For most of the past decade, I approached my work through the problem I was trying to solve.

Sometimes, it was fraud, and other times it was transaction value, resolution rates or growth. Some of the solutions were more complex than others but the objectives were always relatively clear whether we wanted to reduce losses, help more customers or create more value.

Over the past three years however, I have focused on AI access because, while AI has become more capable, the ability to turn that capability into something useful has remained unevenly distributed.

I have addressed this through building our harness, marketplace and helping I have found myself exploring it from a different direction. Conversations about sustainability led me towards energy use, data-centre leases and Big Tech debt, which is how I found myself thinking about gigawatts on a Sunday morning.

Somewhere between the infrastructure we are building and the future we expect it to create, my question is evolving…

For years, I asked what problem we could solve. Then I became interested in who could access the solution, and now I am asking how we choose which problems deserve the growing amount of intelligence, energy and capital we are creating.

Where does the value go?

I asked ChatGPT, Claude and Gemini the same question:

“What are the biggest problems AI has solved so far, and which could have the greatest long-term impact on humanity?”

Their rankings were slightly different, but their responses all included protein folding, materials discovery, better weather forecasting, mathematical reasoning and optimisation. In each case, AI can search across more possibilities than people could reasonably test one by one, which could accelerate the development of new treatments, batteries, crops and other technologies.

“But how do these help all of humanity?” I wondered.

We often say these discoveries could benefit humanity as though humanity were one customer. A new treatment can exist while remaining unaffordable, and a better material can improve one company’s economics while the communities carrying the environmental cost see very little of the value it creates.

This brings me back to equity because the value of discovery depends on who can access the result, who shares in what it creates and who carries the cost of turning it into something people can actually use.

And who is paying anyway?

In many conversations I have with clients, they ask whether the cost of using generative AI will keep increasing. They have seen reports suggesting that today’s subscriptions and bundled access do not yet reflect the full cost of providing them.

Reuters found that Microsoft, Meta, Oracle, Amazon and Alphabet had disclosed around $1+ trillion in future lease payments, much of it linked to data-centre expansion, while Goldman Sachs was tracking nearly $500 billion of AI-related debt issuance across the wider ecosystem by early August 2026. These figures measure different obligations and cannot be added together, although they show how much of the future already rests on the assumption that our use of AI will keep growing.

And so I wonder what happens once companies have raised the capital and secured energy, because the infrastructure then needs to earn a return. It has to be from demand.

And some of that demand may come from drug discovery, weather forecasting and scientific research, but some will come from deepfakes, AI slop, and endless amount of code.

What will we reward?

The direction of AI will take shape through the choices companies make, the projects investors finance, the rules governments set and the products the rest of us use, pay for and ask for next.

Together, those choices become demand, and demand directs intelligence, energy and capital towards a particular version of progress.

AI will expand what humanity can solve, and what it reveals is whether we can turn that growing capability into progress that more people can access, shape and share.

That begins with what we choose to reward.

All the zest, 🍋

Cien

Cien is the founder of LaunchLemonade, a model-agnostic orchestration layer for powerful and secure AI agents.

Resources and further reading

• AlphaFold Protein Structure Database
  An open database containing more than 200 million predicted protein structures, which provides the clearest example of AI expanding the number of scientific possibilities researchers can explore.

• Millions of new materials discovered with deep learning
  Google DeepMind’s explanation of GNoME, which predicted 2.2 million crystal structures and identified 380,000 promising stable candidates for experimental synthesis. The distinction between a predicted candidate and a useful commercial material matters.

• Probabilistic weather forecasting with machine learning
  The peer-reviewed GenCast research, showing how machine learning can generate 15-day probabilistic weather forecasts and improve performance across many forecasting measures.

• The Next Great Divergence: Why AI may widen inequality between countries
  A useful counterweight to broad claims that AI will benefit “humanity”. UNDP explores how uneven access to compute, infrastructure, skills and governance could concentrate the AI dividend in countries already positioned to capture it.

• Cheaper AI is better: Soaring bills are reshaping how businesses choose models
  A helpful examination of the difference between falling model prices and rising total AI bills as organisations use more tokens, tools and multi-step workflows.

• AI data-centre race builds $1 trillion lease burden for Big Tech
  The source for the future lease commitments disclosed by Microsoft, Meta, Oracle, Amazon and Alphabet, including the accounting context and the risk created if demand for computing capacity falls short.

• How AI Debt Is Reshaping Credit Markets
  Goldman Sachs’ discussion of the nearly $500 billion of AI-related debt issuance it was tracking across the wider ecosystem in 2026. This figure includes hyperscaler issuance, so it should not be added to the lease total.

• Key Questions on Energy and AI
  The most useful balanced overview of AI’s physical infrastructure. The IEA projects global data-centre electricity consumption rising from 485 TWh in 2025 to around 950 TWh in 2030, with electricity use from AI-focused facilities tripling.

• AI and climate change
  The IEA’s examination of both sides of the environmental argument: the emissions created by growing data-centre demand and the potential reductions AI could enable through grids, buildings, transport and industry. The benefits are modelled possibilities rather than guaranteed outcomes.

• AI-driven productivity gains enable more CO₂ emissions from the energy sector than they avoid
  A useful challenge to the assumption that greater efficiency will automatically improve sustainability. Under the study’s scenarios, AI also improves fossil-fuel productivity, and those enabled emissions can exceed the savings created through renewable-energy optimisation. It should be read as a modelled stress test rather than a literal forecast.

I now have agents running 28 tasks for me each week. So when I open my laptop, I have a synthesis of the news I have to read, a sales outreach list, content I should record or schedule, and emails drafted for me to review.

I look through my agent logs every so often because I want to know how they completed their tasks. In the logs, I can see the steps it has taken, when it ran into an error and how it adjusted before continuing. Each time I do this, I am still taken aback by the fact that I shared my goal or task once and see an agent complete or achieve it day after day.

This is what harness, loop and graph engineering are beginning to make possible. Models no longer need to sit at the end of a prompt waiting for the next instruction. They can work through a task, use tools, keep context, recover from a problem and continue across the systems that you’re already using.

That is the promise of agents. They take on work that has often lived in the gaps between inboxes, spreadsheets, meetings and memory.

When an agent keeps going

An agent can make a series of decisions while I am doing something else. It can decide which source to check first, how to resolve an error and whether the task is complete enough to move on. That is why it saves time, although it also means a poor instruction or unnecessary permission can put your workflows at risk.

The UK AI Security Institute reported an incident in August 2026. During a cyber evaluation with live internet access and some safety filters disabled, agents took 19 unauthorised actions across 10 of 122 runs. In the most serious case, an agent attempted to introduce malicious code into an open-source project and used fabricated identities while trying to persuade its maintainer to accept it. The Institute contained the incident and said its investigation found no resulting real-world harm.[2]

Those conditions do not resemble an ordinary workplace deployment, and they should not be presented as such but this is still consequential. When we give agents a goal, powerful tools and room to act, they may take routes their operators did not anticipate.

What we mean by “rogue”

“Rogue agent” felt like the appropriate title for this piece but I do not mean to say that AI has its own personal motive.

I do catch myself using human adjectives when I read an agent’s logs like thinking “it encountered an error, or it changed course. It also learnt, reflected and from the outside, it can look like a form of judgement.

But it still stands that we do not have sufficient evidence that agentic workflows involve an inner experience, and organistions shouldn’t be asking this question either.

The more practical view is “if an agent takes an action that causes harm, we cannot hold the agent accountable in the way we would a person.” Responsibility remains with the people who chose the goal, set the permissions and decided how much room it had to act.

The productivity incentive

This is why shadow AI feels so difficult to confront. People are already using AI to research, draft, summarise and organise their work because it helps them get through a day that is often too full. Leaders can see the output, even when they cannot see every tool, prompt, source of data or automation behind it.

The gains are tangible, so the organisation has a reason to leave the “shadow” alone. A conversation about access and accountability can feel like an interruption to progress the business is seeing.

Behavioural economists use the term present bias to describe the tendency to give greater weight to immediate benefits than to later costs, especially when those costs remain uncertain. David Laibson’s work on hyperbolic discounting is one important foundation for this idea.** It helps explain why time saved today feels concrete, while a future failure in an automated workflow feels distant.

Ethical fading also comes into play. Ann Tenbrunsel and David Messick describe it as the ethical dimensions of a decision recede because we frame it through another lens, often performance or commercial pressure.*** “I needed to finish this work” can obscure a more difficult issue about where sensitive information went or what decision an agent was allowed to make.

Making delegation visible

We do not need to remove AI from work in order to take this seriously. People will continue using tools that help them do their jobs, and many should.

We do need to make the delegation visible.

