the future and launch future trillion-dollar industries.

In this post, I will share some of the significant learnings and discoveries from this mentoring experience, including:

• Institutional research labs are vital to the US innovation ecosystem, given their long history of bringing game-changer technologies to the world.

• How the I-Corps program prepares scientists and engineers to bring innovations from labs to the market.

Startup founders who are reading this might answer questions such as:

• Is there a path to start a company from these innovations?

• Is being a founder the right direction to take for the next step in your career?

A Brief History of Research Labs

First, we need to understand the importance of research labs to the US innovation ecosystem.

Emerging technologies from institutional research labs funded by large governmental, academic, and corporate institutions have existed for a while. 

One of the more prominent research programs in the world is the Defense Advanced Research Projects Agency ("DARPA"), a research and development agency of the United States Department of Defense. DARPA is responsible for the military development of emerging technologies and plays a vital role in major technological breakthroughs that we are familiar with today, including the Internet, GPS, computer mouse, drones, and many more.

University-affiliated research labs, including MIT and Stanford, have launched many significant innovations such as computer vision, email, graphical user interfaces, and blockchain.

Larry Page and Sergey Brin developed PageRank at Stanford University before founding Google. Nonprofit research labs such as SRI International gave us the first surgery robot, Davinci, the first virtual personal assistant, Siri, and many more. For more information about the history of institutional research labs, here is an excellent read about this subject.

Ideas to Market

The following chart provides a high-level roadmap of how research lab ideas are commercialized and launched to the market.

In the first stage, many university research labs work on foundational research in essential areas such as material science and biology.

Once teams such as our MIT team achieve a technological breakthrough, the next stage is to talk to users and companies in the Customer Discovery process, including Use-Inspired Research and Proofs-of-Concept, as shown in the chart above.

The National Science Foundation ("NSF") / I-Corps program helps the research teams to transition to the next stage by providing an immersive, seven-week experiential training program that prepares scientists and engineers to extend their focus beyond the university laboratory.

"The high-level goal of this program (I-Corps) is to increase the odds that publicly funded research can create impacts on society." - Roman Lubynsky, Executive Director at MIT I-Corps.

I-Corps serves as a bridge connecting the worlds of academia and industry. It encourages university teams to connect with companies, associations, and sometimes government agencies. 

These meetings aim to grasp what users genuinely need and gather valuable insights about the industry environment. These insights guide how to effectively bring their innovations to the market for commercial success.

Each university-affiliated I-Corps team typically consists of an Entrepreneurial Lead (EL), a Technical Lead (TL), and an Industry Mentor (IM). As an IM, my role is to guide the team to derisk essential product and business developments by:

• Help identify customers for interviews and make introductions to arrange some interviews.

• Review and critique team presentations.

• Facilitate reflection on and integration of customer feedback.

• Be a sounding board for business model canvas (BMC) elements, a strategic management template for developing new business models. Here is an excellent guide if you are interested in learning more.

Customer Discovery

The Customer Discovery process aims to guide startups toward building the right products for the right customers. Here are the typical steps:

• Observing and defining a phenomenon (problem or market need)

• Developing a hypothesis about a solution to the problem (business idea)

• Experimenting to test the hypothesis (getting "out of the building")

In my previous Product Manager role, I conducted our company's customer interview process by speaking directly with enterprise customers. Through these interviews, we can extract vital insights guiding our product and business developments.

In comparison, I-Corps program directors pushed teams to gather insights from 100+ customer interviews during the 7-week program and accomplish what typically takes 18 months or more. 

In addition, teams are not allowed to disclose the tech or what they are working on (which makes it challenging) during the interview, as it might bring bias to the meetings. 

Questions are generally structured to extract insights and learnings, such as:

• What are the vital user needs?

• Is there a significant improvement over the existing solution?

• Is there a substantial value to this potential solution?

• What are the primary requirements (from product form factors to regulations)?

At this stage, teams are also trying to figure out a business model from this discovery process. If there is a potential problem/solution fit, teams would determine if they should start a company or partner/license the technologies.

Quality Matters More than Quantity

While finding 100+ companies to interview in such a short span is daunting, I saw many teams are resourceful and quick learners to meet the quota by leveraging intros from their networks. 

While understanding user needs and pain points is vital to building the right product for the right customers, it's equally crucial to understand the degree of the potential customers' willingness to pay for the solution. 

As part of my preparation, I often researched the target company beforehand so that I could guide a portion of the interview conversations towards:

• Understand the target customers' business model and how our product can align with their strategic goals.

• Obtain better contexts to determine if our solution is a potential vitamin or pain killer.

• How does the decision-making process work with all the stakeholders within the company and external partners?

