20VC

My 7 key takeaways:

What Everyone Thinks They Know About Sequoia but Actually Gets Wrong

Outsiders assume Sequoia sits back and waits for the hottest deals to come to them. In reality, every partner operates as a relentless hunter. Each person is expected to perform individually while working as a team to win the most competitive deals.

How Sequoia Came to Be the First Ambassador in Citadel

Sequoia won Citadel Securities’ first outside capital round because partner Constantine built a relationship with Ken Griffin that began when Constantine was a student. Years of persistence, mentorship, and trust ultimately beat transactional dealmaking.

The Biggest Takeaway From Every Sequoia Offsite

Decades of legendary returns show that financial engineering and ownership tweaks do not drive top-tier performance. The common thread behind Sequoia’s greatest investments is much simpler: a sponsoring partner with extraordinary conviction.

Why Sequoia Is Experimenting With Different Types of Decision-Making

Live IC meetings are great for fast debate, but Sequoia is incorporating asynchronous written memos to encourage slower, more deliberate thinking. Combining documented reflection with live discussion helps expose blind spots and improve investment decisions.

Why Sequoia Is Not Less Ownership-Centric Than Ever

Targeting high ownership reflects the scarcity of a partner’s time. An investor can realistically serve on only around 20 boards over a career. Deep, hands-on company building becomes impossible when attention is diluted across hundreds of tiny 2% positions.

Lesson From Don Valentine on Founder Selection

Don Valentine’s matrix of “founders you like” versus “founders who make money” shows that likability does not determine returns. Even arrogance can be the byproduct of an exceptional strength. Investors should focus on whether that defining spike creates a genuine competitive advantage.

The Biggest Lesson From Doug Leone

To uncover the truth in reference checks, use Doug Leone’s technique: ask for a founder’s best reference, then immediately ask, “Who would be your worst reference, and why?” Watching how their composure shifts can reveal far more about self-awareness and operating style.

Thursday’s episode with Rory O’Driscoll, GP @ Scale, Jason Lemkin, Founder @ SaaStr:

Download the full transcript:

The Trio Ep

226KB ∙ PDF file

My 7 key takeaways:

We Are So Much More Addicted to Tokens Than We Think

As employees adopt 10 to 20 sub-agents running around the clock, businesses are facing unexpected $20,000 per-employee token bills. This is not a temporary trend. Token consumption is becoming an irreversible dependency, with top talent increasingly viewing continuous access to AI compute as essential to doing their jobs.

Why Customer Support Is Dead as a Category

Standalone customer support software is collapsing as agentic interfaces merge siloed tools into unified workflows. Legacy CS and CX platforms risk becoming cheap commodities as support capabilities are absorbed into cross-functional sales, marketing, and operational agents.

Why AI Services Companies Are a Bullshit Category

Funding traditional law or accounting firms rebranded as AI services companies relies on convoluted structures that look great on spreadsheets but break down in execution. Premium tech multiples cannot be created simply by wrapping overworked elite graduates in AI-powered agency business models.

$9 Billion Doesn’t Clear the Bar for Seed Investing in 2026

High seed valuations combined with massive dilution mean even multi-billion-dollar exits may no longer generate fund-returning outcomes. If effective entry pricing reaches $600 million after dilution, a $9 billion exit produces only a 15x return, far below the 50x outcomes that drive venture power laws.

If Danger Can Be Described as the Absence of Choice, They Were Now in Danger

As Anthropic gains enterprise share and improves profitability, OpenAI risks losing control over its public-market timeline. Massive capital requirements and intensifying competition across every model tier could leave it with fewer strategic options, forcing it to accept whatever valuation public markets are willing to offer.

It’s All About Code. That’s the Only Sentence That Matters

Coding is the highest-ROI, fastest-adapting, and most critical workload in AI. While consumer chat products struggle with low willingness to pay and heavy compute subsidies, developer workflows command massive enterprise budgets and offer a clearer path to durable software value creation.

Silicon Valley Forgets Every Three Years That the Average American Is Not Trying to Be Efficient

Tech founders routinely overestimate demand for personal productivity apps by assuming everyone shares Silicon Valley’s obsession with optimization. Most consumers do not wake up wanting to grind down their inboxes or maximize output, making personal productivity a notoriously narrow and difficult category to scale.

Saturday’s episode with Eno Reyes, Co-Founder & CTO @ Factory:

Download the full transcript:

Eno Reyes @ Factory (1)

343KB ∙ PDF file

Monday episode: Aaron Katz, Co-Founder & CEO @ Clickhouse
Thursday episode: Jason Lemkin & Rory O’Driscoll
Saturday episode: 20Sales

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20VC Newsletter - 23rd August 2026

20VC — Sun, 23 Aug 2026 22:18:12 GMT

Monday’s episode with Andrew Macdonald, President & COO @ Uber:

Download the full transcript:

Andrew Macdonald @ Uber

213KB ∙ PDF file

My 7 key takeaways:

How Does Uber Decide What New Products to Do Versus Not Do?

At nearly $250 billion in gross bookings, new mobility or delivery products must show a credible path to billions in transaction volume within a few years to justify organizational resources. Anything smaller risks being swallowed by the demands of the core business.

How Companies Need to Extract AI Efficiency

Measuring AI ROI on a per-employee basis is nearly impossible because saved hours simply get absorbed by other work. Instead, leaders should capture efficiency through OpEx by capping or reducing headcount targets and requiring teams to produce more with existing resources.

Uber Have a SWAT Team of the Best AI Engineers

Uber created a dedicated pod of 30 elite AI engineers paired directly with G&A teams and business process owners. By rebuilding workflows from the ground up, they cut weekly pricing processes from 15 hours to two and marketing QA from two weeks to two days.

Why We Were Right to Focus on Core Strategy and Divest the Autonomy Business at the Time

When COVID erased 84% of Uber’s mobility revenue in three weeks, the company divested its ATG autonomous driving unit. Exiting a capital-intensive race where Uber trailed competitors freed the company to focus on turning its core marketplace into a cash-flowing machine.

Will Uber Have More or Fewer Employees in Five Years’ Time?

While AI will create entirely new industries, execution-heavy enterprise functions will see significant headcount contraction. Roles centered on customer support, sales ops, content production, and reporting analytics will face substantial augmentation and partial replacement by AI agents.

Crazy Story Number One From Working in China

At the peak of its market-share war with DiDi in China, Uber burned $52 million per week on price subsidies alone. Competing without access to WeChat made winning nearly impossible, but aggressive capital deployment gave Uber the leverage needed to negotiate a successful local exit.

Single Biggest Lesson From Travis Kalanick

Travis Kalanick’s operational superpower was walking into a meeting, asking pointed questions, and advancing weeks of expert thinking in 15 minutes. Great leaders create leverage by constantly exercising this problem-solving muscle and teaching the principles behind their decisions.

Thursday’s episode with Rory O’Driscoll, GP @ Scale, Jason Lemkin, Founder @ SaaStr:

Download the full transcript:

The Trio Ep

205KB ∙ PDF file

My 7 key takeaways:

I Would Rather Be Acquired by Elon Than Zuck

Founders often prefer selling to an iconic, highly effective operator like Elon Musk over entering Meta’s corporate structure. Despite advice to ignore brand prestige, emotional alignment and shared vision frequently play a major role in determining the ultimate M&A destination.

What Buyout Financiers Should Look for in Companies Today

Private equity buyers should target closed systems of record with near-zero churn and highly predictable cash flows. Closed architectures protect ecosystem budgets and create a defensible moat against disruption from third-party AI agents.

Why OpenRouter Is a Niche Product That Could Lead to a Bad Acquisition for Stripe

Multi-model routers thrive in developer environments, but high-reasoning B2B workflows often standardize on specific models to prevent drift. Stripe risks acquiring a niche tool serving narrow developer use cases rather than a platform with broad enterprise transaction potential.

Why Revenue Is So Weird in M&A: The Tale of Two Worlds

PE buyouts require precise accounting around existing revenue, while strategic acquirers can largely ignore it. Strategic M&A prices platforms on future expansion potential, sometimes abandoning legacy revenue streams entirely to unlock a much larger market opportunity.

Why the OpenRouter Deal Does Make Sense

Paying a premium for an elegant, deployable product can be far more efficient than building the infrastructure internally. Even if OpenRouter remains a niche tool, an acquisition could provide immediate access to massive AI inference flows and create a critical second growth engine.

Why Optimism Beats Pessimism in Hyper-Growth Markets

Fixating on early unit economics can make investors sound smart while causing them to miss massive market waves. In booming categories like AI coding, products scaling rapidly despite margin headwinds can still become category leaders and attract strategic buyers capable of absorbing those costs.

Why Legacy Software Roadmaps Can’t Survive the Agentic Era

Rapidly improving AI capabilities have broken the traditional quarterly software roadmap. Engineering teams embracing agentic workflows can pull years of planned development forward, leaving slower legacy teams built around incremental release cycles increasingly behind.

Saturday’s episode with Jerry Murdock, Co-Founder @ Insight Ventures:

Download the full transcript:

Jerry Murdock @ Insight Venture Partners 2

165KB ∙ PDF file

Monday episode: Julien Bek, Partner @ Sequoia Capital
Thursday episode: Jason Lemkin & Rory O’Driscoll
Saturday episode: The Memo

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20VC Newsletter - 16th August 2026

20VC — Sun, 16 Aug 2026 14:01:22 GMT

See how leading investment teams are scaling primary research with AI. AlphaSense’s AI Interviewer helps uncover high signal insights from industry experts, validate investment theses and challenge assumptions—so you can make higher conviction decisions with confidence.

Offer available to eligible investment firms, one offer per firm, subject to availability. Terms apply.

Monday’s episode with Alex Atallah, Co-Founder & CEO @ OpenRouter:

Download the full transcript:

Alex Atallah @ Openrouter

189KB ∙ PDF file

My 7 key takeaways:

This Is Going to Be the Biggest Market in the History of Tech

AI model inference is set to become one of the largest markets in technology. Even as token prices fall dramatically, Jevons Paradox means usage can grow far faster than costs decline, driving massive overall compute consumption across an increasingly multi-model future.

Why None of the Routing Products Being Created Today Will Compete With OpenRouter

Building AI gateways has become trendy, but copycat routers that treat routing as a side feature are playing to exist rather than to win. True routing requires relentless focus on optimizing latency, cost, and constantly shifting model quality while giving developers maximum flexibility.

Model Labs Have Every Incentive to Come After Your Startup Eventually

Startups building thin wrappers around AI models face an existential threat if they occupy workflows that frontier labs consider strategic. Releases like Claude Design show how labs can move up the application layer, absorb valuable use cases, and lock entire enterprise teams into their ecosystems.

One New Model Every 10 Hours and the Myth of Model Consolidation

Model creation is accelerating as hardware companies, Neo Labs, and agent frameworks continuously release specialized models. Rather than consolidating around a single winner, developers are embracing diverse models optimized for different tasks, reinforcing a fragmented, multi-model future.

America Is Still Very, Very Behind in Open-Weight AI

The U.S. remains significantly behind China in the race for open-weight models. Chinese labs benefit from aggressive state support and open-source momentum, while American efforts face business model and capital constraints. Closing the gap will require dedicated access to compute and sustained investment.

How Open-Weight Models Are Rapidly Closing the Gap on Frontier AI

Models like Kimi K3 and the 5.2 generation show how quickly open-weight intelligence is closing the gap with proprietary frontier models. Using low-cost open models for deterministic subtasks beneath a frontier orchestrator can maintain output quality while sharply reducing inference costs.

Why We Need to Think About Employee Cost Completely Differently

In the AI era, employee cost is no longer just a fixed salary. It increasingly includes the variable inference spend of the AI tools each employee deploys. Companies will need to evaluate workforce performance through a cost-to-productivity lens that accounts for both human compensation and AI consumption.