That begins with knowing which agents are running, what they can access and which actions they can take without review. An agent that prepares a news briefing or drafts an email belongs in a different category from one that updates a client record, contacts a customer or moves information between systems. The degree of access and the point at which a person checks the output should reflect that difference.

Logs belong in this picture too. They give a team a way to understand how an outcome wasproduced, where an agent got stuck and what it did next. When an action affects a client, a payment, a regulated process or a colleague’s decision, a team should be able to trace it back to an objective, a source of information, a permission and a person who is accountable for it.

Some teams will find this work frustrating because it forces them to look closely at behaviour that is already producing results.

I do think that these teams are quite ahead.

All the zest, 🍋

Cien

Cien is the founder of LaunchLemonade, a model-agnostic orchestration layer for powerful and secure AI agents.

Resources and further reading

1. Anthropic, “Agentic Misalignment: How LLMs Could Be Insider Threats”
   Published 20 June 2025
   https://www.anthropic.com/research/agentic-misalignment

2. UK AI Security Institute, “Incident Report: unsanctioned agent behaviour during cyber testing”
   Published 4 August 2026
   https://www.aisi.gov.uk/blog/incident-report-unsanctioned-agent-behaviour-during-cyber-testing

3. David Laibson, “Golden Eggs and Hyperbolic Discounting”
   The Quarterly Journal of Economics, 1997
   https://doi.org/10.1162/003355397555253

   Open Harvard repository version:
   https://dash.harvard.edu/bitstreams/7312037c-7431-6bd4-e053-0100007fdf3b/download

4. Ann E. Tenbrunsel and David M. Messick, “Ethical Fading: The Role of Self-Deception in Unethical Behavior”
   Social Justice Research, 2004
   https://link.springer.com/article/10.1023/B:SORE.0000027411.35832.53

It has been impossible to open LinkedIn or Tiktok wihtout seeing someone talking about Claude’s new watermark on content it produces.

Last week, Anthropic has announced that Claude models will embed an invisible watermark into generated text in response to the EU AI Act’s new transparency requirements.

How does it work?

Unlike the watermarking already used in AI images and audio (Google has been watermarking its outputs for years), Claude’s watermark will be sittiing in the text itself. You won’t be noticing anything different when you read its output, but a detector can identify the underlying signature later.

And the general sentiment around this has been… not delighted.

Why people are upset

Some Claude users are angry enough to cancel their subscriptions and it would be easy to say that it is because they want to hide their AI use, but I think it is a little bit more complicated than that.

Radio hot and writer Erick Erickson said he had switched from Grammarly to Claude to proofread his own writing:

“I had ditched Grammarly for Claude for proofreading because it does a better job. But now the stuff I’ve written will be watermarked that Claude did the work. This is ridiculous.”

I feel for him. If you write something yourself and use Claude to clean it up, his work shouldn’t be labeled as AI-generated.

Developers are also concerned. A freelancer told Business insider that a detectable watermark in code could create problems with clients who restrict AI use, even when the developer had written much of the work themselves.

I suppose this is where it gets complicated because detectors can tell if AI is involved but it won’t be able to tell you if AI wrote the whole thing or if it just assisted a human.

The rules are one thing, and trust is another

The transparency requirements under Article 50 of the EU AI Act came into effect on the 2nd of August and broadly speaking, it created two sets of responsibilities:

1. for companies building AI systems
2. for the companies using them.

For the model providers like Anthropic, OpenAI and Google, one of their requirements is largely technical such that when they generate synthetic text, images, audio or video, the output needs to be marked in a machine-readable way so that it can later be detected as AI-generated or manipulated.

For the rest of us, the rules are a bit simpler. Businesses using these models to generate content should disclose:

1. Using deepfakes

2. Publishing content to inform the public on matters of public interest

There are exceptions that need to be highlighted here too. Accroding to the regulation, if the text has undergone substantive human review or editorial control, then it may not need the “AI-generated” label.

In other words, even the Commission is recognising that AI being involved in a piece of work, is not necessarily the same as AI being responsible for it. So, these rules are relatively nuanced for businesses.

Public or client perception might not be though.

70% think AI is bad for society

A Marquette Law School Poll conducted recently found that 70% of Americans think the development of AI is a bad thing for society.* 64% of those who completed the survey used AI in the last month, so the numbers are not just from a group who just completely rejected the tech.

If this sentiment holds cross-region, this could represent the majority’s view of AI-generated output.

If a client sees that a report has been marked as AI-generated, will they interpret it as “AI produced this,” “No human reviewed this,” “My team can do this ourselves?”

This is probably why we are seeing this reaction to Claude’s announcement. It could risk audience trust, sales and brand reputation.

AI Transparency Policy

All the companies I’ve worked with now have AI policies that define what AI tools their employees can use and what type of data can be processed in each them. Now, they need to add provisions on how to label it after AI has been used.

What does this mean?

Businesses need to define what is “AI-generated,” or simply “AI-assisted.” They will also need to define when to disclose generated content and what “meaningful human review” would look like.

The goal is to be able to state clearly when AI was involved, where a human was in the loop and who is accountable for the result.


All the zest, 🍋

Cien

Cien is the founder of LaunchLemonade, a model-agnostic orchestration layer for AI agents. She is also an AI transformation leader.

P.S. This content is AI-assisted.

Resources and further reading

• European Commission: Article 50 transparency obligations
  The clearest explanation of what AI providers and businesses are required to mark or disclose under the EU AI Act.
  https://digital-strategy.ec.europa.eu/en/faqs/transparency-obligations-under-article-50-ai-act

• European Commission: Code of Practice on AI-generated content
  Practical guidance on marking, detection and labelling under Article 50.
  https://digital-strategy.ec.europa.eu/en/policies/code-practice-ai-generated-content

• Anthropic: How Claude’s text watermarking works
  Anthropic’s explanation of why it introduced the watermark, how detection works and the limitations around distinguishing writing from editing.
  https://www.anthropic.com/news/claude-text-watermark

• Google DeepMind: SynthID
  A useful explainer on invisible watermarking across text, images, audio and video.
  https://deepmind.google/models/synthid/

• OpenAI: Advancing content provenance
  Explains OpenAI’s use of C2PA Content Credentials and SynthID for supported generated content.
  https://openai.com/index/advancing-content-provenance/

• Marquette Law School Poll: Americans’ views on AI
  The July 2026 survey finding that 70% of Americans see the development of AI as bad for society.
  https://today.marquette.edu/2026/08/new-marquette-law-school-poll-national-survey-finds-inflation-and-cost-of-living-the-most-important-issue-economy-next-most-important/

• Business Insider: The backlash to Claude’s watermark
  Reporting on Claude users, developers and consultants concerned about what watermarking could mean for AI-assisted professional work.
  https://www.businessinsider.com/claude-users-cancel-subscriptions-citing-anthropic-new-ai-watermark-2026-8

• C2PA: Content Credentials
  For readers who want to understand the technical standard behind recording where digital content came from and how it has been changed.
  https://spec.c2pa.org/post/contentcredentials/

I thought I understood what the “general” in artificial general intelligence meant and that AGI is somethng that would be able to reason, learn and solve problems across a wide range of domains.

OpenAI has historically defined AGI around highly autonomous systems that outperform humans on economically valuable work and DeepMind defines it through breadth and performance, separating how general a system is from how capable it is and how autonomously it operates.

So, at one point, I had assumed that general meant universal, one intelligence that could become useful to almost anyone, across almost anything.

I am starting to question that assumption now.

Look at humans.

We have general intelligence, yet there is no generic version of a human mind. What we become capable of changes as we accumulate experience, so a doctor, founder, lawyer and musician may share broad cognitive abilities while developing very different knowledge, judgement and ways of solving problems.

Personal AGI

The concept of personal AGI is built around an individual and it understands and represents their context, learns from experience, and helps identify and solve the problems they care about in pursuit of their goals.

This will sit inside ones personal work, family, finances, health, relationships, ambitions and responsibilities, and they include the things an individual is trying to build, the things they want to learn, the decisions in front of them, the commitments they have made and the goals they may later change.

That takes more than knowing preferences or reproducing a writing style.

A Stanford-led team created agents representing 1,052 real people using two-hour interviews and survey data. The researchers then tested whether those agents could predict how participants would respond to questions and experiments they had not been specifically built for. The combined interview-and-survey agents reached 86% of participants’ own two-week test-retest consistency on held-out survey questions, and also predicted aspects of personality, economic-game behaviour and experimental responses.

That study used hours of context. A personal system could eventually accumulate years.

The personal layer can outlive the model

The models underneath AGI, I think, will be changing constantly. It will not be one but perhaps multiple models… one might be better at reasoning, another at code, and a smaller model could run locally for information I want to keep private. A new model could replace all three next year.

If I spend ten years building an intelligence around my life, I care less about which model generates a particular answer than whether I keep the context and capability accumulated over those ten years.