Derisk Market, Sales & Distribution

For many startups, finding the initial market segment to target the initial customers (aka the beachhead market) for a new product is challenging when there are multiple possible markets to consider. 

While the I-Corps program provides a structure for teams to conduct as many interviews as possible during the 7-week program, my main feedback to the directors was teams should spend more time on deeper conversations that can help them figure out:

• What's the right beachhead market to enter that aligns towards near-term and long-term expansion to avoid the risk of a niche market player?

• Derisk the long sales cycle process that often plagues many early-stage companies.

• Understand the complexity of the ecosystem to provide insights into distribution/partnership plans and figure out their trade-offs.

Self-Discovery - Should I be a Founder or Work For a Startup?

For many scientists and engineers, the I-Corps program forces many intelligent PhDs to go outside their comfort zones (i.e., labs 😀 ) and speak to as many people as possible.

Through this experience, they can get a glimpse of startup life to determine if entrepreneurship or working for startups is the right path for their career.

My Reflections

Mentoring at the I-Corps program certainly provided some flashbacks of my former life as a startup founder and product manager. 

Many founders could benefit from a similar rigorous structure that can help them validate their ideas and learn how to build products and companies properly in the early days by spending more time with customers instead of fundraising.

Overall, it has been a great experience to see the future class of founders and how tech comes out of the lab to the market. The mentoring experience aligns with my goals as an early-stage investor.

In addition, the I-Corps program provides teams with plenty of insights but only some of the answers they seek. 

In many instances, some teams might need additional customer discovery to figure out their beachhead market and determine whether starting a company is the next step.

Lastly, for anyone asking if any possible deal flow is coming out from this mentoring experience–that will be a subject for a future post. 

My current focus is to continue nurturing these future founders' deep relationships and learn more about the innovations.

Final Note

If you find posts like this helpful, please subscribe to Ante Up. For founders working on exciting problems, check out my Investment Thesis 1.0.

For any potential founder currently at Big Tech or fast-growing startups, my fellow angel investor friends and I would love to chat and connect you with our network of founders, experts, and investors.

If you want to learn more or have more questions, please contact me on X (fka Twitter).

Thanks to Jen Liao, Jaireh Tecarro, Alejandro Sevilla, and Haining Gao for reading the drafts of this post.

Chris

PS: Sign up☟ if you find this interesting. Email me if you have any excellent topics for me to explore more!

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For the founders reading this post, I want to provide a comprehensive guide on fundraising effectively that aligns with your startup’s business goals from ideas to Series A.

I will show you how to balance those two objectives while figuring out the following:

• How much money do you need to raise to hit the following milestones?

• How much dilution should you be comfortable with for each funding round?

Dilution refers to the part of the company you give to investors in exchange for their investment in your company’s equity.

In the following posts, I will provide other distinct fundraising methods, including bootstrapping and applying for accelerators such as Y Combinator, that you can consider. 

Roadmap

First, we will use the following Product-Market Fit (“PMF”) guide as our roadmap to help us identify our business goals and structure fundraising accordingly.

Note that many builders’ actual product development experience is often less linear than the above guide shows.

Nevertheless, this guide provides an excellent exercise on how to align fundraising with business goals. 

Let’s assume we are first-time founders with a limited network trying to figure out how to launch a B2B enterprise software company. Here is the snapshot of our fundraising roadmap.

Idea Phase

In the first phase, the primary goal for anyone considering starting a company is to validate the Market Opportunity by understanding your customer’s problem.

“Around 35% of startups fail because there is no need for their product in the market.” - Problem Validation And Customer Discovery Interviews.

In the pre-launch stage, we first prioritize understanding our customer’s problems through customer discovery interviews. Before quitting our job to start this company, we should validate the customers’ key pain points. You can read more about this here.

Since customer conversations require only slides, questionnaires, and time commitment, there is no need to start a company and do any fundraising officially.

Concept Phase

In the concept phase, our business goals are to prioritize the key customers’ problems from the customer discovery interviews and ideate/design a solution.

Since this phase requires an early product design ranging from initial mockups to prototypes, we can raise a small amount of money to undertake this project.

At this point, we can also consider officially starting the company (at least part-time).

Here are some key considerations:

• Can we do the early product design ourselves or hire/outsource a UI/UX designer/engineer?

• Can we use our money to fund all the ongoing costs (bootstrap)?

If the answer is “no,” we need a $100,000 budget to cover the initial cost, with additional reserve for iterations. We can consider the following:

• Register the company using Stripe Atlas and get a business account with neo-banks such as Mercury. Given the recent banking crisis with SVB and First Republic Bank, you should do additional due diligence on finding the right banking partner for your startup.