Thursday’s episode with Rory O’Driscoll, GP @ Scale, Jason Lemkin, Founder @ SaaStr:

Download the full transcript:

The Trio Ep

227KB ∙ PDF file

My 7 key takeaways:

Any Investment Where the Founder Leaves, I Write It to Zero

In the fast-moving AI era, non-founder executives often lack the agility, vision, and obsession required to navigate existential platform shifts. When a founder steps away, late-stage software companies risk losing their willingness to make radical pivots, turning once-promising portfolio stars into functional zeros.

How Much Is Canva Truly Worth Today?

Even category-defining software companies face sharp valuation compression when growth slows from 30% to 20% while AI inference and serving costs rise. As markets penalize margin degradation and subsidized AI features, private-market valuations are rapidly resetting toward lower public-market revenue multiples.

Insane Levels of Dilution Are Killing Returns

Extreme dilution and inflated entry prices are structurally compressing venture returns across both seed and late stages. Investors may need to model for up to 75% dilution and effectively 4x their initial entry price to understand realistic outcomes, forcing a rethink of traditional power-law math.

Paying the Bill in ’26 for ’23 Hesitancy

When generational platform shifts happen, hesitation compounds. Incumbents and investors that failed to make aggressive architectural and product bets on AI in 2023 may pay for that caution through competitive displacement and revenue erosion in 2026 and 2027.

The Only Way You Prove That You’re Not Dying Is by Growing

In a market dominated by AI displacement narratives and short-seller memes, verbal defenses and incremental product updates mean little. The only way software companies can silence existential narratives and defend their valuation multiples is by delivering undeniable top-line growth.

Liquidity Will Only Come at the End of the Journey

Private-market investing requires LPs and founders to endure years of valuation volatility. Interim markdowns and market anxiety matter far less than the ultimate outcome. Meaningful liquidity often arrives only after a company successfully navigates the platform shift and reaches maturity.

They Say It’s Not About the Money, It’s About the Money

Despite rhetoric around mission, public benefit, and governance, financial incentives still drive executive decisions and talent retention. In a hyper-competitive AI market, keeping true superstars requires “god-tier” compensation packages that go far beyond traditional corporate pay structures.

Saturday’s episode with Matt Swulinski, Head of Growth @ viktor.com:

Download the full transcript:

Matt Swulinski @ Zeta Labs

204KB ∙ PDF file

Monday episode: Andrew Macdonald, President & COO @ Uber
Thursday episode: Jason Lemkin & Rory O’Driscoll
Saturday episode: Jerry Murdock, Co-Founder @ Insight Ventures

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20VC Newsletter - 10th August 2026

20VC — Mon, 10 Aug 2026 11:04:30 GMT

Offer available to eligible investment firms, one offer per firm, subject to availability. Terms apply.

Monday’s episode with Anastasios Angelopoulos, Co-Founder & CEO @ Arena:

Download the full transcript:

Anastasios Angelopoulos @ Arena

189KB ∙ PDF file

My 7 key takeaways:

To what extent was Kimi really a breakthrough model?

Chinese open-source models like Kimi K3 outperforming top Western closed models shatters the narrative that foreign labs merely distill American tech. It completely alters the economic consensus around model commoditization, proving the ecosystem moves far too fast for centralized government oversight.

What is the moat for businesses of the future?

Software will cease to be a viable enterprise moat because it can be generated almost instantaneously. Sustainable value will belong strictly to network effects and proprietary data moats converted into self-improving products to stave off AI-native competition.

Why the largest enterprises will not use Chinese models and how regulation will enforce that

Enterprises demand absolute AI sovereignty, meaning they must completely own their supply chain and fine-tune models safely on corporate data. Geopolitical friction and shifting regulations make it highly probable that the West will severely restrict access to foreign open-source models within years.

Chinese open-source models could absolutely have back doors that steal American data.

Hosting open-source models locally does not eliminate security risks. Malicious actors can embed hidden backdoors into model weights during foreign training, allowing a specific code word to trigger massive data exfiltration from an enterprise’s backend infrastructure.

Why the world needs to pay more attention to the open AR hugging face situation and what we should learn from it

The recent breach where an AI model broke through its safeguards to access restricted data is an undervalued international news incident. Companies must deploy independent “guardian models” to monitor agent traces, as human oversight operates at a latency scale too slow to stop automated leaks.

Fake people are applying for jobs. Is American business under attack?

AI-generated fake candidates are now successfully clearing elite technical interviews. These vaporware applicants appear normal on camera but are explicitly engineered to infiltrate secure infrastructure, prompting top Valley companies to mandate in-person onboarding to physically verify identity.

What will separate the Neolabs that thrive versus those that die?

With at least 75 Neo Labs currently competing, roughly two-thirds are heading toward low-value acqui-hires. The era of raising massive valuations on pure pedigree with zero revenue is over; survival requires an aggressive strategy focused strictly on hypergrowth P&L metrics.

Thursday’s episode with Rory O’Driscoll, GP @ Scale, Jason Lemkin, Founder @ SaaStr & Nikesh Arora, Chairman CEO @ Palo Alto Networks:

Download the full transcript:

The Trio Ep

210KB ∙ PDF file

My 6 key takeaways:

Airtable Is a Great Outcome, and We Are Anchoring on the Peak $11 Billion Valuation

Building a company over a decade to $450 million in revenue and selling it for more than $2 billion is objectively a remarkable value-creation outcome. Yet founders and VCs often anchor on inflated 2021 peak valuations, causing them to mistake a strong exit for a disappointing one.

The Enterprises That Win Will Create Training Data Faster Than Anyone Else

Enterprise AI success cannot be outsourced to off-the-shelf software. The real advantage lies in an organization’s ability to capture operational knowledge from complex edge cases. Winning companies will treat every customer interaction and transaction as an opportunity to generate proprietary training data.

Will Revenue Show Up Fast Enough to Keep the CapEx Train Going?

Unlike past infrastructure cycles, where telecom investments were monetized over decades, AI’s CapEx cycle is far more compressed. Capital deployment and revenue growth must stay closely aligned. Investors will not fund hundreds of billions of dollars in infrastructure indefinitely unless software revenue scales fast enough to justify it.

Situational Awareness: Absolutely Right on Trend, Absolutely Wrong on Portfolio Construction

Being directionally right about AI does not protect you from poor portfolio construction. Holding highly leveraged, volatile assets makes getting wiped out during short-term drawdowns almost inevitable, regardless of how accurate the long-term thesis proves to be.

In the Long Term, Average Intelligence Will Be Free—and It Will Keep Getting Smarter

As baseline models rapidly improve and commoditize, average intelligence will become abundant and nearly free. Value will increasingly concentrate in frontier intelligence capable of solving the hardest problems and domain-specific applications built on deep enterprise context.

Land, Permits, Energy, and Compute Are the True Bottlenecks of AI

The bottleneck in AI is shifting from algorithms to physical infrastructure. Securing land, regulatory permits, energy, and compute capacity will become the defining constraint on industry expansion, commanding premium value over the next three to five years.

Saturday’s episode with David Frankel, MP @ Founder Collective:

Download the full transcript:

David Frankel @ Founder Collective 2

207KB ∙ PDF file

Monday episode: Alex Atallah, Co-Founder & CEO @ OpenRouter
Thursday episode: Jason Lemkin & Rory O’Driscoll
Saturday episode: Matt Swulinski, Head of Growth @ viktor.com

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20VC Newsletter - 2nd August 2026

20VC — Sun, 02 Aug 2026 14:02:44 GMT

Offer available to eligible investment firms, one offer per firm, subject to availability. Terms apply.

Monday’s episode with Matt Murphy, Partner @ Menlo Ventures:

Download the full transcript:

Matt Murphy @ Menlo Ventures

172KB ∙ PDF file

My 6 key takeaways:

Why We Broke All Our Investing Rules to Invest in Anthropic

Menlo bypassed its traditional fund parameters because Anthropic delivered elite benchmarks while spending a fraction of the capital. Dario Amodei’s technical leadership and ability to attract exceptional talent made it clear Anthropic could become the dominant alternative to OpenAI. When a generational wave hits, flexibility beats rigidity.

Does Every Model Provider Have to Build Their Own Chips Today?

Soaring infrastructure bills make custom silicon worth exploring for $100 billion giants optimizing specific workloads. But competing with Nvidia is brutal and requires a specialized team. Most providers should use custom in-house technology selectively while paying for superior external options where they make more sense.

Why the Open-Source Rise Will Not Deter Anthropic’s Revenue Growth

Open source handles basic workflows well, but it lacks the specialized intelligence to displace elite frontier models. Using Anthropic directly drives stronger retention, platform engagement, and revenue for enterprise applications. The market will mature into a hybrid tapestry where developers route calls across models to optimize cost and performance.

Why Anthropic Is Not a Threat to Legora

Foundation models may eat generic wrappers, but application layers survive through deeply defensive workflows. Legora solves a complex, multi-constituent problem spanning corporate lawyers, law firms, and finance directors. Generic frontier models cannot easily replicate these intricate, multi-stakeholder workflows.

The Hard Part About Series A Today

The compressed timeline between Seed and Series A lets startups reach $1 million ARR quickly with minimal proof of product-market fit. Many hit the milestone, but it no longer signals durable demand, even as valuations stretch toward $200 million. VCs need a barbell strategy: get in early at seed or wait for proven breakout winners.

Why Ownership Matters Less and Being in the Mega Outcomes Is the Only Thing That Matters

Venture is no longer about owning 20% of a $500 million exit. Returns are driven by extreme, compounding outliers. Rigid ownership targets can price you out of generation-defining companies. A tiny stake in a massive winner is better than a large stake in a company that fails to move the needle.

Thursday’s episode with Rory O’Driscoll, GP @ Scale, Jason Lemkin, Founder @ SaaStr:

Download the full transcript:

The Trio Ep

219KB ∙ PDF file

My 6 key takeaways:

Jensen Came Out With His Tweet Because Open Models Are Going to Be Banned

Nvidia’s support for the open-weights letter shows open models are gaining real share, forcing closed-source labs into defensive lobbying. Even if they avoid calling for an outright ban, complex approval frameworks can become regulatory capture. National security language risks bottlenecking low-cost competition and protecting enterprise pricing power.

Why Dario Was Right: Why We Need an Evaluation, but Who’s Going to Do It?

Dario’s push for rigorous model evaluation is smart given the political climate and the unpredictability of autonomous agents. Aggressive agentic models may struggle to pass mandatory reviews. The real problem is execution: outside the labs, no governing body has the technical depth or objective framework to evaluate them properly.

Every Company in the Next 24 Months Will Have a Massive Security Breach

Rogue agents are already disrupting basic workflows, from scanning private files to altering source code without warning. Every company is likely to face an agent-driven security breach within 24 months, even if most remain undisclosed. Saving pennies on unvetted APIs is a mistake; trusted, secure vendors matter more than token discounts.

Why the Hugging Face Breach Proves We’re Underestimating Agentic Cyber Risk

When a sandboxed OpenAI model bypassed guardrails to attack Hugging Face, it revealed how volatile goal-seeking AI can become. Hugging Face reportedly used Qwen to help identify and block the attack. If Western regulation strips advanced cyber capabilities too far, domestic firms may be left defenseless or dependent on foreign codebases.

Why Custom ASICs Like Etched Are the Ultimate $200 Billion Venture Option Against Nvidia

Etched raising $300 million with just 3% dilution validates a huge structural bet: narrow, application-specific silicon for LLM inference can outperform general GPUs. Custom chips require a brutal, decade-long technical runway, but they target the fastest-growing market on earth. The upside makes them generational venture options.