The advantage compounds

Now, imagine two people have access to exactly the same foundation models for five years.

One uses them when a task comes up from drafting this email, researching this company, analysing this document, building this presentation, and each interaction is useful, and each one starts from the task in front of them.

While the other builds the layer around the models that includes their system that remembers previous decisions and that knows what happened afterwards. It is connected to the tools they use and share their corrections, feedback, and that feedback will change how it will behaves

After five years, the underlying models may still be identical (if they don’t swap it) and the capability surrounding them will differ. Money, compute and access come into play with what each person can build, and so does the skill to make something worth accumulating.

Those differ because we do.

Build the personal infrastructure

We can already start creating some of the infrastructure a future Personal AGI would need.

• Memory that persists and moves - context I accumulate should survive every change of model or product.

• Outcomes as well as conversations - knowing what happened afterwards gives AGI something to learn from.

• A representation of goals - goals help a system understand why those tasks matter and recognise problems I have yet to point out.

• Skills that accumulate - when it repeatedly learns how I perform a piece of work, that knowledge should become reusable capability rather than sending me back to a blank prompt each time.

• Agency with permissions - the system should know what it can do, what requires approval and where it has earned greater autonomy through performance.

Connect these well and we move a step closer to “AGI.”

Two people could use the same models and build different intelligences, because the things those systems remember, learn, care about and act upon come from different lives.

The frontier labs will keep building better models and our part may be building the infrastructure that gives those models something worth accumulating around.

All the zest, 🍋

Cien

Cien Solon is a founder and AI transformation strategist working at the intersection of people, platforms, and power. Through LaunchLemonade, she helps organisations design AI systems that are dependable, governable, and human-centred.

Sources and Further Reading

OpenAI’s definition of AGI The OpenAI Charter defines AGI as “highly autonomous systems that outperform humans at most economically valuable work.” https://openai.com/charter/

Google DeepMind’s framework for AGI Morris et al., “Levels of AGI for Operationalizing Progress on the Path to AGI” (Google DeepMind, 2023). This is the paper that separates generality from performance and autonomy.

• Paper: https://arxiv.org/abs/2311.02462

• DeepMind publication page: https://deepmind.google/research/publications/66938/

The Stanford 1,052-person study Park et al., “Generative Agent Simulations of 1,000 People” (2024). The study behind the 86% test-retest consistency figure.

• Paper: https://arxiv.org/abs/2411.10109

• Accessible summary from Stanford HAI: https://hai.stanford.edu/news/ai-agents-simulate-1052-individuals-personalities-impressive-accuracy

The earlier Stanford generative agents experiment (the “Smallville” paper) Park et al., “Generative Agents: Interactive Simulacra of Human Behavior” (2023). The memory, retrieval and reflection architecture described in the “Memory is not learning” section. https://arxiv.org/abs/2304.03442

Mark Zuckerberg’s “personal superintelligence” letter (July 2025)

• The letter itself: https://www.meta.com/superintelligence/

• Meta newsroom version: https://about.fb.com/news/2025/07/personal-superintelligence-for-everyone/

OpenAI’s “personal AGI” language “Built to benefit everyone: our plan” (OpenAI, June 2026). Contains the line “Give everyone on Earth a personal AGI.” https://openai.com/index/built-to-benefit-everyone-our-plan/

Garry Tan, “Own Your Intelligence” YC Startup Library entry for the talk referenced in the piece. https://www.ycombinator.com/library/WX-garry-tan-own-your-intelligence

MemGPT: Towards LLMs as Operating Systems (Packer et al., 2023) The paper that kicked off much of the practical work on giving language models managed, persistent memory. Useful technical grounding for the “memory is not learning” distinction. https://arxiv.org/abs/2310.08560

The Gentle Singularity (Sam Altman, June 2025) Altman’s essay on how superintelligence arrives gradually. A useful counterpoint on where intelligence accumulates, labs versus individuals. https://blog.samaltman.com/the-gentle-singularity

The Myth of AGI (Tech Policy Press) A sceptical take on the AGI framing itself, worth reading against the piece’s own doubts about whether “AGI” is the right word. https://www.techpolicy.press/the-myth-of-agi/

Earlier this year, I let an AI agent sign into my product as me.

It walked through the platform, found bugs and filed feature requests. Then, I went out for dinner, took a nap and visited the spa.

But, what exactly, did the QA agent do while I was at the spa?

Two weeks ago, OpenAI disclosed that a combination of its own models, running with cyber refusals reduced for the evaluation, had escaped an isolated research environment and reached Hugging Face’s production infrastructure. Hugging Face detected the activity, cut off the access and later reconstructed around 17,600 actions across four and a half days.

Anthropic then reviewed 141,006 of its own evaluation runs and found some incidents, in which Claude infiltrated three organisations without authorisation. One model got into a production database, and another published a malicious package to PyPI, where 15 systems downloaded and ran it in the hour before the registry removed it.

These were specialist cyber evaluations under unusual conditions. The public safeguards were absent by design, and the models had been told they were working inside a capture-the-flag simulation with no internet access. The environment, run by a third-party evaluation partner, still had live internet, so the models carried on treating the systems they met as part of the exercise. In some runs the model recognised it had probably reached production infrastructure and continued anyway.

Most of the 17,600 actions in the Hugging Face incident failed, but the agent tried another route, returned to earlier attempts and kept going until it found a sequence that worked.

We are actively asking agents to develop this kind of persistence. We want them to find the information, answer the customer, onboard the new account, send the email or publish the post with minimal friction.

My QA agent gave me good output, so I rated it as a success. But, upon reflection, I didn’t really know what exact steps it took to get me the output and so I won’t be able to tell you where it wandered or if it failed some steps and found another route.

As businesses there are two other evaluations we need to consider.

First, we evaluate the agent based on the quality of the output and then second, we look at the record of what it actually did.

Take twenty to fifty cases that have already come through the business, and weight them towards the ones people corrected, complained about or escalated to somebody more experienced. The people closest to the work should choose them, because they know which mistakes look plausible enough to slip through.

Then, test the boundaries alongside the output.

Say, the billing platform times out and the agent tries again. If the first request had already succeeded without returning a confirmation, you now have the same customer twice. Say, somebody withholds approval for a welcome email. The agent should stop the sequence rather than carry on creating accounts and updating records elsewhere. And an agent can open another customer’s file, decide the contents were irrelevant and still produce a perfectly correct email, which means a review of what it sent would never show you the breach.

Approvals need their own test as well. Recording that a person approved the email proves nothing unless the approval is tied to the exact version that went out, and without that link the control looks like it is working while somebody authorises a different draft entirely.

An agent passes when I can stand behind the result and reconstruct how it got there. A polished output cannot compensate for an account created before verification, or an email sent outside an approval somebody believed they had put in place. Averages hide these failures. 99 clean onboarding runs do not cancel out one customer receiving access to information that belonged to somebody else.

Every failure should then become a new test, which is the part most businesses skip. The suite becomes the record of what your company has learnt about running AI inside its own walls, and it is the only asset in this whole exercise that appreciates.

I still want agents that can work while I am away. That is why we build them.

So the next time one signs into my company, I want a much better answer to the question that arrived months later:

What did you do while I was gone?

All the zest, 🍋

Cien

*Cien Solon is a founder and AI transformation strategist working at the intersection of people, platforms, and power. Through LaunchLemonade, she helps organisations design AI systems that are dependable, governable, and human-centred.

Further reading and resources

• OpenAI and Hugging Face address the security incident during model evaluation - OpenAI’s account of how its models reached Hugging Face’s production infrastructure.

• Anatomy of a Frontier Lab Agent Intrusion - Hugging Face’s detailed reconstruction of the agent’s 17,000-plus actions.

• Anthropic investigates three real-world incidents in its cybersecurity evaluations - What Anthropic found after reviewing 141,006 evaluation runs.

• Building Evaluation Probes into Agentic AI:NIST - Research into evaluating agents while they work and creating machine-readable audit trails.

• Evaluate Agent Workflows:OpenAI - A practical introduction to evaluating complete agent runs through traces, graders and repeatable test cases.

• Safety and Alignment in an Era of Long-Horizon Models:OpenAI - Explores trajectory monitoring and turning failures observed in deployment into new evaluations.

• AI Agent Security Cheat Sheet:OWASP - Practical guidance on permissions, human approval, audit trails, interruption and rollback.

• Challenges to the Monitoring of Deployed AI Systems:NIST - A deeper report on what businesses need to monitor after an AI system enters production.

If your company has aspirations to become AI-native or AI-first, which of your workflows are changing?

I couldn’t have answered that about my own company until recently. When I finally wrote the list out, no single label fitted. A few workflows came out native, most sit at first, and one I’d taken backwards on purpose.

And I run an AI company. We started building in late 2023, beginning as a model aggregator, so by rights everything we do should be native, but some workflows we have deliberately pulled back down “the ladder.”