• Raise $100K in the Friends & Family round, including any angel investor from your network. I will use Simple Agreement for Future Equity (SAFEs) as the main instrument for early-stage fundraising. Here is the SAFE document template. 

• Using SAFE saves us $25-50K of legal fees usually associated with issuing equity.

• Since the startup is still in the idea/concept phase, let’s set the price at a “generous” $3M post-money valuation.

Let’s assume our company starts with an initial 10,000,000 common shares. We also created an option pool for employees, advisors, and consultants throughout the company's life.

Here is our pro forma cap table at inception.

Don’t forget that SAFE investors are not on the cap table until the SAFE investments convert at the following priced round (ex: Series A). 

Here is a separate table for us to keep track of any potential dilution. After the friends & family round, we estimate that our founders' ownership is around 89.2% by subtracting the SAFE dilution and option pool.

At this moment, we will start planning our future fundraising by building our investor network in the following buckets:

1. Angel investors from operators/investors like myself and my friends at Hustle Fund Angel Squad.

2. Micro VC/small funds ($10M or less fund size) & syndicates like PaperJet Ventures.

3. Institutional VC/seed fund.

Committing Phase

After iterating our product concept, our next goal is to launch its beta version to target early adopters. At the same, it’s time to make our first hires, especially for the engineering and design teams, depending on needs.

We will raise a $250K pre-seed round for this phase at a $6 million post-money valuation. This budget assumes we are operating very lean by forgoing any founders’ salary and going fully remote.

We primarily target value-added angel investors for the first fundraising and are open to accepting checks as small as $5K. While the checks are small, early angels can add value through introductions to their network of other angels and connections with institutional VCs who could be in your cap table as you progress. 

In Freshpaint’s insightful post, angels’ value-adds illustrate how any unnamed angel investor’s $5K check was responsible for over 50% of its seed round.

Value-add angels/operators are very helpful in the early days, ranging from providing warm intros to other investors and sharing expertise in crucial areas such as product design and GTM to providing reputation signals.  

We will build a pitch deck based on our founders/market fit narrative, market size, and highlights of our current learnings and tractions.

The table below shows the potential SAFE dilution after the pre-seed round. We estimate that our founders' ownership is around 85.0%.

Validating Phase

In this phase, our goals are to launch product version 1.0 and show our product is working with key metrics that can set us up for Series A funding.

What You Should Expect

In this phase, startups spend more time experimenting (sometimes pivoting from initial ideas) before things start to work. There is a high default dead risk as founders often underestimate the time to build and find PMF and realize that their companies need more capital than initially estimated.

High Hurdle to Series A

In the current fundraising climate, there is a high hurdle for B2B enterprise software startups raising Series A.  I asked an experienced early-stage VC to provide the following valuable insights. 

“Vertical/sector matters a lot. I have portfolio companies with less than $1M ARR that were able to raise Series A because it’s GenAI. Some companies at $2.5M+ ARR are capable as well.

On the other hand, some companies with $2M+ ARR struggle to raise this round given their market and vertical. Growth rates matter also.”

For this exercise, we must hit around $1.5-2M ARR with 50-100% year-over-year growth and consistent retention metrics.

Next, we must consider that our team will need various sales, partnerships, and marketing channels to acquire enough customers to hit the key metrics. Given this requirement, we must grow our product, design, engineering team, and early hirings in other areas, especially customer support/service, to anticipate incoming customers.

We will raise a $1.75 million seed round based on the above budget plus office-related costs. It also requires us to plan for an 18-24 months runway toward Series A. If you need help calculating the runway for your startup, read this guide from Brex.

Raising a seed round also means founders can finally compensate themselves. According to this guide, a founder's salary is typically 50K at Pre-Seed, 100K at Seed, and 150K at Series A.

Given the size of the round, I recommend raising it in 2 tranches:

By first reaching out to angel investors and small funds/syndicates for Seed I, we can close the funding faster and use the funds to build the product and momentum.

Raising from institutional venture funds takes longer than anticipated, a critical risk for many early-stage startups struggling with capital.

Raising in increments also helps us to be disciplined with costs and avoid over-hiring.

We can start to reach VC firms as soon as we hit some momentum in Seed I fundraising or some inflection in our product development and sales.

The table below shows the potential SAFE dilution after the two tranches of the seed round. We estimate that our founders' ownership is around 69.2%.

Scaling Phase

In the last phase (for this exercise), we aim to scale our sales to early adopters with a repeatable sales process and transition towards building the company.

A repeatable sales process means we have a proven sales playbook that can identify primary users, decision-makers, buyers, and influencers. We have successfully fine-tuned our product, pricing, and messaging. We are close to PMF or about to hit it during this phase.