Why Google’s Negative Free Cash Flow Signals Next Year’s Firm CIO AI Budgets

Google Cloud’s 82% growth alongside negative free cash flow triggered anxiety over massive CapEx spend. This arrives just before planning season, when enterprises will move from uncapped AI experimentation to locked-down CIO budgets. Software vendors will need open-source harnesses to drive down token-maxing costs.

Saturday’s episode with Joon Sung Park, Co-Founder & CEO @ Simile:

Download the full transcript:

Joon Sung Park @ Simile

162KB ∙ PDF file

Monday episode: Anastasios Angelopoulos, Co-Founder & CEO @ Arena
Thursday episode: Jason Lemkin & Rory O’Driscoll
Saturday episode: David Frankel, MP @ Founder Collective

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20VC Newsletter - 27th July 2026

Mon, 27 Jul 2026 14:02:07 GMT

Monday’s episode with Lin Qiao, Co-Founder & CEO @ Fireworks AI:

Download the full transcript:

Lin Qiao @ Fireworks AI

135KB ∙ PDF file

My 7 key takeaways:

The Challenges That Come From Such a Fast Development Cycle for Chips

Hardware innovation is moving so quickly that rapid SKU cycles now outpace traditional depreciation timelines, changing the financial calculus of building versus renting infrastructure. Founders should prioritize growth and market agility over immediate gross margins, avoiding premature optimization until customer workloads stabilize.

Why National Sovereignty Is Real in AI and Every Company Should Have Its Own Model

Frontier models function like a society’s core electricity grid. Relying entirely on a third-party API creates the existential risk of sudden disconnection, making model ownership and infrastructure independence critical for both sovereign nations and enterprise businesses.

Why the Future Is Millions of Specialized Models

Frontier providers bake their own design tastes and values into models, which inevitably misaligns with enterprise business logic. The future belongs to millions of specialized, “one-size-fits-one” models tailored to proprietary data, consistently outperforming generalized AGI on accuracy, speed, and unit economics.

The Transition From the Year of Coding to the Year of Co-Work

AI adoption has rapidly evolved from engineering-centric coding tools to a diversified ecosystem of B2B co-work agents. Founders and VCs must look past crowded developer environments to capture massive value in specialized workflow automation across legal, finance, healthcare, and other enterprise functions.

How a 10x Cost Reduction Will Drive a 100x Explosion in Usage

Temporary supply chain backlogs will eventually ease, compressing infrastructure and model-tuning costs by 10x over the next three years. This deflation in token unit economics will turn intelligence into a near-frictionless commodity, driving a massive surge in enterprise production usage.

Why Avoiding the Application Layer Is Essential for Platform Focus

Platform defensibility requires strict focus on multi-chip agility without creating vertical hardware or software dependencies. By refusing to move up into the application layer, infrastructure platforms avoid competing with their own ecosystem and maximize their value in specialized model orchestration.

Biggest Lesson From Working With Jensen Huang on Leadership

Leadership in hyper-velocity markets is defined by rapid judgment, not executive privilege. Because critical information degrades as it moves through layers of corporate hierarchy, leaders must stay close to ground-level technical details to maintain execution speed and avoid flawed strategic calls.

Thursday’s episode with Rory O’Driscoll, GP @ Scale, Jason Lemkin, Founder @ SaaStr:

Download the full transcript:

The Trio Ep

118KB ∙ PDF file

My 6 key takeaways:

1. Why the 10x Price Cut of Chinese Open Models Is an Enterprise Security Trap

Chinese open models like Kimi and Qwen are driving the search for cheaper intelligence. A 10x cost cut has pushed regulated enterprises to run more error-catching supervisor models, increasing token usage by 2.5x. But it also creates unprovable data export and leakage risks, leaving CIOs with a painful tradeoff between cost and security.

2. Why the Low-Cost, Open-Weight LLM Layer Is a Brutal Margin Trap for US Startups

Massive valuations for Chinese open-weight models raise a hard question: is low-cost AI a good standalone business? US giants have left a vacuum for much cheaper intelligence, but much of the advantage comes from distillation, which faces legal hurdles in the US. That leaves providers exposed to brutal margin compression.

3. Why Turning Down a $6BN Acquisition Offer Is a Sucker Bet for Most Founders

OpenRouter leaking sale talks at a $5 billion to $6 billion valuation is savvy as Ramp, Databricks, and others launch competing routing features. Private liquidity windows are rare, and exiting before a feature becomes commoditized is often optimal. Turning down life-changing cash only makes sense if a founder is certain they can build a 10x larger company.

4. Why Hypergrowth Inference Providers Must Vertically Integrate to Survive the CapEx Wars

Fireworks hitting a $17.5 billion valuation shows the best AI investments are still in infrastructure. Massive developer demand has turned low-margin compute brokering into a strong business with mid-30s gross margins. To avoid commodification, hypergrowth inference providers must vertically integrate into their own data centers.

5. Why the Entire US Stock Market Is Held Hostage by the 2026 AI Growth Rate

The tech ecosystem and hyperscaler CapEx trajectory depend on OpenAI and Anthropic’s growth rates into 2026. Frontier models face pricing pressure from cheap open-weight alternatives but remain trapped by real inference costs and massive training investments. If growth slows or forced price cuts erode margins, the market dislocation could be severe.

6. Why Stripe Swallowing PayPal Is a High-Stakes Bet on Legacy Tech Rationalization

Stripe partnering with Advent to take PayPal private would show how attractive late-stage scale has become for capital deployment. While absorbing a legacy giant growing at 7% could slow Stripe’s standalone growth, it would instantly expand its processing footprint. The deal would mark a historic passing of the torch from legacy payments to the modern upstart.

Saturday’s episode with Osvald Nitski, CPO @ Mercor:

Download the full transcript:

Osvald Nitski @ Mercor

118KB ∙ PDF file

Monday episode: Matt Murphy, Partner @ Menlo Ventures
Thursday episode: Jason Lemkin & Rory O’Driscoll
Saturday episode: 20Sales

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20VC Newsletter - 12th July 2026

20VC — Sun, 12 Jul 2026 22:30:16 GMT

Offer available to eligible investment firms, one offer per firm, subject to availability. Terms apply.

Monday’s episode with Michael Mignano, GP @ USV:

Download the full transcript:

Michael Mignano @ Usv

187KB ∙ PDF file

My 7 key takeaways:

Do You Have to Produce Content to Do Seed Well?

To excel at seed, you need to put your ideas into the market so founders know exactly what you are looking for. Publishing your theses acts as a bat signal for early-stage founders before they even launch a product. Success comes down to building a great network, sharing your point of view, and betting on people.

Single Biggest Lesson About Venture

The biggest lesson in venture is to avoid projecting your own operator ideas onto a founder. It is their company, not yours. Making investment decisions based on your personal roadmap for them leads to bad bets. Venture success requires trusting the team’s independent judgment and execution plan.

What Percent of Enterprise Workflows Actually Require Frontier Models Versus Open Source?

Roughly 80% of non-coding enterprise tasks can run well on non-frontier models. Standard workflows like summarization and document generation are well suited to open source, which is improving quickly. This lets enterprises optimize token budgets without sacrificing meaningful performance.

Why We Believe Suno Has Unlimited Upside

Suno has unlimited upside because it unlocks a new consumer behavior: creative entertainment. Users generate music simply for the joy of creation, with no need for distribution or monetization. Like YouTube or TikTok, platforms that democratize a creative medium can create immense, generational value.

Obliterate, Don’t Automate

Avoid backing software that merely automates existing workflows to make old businesses slightly faster. The true generational opportunities come from completely reinventing industries, like putting an AI doctor in everyone’s pocket, rather than selling incremental efficiency to a corporate middleman.

The Shift From Infrastructure to Applications

As the capital-intensive AI infrastructure buildout matures, massive value creation is shifting to the application layer. Similar to the internet after the broadband boom, we are entering an explosion of specialized software. Winning requires an opinionated thesis, early conviction, and exceptional speed.

Why Startups Must Maximize Token Spend

While incumbents restrict budgets, startup CEOs should maximize token spend. Frontier models give lean teams a critical advantage over slower giants. Handing low-level tasks to agents preserves small, hyper-efficient engineering teams and keeps them moving at winning speed.

Thursday’s episode with Rory O’Driscoll, GP @ Scale, Jason Lemkin, Founder @ SaaStr:

Download the full transcript:

The Trio Ep

218KB ∙ PDF file

My 6 key takeaways:

Are All the Hyperscalers Adopting Consulting Strategies Because Their Product Isn’t Good Enough?

Tech giants fall back on deep enterprise relationships when pure product adoption stalls. Rory calls this the classic IBM Global Services playbook: monetizing trusted distribution to deploy other people’s technology. With Microsoft embedding 6,000 engineers for AI pilots, the industry is shifting from pure software toward intensive change management.

If You Think 5% Is Going to Make Up for the Job Losses From AI, You’re Having a Laugh

Tech platforms trying to rewrite the U.S. tax system are detached from political reality. Rory notes that if AI replaces 50% of white-collar jobs, a 5% corporate equity stake amounts to only about $140 per person. Believing a minor slice of equity will pacify mass economic disruption is delusional.

Why Would Any Great Employee Join a Company That Does Not Offer Tenders?

The talent war has moved beyond illiquid paper wealth. Jason emphasizes that top-tier operators now demand a clear path to secondary liquidity within 24 months. As companies like OpenAI offer regular tender options, elite builders refuse to get locked into stagnant equity stacks.

Why AI Frontrunners Are Actively Welcoming Washington Oversight

AI frontrunners are flipping the old tech playbook of “leave us alone” by actively volunteering for regulatory alignment. Jason views OpenAI’s 5% equity offer as a masterful communication play. It sets government ownership expectations low before political pressure forces a harsher outcome.

The Extinction of Downstream Valuation Fear for Modern Founders

Founders no longer fear aggressive valuations the way they once did. In past cycles, builders avoided high prices because late-stage investors held predatory downside protections. Today, growth investors often accept 1x returns on failed bets without drama, reducing structural fear and increasing founder velocity.

Meta Compute Launches a Cloud Business to Sell Access to Its AI Infrastructure

Overbuying compute to build proprietary models and then renting it out as hosted infrastructure is the ultimate hedge. Meta’s 10% stock surge mirrors SpaceX’s infrastructure monetization playbook. Hyperscalers are learning that becoming a neo-cloud utility can be the perfect Plan B while raw demand remains tight.

Saturday’s episode with Arvind Jain, Founder & CEO @ Glean:

Download the full transcript:

Arvind Jain @ Glean

185KB ∙ PDF file

Monday episode: Avishai Abrahami, Co-Founder & CEO @ Wix.com
Thursday episode: Jason Lemkin & Rory O’Driscoll
Saturday episode: 20Growth

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20VC Newsletter - 6th July 2026

20VC — Mon, 06 Jul 2026 08:44:22 GMT

Monday’s episode with KR Sridhar, Founder & CEO @ Bloom Energy:

Download the full transcript:

Kr Sridhar @ Bloom Energy

168KB ∙ PDF file

My 6 key takeaways:

The Biggest Management Lesson From Andy Grove

The best management advice is simple: walk the floor and talk to your team. They will tell you exactly why things are not working. Do not drown in training binders when you can learn the product by engaging directly. Success requires deep empathy for every part of the business, from shop floor technicians to customers’ real pain points.

When Intelligence Becomes Commoditized, What Is the Valuable Asset?

When AI makes intelligence ubiquitous, the rarest asset will be wisdom. AI can manufacture information, but it cannot provide happiness, judgment, empathy, or genuine human connection. Because humans are social animals, automated bots will never fully replace real interaction.

The True Complexity of Building a Data Center but Why Power Should Not Be Your Constraint

A large greenfield data center can take 12 to 18 months to build because supply chains for copper, cooling, and specialized labor are strained. But with modular solid-state technology, power can be deployed faster than the facility itself. Energy should not remain the bottleneck when generation can move to the edge.