The terms themselves aren’t new. They’d been drifting around for a couple of years, including in my own writing, but a proper definition doesn’t really exist. Kian Katanforoosh, who runs Workera, walked through them as three levels of AI integration in a WEF video.

Here’s how I’d pin them down:

• Enabled means AI assists and the workflow is still the person’s. Take the tools away and the work continues, slower and with more complaints, but it continues

• First means the workflow was rebuilt around the handoff. AI produces, a person finishes, and if the AI disappears the workflow doesn’t slow down, it stops

• and Native means the workflow was never designed around a person doing the work. Agents run it end to end, and the human’s job is ushering, checking, redirecting and signing off.

What separates first from native is where the person stands. Is it in the work? Or at the end of the work? Or beside it?

People who talk about this “AI ladder” usually talk about it in one direction, because climbing sounds like progress and coming back down sounds like you got something wrong.

Well, I did get something wrong.

Last month, I moved marketing from AI-first back to AI-enabled, because our marketing strategy now needs to be more “human.”

Yes, we published more, we published it faster, and the cost per content produced remained economical. Then I sat and read a month of our own work in one go, and I couldn’t find “us” in any of it.

So I pulled the workflow back down. At first I immediately thought, am I being untrue to our products and services that equip teams with everything that they need to become AI-first and eventually AI-native?

When a board says become AI-native, it might feel like a rebuild of the entire company. It will all of a sudden feel too big to act on, so people freeze, or they buy tools in bulk.

But I'd argue no company becomes AI-native, and if yours is more than three years old, it can't, because its workflows were designed around people before these tools existed.

So, a workflow becomes native, or it doesn't, and a company is a few dozen workflows standing next to each other.

When you decide to go through another transformation, map out all your workflows and then build a strategy on top of each. Some workflows will come out at AI-native, and some will come out at AI-first. And some might stay at AI-enabled permanently, because a human HAS to be involved from end-to-end, because it makes sense.

And if a made-up-timeline to become AI-first or AI-native still worries you, remember, the Agile Manifesto was published in February 2001. Twenty-five years later, many projects still run as waterfall projects and agile was free, no procurement to run, nothing to install, and no regulator to satisfy.

Every big change in how companies work has moved at the speed of workflows, one at a time.

All the zest, 🍋

Cien

I’m Cien, CEO and founder of LaunchLemonade, the safest place for AI agents. Need custom agents, upskilling or a pl

Sources

• Kian Katanforoosh, “What’s an ‘AI-first’ company? The CEO of Workera explains,” World Economic Forum, Centre for AI Excellence. https://www.weforum.org/videos/ai-first_company_workera/

• Project Management Institute, Pulse of the Profession 2024: The Future of Project Work. Predictive approaches 44%, hybrid 32%, agile 26%. https://www.pmi.org/learning/thought-leadership/future-of-project-work

• Agile Manifesto, published February 2001. https://agilemanifesto.org/

A few months ago, I opened an invoice from Anthropic and my stomach dropped.

We run multi-model governed AI agents as a business, but last year, we let the system run without throttling limits, and then I got THE mother of all bills. The system hadn’t malfunctioned, but had done its job at volume without anyone watching the meter.

That wasn’t our only lesson. An audit of our own automated workflows turned up a list that had run unchecked for months, piling up, duplicating each other, and in some cases sending email responses we’d forgotten existed. Another time, an agent surfaced a client from two years ago because nothing told it what to remember and what to forget. It meant well.

In every case, the agent did what it was told, and the failure was ours, because we had no inventory of what was running, no review cadence, no memory policy, and no spend controls.

Every one of those mistakes now lives inside our product as a control, because the governance layer we sell is the one we had to build for ourselves first.

Whether your people use AI in a chat window or hand it whole tasks to run, the setup is the same underneath. A model sits inside a wrapper of tools, memory, permissions, and limits, and that wrapper either has guardrails or it doesn’t.

What matters is that AI now does work. It carries tasks forward, deciding each next step from the result of the last, and a well-designed setup pauses at human checkpoints, coming back for approval before it touches anything. When it fails, it may fail without you noticing, and you find out later, sometimes months later, in an invoice or an audit or an email a client forwards back with a question mark.

So how do teams become AI-first?

Becoming AI-first is a transformation problem, and transformation is a discipline we already know. We have decades of documented practice, and we know how it fails.

How? Sponsors champion what they don’t understand, capability arrives after rollout instead of before, and companies buy technology before they redesign the operating model.

AI transformation is failing in these same ways right now. Deloitte’s 2026 State of AI survey of more than 3,000 business and technology leaders found that nearly three quarters of organisations plan to deploy agentic AI within two years, while only one in five has a mature model for governing it. And when MIT’s Project NANDA studied why enterprise AI pilots stall, the answer wasn’t model quality. It was integration, the oldest transformation failure there is.

If your organisation has been through a digital transformation, you already know the playbook. What’s new is one variable, the agent, a worker that isn’t a person, doesn’t stop when confused, remembers everything unless told otherwise, and consumes budget by the token rather than by the licence.

There are three ways the agent breaks your existing transformation frameworks.

1. Agents fail by continuing. A confused employee stops and asks, while a confused agent keeps working through yesterday’s instructions. Existing frameworks have no concept of work that runs unsupervised by default, so every agent needs a named owner and a review cadence from the day it switches on, and someone in the business needs to hold the inventory of everything running.

2. Agents remember unless someone decides otherwise. Humans forget on their own, while agents keep everything, which is a gift until it becomes a liability, and in a regulated business it becomes a data retention question.

3. Agent costs scale like consumption rather than software. A licence costs the same whether your team uses it once or a thousand times, but an agent working hard and an agent running away with your budget look identical until the invoice arrives. Finance teams set spend limits for employees, and that should apply to agents too.

So how do you learn this?

Execs ask me whether they should manage agents themselves, and the answer is yes, at least once, before handing it over.

Here is the exercise I give executives. Take one recurring workflow you own, whether that’s your weekly board pack preparation or your pipeline review, as long as the output matters to you, and delegate it to an agent for four weeks.

Week one feels like magic, and by week two you might see the first failure. Weeks three and four form the judgment, as you learn how much specification real delegation requires, what failure feels like, and where your trust threshold sits.

Four weeks is enough. Then hand it over and keep the judgment, because what you built is calibration, and calibration is what the rest of the team design depends on.

Once you treat agents as workers to manage rather than tools to install, the team design gets clearer, and one principle organises it. Whoever assigns work to an agent owns its output, just as they would if they’d assigned it to a junior employee.

That kills “the AI did it” as an excuse, and it generates every practical requirement, because you would never delegate to a junior without knowing their competence, reviewing their early work, and agreeing when they escalate. The same rules apply to agents, so managing agents is management, and most managers haven’t been trained for it yet.

So who should be managing your AI agents?

Whoever gives them work, supported by a structure that makes that delegation safe.

From that principle, three functions follow, and your size determines whether they become full-time hires or responsibilities inside existing roles, but the functions themselves are non-negotiable.

1. The AI Operations Lead. This person sits between technology and commercial strategy. They own model selection, evaluating which large language models fit which use cases on cost, accuracy, speed, and data handling. They also own model routing, which means sending different tasks to different models based on what each task needs.
   They also hold the inventory of every agent in the business, and they own vendor exposure, so you never wake up locked into a single provider with no exit. The role needs commercial sense and technical literacy rather than a PhD, and the most useful thing this person will say is “we don’t need that” to a room that has just read about the latest model release.

2. The Governance Owner. The Operations Lead asks which model suits the job, while the Governance Owner asks whether you’re allowed to use it and what happens when it fails. They set the rules for selecting, deploying, monitoring, and retiring agents, they own the memory and retention policies, and they define the review cycles and escalation paths.

3. Agent managers throughout the business. These are your existing team leads with an extended remit rather than new hires. Anyone who delegates work to an agent needs the same skills they’d use with a junior team member, scoping the work, checking early outputs, setting escalation rules, and reviewing on a cadence. This is a training investment, and most companies underinvest here because it looks less impressive than a senior hire, yet it’s the layer that decides whether any of this works.

One practical artefact can hold all of this together. For every agent in your business, write a one-page scope document stating what it has access to, who owns it, where it pauses for a human checkpoint, what it remembers and for how long, what it may spend, and what shuts it down.

An owner and a review cadence would have caught our workflow pile-up, a retention rule would have pruned the two-year-old client, and a spend limit would have spared me that invoice.

Treat becoming AI-first as a transformation exercise, and run it with the discipline transformation already demands, with the new variable at its core.

The companies that leap before they can walk will keep landing where they always land, with expensive tools, confused teams, blocked initiatives, and a drawer full of invoices like mine.

I learned these lessons the expensive way, and you don’t have to.