Achieving this milestone also allows us to gradually transition from founders-led sales to handling all the GTM and sales activities to a sales team we need to build. In addition, this company-building step will shift our attention towards expanding our products or services into new markets. 

We will raise $10 million of Series A at a $40M post-money valuation. Series A's usual range of dilution is around 15-25%.

Given the market uncertainty affecting Series B, it’s prudent to plan Series A to account for an additional runway of up to 24 months to prepare for possible prolonged fundraising in the following Series B round in exchange for giving up 25% dilution ($10M round/$40M post).

Here is what our cap table looks like after Series A. I will share how priced round conversion works in the next post.

Play the Long Game

For any founder who thinks about optimizing for high ownership and valuation after looking at this example, I advise them to take a long view of their companies.

Startups’ valuations, driven by supply and demand, depend on many factors, including stages, sector, macroeconomic (interest), competition, and relative comps.

Instead of over-optimizing your fundraising structure by targeting the lowest dilution for each round, here are my advice:

• Raise quickly on fair pricing with the right investors on the cap table so you can get back to the business of building the product/company.

• There is less pressure to grow to justify any inflated valuation.

• Missing key milestones, especially during a market downturn, can lead to down rounds with investors asking for more control.

• When investors have control, they could replace you with external CEO if the company’s growth is below expectations.

When it comes to fundraising, you should always align with business goals first. Once you execute, you will be in a stronger position to control your destiny and optimize for the best terms in the future.

Final Note

For founders working on exciting problems, check out my Investment Thesis 1.0.

If you want to learn more or have more questions, please get in touch with me on Twitter.

Thanks to Jen Liao and Jaireh Tecarro for providing valuable insights and reading drafts of this post.

Chris

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In the last post, I announced the launch of my new syndicate, PaperJet Ventures (“PaperJet”), after investing in tech startups for the past two years. Here is my investment thesis in two versions - teaser and extended (which I highly recommend):

Teaser

Innovation drives us forward. Artificial Intelligence (“AI”), semiconductors, and battery tech are not just changing the game but creating a new one. PaperJet seeks disruptors who can transform how we work and move.

We’re interested in Deep Tech/Climate, AI, and Vertical Software as a Service (SaaS).

Deep Tech/Climate

The continuous improvement of battery technology and its declining cost make it a great time to invest in startups that can power up more than just cars.

Self-driving cars, saving energy, and better ways to make things are essential to us. PaperJet wants to work with technical founders with a laser focus on solving fundamental problems holding the industry back and preventing widespread adoptions.

Artificial Intelligence (AI)

Applied AI is transforming the world. With “Copilot for professions,” we can boost efficiency and improve job satisfaction in sectors like finance and law. PaperJet seeks experts with domain expertise to build Large Language Models (LLM)-powered services.

Vertical Software as a Service (SaaS)

In traditional industries, SaaS solutions will improve processes and digitize work. PaperJet searches for founders with experience in the telecom, energy, and natural resources industries.

I recommend reading the following extended version of the investment thesis to learn more.

Investment Thesis 1.0 - Extended Version

PaperJet Ventures invests in Disruptive Innovation at the earliest stages.

Technology breakthroughs such as AI, advanced semiconductors, and battery technology enable and accelerate innovation.

As these technologies continue to evolve and, in some cases, converge, PaperJet is looking for disruptive innovations to transform how we move and work. Here are PaperJet’s three focus areas.

Deep Tech/Climate

The proliferation of Electric Vehicles (EVs) has revitalized the once-stagnant auto industry and propelled it forward. Extensive climate-oriented programs like the Inflation Reduction Act, the global phase-out of fossil fuel vehicles, and strong public support are additional tailwinds shifting the world toward sustainable energy.

The continuous improvement of battery technology and its declining cost curve (as shown below) provide the main catalysts for PaperJet investing in startups that can electrify other major transportation, including aerospace/aviation, marine, and commercial/cargo.

Key players such as Tesla, CATL, LG, Panasonic, and Redwood Materials are making significant inroads in cell design, advanced battery materials, and vertically integrated battery production and recycling. 

As 300+ “Gigafactories” are coming online by 2030, the expected economies of scale will drive battery costs downwards in the long term.

PaperJet has some views about solving significant constraints holding the industry back and preventing widespread adoptions.

• Accelerate adoptions with breakthrough technologies, including autonomous mobility, next-generation powertrains, electric propulsion systems, energy storage, and charging infrastructure.

• Solve current supply chain bottlenecks for crucial components (semiconductors and sensors) and parts through localization & integration.

• Achieve faster economies of scale, including volume production, through advanced manufacturing with scalable solutions such as vertical integration & automation.

• Reduce the idea-to-revenue/commercialization cycle, and improve visibility to risk and performance with software-enabled solutions.