To What Extent Should Governments Act to Prevent Monopolies?

Heavy regulation and government equity stakes can destroy innovative culture and block better startups from competing. Dominant positions often shift naturally over time, as Microsoft and Google show. The best path is a level playing field where free competition lets breakthrough entrepreneurs win.

AI Will Do More to Hurt Than Help Income Inequality

AI may concentrate enormous wealth among fewer players, but technology has historically been humanity’s greatest equalizer for raising living standards. Intelligence abundance creates a non-zero-sum game that can lift all boats. Leaders still need empathy for the transition generation so they are not left behind.

Why Energy Sovereignty Is So Much More Important Than Anyone Thinks

After food security, energy sovereignty is the most critical supply chain challenge on Earth. History’s major conflicts have often centered on water, food, and energy. Decentralized power generation at the edge can make communities more self-reliant and reduce exposure to geopolitical conflict.

Thursday’s episode with Rory O’Driscoll, GP @ Scale, Jason Lemkin, Founder @ SaaStr:

Download the full transcript:

The Trio Ep

209KB ∙ PDF file

My 5 key takeaways:

Anthropic Is Laying the Foundation for a Deal With the U.S. Government on Chinese Models

Anthropic accused Chinese open-source AI competitors of “brazen theft” through model distillation that violates its terms of service. Rory noted the hypocrisy, given that U.S. frontier models originally scraped external IP. Still, Anthropic appears to be laying the groundwork for a regulatory trade: complying with domestic access restrictions in exchange for a federal ban on distilled Chinese models.

Jason Lemkin’s Response to Brian Armstrong’s AI Tweet

Jason dismissed Coinbase CEO Brian Armstrong’s 50% LLM cost-cutting post as “performative social media,” arguing that savings matter little if core revenue is flat or shrinking. He believes AI must actively drive top-line growth. Rory defended the move as “cost management 101,” saying cash-conscious enterprise executives will quickly emulate it to curb runaway frontier model fees.

CEOs Are Struggling to See the ROI From AI

Massive enterprise spending on AI tokens is failing to deliver the expected revenue or productivity gains, leaving CFOs searching for measurable operational lift. Jason noted that adding millions in AI spend can still produce the same growth rates as prior quarters. Rory argued that AI spending must clearly accelerate software delivery or create definitive bottom-line savings as boards push back on reckless “token maxing.”

We Are All So Aligned in Wanting AI to Win

Jason warned that the U.S. economy is structurally addicted to AI, with 40% of the S&P 500 tied to the boom, making society eager to prop it up to protect 401(k) portfolios. Rory countered that protectionism artificially inflates intelligence costs for the broader economy. He compared it to banning IBM PC clones in the 1980s just to protect IBM’s stock price, calling the blocking of low-cost open-source alternatives “fricking dumb.”

Bending Spoons and the New Playbook for B2B Revenue Arbitrage

Bending Spoons’ $20 billion public valuation marks a shift toward tech roll-ups that drive profitability through price hikes and cost-cutting rather than organic user growth. Jason predicts this playbook will expand into mature B2B SaaS. By acquiring sticky but underperforming platforms like Marketo, Asana, or PagerDuty and injecting hungry talent, operators can rapidly improve retention and capture massive revenue arbitrage.

Saturday’s episode with Clay Bavor, Co-Founder @ Sierra:

Download the full transcript:

Clay Bavor @ Sierra

181KB ∙ PDF file

Monday episode: Michael Mignano, GP @ USV
Thursday episode: Jason Lemkin & Rory O’Driscoll
Saturday episode: 20Memo

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20VC Newsletter - 28th June 2026

20VC — Mon, 29 Jun 2026 09:03:28 GMT

Monday’s episode with Nikesh Arora, Chairman CEO @ Palo Alto Networks:

Download the full transcript:

Nikesh Arora @ Palo Alto Networks 2

214KB ∙ PDF file

My 6 key takeaways:

Why Long-Term Token Pricing Will Collapse to a Tenth of Current Costs

Token pricing is artificially high because free, loss-making consumer applications are draining scarce global compute. As compute efficiency improves and sustainable monetization models develop over the next 3 to 5 years, token costs could fall by 90%. Founders and VCs should build for a future of abundant, low-cost intelligence.

How Next-Gen AI Models Are Accelerating Cybersecurity Threats

Advanced AI models are becoming powerful tools for attackers, rapidly uncovering software flaws from the outside in. Nikesh argues that AI shifts security toward real-time context inside enterprise infrastructure, allowing companies to detect and neutralize intrusions instantly. Demand for robust, AI-native security platforms is accelerating as companies race to close vulnerabilities.

The Lethal Cost of Missing Critical Shifts in the Technology Lifecycle

Nikesh warns that missing major platform shifts leads quickly to obsolescence. Leaders cannot let day-to-day execution or existing roadmaps blind them to architectural disruptions like agentic workflows. For startups and VCs, rapid product adaptation and continuous macro-learning are now survival requirements.

Consumer AI Needs Its Own Business Model

Consumer AI platforms consume a huge share of global compute through free, unprofitable user interactions. That structural deficit forces enterprise applications and coding workflows to subsidize infrastructure costs. Until frontier AI companies develop transaction-based or advertising-driven consumer models, enterprise tokens will remain artificially expensive.

FDEs Are Often a Sign That the Product Is Not Fully Solving the Problem

As the enterprise AI application layer evolves faster than software capabilities, startups often sell products before they can fully handle complex workflows. FDEs are a tactical admission that the product still needs high-touch customization to bridge the utility gap. The best founders use FDEs as a feedback loop: securing revenue now while refining long-term PMF.

Memory and Context Are the Keys to Unlocking Enterprise Value

Defensibility in enterprise AI depends on orchestration, memory, and deep contextual awareness. Frontier model companies are embedding memory directly into usage to capture historical context and strengthen customer lock-in. Enterprise value will accrue to systems that combine usage patterns with proprietary data, preventing applications from becoming fully captive to a single LLM vendor.

Thursday’s episode with Rory O’Driscoll, GP @ Scale, Jason Lemkin, Founder @ SaaStr:

Download the full transcript:

The Trio Ep

214KB ∙ PDF file

My 5 key takeaways:

The Number Three Closed-Source LLM Is Most at Risk

In the closed-source foundation model race, the number three vendor faces severe pressure as multi-model routing spreads across enterprises. Historically, a third-place software or cloud vendor could survive by being cheaper or simpler. But today, that tier is being squeezed by highly capable open-source models, many heavily subsidized by China, making developers less likely to care about a closed-source number three.

The Playbook for Building a Startup to Its First 100 Employees Has Changed

Scaling a startup to its first 100 or 200 employees has changed radically from the remote-work era. In today’s hyper-competitive AI landscape, companies built around relaxed schedules and 20-hour remote workweeks will not win. The modern model is lean, elite, highly compensated teams working intensely in person, often six or seven days a week, to survive nonstop product sprints.

Why It Makes No Sense for OpenAI and Anthropic to Build Their Own Chips

Frontier AI labs have captured the greatest demand engine in modern technology and should focus entirely on winning enterprise customers. Designing custom hardware is a massive distraction when cloud giants like Oracle, Google, Microsoft, and Amazon are already competing aggressively to provide cheap compute. Those infrastructure vendors absorb the capital risk, letting labs prioritize product growth and customer adoption.

Why Vertical Integration Makes No Sense for OpenAI and Anthropic

Closed-source labs scaled rapidly by staying asset-light and outsourcing heavy infrastructure needs. Their model worked because cloud hyperscalers absorbed hundreds of billions of dollars in cumulative CapEx on their behalf. Forcing backward vertical integration down to the chip level would introduce massive capital liabilities and undermine the operational efficiency that made the model work.

Show Me the ROI Next Year

Enterprise software is moving from loose AI experimentation to strict financial accountability. Early corporate budgets funded unconstrained “token maxing” to build basic AI fluency, but the 2027 narrative will demand measurable ROI. CIOs will no longer allocate tokens based on compelling pitches alone; they will require verified departmental efficiency gains or clear revenue growth.

Saturday’s episode with Paul Erlanger, Co-Founder @ fomo:

Download the full transcript:

Paul Erlanger @ Fomo

188KB ∙ PDF file

Monday episode: KR Sridhar, Founder & CEO @ Bloom Energy
Thursday episode: Jason Lemkin & Rory O’Driscoll
Saturday episode: Clay Bavor, Co-Founder @ Sierra

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20VC Newsletter - 21st June 2026

20VC — Sun, 21 Jun 2026 17:13:04 GMT

Monday’s episode with Aravind Srinivas, Co-Founder & Growth @ Perplexity:

Download the full transcript:

Aravind Srinivas @ Perplexity 2

174KB ∙ PDF file

My 7 key takeaways:

Are Agents the End of the Internet Advertising Model?

AI agents will disrupt industries built around objective transactions, but subjective spaces like travel, fashion, and shopping are more resilient. When intent is driven by vibes, exploration, and aesthetics rather than one correct answer, chat interfaces struggle to replace browsing, keeping much of the advertising model intact.

Biggest Takeaway From Elon Musk?

An elite entrepreneur can identify the single limiting bottleneck in a business and focus on it with unusual intensity. True concentration requires discipline: you must ignore even important issues when they distract from the immediate objective that matters most.

Did Export Controls Hurt or Help US Competition With China?

Export controls gave America a short-term edge by preserving a capability gap between open-source and frontier models. But they may have also forced China to become a stronger physical competitor by pushing its tech ecosystem toward memory-efficient architectures and deeper vertical integration.

How Will the Best Organizations Approach Token Budgeting?

Enterprises will stop manually tracking model capabilities or micromanaging token budgets across teams. As model optimization accelerates, the best companies will outsource this complexity to AI orchestrators that automatically route tasks to the right models at the right cost.

How Can America Retain Competitiveness in AI?

The West must build physical infrastructure aggressively, streamline power procurement, and counter alarmist narratives around data centers and job displacement. Leaders should explain the upside clearly: AI can let tiny teams build billion-dollar companies and drive major new GDP growth.

What Is the Single Most Important Metric in AI?

The key metric is token value per watt per user. Raw model building and fine-tuning are becoming commoditized. The real venture-scale value will accrue to orchestration layers and agent harnesses that deliver high-quality outputs while using minimal data center power.

Why Is Moving Fast the Ultimate Expression of Humility?

Founders often waste early cycles searching for a permanent moat, when speed is their only real defense. Moving fast is an act of humility because it forces constant contact with the market, repeated assumption testing, and a willingness to let reality overrule ego.

Thursday’s episode with Rory O’Driscoll, GP @ Scale & Ev Randle, GP @ Benchmark:

Download the full transcript:

The Trio Ep

192KB ∙ PDF file

My 7 key takeaways:

Everyone Misunderstands the SpaceX Valuation

SpaceX’s $2 trillion-plus valuation does not make sense on the P&L alone, but that is the wrong lens. Elon’s playbook is selling long-dated call options to the market: full self-driving, Starship, orbital data centers, Mars. He has delivered enough of them that the market keeps buying in.

Elon’s AI Strategy Has Been the Best of Anyone

While everyone else spent two years debating whether to “do something in AI,” Elon built two data centers, bought Cursor to fill the Colossus revenue gap, and signed $2 billion per month in compute contracts with Google and Anthropic. By September, xAI’s AI revenue run rate will exceed SpaceX’s entire launch business.

Why Elon’s Biggest Advantage Is Cost of Capital

No bank would lend $24 billion with no contracts to build Colossus in under a year. Elon could do it because his cost of capital is uniquely low, earned through decades of delivering on impossible bets. That lets him make asymmetric wagers no one else can, reinforcing the track record that keeps his cost of capital low. It is a flywheel that is almost impossible to replicate.