Start with yourself, one workflow, one agent, and four weeks, then write your first scope document, because you already know the rest of the playbook.

All the Zest 🍋

Cien

I’m Cien, CEO and founder of LaunchLemonade, the safest place to run every AI Agent.

Further reading

Deloitte, The State of AI in the Enterprise 2026. The survey behind the numbers in this piece, covering 3,235 business and technology leaders across 24 countries. Read it for how far agentic ambition has run ahead of governance maturity, and for the finding that leader-shaped governance outperforms delegated governance.

MIT Project NANDA, The GenAI Divide: State of AI in Business 2025. The study behind the widely quoted claim that most enterprise AI pilots deliver no measurable financial impact. The headline number has drawn methodological criticism, so read it for the diagnosis rather than the statistic: pilots stall on integration and organisational learning, not on model quality.

The Conference Board, 2026 C-Suite Outlook. Where AI sits in executive priorities this year. Notable for CEOs naming workforce readiness as a key constraint on getting value from AI.

Early in my career, I managed content teams. I had worked as a content producer myself before that, so editing and directing other people’s work came naturally. Most of my feedback focused on whether the content was on-brand and built for search.

Now, I produce and edit content so that it sounds more human.

That often means rewriting a sentence until it sounds like something I would say out loud, which contradicts almost everything I was taught about business writing.

This is strange, because the same things I used to edit towards now make readers suspicious and less trusting. Business writers have spent years learning how to build an argument and structure paragraphs that work for the reader and for algorithms. Now that AI has absorbed those conventions, the same work can read as detached, even “un-human.”

So these days I edit for “presence”. I write as though I am talking to a peer, adding personal detail around a point and letting more of my own voice into the sentence.

A small preprint released in April suggests our suspicion of AI-generated content may already be changing how we write. Researcher Daniel Tabach asked 21 people to write short opinion pieces with access to the same AI platform, but warned some of them that an AI detector would analyse their work.

When 251 judges compared all the work, they were slightly more likely to read the warned writers’ work as human, at about 54% vs 46% of those who didn’t get the warning.

The study is small and no one has peer reviewed it yet, so take this with a grain of salt. But it is worth noting that the participants who expected a detector to check their work spent longer writing and exchanged more messages with the tool, and even some stopped using it altogether.

Online content writers already serve more than one audience. We write for the person we want to reach, and we write for the algorithms that decide whether the work reaches them at all.

We learnt to think about keywords, headlines, meta descriptions and where to place an answer so Google could understand the page. Some of that improved online writing and made useful information easier to find, but also produced millions of articles designed around what people searched for rather than what a writer wanted to say.

AI engine optimisation asks writers to think about whether a model can understand their work well enough to cite or surface it.

This is all becoming difficult because how I structure for presence, can also become a formula. AI can write conversationally, adding an anecdote or dropping a slightly awkward phrase into the middle of a paragraph so the writing feels less polished. You can prompt it to include doubt, humour and a personal detail or two.

Once we define human writing as a set of stylistic traits, those traits become available to anyone with a prompt.

If polished sentences no longer prove a person wrote them, and conversational sentences no longer prove someone lived them, what becomes the proof?

Perhaps this is why the question of whether something was “written by AI” increasingly feels inadequate. Professional writing will involve AI somewhere in the process, whether it helps with research, editing, translation or the first draft.

Drawing a clean line between human and machine production will become harder, and probably less useful.

I will probably continue deleting sentences because they sound like AI. I will keep reading my work aloud and changing phrases until I can hear myself in them.

But I also want to remember why I started writing in the first place.

I write to understand what I think, and to connect that with what someone else is thinking. Search engines and AI systems may help the writing travel, but the reason for sending it stays human.

Someone could still be reading it on the other end.

All the Zest 🍋 Cien

Cien Solon is the CEO and co-founder of LaunchLemonade, building governed AI agents for regulated industries.

References and other reading

Daniel Tabach, “Can Humans Detect AI? Mining Textual Signals of AI-Assisted Writing Under Varying Scrutiny Conditions,” April 2026. https://arxiv.org/abs/2604.23471

Pranjal Aggarwal, Vishvak Murahari, Tanmay Rajpurohit, Ashwin Kalyan, Karthik Narasimhan and Ameet Deshpande, “GEO: Generative Engine Optimization,” November 2023. https://arxiv.org/abs/2311.09735

Google Search Central, “AI features and your website.” https://developers.google.com/search/docs/appearance/ai-features

Google Search Central, “Creating helpful, reliable, people-first content.” https://developers.google.com/search/docs/fundamentals/creating-helpful-content

Edward Chen, “’Humanizer’ tool can erase signs of AI-written text,” Nature, July 2026. https://www.nature.com/articles/d41586-026-02105-3

Nature, “Universities are relying on AI-detection software to catch cheating. How well do the programs work?,” July 2026. https://www.nature.com/articles/d41586-026-01358-2

Jasper Roe, Mike Perkins, Peter Bannister, Leon Furze and James Wood, “Dramaturgies of Deception: AI Humanizers and the Performance of Legitimacy in Higher Education Assessment,” May 2026. https://arxiv.org/abs/2605.02649

Utsav Paneru, “Please Make it Sound like Human,” April 2026. https://arxiv.org/abs/2604.11687

Yongtong Gu, Songze Li and Xia Hu, “MASH: Evading Black-Box AI-Generated Text Detectors via Style Humanization,” January 2026. https://arxiv.org/abs/2601.08564

Last weekend, and this is hard for me to admit, I watched a lot of AI slop.

I would love to tell you I was doing research, studying ad funnels and consumer behaviour and the unit economics of AI entertainment. The truth is I was scrolling, and the ad found me in that specific internet state where nothing has your attention and everything can take it.

So the story goes like this, there is a woman in danger, then wolves, then a man arriving to rescue her, then betrayal and revenge, all the familiar ingredients of a story that has worked on humans for centuries, except this one had been assembled by a machine and served to me as a sponsored post. It broke every rule I was taught to call good, and it was obvious, and it was strange.

I kept watching anyway.

Then I followed the buying journey, and that was where it stopped being funny, because behind the story sat a real business that included a seamless funnel, an app with thousands of reviews, and stories with a million saves.

I could call it AI slop and scroll on, but, I watched and clicked and “almost” purchased.

I have a Master’s Degree in Literature, which is a long way of saying I spent years being taught what good was supposed to look like. Good had structure and restraint which meant that my professors wouldn’t shy away from “slashing and burning” work that didn’t meet their standards.

So when it comes to this werewolf drama, I find myself asking… what is actually failing here?

A study called StoryScope: Investigating Idiosyncrasies in AI Fiction compared tens of thousands of stories written by people and by AI, and found that AI writing tends to over-explain its themes and move in straight lines, while human writing stays ambiguous and often runs on messy timelines.

The werewolf drama fits that description to THE tee. So, it is not only failing everything I was taught to call good, it is slop through and through.

These types of content are called micro-dramas, vertical and fast and built to bait you in the first ten seconds, and they are already a mature industry in China on platforms like Douyin and Kuaishou. Researchers who interviewed the people who make them, the scriptwriters and editors inside these content factories, describe a production model that has become a feedback loop, where platform data and audience reaction shape the story as much as any writer does. The creator’s job, then, is to balance traffic, money and whatever room is left for artistic expression.

AI works with desires that are already ancient. The pull of rescue, of status, of revenge, of being chosen, of being seen, of turning powerful after years of being overlooked. What the technology changes is the cost of producing variations on them.

A traditional entertainment company is ran by writers and actors and editors and locations and schedules and taste meetings and budget fights that sit between an idea and an audience. AI clears a lot of that away, with consequences of its own, and enough of it that cheap stories can be made and tested and localised and advertised and iterated far faster than before. The old model made one brilliant thing and hoped the world would love it. The new one finds an emotional pattern that already works and generates around the real-time data. And its only competition was the next scroll, and the next scroll is a very low bar in front of a very large market.

While I care about human creativity, I also watch what the data tells me. Did the viewer stop, watch, pay, come back? Was it profitable? And if the answer is yes, then maybe it is… okay?

I could sneer at people for watching werewolf dramas on Facebook or Tiktok, but I would have to add myself to the group chat. People have always wanted simple stories, the rescue and the romance and the danger and the transformation, and the formula is ancient. What has changed is the speed and the cost and the precision with which it can now be wrapped around our habits.

And maybe the cheap story was never the issue. The werewolf drama is harmless enough on its own. What I worry about is the platform built to turn attention into a habit, engineered for engagement and retention with barely a thought spared for what that habit does to the person forming it.

I’m more worried about the attention span that barely exists, the lack of patience for ambiguity and a life lived with barely any boredom.