• Increase the knowledge talent pool in the sector.

PaperJet invests in startups looking to solve these problems or provide the necessary infrastructure for other companies to solve them. Not only these innovative startups can disrupt the sector, but they also create a new ecosystem that can revitalize the stagnant manufacturing sector that badly needs innovations.

PaperJet is looking for technical founders with a laser focus on problem-solving who create an agile and execution-first company culture that can attract capital and talent. 

Artificial Intelligence (AI)

The rise of Generative AI and Large Language Models (LLM) provides the possibilities of how applied AI can transform our current work. 

Github Copilot provides an example of how an AI tool can help engineers become 100x more efficient in creating and deploying code while making their work more meaningful and enjoyable. 

AI’s breakthrough has made inroads in healthcare, robotics, and other fields outside of tech.

As these models and their applications evolve, PaperJet wants to invest in “Copilot for professions” in idea-intensive industries such as financial services, accounting, legal, and consulting.

With the correct AI tools, professionals can spend less time on routine activities such as collecting information, analyzing data, or preparing reports. Since these professions have much higher client-facing job requirements, actual work will involve more expertise application, interaction with key stakeholders, and people management. The expected outcome will address these industries’ problems with employee turnover, low job satisfaction, misalignment of pay structure to outcomes/impacts, overhiring, and more.

Given how fast AI is evolving, PaperJet seeks founders with a strong balance of domain expertise, intellectual curiosity, and agility to identify technological shifts to create new opportunities.

Vertical SaaS

Finally, another key focus is vertical SaaS solutions for traditional industries, such as telecommunications, energy, natural resources, and utility. Before any possibility of applied AI in these industries, there is a more significant and immediate need for enterprise SaaS solutions to solve major pain points in their business processes while digitizing knowledge work to enable collaboration and workflow automation.

PaperJet is looking for founders with the right domain experiences within telecom, energy, utility, and natural resources since these industries provide the critical infrastructures that power the startups within our deep tech and AI focus areas.

As these SaaS companies can collect their customers’ proprietary data and train them with the foundational models, their products become more useful with AI-enabled opportunities ranging from workflow automation to valuable analytics and insights.

Final Note

For founders working on interesting problems, especially within those core focus areas, please email me at christopher.j.kong@gmail.com.

If you are an accredited investor interested in angel investing, consider joining PaperJet Ventures.

If you want to learn more or have more questions, please contact me on Twitter.

Thanks to Jen Liao and Jaireh Tecarro for providing valuable insights and reading drafts of this post.

Chris

PS: Sign up☟ if you find this interesting. Email me if you have any excellent topics for me to explore more!

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After investing in tech startups and building a network of founders, experts, and investors for the past two years, I'm taking the next natural step and starting my syndicate, PaperJet Ventures.

What is a syndicate?

A syndicate is a group of investors that pools their capital to invest in deals (SPVs). As the syndicate lead, I first source a deal and then invite other accredited investors to support the investment in exchange for them to pay a carry, which is a percentage of the investment's profits.

Why Now?

Since I started my angel investing journey in early 2021, I have invested in over 50 startups. You can see a summary list on my Notion page. 

I've learned much on my rollercoaster journey, from the bull market's peak to the downturn driven by an uncertain macroeconomic environment, with rising interest rates and recessionary fears. I have documented my learnings on this blog.

These learnings help me refine my decision-making process. As the market exuberance has ended, we are reaching an exciting point as investors.

There is no better time to invest in startups.

Valuations have come down considerably to historical "normalized" levels.

The challenging fundraising environment has created a new cohort of founders who are more gritty and resourceful in dealing with near-term uncertainty.

I am excited to see more founders have a longer-term mindset with a greater focus on managing their startups' burn rate and building sustainable businesses in these uncertain times.

PaperJet Ventures aligns with my plan to scale my angel investing in hard tech, AI/ML, and SaaS. I can continue to build my network, knowledge, and capital through PaperJet Ventures to capture more investing opportunities, especially with my angel investor friends (many from Hustle Fund's Angel Squad).

What is PaperJet Ventures About?

PaperJet Ventures invests in founders with bold and disruptive ideas at the earliest stages. For founders, PaperJet Ventures provides real tactical support for promising founders with my deep finance background and Silicon Valley experience.

With my finance skillset, I have the knowledge and tactical skills to advise founders to navigate complex fundraising and investor management processes. I'll work on behalf of your interest, from tactics and deal mechanics to sharing "insider" knowledge.

As a former founder, I learned firsthand how challenging it was to build companies from zero to one. As a former product manager, I have the experience and insights to help early-stage startups to find their product market fit.

With these experiences and strong empathy, I am the right ally for the founder on the cap table.