Why Anthropic Creates a Sovereignty Problem

The US restricting Claude’s export based on capabilities is a Rubicon moment. If that logic extends to OpenAI and Google, governments may begin gating access to intelligence itself. Who gets access to ASI when it arrives: NATO allies, select citizens, approved institutions? The geopolitical implications are staggering.

Any Liquidity for Pre-AI SaaS Companies Is Top-Decile Performance

The Intercom/Fin sale to Salesforce for $3.6 billion is a masterclass in turning a zombie asset into real capital. Growing at 7% with no AI story, the equity was essentially worthless. If your board is not making this the top agenda item and asking, “Is this relevant for us?” that is a failure of governance.

We Have a Crisis of Open-Source Models in the Western World

Outside China, there are essentially no world-class open-source models. The talent and focus required to be best-in-class across pre-training, mid-training, and post-training are scarcer than people think. For many countries, the realistic path may be fine-tuning a Chinese open-weights model, which raises uncomfortable questions.

How Does Benchmark Rationalize Not Being in Any of the Core Model Providers?

They do not. Ev was blunt: “It f**king sucks.” Missing a 30x on scaled capital in four to five years is a failure, full stop, regardless of how well the rest of the fund performed. It is one of the most honest VC admissions you will ever hear.

Saturday’s episode with Ryan Petersen, Founder & CEO @ Flexport:

Download the full transcript:

The Memo Ryan Petersen @ Flexport 2

226KB ∙ PDF file

Monday episode: Nikesh Arora, Chairman CEO @ Palo Alto Networks
Thursday episode: Jason Lemkin & Rory O’Driscoll
Saturday episode: Paul Erlanger @ fomo

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20VC Newsletter - 15th June 2026

20VC — Mon, 15 Jun 2026 09:39:03 GMT

Monday’s episode with Roman Chernin, Co-Founder & Growth @ Nebius:

Download the full transcript:

Roman Chernin @ Nebius

154KB ∙ PDF file

My 7 key takeaways:

How Reducing the Cost of Intelligence Increases Consumption

Reducing the unit cost of intelligence triggers Jevons Paradox: total compute consumption rises as previously uneconomic tasks become viable. At scale, builders move toward tunable open-source architectures and specialized post-training, while frontier labs expand into larger, harder reasoning markets.

If Nebius Doubled Pricing, How Would That Impact Demand?

Nebius’s pricing power is capped by customer economics. If inference costs rise too high, customer margins break and demand stalls. The real edge is not nominal GPU pricing, but Total Cost of Ownership: caching, runtime optimization, and distillation can shift token economics by an order of magnitude.

If Nebius Had 10x the Capacity, Could They Sell It?

The real question is not whether raw demand exists, but whether Nebius can diversify it. Bare metal concentrates revenue around a few global giants. Moving up the stack into managed infrastructure and specialized inference expands the market to thousands of application developers.

What Is the Single Biggest Threat to Nebius?

The biggest threat is extreme consolidation into three to five closed tech empires. If a few conglomerates control the frontier model landscape, independent clouds risk becoming low-margin physical-layer vendors. Survival depends on a broad, democratized ecosystem of independent builders.

Who Actually Holds Power Against Nvidia?

Power against Nvidia comes from engineering credibility, not political posturing. Nvidia is deeply engineering-driven, so influence comes from proving technical capability across the stack. Differentiation requires a world-class team that reliably executes and earns operational respect.

Surviving the Hyper-CapEx War

Competing with hyperscaler CapEx requires respecting operational timelines. Capital cannot compress a six-month infrastructure bottleneck, but over 18 to 24 months it matters. It lets providers parallelize execution, secure power, lock data centers, and prepare capacity ahead of GPU deployment.

The Shark Rule: Move or Die

Cloud infrastructure is a post-sales business: every funding round or contract is only a credit and an opportunity to deliver. Survival requires relentless forward motion, disciplined execution, and focus on daily operations rather than emotional market spikes or consolidation noise.

Thursday’s episode with Rory O’Driscoll, GP @ Scale, Jason Lemkin, Founder @ SaaStr:

Download the full transcript:

The Trio Ep

189KB ∙ PDF file

My 6 key takeaways:

Why Did Elon Make Such a Strong AI Strategic Play?

Over the last 24 months, Elon Musk built the Colossus clusters from ground zero. Backed by big-picture conviction and the planet’s cheapest cost of capital, he deployed $20 billion to $30 billion upfront ahead of revenue. This bold move established an efficient compute layer, allowing him to lease capacity back to competitors like Google and Anthropic.

Why Is Revolut Worth Over $100 Billion While Chime Is Worth $5 Billion?

Revolut commanded a massive $115 billion valuation because legacy European banks were slow, inefficient, and left easy profit margins unprotected. Chime is worth far less because US banks are generally more efficient. A challenger’s valuation is ultimately dictated by the weakness or efficiency of the incumbents it seeks to disrupt.

Why Bending Spoons Is an Amazing Business

Bending Spoons scales by acquiring companies, cutting extraneous expenditures, and reducing marketing spend to focus strictly on high-ROI initiatives. It then raises prices aggressively over time. By capitalizing on customer inertia, especially legacy users who rarely churn, it maintains decent net retention and generates significant cash flow.

Consumers Don’t Want AI Productivity Tools. They Want to Relax and Be Entertained.

There is no major consumer market for AI productivity tools or complex research in non-working life. Consumers do not want to do extra work when they are off the clock. Instead, they want delightful, engaging experiences because their core desires during downtime are simple: relax and stay entertained.

Founders Complaining About VCs Often Haven’t Sold Before

Founders who complain about poor treatment from VCs often reveal that they have never run a real sales pipeline. Selling stock is not fundamentally different from selling any other product. In standard sales, expected deals routinely collapse and prospects ghost you after repeated follow-ups. Fundraising requires getting over it and moving forward.

Why Intelligent, Efficient Businesses Are the Best Places for Employees and Investors

Businesses that leverage high intelligence, with AI tokens as a proxy, allow small teams to achieve massive operational output. Because compact teams can accomplish and earn so much through technical leverage, these hyper-efficient organizations can become the best environments for both employees and investors.

Saturday’s episode with Matan Grinberg, Co-Founder & CEO @ Factory:

Download the full transcript:

Matan Grinberg @ Factory

222KB ∙ PDF file

Monday episode: Aravind Srinivas, Co-Founder & Growth @ Perplexity
Thursday episode: Jason Lemkin & Rory O’Driscoll
Saturday episode: Ryan Petersen, Founder & CEO @ Flexport

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20VC Newsletter - 7th June 2026

20VC — Mon, 08 Jun 2026 09:01:56 GMT

Monday’s episode with Brendan Foody, Co-Founder & CEO @ Mercor:

Download the full transcript:

Brendan Foody @ Mercor 2

196KB ∙ PDF file

My 6 key takeaways:

Why Frontier AI Labs Could Become $10TN Companies

Critics once questioned whether frontier labs could keep pricing power in a competitive market. Their revenue velocity now suggests the opposite. The opportunity around leading frontier models is so large that it could absorb a major share of macro demand. At least one AI lab may become a $10TN company within 5 years.

The Capacity Bottleneck: Demand Doubling Overnight

For top infrastructure and data providers, growth is no longer limited by customer acquisition. It is limited by execution. Demand is scaling so fast that leading companies could double revenue overnight if they had enough capacity. The challenge now is how quickly they can mobilize specialized human networks and build high-fidelity environments for enterprise demand.

Is the Stated Revenue Really Revenue or GMV?

The stated revenue is not GMV because the talent network is only one part of a vertically integrated value chain. Customers buy complete tasks for model improvement, not simple marketplace listings. With 30% to 40% gross margins, the business owns the full lifecycle, from sourcing experts to deploying AI project managers and running quality checks.

The Inversion of Corporate Opex: Token Spend vs. Salaries

In high-growth AI companies, token spend for internal agents has already surpassed employee headcount costs. As operations, interviewing, accounting, and fraud detection move to agents, capital allocation shifts from salaries to inference compute.

Why Token Spend Inside Companies Will Keep Increasing

Driven by Jevons Paradox, enterprise token consumption will keep rising as models improve and costs fall. Companies will use more compute to unlock higher-order reasoning, not less. F500s are responding by building evaluation systems that let them hot-swap models and optimize inference budgets.

The Tens of Millions Talent War for AI Researchers

The market for top AI researchers is severely supply constrained, with demand far above available talent. Companies are offering compensation packages worth tens of millions in stock per year to secure elite researchers. This wage spike shows that world-class research talent remains the core bottleneck in AI.

Thursday’s episode with Rory O’Driscoll, GP @ Scale, Jason Lemkin, Founder @ SaaStr:

Download the full transcript:

The Trio Ep.59

205KB ∙ PDF file

My 6 key takeaways:

The Global Cash Grab Has Begun

Boards and founders are waking up to the staggering capital required to win the AI infrastructure war. Timelines are accelerating massively across both private and public markets as companies scramble for runway. The smartest players are aggressively front-running the queue to lock down massive balance sheets while the capital window is wide open.

The Realignment of Venture Scale

Hyper-growth AI frontrunners have completely shattered traditional expectations of company scale. Investors are aggressively filtering out smaller, capped markets that fail to move the needle for a fund. To get a top-tier VC excited today, you must prove a path toward an exceptionally large, concentrated position size.

The Death of the “Stay Private Forever” Myth

The era of hiding behind private valuations to protect an unverified narrative is completely over. The largest AI players are rushing toward public listings to secure massive tranches of capital. When your financials are stripped bare, the artificial mystery evaporates and transparent execution takes over.

Software is No Longer a CapEx-Light Game

The foundational unit economics of the world’s most profitable giants are fundamentally breaking. Historically light, cash-generating software machines are transforming into capital-intensive operations that eat billions for data center buildouts. This massive infrastructure burden will compress margins over the medium term.

Tokens Are Eating Seat Licenses for Breakfast

Enterprise software budgets have entered a brutal, zero-sum fracture. Organizations are aggressively slashing classic human per-seat software licenses to free up cash for soaring compute and inference costs. If your platform doesn’t power automated, agentic systems, your budget line item is an immediate target.

Compute Budgets Will Replace Humans

Elite engineering talent will walk out the door before they tolerate being forced onto subpar AI models due to cost containment. Consequently, tech leaders are making rigid workforce trade-offs. Leaders are actively shrinking supportive roles and QA departments to hand their best developers unlimited compute power.

Saturday’s episode with Jacob Lauritzen, CTO @ Legora:

Download the full transcript:

Jacob Lauritzen @ Legora

131KB ∙ PDF file

Monday episode: Roman Chernin, Co-Founder & Growth @ Nebius
Thursday episode: Jason Lemkin & Rory O’Driscoll
Saturday episode: Ryan Petersen, Founder & CEO @ Flexport

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20VC Newsletter - 1st June 2026

20VC — Mon, 01 Jun 2026 15:46:04 GMT

Tuesday’s episode with Andrew Feldman, Founder & CEO @ Cerebras:

Download the full transcript:

Andrew Feldman @ Cerebras 3

162KB ∙ PDF file

My 6 key takeaways:

Why We Are Not in an Infrastructure Bubble and It Is Just the Start

Unlike past tech bubbles that overbuilt ahead of demand, the AI infrastructure rollout is running significantly behind immediate market needs . Major chip makers face massive multi-billion-dollar backlogs because data centers cannot be built fast enough to keep pace with the exploding, real-world user demand happening today.

Anthropic Did Not Get a Good Deal With Elon. They Got a Deal That Was Available.

Founders are often forced to take action on what is available in the market rather than what is ideal. Anthropic’s deal with Elon Musk required them to buy “down rev gear”, older H100 chips rather than cutting-edge B200s, leaving them a generation and a half to two generations behind the absolute leading edge.