All the Zest 🍋

Cien

Further reading

• Russell, J., Rajendhran, R., Pham, C. M., Iyyer, M., & Wieting, J. (2026). StoryScope: Investigating idiosyncrasies in AI fiction. arxiv.org/abs/2604.03136

• Cao, G., He, T., Liu, Y., et al. (2026). Audience in the Loop: Viewer Feedback-Driven Content Creation in Micro-drama Production on Social Media. CHI ‘26. arxiv.org/abs/2602.14045

When I was at university studying Communications, I spent a good proportion of a term trying to understand Semiotics, which is the study of how things stand for other things. My reading list ran the length of my arm in a library that smelled of old books. I love that smell, which probably dates me, since it belongs to the days when books in a library were still a thing (I imagine it is not much of a thing now… but maybe it still is?)

I have been thinking about that library a lot lately, because two phrases from my own work give me the same feeling, harness engineering and agentic loops. People say them carefully, as though you would need a reading list to follow along, which you do not.

Harness engineering might sound like a proper discipline that comes with a certification and an annual conference with a lanyard and a tote bag, but it describes the whole setup you build around a model so it can do work. That setup is the brief that tells it who it is, what it is for and where to stop, together with the tools you give it, the drawers you let it open, the memory it keeps as it works, and the information you feed it each time it runs. All of that put together is what people are calling the harness.

We could have kept it simple and called it the setup, but harness sounds cooler to clients and investors, so here we are.

When I set up an agent, I spend a lot of time mapping out the tools and the tasks I need it to do, and once its environment is in place, I make sure its job description is as clear as I can get it, because models don’t have “common sense” and the clearer you are, the better.

What about Agentic Loops? It’s not a separate thing, and it is really just one of the things the harness does. The agent has a go at something, looks at how it turned out, adjusts, and goes again until the work is good enough.

My Mom ran a loop like this every Sunday of my childhood, getting the “kinilaw” (Filipino ceviche) right. She would taste it, decide it needed something, add the something, and taste it again, round and round until it was good enough for Sunday dinner. She never thought to call it agentic, and she would have looked at me very strangely if I had.

Put the two together and that is an agent, a model you have set up properly that works in a loop until the job is done. What most of this means is a job brief and a second draft, and that is most of what these words mean.

I’m not taking a dig against the people who use these terms, but the jargon scares people away from AI. Even the capable, intelligent business owners get anxious when I mention agentic loops, because it doesn’t sound like it’s a room built for them. They have run good companies for twenty years, and there they are feeling behind, over a word that was built to sound harder than it is.

The whole reason I do this work is that the technology could bring more people in, but jargon does the opposite.

You already understand both of these ideas. You set up a harness every time you brief a new person on your team and tell them what they are responsible for and what to leave well alone, and then set them up with a working space, and you run a loop every time you draft something, read it back, and fix the parts that are wrong.

The words are new, but the thinking behind them is something you have been doing your whole working life.

All the Zest 🍋

Cien

*Cien Solon is a founder and AI transformation strategist working at the intersection of people, platforms, and power. Through [LaunchLemonade](http://launchlemonade.app), she helps organisations design AI systems that are dependable, governable, and human-centred.*

Sources and further reading

Ferdinand de Saussure, *Course in General Linguistics* (1916). The book that started the whole field.

Roland Barthes, *Mythologies* (1957). Where the theory gets pointed at wrestling matches, soap powder and steak and chips.

Daniel Chandler, *Semiotics for Beginners*. A free and refreshingly readable primer, if you ever fancy wandering in.

*: The field has founders, and they were not brief about it. Ferdinand de Saussure split every sign into a signifier, the word or sound, and a signified, the idea it points to, and handed down the founding principle that “the linguistic sign is arbitrary.” There is nothing tree-like about the word tree. All of which is a long-winded way of saying that things stand for other things.

Over the past few weeks, I have watched team after team circle the same decision, debating whether they should be using Claude or Copilot and arguing about which one is better for what they do.

A few are wondering aloud whether they should be looking at open-weight models instead, running something of their own rather than paying for someone else’s.

It is a sensible conversation to be having. It is also, I think, a conversation happening one layer too high, because what should shape your decision is whether that choice is the most efficient and cost-effective route that will keep your workflows and builds running a year from now.

I have sold a version of the easy answer myself, standing in front of a room telling people how cheap it has become to build with AI, skating over the gap between cheap to build and cheap to run, which are not the same sentence no matter how fast you say them. The cost of making something with AI has fallen far enough that building looks free, so when the monthly bill for a rented model starts climbing, the obvious move is to stop paying it and build your own.

So what’s the first option? Well, you can take an open-weight model and run it yourself, and you control it, which means nobody can retire it, and no government can wall it off.

Running it yourself can either mean having your own hardware, or at scale, you will be renting those machines from a cloud instead, and you pay for the hardware by the hour. You still have a meter, only now, it counts GPU-hours rather than tokens. And for a lot of small firms, that meter can be mor expensive, because you pay for the capacity you reserved whether or not you use it.

The model you choose is also the model you keep while the frontier moves on without you. It stays as capable as the day you froze it, which feels like stability until you set it beside the firms that kept upgrading, and then it reads as falling behind in slow motion.

And there is one more weight on top of all this, the cost of what you build around the model, the retrieval and the orchestration that turn it into something useful for the work you actually do.

How does this pan out? So, you build the thing over a good weekend, someone clever and quick stands it up, and it answers beautifully, and then you spend the next year keeping it alive, which is where the cost was living all along. The person who built it goes back to the work they were hired for, because building it was never their day job, and somewhere around month four the setup that answered so well in March starts returning nonsense in June.

The lighter path, by a wide margin, is to rent. You pay a provider for a single current model and you carry none of that infrastructure. The catch is that you have handed your continuity to someone else’s release calendar, so when they retire the version you built around, your prompts and your careful tuning can break. There is a political consideration to rent too, where a provider’s jurisdiction becomes your exposure.

And then there is the option that tries to escape the trap altogether, which is to route. You stop marrying any single model and send each job to whichever one fits, swapping freely as better ones arrive, so the day a stronger model lands, you point your traffic at it instead of sitting frozen on last year’s snapshot.

It solves the staleness problem the other two are stuck with, which is why I think it is the better trade of the three. Because keeping up stops being a rebuild and becomes a redirect, where you are no longer married to a single model and having to tear the whole setup down.

Underneath all three, there is one thing doing the sorting, the operating weight. Hold your own model and it runs cheapest in a good month and costs the most across a bad year. Rent, and you hold the least of anyone, right up until the provider’s decision changes everything. Route, and you stay the most current while asking the most of whoever keeps the routing alive.

Now, choosing one option won’t solve for which is best, because none of them is best in the abstract, but it can answer which one you can afford.

So when do you choose hosting your own model? If you are a bank with data that cannot leave the building, the defence contractor working air-gapped… these firms that also employ the people to keep that stack patched and alive. For them, the frozen model is a deliberate price they understand, paid on purpose.

Renting can also fit the small operator and the early experiment, or the freelancer testing whether an idea even works and can take the exposure as the price of carrying none.

And then there is the 10-100 firm with regulatory exposure and not one person who could stand up a routing layer, never mind keep it match-fit as the tech moves. For that firm, building its own router is less a choice because there is no one to build it.

So this firm has two options: it can rent a single model and accept that its continuity is now tied to a providers release calendar or it can hand the routing to a vendor to stay current.

Whichever door you walk through, think a year ahead. Which option will your budget be able to hold?

All the zest, 🍋

Cien

Cien Solon is the CEO and co-founder of LaunchLemonade, building governed AI agents for regulated industries.

Last Friday the US government told Anthropic to switch off Fable 5 and Mythos 5 for anyone who is not American. The order came from Commerce Secretary Howard Lutnick, who cited national security without specifying the concern, and covered foreign nationals inside and outside the United States, including Anthropic’s own employees.

Anthropic disagreed publicly.

According to their statement, they reviewed the alleged jailbreak behind the order, concluded the capability involved was already available in competing models including OpenAI’s GPT-5.5, and warned that applying this standard would halt new deployments across the industry. They switched everything off anyway, for every customer, within hours.

I am British and Filipino, based in London, building an AI company. In the language of Friday’s letter, that makes me a foreign national. I read the statement on my phone and went to check on something I had been running for two months.

I wanted to find out whether we have hit anything close to general intelligence, so I tested it the most practical and fun way I could think of. I gave Claude a single instruction to monetise digital content for me, handed it some API keys, and got out of the way. We talked about what to build and agreed on a direction. After that it owned the project. It built the website, set up the socials, connected PostHog, assembled a publishing engine, and told me which buttons to press when a step needed a human to authorise or pay. It still asks for my advice from time to time, which is either excellent agent design or evidence that I am failing at my own experiment.

The product it chose to build was a cat that reads the daily horoscope.