At PaperJet Ventures, I seek deals where I can add value, such as helping founders with fundraising, business development, product/design advisory, and connecting them to the right people! (More on this in the Startup Helper section below.)

Core Areas of Focus

On a high level, PaperJet Ventures’ investment style is identical to how I currently invest based on my own operating experience, industry insights, and network. 

Before working in tech, I spent many years in investment banking, where I gained insights from covering various industries. I closed US$15+ billion of deal transactions in the media, telecommunications, natural resources, transportation, and utility industries during that time. 

These insights gave me the foundational knowledge to perform deep research and due diligence, which has helped in my public and private investing process.

In addition to the network I built via my professional experiences, I was fortunate to find other like-minded investors who shared my passion for startups, especially the ones I connected with from Hustle Fund’s Angel Squad.

Based on these three criteria, PaperJet Ventures' core focus areas include Hard Tech, which is defined as startups that are "working on hard technology problems that have more world-changing potential." Good examples include self-driving cars or building rockets. 

I am particularly excited about founders tackling complex problems to transition the world to the "electrification of transportation" future.

I'm also excited about builders who can solve our supply chain constraints with advanced manufacturing solutions. Many startups operating at the intersection of software and hardware are building software-enabled workflow solutions and automation. 

While AI is experiencing a massive renaissance with the emergence of Chat GPT and Generative AI, I'm particularly excited about the possibilities of "Copilot for Professions" (a term coined by Reid Hoffman recently). 

Shaped by my experience in the corporate world and my previous startup of building AI solutions in the legal tech space, I'm looking for startups that can transform jobs in the financial and legal industries.

Another main focus is vertical enterprise/SaaS solutions within industries where I have solid insights and knowledge. I'm looking primarily for solutions with strong business use cases that provide clear value for customers with solid unit economics.

Sitenna, PaperJet Ventures' first syndication deal, is an excellent example of this core focus.

Here is PaperJet Ventures’ Investment Thesis 1.0.

Why Me?

Angel Investor & Startup Helper

I invest primarily in pre-seed and seed-stage companies. I focus on sectors where I can add value by connecting to the right people & having deep domain experience. To better understand my investing strategy, check out the blog here.

The best part about being on the cap table as an investor is the opportunity to form deep relationships with founders and be helpful in problem-solving in the early days.

Here are some testimonials from founders that I backed:

The investor you want on your cap table! YC has a saying - "don't ignore the small cheques." This couldn't be more true for Chris.

He is incredibly supportive commercially, operationally, and on follow on investment. His network becomes your network, and he's there if you need any advice or support. I'm delighted to have Chris as an investor in Sitenna - Daniel Campion, Co-founder & CEO of Sitenna (YC S21)

Chris was the fourth person to invest in H3X and has consistently been one of the most helpful angels on our cap table. He is always willing to help and knows how to identify strong teams and pick winners. It's been a pleasure to work with him the last couple of years. - Jason Sylvestre, Co-founder & CEO of H3X (YC W21)

Here are some notable examples of things I have done for founders:

Fundraising

• Helped founders raise over $400K (as of 1/24/23) from introductions to VCs, syndicate leads, and angel investors and running successful deal syndications.

• Provided a hard tech startup founder with valuable market insights and deal terms comparables to help him prepare for the upcoming Series A fundraising.

Product & Design

• Collaborated with designer/angel investor Jaireh Teccaro to help founders of a fintech startup to improve its iOS app onboarding user journey.

Biz Dev & Sales

• Introduced founders to potential customers (including a tier 1 company with over $100 billion market capitalization) and strategic partners.

• Introduced an angel/operator for a startup advisor role in partnerships and contract negotiation.

Non-dilutive Funding

• I introduced three climate tech founders to a consulting firm that specialized in helping startups to apply for federal grant programs.

As referenced earlier, I have had a strong network with my angel investor/operator friends since I joined Hustle Fund's Angel Squad.

Over time, I build deep relationships and identify my angel investor friends' unique "superpowers," ranging from design advisory and fundraising to brand marketing/SEO. 

For any founder reading this, PaperJet Ventures' most unique differentiator is the people behind it. There is a team of "Avengers" who can bring tremendous experience and expertise to guide you in your early-day journey. 

We are with you all the way: from your introductory meeting through due diligence and post-investment – you will have access to our network of operators/investors to answer your "founders ask" questions.

To summarize, my biggest differentiators as an investor are

• My deep finance/banking background provides founders with tactics and insider knowledge to help them navigate the fundraising process.

• My industry knowledge allows me to do deep market research and due diligence within my core focus areas.

• Previous operator experience, where I can empathize and problem-solve with founders. 