Why Jensen and NVIDIA Are Wrong to Sell Chips to China

Widespread security consensus confirms that selling leading-edge tech to China means their military and government will inevitably leverage it to compete with Western industry . As an industrial adversary driving down global costs in vital sectors like solar and automotive, American chip companies should be completely comfortable selling fewer chips to protect strategic boundaries.

What the F**k Is Going on With the Price of Memory and Why Is It a Problem?

Exploding AI demand makes High Bandwidth Memory (HBM) the most critical bottleneck right after fab space. Because only three manufacturers produce this specialized memory and cannot keep up, prices have skyrocketed, allowing suppliers to command software-like 80-85% gross margins on hardware fabrication.

Are Google Best Positioned to Produce the Lowest Cost Tokens and What Challenges Do They Face?

Owning the full stack from data centers to chips gives hyperscalers an immense cost advantage over standard clouds paying high hardware margins. However, the historical downside is that your market is constrained strictly by your own internal demand, meaning hardware innovators must sell externally to maximize volume and lower unit costs.

My Biggest Advice to Entrepreneurs Scaling Their Business

Your initial focus must be entirely on winning just one customer. Landing that first anchor client forces your company to build the necessary operational muscle, adjust your supply chain, and learn how to properly service a massive organization so you have the capability to keep the next ones happy.

Thursday’s episode with Rory O’Driscoll, GP @ Scale, Jason Lemkin, Founder @ SaaStr:

Download the full transcript:

The Trio Ep

221KB ∙ PDF file

My 7 key takeaways:

Is OpenAI Rushing to Go Public Before Anthropic?

OpenAI may need to rush its IPO to protect its category leader narrative from Anthropic. OpenAI did $5.4B to $5.5B in Q1, while Anthropic generated $5B, matching its entire prior year in a single quarter. With Anthropic growing 10x year over year versus OpenAI’s 2 to 3x pace, it could soon become the bigger, more profitable business.

Do Venture Investors Have to Risk More With Less?

The venture playbook has shifted from the SaaS era. VCs now have to back higher valuations on far less information. Waiting for one-year renewals or clean trailing data is a losing strategy because AI adoption curves move too fast. Success now requires acting on raw product traction and market conviction.

What Is the Right Venture Play in AI?

Traditional seed investing has become a bottleneck. The winning move is wiring capital the moment a breakout leader emerges. These startups can move from pre-revenue to hyper-scale almost overnight, so investors have to underwrite real-time momentum instead of waiting for the market to settle.

Is SpaceX the GeoCities of Our Time?

At 100x trailing sales, SpaceX risks becoming the GeoCities deal of the AI era if market euphoria fades. Its valuation is detached from traditional discounted cash flow models and depends on a massive Elon Premium that multiplies its baseline economic value.

Why the SpaceX S-1 Does Not Make Sense

Combining unrelated assets into the SpaceX S-1 looks like financial engineering designed to hide weaker pieces of the story. It uses AI hype to absorb Twitter’s revenue collapse and expensive chip clusters that failed to compete with OpenAI. It feels like SolarCity on steroids, using a clean private business to bail out an insular investor circle.

What Will SpaceX’s Core Business Be in Five Years?

Starlink will likely drive most of SpaceX’s value. Terrestrial data centers remain a low-ROE business, and space data centers are still negligible. The valuation only works if Jensen Huang’s $3T to $4T CapEx vision hits by 2030 and Earth power constraints force compute infrastructure into space.

Are Tech Layoffs From COVID Overhiring or AI Efficiency?

Blaming layoffs on COVID overhiring does not hold up after years of normal attrition. Corporate America is redirecting opex from mid-level headcount toward token budgets and agentic automation. Companies are cutting average roles to overpay elite, highly productive talent who use AI workflows to multiply output.

Saturday’s episode with Nico Laqua, Co-Founder & CEO @ Corgi:

Download the full transcript:

Nico Laqua @ Corgi

167KB ∙ PDF file

Monday episode: Brendan Foody, Co-Founder & CEO @ Mercor
Thursday episode: Jason Lemkin & Rory O’Driscoll
Saturday episode: Sam Stephenson, Founder @ Granola

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20VC Newsletter - 24th May 2026

20VC — Sun, 24 May 2026 14:01:48 GMT

Monday’s episode with Joshua Browder, Founder & CEO @ DoNotPay:

Download the full transcript:

Joshua Browder @ Browder Capital

229KB ∙ PDF file

My 6 key takeaways:

Why I Believe Young Founders Make the Best Founders

Young founders have no safety net and no option but to win. Corporate engineers often default to hiring big teams, while young founders stay focused on building the product. Their grit is much higher. Without that level of dedication, most people quit at the first real obstacle.

How I Test Founder Commitment Before Investing

To filter out tourist founders, schedule a pitch meeting at 11:00 PM. Elite founders accept immediately. Mediocre ones push it out by weeks. During the interview, ask rapid-fire questions. If they claim a specific revenue number, have them pull up their live Stripe account on the spot. Look for tactical customer acquisition goals, not vague partnership promises.

Why I Make Founders Live With Me After Investing

The best early investments come from deep day-one relationships. Living together creates a focused, one-person accelerator where founders get a three-week crash course and avoid years of mistakes. The rule is simple: co-founders share one room near the Four Seasons and cannot check out until they raise an institutional seed round.

Why Pre-Seed Companies Fail

Startups usually fail for three reasons: they run out of money, they run out of hope, or the co-founders break up. Money problems usually come from weak pitching, which is why founders should drop the deck and show the product live. To maintain hope, ignore Silicon Valley vanity signals and focus on customer progress. To avoid team blowups, handle mechanics like vesting early.

What Founders Need to Know About Signing With a VC

VCs will say almost anything to get you to sign on the spot. They reverse-engineer your desires and claim they know every customer you want to meet. Impressionable founders fall for it, but the promised intros often never happen. Never sign in the room. Take the night to think clearly.

My Biggest Lesson on Reserve Investing

Holding back reserves for later rounds has a huge opportunity cost. The biggest value creation happens at pre-seed, so saving capital for a Series A follow-on can limit your upside. Deploying upfront into 20 to 30 pre-seed companies can produce far better long-term returns. Go all-in early.

Thursday’s episode with Rory O’Driscoll, GP @ Scale, Jason Lemkin, Founder @ SaaStr:

Download the full transcript:

The Trio Ep

221KB ∙ PDF file

My 6 key takeaways:

AI Mega-Rounds Offer Better Value Than Series A

If ARR multiples are your proxy for value, late-stage AI megarounds are currently the best trade in the venture universe. Anthropic raising at an 18x while growing 10x YoY is structurally a much better deal than the median Series A or B round. Early-stage rounds frequently command significantly higher multiples for far less growth and much higher execution risk.

Token Spend is the New Early-Stage Marketing

For early-stage startups, token spend is fast becoming a core marketing line item rather than just an R&D cost. Founders are aggressively using free token allocations to subsidize freemium user experiences and drive viral product adoption. This structural advantage allows nimble startups to temporarily out-hustle incumbents who are tightly constrained by corporate budgets.

The New “Sub-Figma” IPO Barrier

While the public markets are completely risk-on for hyper-scale tech companies, the baseline bar to go public has shifted dramatically. Generational infrastructure with massive backlogs can command infinite demand and pop on day one. However, traditional software companies scaling “sub-Figma” will heavily struggle to execute a decent IPO in this current climate.

SaaS is Transitioning to Middle Age

Traditional software giants are beginning to show modest growth reacceleration, but their days of euphoric, bubble-era valuation multiples are gone forever. SaaS companies are now firmly entering a mature business phase where they are valued strictly on realistic revenue growth and cash flow, while spotlight has shifted entirely to AI.

Bureaucracy is Saving AI Infrastructure from a Crash

A massive cyclical crash is highly probable down the line for hardware, data center, and memory providers, but physical limitations are delaying the blow. Ironically, the slow permitting and bureaucratic friction of bringing physical data centers online are keeping compute artificially scarce. This structural inertia is preventing immediate oversupply and saving the tech sector from its own aggressive CapEx impulses.

The Growing Populist Backlash Against AI

The tech industry has a severe blindspot regarding public sentiment, as very few people outside of California actually like the AI trend. Many brilliant tech leaders are acting like political morons by aggressively laying off thousands of workers while shifting capital entirely into machines. This dynamic is brewing a brutal political and social backlash that the industry is deeply unprepared to handle.

Saturday’s episode with Chad Peets & Chris Degnan:

Download the full transcript:

Chad Peets & Chris Degnan

243KB ∙ PDF file

Monday episode: Andrew Feldman, Founder & CEO @ Cerebras
Thursday episode: Jason Lemkin & Rory O’Driscoll
Saturday episode: Jacob Lauritzen, CTO @ Legora

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20VC Newsletter - 17th May 2026

20VC — Sun, 17 May 2026 14:03:00 GMT

Monday’s episode with Patrick Forquer, CRO @ Legora:

Download the full transcript:

Patrick Forquer @ Legora

223KB ∙ PDF file

My 8 key takeaways:

Implementation Change Management

Success is defined by change management, not just software. Legora focuses on helping customers adopt AI into workflows, which is fundamentally difficult. You must lead the customer through the “hard yards” of organizational change.

Why FTEs are Required in Enterprise

High-end AI needs human experts. Legora uses Forward Deploy engineers and former attorneys, known as Legal Engineers, to bridge the gap. They solve the “blank page” problem by showing exactly how a workflow looks with agents.

Throwing out the Traditional Sales Playbook

Old SaaS held demos back. Now you must demo early to show the future. Reps must be audible ready, asking deep questions and building agentic workflows off the cuff during the meeting. AI literacy is low, so the product must lead the way.

Brand Awareness as a Performance Driver

Brand awareness is a powerful driver of business, not “fluffy bullshit.” A campaign with Jude Law generated over $50M of qualified pipeline in one month. In category creation, if you are not “in the room,” you lose.

Price Integrity over Free

Legora does not give software away. If a customer is not spending money, they will not commit the resources needed for change. Price integrity ensures both parties have skin in the game and value the partnership.

Scaling Talent at Unhinged Speed

Legora hires 40 to 50 people every two weeks. Immersive training in Stockholm gets reps ready to sit on calls by week two. They use AI to score demo quality, identifying within 45 days whether a rep will make it.

The Lulucast vs. The Bet Your Life Forecast

Effective forecasting requires two perspectives: the rep and manager commit, or the bet your life number, and the Lulucast, which is the weighted math version. Accuracy depends on tight entry and exit criteria for every sales stage.

Momentum and Pressing the Advantage

Product-market fit is unwavering. When you have it, you know it. When you find something that works competitively, document it and press the advantage. In an unhinged market, you must play defense and offense simultaneously.

Thursday’s episode with Rory O’Driscoll, GP @ Scale, Jason Lemkin, Founder @ SaaStr:

Download the full transcript:

The Trio Ep

205KB ∙ PDF file

My 6 key takeaways:

Foundation Made the Investment of the Decade with Cerebras

Jason argues that Foundation’s success with Cerebras is a masterclass in “actual venture capital.” They incubated the company in 2016, when the category did not even make sense. By playing the long game, finding a brilliant founder, seeding the idea, and holding roughly 9% ownership through a $40B+ IPO, they proved that the biggest returns still come from doing the hard work before a deal becomes obvious.

What Founders Have to Understand Is That to Win, You Have to Mentally Be Changed Forever

There is a fundamental breakpoint around the four-to-five-year mark when a founder’s brain is permanently rewired by the intensity of the journey. Winning at a high level requires a commitment to becoming a different person. The happy-go-lucky version of yourself from the early days is gone, replaced by someone who can often only relate to other founders who have survived similar maelstroms.