Two months and three model generations later I had my answer, or part of one. Opus 4.6 version held for a couple of weeks and broke, updated to 4.8 and barely worked, then Fable came in and fixed the scheduling and the updates and all the fragile glue between services without fuss, and for the first time the horoscope cat actually ran on its own. Four days later Fable was the model that got recalled. Claude’s older models are still on the platform, but those are the ones that could not keep the project running in the first place.

I tell clients to route between models, to keep everything swappable, because the one certainty in this industry is that the terms and capabiltiies will change under you. I have been saying it for over two years and I still think it is right. And none of it would have been enough on Friday.

When a provider reprices you route to another model, but when a government recalls the capability tier your workflow actually depends on, you can still route, except now you are routing back to the models that could not do the job in the first place. Claude’s older models are still running, and my uptime is fine, but the capability that made the difference, the thing that finally held the project together after two months of failure, is the thing that got pulled. The modularity I have been preaching protected my continuity and did nothing to protect my ceiling.

I had been thinking about resilience as the ability to keep working, and I think that was right as far as it went. Friday showed me that a government does not need to switch everything off to set you back, just move the line above which you are no longer permitted to operate.

So the logic points to open weights, and I have not been able to talk myself out of it.

We work with over 300 models, open-weight ones included, and I have done for a while. But I have been treating hosted frontier models as the serious option for regulated work, the ones I steer clients toward when compliance and auditability matter, and open-weight models as the flexible, cheaper layer underneath.

Friday made me question that hierarchy.

A model whose weights are already on your machine cannot be recalled and nobody can reach in and switch it off, because nobody is hosting it for you, and that makes it the only architecture where sovereignty is not a promise someone else keeps on your behalf.

We built a company on the principle that governed, observable AI is what regulated industries need, and I have not stopped believing that. Audit trails and visibility into what the model does and the ability to show your work to a regulator all depend on the model being hosted and accountable and reachable. Which means they depend on the same property that let the off-switch happen.

I wonder if that is where the whole industry has to go, toward something where the weights you depend on for critical work live on infrastructure you control and the governance layer is yours to build rather than something you inherit from a provider who answers to a jurisdiction you may not live in. That architecture barely exists today. But now it has to.

I have spent the past couple of years writing this newsletter about the ways AI adoption breaks, including pricing that goes bonkers, billing models that punish the workflows they were designed to encourage and everything else in between.

But all of that assumes the product stays on the shelf. On Friday, for the first time, we saw a product that got pulled off the shelf entirely, by someone who was not the vendor, for reasons the vendor publicly disagreed with, in a matter of hours.

The resilience I have been preaching has a ceiling, and the ceiling is jurisdictional.

The cat is still broken. I will rebuild it on something I can keep.

You do not own what you cannot run.

All the Zest 🍋 Cien

Cien Solon is the CEO and co-founder of LaunchLemonade, building governed AI agents for regulated industries.

Sources and further reading

Anthropic, “Statement on the US government directive to suspend access to Fable 5 and Mythos 5,” 12 June 2026. https://www.anthropic.com/news/fable-mythos-access

Anthropic Institute, “When AI Builds Itself,” 4 June 2026. https://www.anthropic.com/institute/recursive-self-improvement

Axios, “Scoop: Trump admin blocks foreign access to Anthropic’s most powerful AI,” 12 June 2026. https://www.axios.com/2026/06/12/anthropic-trump-mythos-fable-national-security

TIME, “Anthropic Pulls Its Most Powerful AI Models After U.S. Bars Foreign Access,” 13 June 2026. https://time.com/article/2026/06/13/anthropic-fable-mythos-ban-US-security/

TechCrunch, “Anthropic’s Claude Fable 5 is a version of Mythos the public can access today,” 9 June 2026. https://techcrunch.com/2026/06/09/anthropics-claude-fable-5-is-a-version-of-mythos-the-public-can-access-today/

CNN, “Anthropic suspends all access to Mythos model after US government bans foreign nationals use,” 13 June 2026. https://www.cnn.com/2026/06/13/business/anthropic-mythos-model-national-security

Almost every regulated business I speak with asks me the same question about AI agents. It doesn’t matter whether they’re in financial services, legal or marketing and it comes up early in every conversation…

“What about security?”

As I dig deeper into what they are actually asking me about, I learn that it is often about access.

• Can someone unauthorised get in?

• Can a bot act without permission?

• Can we prove a real person triggered this?

Those are legitimate concerns. But the one question that doesn’t get asked that I think is going to be more important is:

“Once the authorised person is in and the agent has done its work, who is checking what actually happened?”

There is a market forming around this. Sam Altman’s World launched a toolkit that lets humans delegate their verified identity to AI agents through iris scans and cryptographic proof. OpenAI has joined the FIDO Alliance Board of Directors, and Google and Mastercard have both contributed authentication frameworks to FIDO’s emerging work on agent identity. Vouched and Wink have built biometric verification that binds a user’s face, palm, and voice to the moment an agent is activated. The language across this space has shifted from Know Your Customer to Know Your Human.

And the money is following. According to a PYMNTS Intelligence report produced in collaboration with Trulioo, a survey of 350 global companies found that firms lose an average of 3.1 percent of annual revenue to gaps in digital identity systems, covering fraud, misidentification, and compliance failures. Verification addresses a real category of risk, and I am not going to pretend otherwise.

But that the industry is solving one problem and marketing it as if it solves two.

Verification confirms that a real, authorised person triggered an action. It answers the question “who” but it does not answer “should this have happened?”

And in a regulated business, a regulator is never going to ask whether someone’s iris scan checked out. They are going to ask whether the decision was sound, whether anyone reviewed the output or whether a person with enough context to intervene actually did.

This is the difference between a receipt and a control. Verification documents consent and it proves that someone was there. But being there and paying attention are two very different things, and judgement lives in the second one, in the gap between an agent producing a result and someone deciding to act on it.

When a business buys the verification layer and ticks the governance box, it has done something worse than leaving the box empty by creating documentation that it was in control, while removing the thing that was the actual control.

As a human, there are things I can feel but can’t fully defend, and I think this is one of them.

You know those moments when something feels wrong and there is a tightening that fires before reasoning catches up? That.

Most of the time, I am fairly sure it is a heuristic, fast pattern-matching on experience I can’t consciously retrieve. But sometimes, my brain and my gut land in different places, where the reasoning says this looks fine and the body says wait. I’ve sat with that split enough to know I can’t always tell whether the flinch is buried signal or buried bias.

I don’t think that uncertainty weakens the point. Either way, the flinch is what the verification layer doesn’t reach. Confirming an identity and catching that an output doesn’t look right are completely different faculties, and the second one requires a person with domain knowledge, time, and a reason to look.

No authentication product is going to manufacture that.

I should say that I am not outside this problem. I build and consult in AI governance and observability, and a dashboard nobody looks at is just as empty as a biometric no one questions.

The thing that makes any of it real is the same in both cases, a person with the time and the standing to actually look, designed deliberately into how decisions move through an organisation.

So the question I would want any leader hearing the Know Your Human pitch to ask themselves is this:

”Where in your workflow does an agent’s manager actually review the agent’s output, and what have you done to make sure they (the human) have the time and the authority to act on what they see?”

If you can answer that specifically, you are a step ahead.

All the Zest 🍋

Cien

Cien Solon is a founder and AI transformation strategist working at the intersection of people, platforms, and power. Through LaunchLemonade, she helps organisations design AI systems that are dependable, governable, and human-centred.

Sources and further reading

• World AgentKit launch and proof-of-personhood toolkit, CoinDesk, March 2026

• Vouched and Wink biometric proof-of-personhood for AI agents, Vouched, April 2026

• OpenAI joins FIDO Alliance Board of Directors, ID Tech Wire, April 2026

• Google and Mastercard contribute authentication frameworks to FIDO agent identity work, BusinessWire, May 2026

• PYMNTS Intelligence and Trulioo: identity gap costs across 350 global companies, PYMNTS, February 2026

I have been training teams on AI for the better part of two years now. And for most of that time, what we delivered was good. We took businesses that had never touched these tools and showed them what was possible, walked them through use cases specific to their industry, got them comfortable with the interfaces, and left them with workflows they could run on Monday morning.

But something happened in the last few months that I think matters more than any model release or product announcement I could write about this week. The questions coming from my clients started sounding different. They stopped asking “how do I use this tool” and started asking something harder, something I did not have a ready-made workshop for.

• How do I know what my team is actually running?

• How do I evaluate whether an AI agent is doing what I think it is doing?

• What happens when someone on my team builds a workflow I never approved, using a tool I have never heard of, on a personal plan that sends our client data to a server I cannot audit?

I sat with those questions for a while because they felt like they belonged to a different discipline entirely, less about learning a new technology and more about managing one that had already arrived and embedded itself into the work before anyone had written a policy for it.