• Network, where I can connect founders to the right people.

Future Resources for Founders

For 2023, I have ideas to share best practices and valuable resources for founders:

• Finance series on fundraising, cap table, financial modeling/budget, and more.

• Job hiring board for portfolio companies.

• Overview of non-dilutive financing programs and other non-equity funding, including equipment financing and debt.

• Office hour for founders to review designs with Jaireh Teccaro and other product managers/designers.

• A series dedicated to future/next-generation founders coming from major engineering programs.

Final Note

For founders working on interesting problems, especially within those core focus areas, please email me at christopher.j.kong@gmail.com.

If you are an accredited investor interested in angel investing, consider joining PaperJet Ventures. There is no pressure to invest as I want to provide more access for everyone to get your "deal review reps" before writing any checks.

If you want to learn more or have more questions about this, please contact me on Twitter.

Thanks to Jen Liao and Jaireh Tecarro for providing valuable insights and reading drafts of this post.

Chris

PS: Sign up☟ if you find this interesting. Email me if you have any excellent topics for me to explore more!

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I shared my argument why now is the best time to start searching for the next rocketship startup to join in the next 6 to 9 months. 

Even if you are not necessarily looking for a job, it's worth getting some market insights and ideas on looking for any breakout companies for future considerations. 

If you haven't seen Part 1, I used the investors' framework and went through:

• Timing & Risk Assumptions - provide market insights for better decision-making.

• What to Look For In Startups? - give heuristics on what to look for in startups.

• Where to Begin Your Search? - how to filter out startups by stage.

• What Are Some Key Things You Should Know About the Current Market? - provide some color on "default alive" & bridge rounds, runway, and liquidation preference.

In Part 2, I will go through:

1. What happens If There Is a Recession? - figure out which startups are vulnerable to recession.

2. What About Hardware & Deep/Hard Tech Startups? - learn about non-software companies' key risks.

3. The Exception to the Guidelines - Self-Founded ex-Founders & Bootstrapper - share the insights of startups with a different approach to fundraising.

What happens If There Is a Recession?

As an angel investor and active stock market investor, I think about the potential of the future and the near-term risks of any investment. As startup employees, your decision-making should incorporate both potential and near-term risks, including the possibility of a recession.

In the challenging fundraising environment, many startups have cut costs to be “default alive,” as mentioned in the last post. 

As we enter the real possibility of a recession, revenue growth in some sectors is more vulnerable than others. Some of the questions that you should be asking include:

• Who are the primary customers?

• What’s the problem the startup is trying to solve for its customer?

• Is the product a painkiller or a vitamin?

Suppose a B2B enterprise startup sells SaaS products to other startups and mid-market companies. In that case, there is a possibility that their customers might not renew the contracts since these companies are looking to cut costs themselves.  

If there is a recession, consumers might not spend as much on non-essential products & services, which could impact certain e-commerce/marketplace companies. 

In addition, the current environment has affected many Buy Now Pay Later ("BNPL") startups. The most prominent one is the 85% decline in Klarna's valuation in the recent funding. With interest remaining high, the outlook for BNPL and their fintech partners is unfavorable.

The second quarter earnings season provides investors with plenty of outlook about corporate and consumer spending. Those market insights provide some excellent proxy of what to expect for startups' near-term outlook.

What About Hardware & Deep/Hard Tech Startups?

As an angel investor, I have a deep passion for deep tech (electrification of transportation, sustainable energy, and space exploration) and hardware startups trying to solve challenging problems.

These companies could be the next Tesla & SpaceX because they can CAPTURE & GROW massive markets with breakthrough technology. 

If you are an engineer, designer, and data scientist thinking about joining non-software tech startups, here are some good reads:

1. Technologies Are Converging

According to Ark Investment Big Ideas 2022, five innovation platforms are evolving and converging simultaneously: Artificial Intelligence, Robotics, Energy Storage, DNA Sequencing, and Blockchain Technology. You can read more here.

2. Recurring Model for Hardware Companies

As hardware companies are moving from a traditional sales model to subscription services that resemble software companies, there are more opportunities than ever for multi-discipline engineers and other knowledge workers. Silicon Valley Bank and Eclipse Ventures recently published this excellent report on the trend.

3. Solving Hard Problems

Andreessen Horowitz has been very vocal for more founders to build companies that can solve challenging problems, including manufacturing, defense, housing, etc. If you are interested in such companies, you should read some of their essays, including this.

While we can still use some of the Product-Market Fit guidelines listed in the first post, here are some near-term risks for the broader hardware & deep tech sectors:

Runway, Sales Cycle & Dilution

Traditional hardware and deep tech companies require much more capital than software companies. In a challenging environment with a strong recession possibility, your goal is to find a well-capitalized company with a long runway to sustain this cycle. 