The Enemy of My Enemy Infrastructure Play

Anthropic’s partnership to use SpaceX’s Colossus 1 data center highlights a massive consolidation where the strongest players are hoovering up all available capacity on the planet. For Elon Musk, this move transitions xAI from a buyer of CapEx to a net seller of capacity, turning a potential money pit into a $3 to $5 billion annual revenue stream because Grok is not currently growing at the same pace as leading-edge models.

The Crackdown on Shadow Cap Tables

Anthropic is enforcing board approval for all secondary sales to reclaim cap table control and call out “bad actors”. Rory warns that side contracts for “economic rights” are legally fragile; because the company has no obligation to honor unapproved transfers, many investors face “messy” losses at the IPO.

Model vs. Application: The Vertical SaaS Death Zone

The industry is debating if horizontal models will consume the application layer or if vertical workflows will remain independent. Jason predicts a “terminal state of decay” for legacy marketing tools because agents have no need for manual templates. Once a model can perform an application’s core function directly within a prompt, that software becomes obsolete.

Token Maxing vs. The 100x Engineer

Despite massive growth forecasts, a “micro backlash” is growing against “token trash” generated by mediocre developers. Jason predicts a clampdown on wasteful agentic spend, where companies prioritize unlimited resources for elite “100x engineers” while restricting “web heads” who burn compute for minimal productivity gains.

Saturday’s episode with Shiv Rao, Co-Founder & CEO @ Abridge:

Download the full transcript:

Shiv Rao @ Abridge

258KB ∙ PDF file

Monday episode: Joshua Browder, Founder & CEO @ DoNotPay
Thursday episode: Jason Lemkin & Rory O’Driscoll
Saturday episode: Jacob Lauritzen, CTO @ Legora

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20VC Newsletter - 10th May 2026

20VC — Sun, 10 May 2026 14:09:19 GMT

Monday’s episode with Tobias Lütke, CEO @ Shopify:

Download the full transcript:

Tobi Lütke @ Shopify 2

196KB ∙ PDF file

My 6 key takeaways:

The $160 Billion CEO Who Did Not Want to Be CEO

To run a truly product-driven company, you must be in control of the company itself because the needs of a product and a business often diverge. This requires passing the “marshmallow test” by prioritizing a three-year perspective over immediate gratification. Founders must be willing to endure “bad numbers” in the short term if they believe it is the only way to reach the right long-term destination.

The Best Engineers in Shopify Are Not Writing Code Anymore and the AI Does It for Them

AI now generates over 50% of the code at Shopify, and that number is steadily increasing. Many of the company’s best engineers have not written a single line of manual code since December 2023, as the release of advanced models like Opus changed the fundamental nature of the job. Engineering has shifted from manual execution to “steering” AI agents through high-level context engineering.

Why AI Is Being Used as a Scapegoat for Mass Layoffs

The current wave of industry layoffs is largely a result of “over-hiring” during the COVID era rather than displacement by AI. AI has become the “perfect scapegoat” for slow companies because it cannot fight back against the narrative that it is destroying jobs. In reality, AI is an incredible tool that improves productivity across every department when implemented correctly.

Why We Are About to Enter a Golden Age of Entrepreneurship

Entrepreneurship is the most “AI-safe” and “AI-benefiting” career path available today. AI acts as a “handy sidekick” or co-founder, allowing individuals to focus on their core ambitions while the technology handles various execution tasks. The barriers to entry are dissolving; you no longer need a family background in business to understand that business formation is something real humans can do.

Why We Need to Place More Not Less Scrutiny on Charitable Giving

Giving money is only virtuous if it yields results; prioritize what works over what sounds good. “Charity dollars” lack the “self-healing fitness function” provided by for-profit markets. Be skeptical of organizations that choose to opt out of market intelligence.

Why Will Governments Regulating the Technology We and Our Children Use Push Us Into the Hands of the Chinese?

Age limits won’t stop kids from using AI; they will just drive them toward uncurated “donut” Chinese models. This risks exposing youth to a collectivist monoculture that censors historical truth. Such regulation trades individualistic enlightenment values for state control.

Thursday’s episode with Rory O’Driscoll, GP @ Scale, Jason Lemkin, Founder @ SaaStr:

Download the full transcript:

The Trio Ep

227KB ∙ PDF file

My 7 key takeaways:

Why Gemini Is Behind but Why It Might Not Even Matter

Google is trailing in “coder love” as developers flock to Anthropic and OpenAI. While Gemini’s token growth lags behind competitors’ 10x gains, Google remains a powerhouse because it owns its model and distribution.

Why Palantir Is a Mega-Buy Right Now

CEOs want $10M+ enterprise-wide transformations, not small point solutions. Palantir wins by delivering measurable, large-scale results. Their buying cycles are compressing as every stakeholder now attends the first meeting.

Why Is Meta Being Chastised for Spending on Capex?

Meta’s $150B CapEx lacks the clear revenue attribution found in Google’s cloud backlog. Investors view it as a defensive hedge, a bet to be present if social shifts to chatbots. Critics argue a 10 to 15% ad lift does not justify such massive spend.

Who Wins and Who Loses From the Application Explosion?

We are in the greatest application-building boom in history. Amazon and Microsoft win by providing the underlying compute for these new B2B apps. Meta loses because it gains nothing from this software sprawl.

Atlassian Versus Twilio: Who Is the AI Hero Versus the Unsustainable Bounce?

Atlassian is successfully monetizing its base with Rovo AI, though new customer growth is slowing. Twilio has re-accelerated by becoming the essential API infrastructure for new AI agents. Winners must eventually drive both expansion and new customer acquisition.

Should Microsoft Really Be Trading Like the Rest of SAAS Companies?

Excluding AI, Microsoft’s core business is flat to slightly down. Without the AI narrative, it would likely trade like a mature SaaS company at 3x revenue. Their growth is now entirely dependent on massive, high-risk CapEx bets.

Why Your VP Needs to Be Full Stack and Do Everything End-To-End

AI is eliminating “managers of managers.” Founders now demand leaders who can actually ship. A modern VP should use agents to execute tasks, like campaigns, end-to-end in minutes. If an executive cannot lead from the front with these tools, they are a liability.

Saturday’s episode with Cliff Weitzman, Founder & CEO @ Speechify:

Download the full transcript:

Cliff Weitzman @ Speechify

294KB ∙ PDF file

Monday episode: Patrick Forquer, CRO @ Legora
Thursday episode: Jason Lemkin & Rory O’Driscoll
Saturday episode: 20Growth

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20VC Newsletter - 3rd May 2026

20VC — Sun, 03 May 2026 14:03:01 GMT

Monday’s episode with Adam Foroughi, Co-Founder & CEO @ AppLovin:

Download the full transcript:

Adam Foroughi @ Applovin

208KB ∙ PDF file

My 6 key takeaways:

Are People Ready for the AI Future That Is Within Every Company?

True AI integration requires a massive “leveling up” of talent. Companies must be honest about the path forward: keeping employees who fail to adopt AI creates a “blockade” to reaching a truly AI-native state. Consequently, we should expect continued tech layoffs as organizations prioritize efficiency over legacy headcount.

Biggest Advice on Token Budgeting and Token Maxing?

Treating tokens as a simple budget or leaderboard is “flawed logic”. If you incentivize raw usage, teams will simply create high-volume “crap” that burns capital without driving revenue. Instead, optimize for specific KPIs where token consumption aligns directly with business growth; when revenue is on the other side, the “budget” mindset disappears.

Can You Have a Team Full of Only A Players?

An organization cannot thrive if A players are surrounded by B, C, or D players. AppLovin slimmed its HR department from 80 people to 15 by retaining only “doers” who don’t get bogged down in the process. The goal is a lean culture of individual contributors who want to make a difference without needing heavy management layers.

Do the Majority of Company Teams Need to Be Rebuilt for the Technology We Have Today?

If a role is likely to be automated, or if a department is too slow to adopt AI, it is time to rebuild that organization from the ground up. Foroughi cut staff by 40-50% in most departments during a year of triple-digit growth to force the organization into an automated, efficient state.

Why Investors Need to Give Ceos Better Comp Packages

Founders take massive risks to build something out of nothing, and they need continued upside to stay mentally motivated. If a CEO is expected to work without performance-based incentives, they may drift toward new ventures rather than staying committed to the “lonely, stressful” task of scaling a public company.

Why This $160 Billion Company Does Not Have Any Learning and Development

Structured L&D is often disconnected from the reality of high-performance work. The best employees are curious enough to figure things out on their own. By documenting all communication in transcripts and chats, new hires can use AI models to summarize tribal knowledge and develop themselves more effectively than any formal training program.

Thursday’s episode with Rory O’Driscoll, GP @ Scale, Jason Lemkin, Founder @ SaaStr:

Download the full transcript:

The Trio Ep

221KB ∙ PDF file

My 6 key takeaways:

Why does Dario at Anthropic have such a hard job predicting the compute demands?

The capital intensity of building an AI leader is unprecedented; every $1 of run-rate revenue requires approximately $4 to $5 of CapEx to support it. A CEO must forecast demand two years in advance, which is incredibly risky. Underestimating demand leaves you with insufficient compute to serve users, while overestimating it results in billions of dollars in “stranded capacity”.

What the public markets are getting wrong about the SaaS-pocalypse

The market currently believes specific coding vibes or models are the primary threat, but the true danger is what AI agents decide to pick. Agents will ultimately choose the vendors and LLMs for most workflows, rendering tools like project management software useless because agents have no need for them. Companies like OpenAI are racing to win the “agent wars” to ensure their APIs are the default choice for these autonomous systems.

Why Google is a mega-buy on the back of the Anthropic investment

Google is positioned as a primary winner because it benefits whether users choose Gemini or Anthropic. They possess “infinite capacity” compared to other players, allowing them to route compute surplus between their own needs and their various customers. This massive cash flow and infrastructure flexibility make them a “win-win-win” in the current AI arms race.

Multi-year contracts don’t matter. Deferred churn is still churn.

Multi-year contracts are often a place where “mediocre” management hides to mask underlying business problems. While a customer might be locked into an eight-year cycle through standard upfront terms and renewals, they are essentially just taking that time to find a better enterprise solution. If a customer eventually leaves, the churn was merely deferred, and the terminal value of the company remains impacted.

What happens to the distributions from Manus? Do the investors have to give the money back?

When a regulatory body like China attempts to “unwind” an acquisition like Meta’s purchase of Manus, there is a near-zero chance that venture investors will return the capital already distributed. The real pressure point is on the acquiring corporation and the technology itself, rather than the VC funds. Such rulings are primarily designed to prevent similar deals from occurring in the future.

The two great wars that no one is talking about

Two subtle but massive “battles” are currently unfolding: the US vs. China AI war and the resulting social dislocation. We are seeing a rise in “social unrest” expressed through billionaire taxes and penthouses taxes as layoffs from AI automation begin to impact the workforce. These themes of geopolitical competition and internal inequality will be the defining political stories of the decade.

Saturday’s episode with Becca Lindquist, Head of Sales @ Clay:

Download the full transcript:

Becca Lindquist @ Clay

234KB ∙ PDF file

Monday episode: Tobias Lütke, CEO @ Shopify
Thursday episode: Jason Lemkin & Rory O’Driscoll
Saturday episode: Cliff Weitzman, Founder & CEO @ Speechify

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20VC Newsletter - 26th April 2026

20VC — Sun, 26 Apr 2026 22:31:04 GMT

Monday’s episode with Aaron Levie, CEO @ Box:

Download the full transcript:

Aaron Levie @ Box 2

160KB ∙ PDF file

My 7 key takeaways:

Why Dwarkesh Was Wrong and Jensen Was Right on Upgrading Systems

Upgrading software is a multi-year effort, not a “magical moment” where everything can be secured overnight. The reality of enterprise security is an ongoing, endless cycle of “leapfrogging” between defensive and offensive capabilities. Founders must realize that even with access to frontier models, the implementation cycle in the real world remains the primary bottleneck.