In addition, these companies have very long sales cycles (depending on the company's specific sector and stages). While it's nice to see companies with a healthy total value of revenue contracts and massive Letters of Intent (LOIs), you should assume that it could take a long time before commercial revenue comes to their bank account.

Knowing how much runway a company has given the actual cash in its bank account is vital. For example, companies with a runway of 18-24 months left can provide you with some comfort if you consider leaving your current role. 

Another consideration includes non-US startups since raising the next round of funding takes longer for international companies. It would help if you considered whether these companies could raise huge rounds from US investors, given the concentration of capital here.

Investors have the upper hand when dictating terms for any company looking to raise a Series A / B in the next 6-12 months. Instead of paying attention to how much the company is valued, look at the dilution % or (funding round / post-money valuation). 

According to Y Combinator's guide, "most rounds will require up to 20% dilution, and founders should try to avoid more than 25%." High dilution, such as 40%, could impact how much your stock options could be worth.

Key Business Risks

Instead of using software companies’ PMF as guidelines, key business risks include technology validation. For any deep tech companies I invest in, I rely on industry experts to help me validate the technology. For any employee considering joining hardware and deep tech companies, I recommend finding experts to validate this risk if you don’t have a relevant industry background.

Depending on the stage, hardware and deep tech companies will focus most of their time on building the technology. Key business model validation includes solving key customers’ pain points with demonstrable Return on Investment (“ROIs”), the initial pricing model given the core value proposition, and the paths toward growing the operating margin and profitability.

Supply chain constraints are the primary problems many hardware companies face during the pandemic. This issue remains a significant concern for the foreseeable future. My favorite question to ask deep tech founders is how they think about external risks beyond their control. As an employee, you should ask this question to understand how founders think about those risks with some plans to de-risk them. 

Before considering any hardware & deep tech startups, you should understand better the near-term challenging fundraising period, runway risk, long sales cycle, and other external factors.

The Exception to the Guidelines - Self-Founded ex-Founders & Bootstrapper

As mentioned above, the exception to these guidelines is former founders who can bankroll their latest startups. There are plenty of such repeat founders who bootstrap their following companies. A good example is Dustin Moskovitz, co-founder of Facebook, who bootstrapped his next company, Asana (NYSE: ASAN), early on before raising any venture round. 

As suggested by my fellow angel investor friend, Jaireh Tecarro, even if the founders aren't blue-chip names such as Travis or Adam, it's worth checking out if they have prior exits in the past. Any major exit in the range of $100+ million means this type of founder is capable of funding their startup on their own in the early days.

Usually, tech employees associate breakout startups based on huge funding news or TechCrunch articles. While it's easier to identify startups that can raise massive rounds from VCs, hidden gems come from founders who take a disciplined approach by initially bootstrapping their startups and focusing on profitability first.

In the recent episode of Y Combinator’s Same, Same But Different (highly recommend), Vanta’s CEO Christina Cacioppo and Zapier CEO Wade Foster talked about their decision to bootstrap their companies early on.

Vanta didn’t raise a Series A round until they built products that customers wanted and found their product market fit. Zapier, valued at $5 billion, has only raised a $1.3 million seed round in 2012 and has been profitable since 2014.

Any employee that has joined startups that have taken this different approach would have benefitted from lucrative employee stock options since the company has retained strong ownership and control from external investors.

On the opposite spectrum, you should avoid bootstrapped startups entirely like Basecamp. In most cases, the founders are reluctant to award any equity compensation to employees. Mailchimp is another example where Intuit acquired the company for $12 billion. Unfortunately, their employees were not one of the benefactors from the exit. The other risk of bootstrapped startups is they could continuously grow at the trajectory of small businesses without any liquidity exit on the horizon.

Final Note

As an investor, I can diversify my portfolio to reduce risks. If I build a broad enough portfolio, the winners might become outsized to pull up my returns across the board. 

As an employee (for most people), you are all in with your wealth creation and income with the company that employs you. Investing allows you to diversify your risks.

In addition, angel investing should be part of your networking strategies. Investing allows you to identify breakout companies for future job opportunities.

Learn + Join Community

• Diversify Your Wealth Building By Invest (see post)

• Build your network

• Broaden your horizon

One day, working for your portfolio company could be your next dream role. 

Also, if you receive a job offer from one of these startups, check out my prior post on how to understand equity compensation.

If you are interested in learning more or have more questions about this, please contact me on Twitter.

Thanks to Jen Liao and Jaireh Tecarro for providing valuable insights and reading drafts of this post.

Chris

PS: Sign up☟ if you find this interesting. Email me if you have any excellent topics for me to explore more!

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