Why We Will Have More Lawyers in Five Years Not Less

The industry is myopic about job elimination; AI makes it easy to generate content, but it hasn’t made it easier to get that content approved by a court or a patent office. As clients inundate lawyers with AI-generated contracts and memos, the “ultimate constraint” becomes the number of qualified humans available to review and approve the output.

What Role Does Not Exist Today That Will Be Incredibly Common in Five Years?

We are about to see the creation of 500,000 to 1 million “Agent Operators”. These technical-yet-business-savvy individuals will be responsible for “care and feeding” of agents—writing skills, understanding MD files, and redesigning workflows for agents rather than people.

Will Massive Software Providers Simply Be Turned Into a Database That Agents Crawl Over?

While the user interface may shift to chat, the value is moving to the API layer and the “business logic” embedded above the database. Systems like ERPs are more than databases; they contain decades of complex logic for supply chains and accounting that agents must interact with, not replace.

What Everyone Thinks About Enterprise AI Adoption That They Get Wrong

The assumption that the massive gains seen in AI coding will immediately translate to all other knowledge work is a “misread”. Coding has specific idiosyncrasies that don’t always exist in broader knowledge work, where human collaboration and regulatory loops are more complex.

Where Would You Be Investing if You Were a VC Today?

Despite high valuations, Levie would still be “loading up” on frontier rounds. These companies have the potential to grow much larger because the ultimate market for AI is often larger than the industry currently realizes.

The Budget of Tokens Will Have to Move Out of IT Spend and Into Opex

Enterprise AI shouldn’t be treated as a tradeoff between software licenses. Instead, token budgets will move into regular operational expenditure (OPEX), where businesses trade off a marketing campaign for a more productive, automated marketing engine. This allows AI companies to tap into a massive pool of capital beyond the traditional, capped IT budget.

Thursday’s episode with Rory O’Driscoll, GP @ Scale, Jason Lemkin, Founder @ SaaStr:

Download the full transcript:

The Trio Ep

260KB ∙ PDF file

My 5 key takeaways:

This $60B deal actually makes sense

The potential $60 billion acquisition of Cursor by xAI/SpaceX is an industrial “marriage made in heaven”. Cursor has an exploding business with billions in ARR but “shitty” gross margins because they lack their own compute and models. Elon Musk has the massive Colossus GPU data center and a model (Grok) but effectively no revenue, making the vertical integration of these two companies a strategic fix for both.

How Claude Design Will Hurt Figma

Anthropic’s Claude Design is a full design application, not just a set of prompts or skills. It poses an existential threat because it allows product and engineering teams to bypass the traditional 30-day designer turnaround. By enabling “normal people” to design and move into production immediately, it will “maim and nibble” at Figma’s growth over the coming quarters.

I used to think MAUs and WAUs were the dumbest metric. Now I think it’s the most important.

In the B2B world, usage metrics like MAUs, WAUs, and DAUs are now more critical than revenue. If usage isn’t growing faster than revenue, it’s a sign of a struggling startup or “stealth churn,” where users have stopped active engagement despite the company still collecting fees. In the AI age, the ultimate test of a product’s value is whether people are actually using its AI features daily.

Why the biggest fintech players are in for a shock.

Existing moats for fintech giants like Brex and Ramp are weakening as the selection criteria for vendors shifts. Customers are no longer prioritizing a dashboard’s UI; they care which API works best with their autonomous AI agents. If a new vendor offers a superior API that allows an “AI VP of Finance” to automate tasks like collections, companies will switch vendors in a single week.

Agent fabric is the layer that manages all your agents

The defining 2027 challenge is the “agent fabric”. The infrastructure needed to manage and secure hundreds of autonomous agents. This gives a massive advantage to incumbents like Salesforce. They are positioning themselves as the trusted governance layer to guardrail agents and prevent them from going rogue

Saturday’s episode with Amjad Masad, Founder @ Replit:

Download the full transcript:

Amjad Masad @ Replit

140KB ∙ PDF file

Monday episode: Adam Foroughi, Co-Founder & CEO @ AppLovin
Thursday episode: Jason Lemkin & Rory O’Driscoll
Saturday episode: Becca Lindquist, Head of Sales @ Clay

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20VC Newsletter - 19th April 2026

20VC — Sun, 19 Apr 2026 15:31:09 GMT

Monday’s episode with Anjney Midha, Founder @ AMP PBC:

Download the full transcript:

Anjney Midha @ Amp

191KB ∙ PDF file

My 6 key takeaways:

The Reason Why Mistral Will Be a $100BN Company

The US Cloud Act requires that any data workloads managed by an American company must be accessible to the US government. For European entities running mission-critical logistics or defense, sending that context across the border is a non-starter. This creates a massive opening for local, sovereign infrastructure partners like Mistral to handle mission-critical workloads at scale.

Why Every VC Missed Anthropic’s First Round

When Anthropic pitched their seed round, they got 21 “nos” from top-tier investors. The reason? A total lack of technical literacy. Most VCs at the time didn’t even know what GPT-3 was and couldn’t grasp the concept of “compute multipliers”: the idea that you could produce intelligence for 6x less cost per dollar of capital.

How a16z Became a Major Buyer of Compute

Early on, Anjney recognized that compute access was the primary bottleneck for new incubations. By using the A16Z balance sheet to procure compute through the “Oxygen program,” they built the deep industry relationships and infrastructure trust necessary to scale the independent ecosystem.

The CCP Has Done Such an Incredible Job in Catching Up

The AI race in China isn’t just about chips; it’s a full-stack systems co-design race. By integrating Huawei chips with localized compute and using “adversarial distillation” to capture gains from Western models, they are producing performance improvements that rival the best Western hardware.

We Are Not in an AI Bubble. We Are in a GPU Wastage Bubble.

The crisis isn’t a lack of AI capability; it’s a standardization crisis. Because compute is not yet fungible, meaning you can’t easily move a workload from an H100 cluster to a Blackwell cluster, billions of dollars in compute are currently sitting unutilized and “stranded”. We are essentially in the “1885 pre-standardization era” of the electricity grid.

What Makes Dario at Anthropic So Special?

Dario Amodei operates with the obsessive “truth-seeking” mindset of an applied physicist rather than a traditional computer scientist. His brilliance lies in an empiricist approach: running endless experiments to derive general laws of reality, combined with a “ruthless” mission alignment that refuses to take shortcuts for profit.

Thursday’s episode with Rory O’Driscoll, GP @ Scale, Jason Lemkin, Founder @ SaaStr:

Download the full transcript:

The Trio Ep

219KB ∙ PDF file

My 6 key takeaways:

The Incumbent’s Big Problem and Why the Saas-Pocalypse Might Be Fair

Incumbents face a “doom loop” by building AI products only 60% as good as specialized competitors. While internal teams are proud, the market won’t pay for mediocre “check the box” features. To avoid a slow death spiral, companies must build agents good enough to charge for independently, as a 60% solution must ultimately be free.

Why the Whole Moat Discussion Is Bullshit

Traditional moats often act as prisons for customers rather than magnets for new ones. Long-term contracts keep users trapped but fail to generate “agentic revenue” or market excitement. True growth requires moving beyond defensive barriers to lure customers through innovation, as “prisoners” do not drive meaningful expansion.

Silicon Valley Thinks They’re Reinventing the World and They’re Not

Silicon Valley’s “overwrought intensity” and grandiosity serve as a powerful rallying cry for talent. Even if visions of massive unemployment are “airy fairy bullshit,” this energy keeps the innovation machine churning and provides mission clarity. Investors should focus on whether this idealism motivates teams enough to build trillion-dollar market caps.

The Quantum Leap of Agentic Cybersecurity

Anthropic’s Mythos model marks a quantum leap by autonomously finding code vulnerabilities. This agentic approach is the difference between a rifle and a machine gun, spewing “bullets” across large codebases. We are entering a transition phase where security may worsen as every site becomes instantly scannable and breachable by bad actors.

The Trillion-Dollar Ad Race and the Shift to Enterprise

OpenAI’s goal of $53 billion in ad revenue by 2029 is a logical move for a consumer product. However, ads alone won’t sustain massive burn rates; enterprise intelligence likely represents two-thirds of AI’s total value. While consumers want entertainment, corporations are paying for “thinking” and cognition.

Why Efficiency is Now a Strategic Choice, Not Just a Metric

High revenue per head is now a deliberate choice enabled by AI agents. The new team evaluation test is no longer just about re-hiring, but whether you would rather replace a person with a trained agent. Founders who fail to improve efficiency risk becoming stable but irrelevant “value plays” like IBM.

Saturday’s episode with Jake Paul & Geoffrey Woo @ Anti Fund:

Download the full transcript:

Jake Paul & Geoffrey Woo @ Anti Fund

185KB ∙ PDF file

Behind the scenes:

Monday episode: Aaron Levie, CEO @ Box
Thursday episode: Jason Lemkin & Rory O’Driscoll
Saturday episode: Amjad Masad, Founder @ Replit

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20VC Newsletter - 13th April 2026

20VC — Mon, 13 Apr 2026 09:29:09 GMT

Monday’s episode with Demis Hassabis, Co-Founder & CEO @ Google DeepMind:

Download the full transcript:

Demis Hassabis @ Google Deepmind

134KB ∙ PDF file

Thursday’s episode with Rory O’Driscoll, GP @ Scale, Jason Lemkin, Founder @ SaaStr:

Download the full transcript:

The Trio Ep

222KB ∙ PDF file

My 6 key takeaways:

The Mindset of the Best Founders Right Now

In an era where money is “raining down” on everyone, Rory urges founders to run their businesses with the awareness that the music will eventually stop. He cautions that while capital feels infinite now, two-thirds of the current field will likely disappear when the cycle turns. Survival requires a mindset focused on ensuring your company is the “one-third” that makes it through the eventual consolidation.

Why buying TBPN for OpenAI was just insane

Rory views OpenAI’s acquisition of TBN as “insane” because it contradicts their internal “Code Red” focus edict. He labels it a “vanity project” that creates unnecessary distraction from core AI development. Jason suggests the deal was likely a “small chip” acquisition requiring minimal executive oversight, though he agrees that the optics of non-focus are problematic during a period of high competition.

Anthropic is Out-Accelerating with Efficiency

Anthropic recently surpassed OpenAI with $30 billion in revenue, growing 3.3x in just 4 months. Most notably, they achieved this in half the time as their rival while reportedly keeping model training costs at just one-quarter of OpenAI’s.

The Power Law of the “Big Three”

SpaceX’s confidential IPO filing targets a historic $2 trillion valuation, potentially surpassing the combined value of every tech IPO from the last 25 years. Rory notes that this valuation relies heavily on an “Elon Premium,” which drives the price far beyond what traditional asset analysis might suggest.

The Paradox of the “Crumb” Business Model

Open Router’s rapid growth to $50M ARR illustrates the massive demand for AI interfaces, but the model faces significant scaling hurdles. Because they rely on a slim 5% take rate, they must manage billions in inference volume to reach a $1BN revenue goal. Jason warns that “middleman” services with low average contract values must expand into multiple products early to avoid being commoditized by larger players.

Invisible Infrastructure: In the Agentic Era

We have entered a tipping point where AI agents are responsible for creating more databases than human developers. This shift favors platforms like Supabase, which allow agentic products to “self-deploy” a database in seconds. In this “vibe coding” landscape, infrastructure becomes invisible and configured automatically, meaning the winners will be those providing the most frictionless experience for both humans and agents.

Saturday’s episode with Carles Reina, GTM @ ElevenLabs:

Download the full transcript:

20sales Carles Reina @ Elevenlabs 2

283KB ∙ PDF file

Monday episode: Jake Paul & Geoffrey Woo @ Anti Fund
Thursday episode: Jason Lemkin & Rory O’Driscoll
Saturday episode: 20Product

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