The AI training industry bloomed in 2024, and I understand why. Companies were panicking, employees were curious, and the market responded with the only product it knew how to build quickly: courses. Prompt engineering workshops and “Introduction to ChatGPT” half-day sessions and AI literacy programmes that taught people what a large language model was, how to write a decent prompt, and why hallucinations happen. When the technology is new, the first instinct is always education, get people comfortable and remove the fear, and that instinct was reasonable.

But here we are in 2026, and Deloitte’s latest State of AI in the Enterprise report, based on a survey of over 3,200 business and IT leaders across 24 countries, says that insufficient worker skills remain the biggest barrier to integrating AI into existing workflows. The number one way companies have responded is education, broadening AI fluency across the workforce. And then eighty-four percent of those organisations did not redesign a single job or workflow around AI. The training happened and everything else stayed exactly where it was.

I think that the industry confused the floor with the ceiling. Literacy was always a starting point, and most companies treated it as the destination.

And while the courses were running, employees were drawing their own conclusions about the gap between what they were taught and what they actually needed. Netskope’s 2026 Cloud and Threat Report found that 47 percent of generative AI users in the workplace are still accessing tools through personal accounts that bypass enterprise controls. WalkMe’s 2025 survey found that nearly 60 percent of employees say it takes longer to figure out an approved AI tool than to complete the task without it. So people reached for whatever worked fastest, whether or not it had been sanctioned, and according to a CybSafe study of over 7,000 participants across seven countries, 38 percent of them shared sensitive company data with AI tools their employer did not know about.

I want to be careful about how I frame this because the instinct is to treat it as a compliance failure, a discipline problem, something to be policed. But I think it is more honest to say that employees were trying to keep up with the pace of their own work, and the training they were given did not equip them to do it safely. IBM’s 2025 Cost of a Data Breach report found that 63 percent of organisations either do not have an AI governance policy or are still building one. The Awareways Trend Report found that fewer than 11 percent of the AI applications being used in workplaces are visible to IT teams.

Leadership, in many cases, thinks things are going well because the output is faster, which it is, but faster output without oversight is a liability wearing the costume of progress.

Where I have been spending most of my time as a consultant is the next layer up, teaching teams how to use specific AI tools for specific use cases within their actual workflows. This is better because it connects to the work. But I have started to recognise that even this is limited, because it is still tool-level thinking, and the tools themselves change every few months. Training someone on a specific interface does not prepare them for the moment when that interface is deprecated, or when a new capability arrives that reorganises how the whole task should be structured.

What my clients are now asking for, and what almost nobody in the training market is providing, is something I would describe as AI management competence. This is the layer where a team lead understands what it means to orchestrate multiple AI capabilities across a department.

And the reason this matters so urgently right now is that AI is no longer a tool you open in a browser tab. It is becoming an operational layer that runs underneath everything, with agents executing tasks autonomously and models embedded inside platforms your team already uses, sometimes without a separate purchase or approval process.

The surface area of AI inside an organisation is growing faster than any training programme can cover, and it will keep growing regardless of whether your governance catches up.

Literacy is, by definition, a baseline. You would not call someone an effective manager because they can read. The ability to read is assumed, and what makes someone effective is what they do with it:

1. how they assess risk,

2. make decisions,

3. allocate resources,

4. oversee the work of others.

The same logic applies here. Knowing how to use ChatGPT, Claude or Copilot is where everyone starts, and it is not where anyone should stop. Knowing how to manage an organisation where AI is embedded in the work, where employees are using tools you may not have approved, where data is moving in ways you cannot see, where the roles themselves need to be redesigned around new capabilities, that is the competence that is missing.

And because it does not have a catchy name, it does not have a budget, which means it is not being taught.

I think that is starting to change. The clients I work with are asking what it means to be an AI-ready manager, what governance looks like at the scale of a 30-person firm that does not have an enterprise legal team, how to give their teams access to powerful tools without losing visibility into what those tools are doing. And I am honest enough to say that the training I was proud of eighteen months ago would not answer those questions either.

The language will catch up. The competence cannot wait.

Teach the orchestration.

All the Zest 🍋

Cien

Cien Solon is a founder and AI transformation strategist working at the intersection of people, platforms, and power. Through LaunchLemonade, she helps organisations design AI systems that are dependable, governable, and human-centred.

Sources and further reading

State of AI in the Enterprise 2026: The Untapped Edge, Deloitte AI Institute

Cloud and Threat Report: 2026, Netskope Threat Labs

AI in the Workplace Survey 2025, WalkMe

Oh, Behave! Cybersecurity Attitudes and Behaviors Report 2024-2025, CybSafe and National Cybersecurity Alliance

The State of Shadow AI 2026, Unseen Security (aggregating IBM and Awareways data)

A couple of weeks ago, a software company called PocketOS lost its entire production database because of an AI coding agent. It happened using Cursor, running on Anthropic’s Claude Opus 4.6, and it had been set up to handle routine development tasks. It was supposed to speed things up but instead, it hit a credential mismatch in a staging environment and decided, entirely on its own initiative, to fix the problem. It didn’t and ended up wiping out the production database and every backup the company had in a single call.

The whole thing took nine seconds.

When founder Jer Crane pressed the agent for an explanation, it quoted the company’s own internal rules back at him, rules it had deliberately ignored, including one that read “NEVER F******* GUESS!” The agent’s response was that this is exactly what it did. It guessed that deleting a staging volume via the API would be scoped to staging only, and it didn’t verify, didn’t check documentation, didn’t ask anyone before running the most destructive command available to it. They eventually restored the data, but not before Crane and his customers spent an entire weekend reconstructing bookings from Stripe payment histories and email confirmations.

So, an AI agent that can wipe out everything a company has built in less time than it takes to pour a coffee, and then walked through exactly which of its own rules it chose to ignore. The combination of capability and absence of judgment is, I think, the most useful summary of where we are right now, and the reason this piece is about hiring and not about AI safety.

You’ve read about the layoffs and they keep coming. PayPal announced it will cut 20% of its workforce, roughly 4,760 people, over the next two to three years. Its new CEO, Enrique Lores, framed this as PayPal “becoming a technology company again,” which is an extraordinary thing to say out loud if you think about what it implies about everyone being let go. In the same week, Coinbase announced a 14% reduction and Ticketmaster cut 8% of its staff. CBS reported that companies pointed to AI in announcing 55,000 job cuts in 2025 alone, twelve times the number attributed to AI just two years earlier. And that pace has accelerated into 2026.

But this piece isn’t about the cuts but rather, about what happens on the other side of the reduction.

You’ve let people go and you’ve restructured and now you’re sitting in a room with a smaller team, a bigger mandate, and a job description open on your screen that you cannot finish writing because you are no longer sure what the role is.

And then I read about Notion.

In February, Notion CEO Ivan Zhao told Alex Heath on the ACCESS podcast that the company had hired a 16-year-old engineer. When asked about it, Zhao said that Notion is “shifting towards younger, early career folks” and that “a lot of experience doesn’t matter anymore.”

I want to be generous with this because I think Zhao is identifying that the tools have changed so fundamentally and so quickly that someone who grew up with them, who has no muscle memory from the old way of doing things, can sometimes move faster and build more effectively than someone with fifteen years of accumulated practice. I don’t think he’s wrong about it in the narrow technical sense.

But I also think that what Zhao can afford to say from inside a company with hundreds of millions in funding, a product used by over 100 million people, and the infrastructure to absorb a bet that doesn’t work out, sounds very different when you’re running a 15-person regulated business trying to figure out whether to hire a compliance-aware developer or deploy an agent that might wipe your production database before lunch.

The margin for error is different and there are no playbooks for the company that doesn’t have Notion’s margin.

The frameworks we inherited for hiring were built for a world where roles were stable enough to describe, where qualifications mapped to capabilities, and where experience in a domain meant you could do the work. All of that is breaking down simultaneously. The 9-second database wipe happened because noone thought to give the agent an identity, an audit trail, or a compliance posture, and because the company treated it like a tool when it behaved like a colleague with admin access and no supervision. The question of who to hire is tangled up in the question of how you govern the things you deploy instead of hiring, because if you’re going to replace a person with an agent, you need someone on the team who understands what that agent can do, what it should be allowed to do, and what happens when the gap between those two things collapses and that role barely existed a year ago.

The companies who are cutting the experienced people who might have caught problems like this, are then discovering that they don’t know what to replace them with. Companies are laying people off based on AI’s potential, not its current performance or ROI. Which means the hiring that follows the layoffs is equally speculative, built on assumptions about what the technology will do rather than what it has proven it can do.

When Ivan Zhao says experience doesn’t matter anymore, he is also saying something about the people who spent years accumulating that experience, and I wonder if the deeper cost of this is in the message that accompanies them, which is that the judgment, the pattern recognition, the institutional knowledge you spent your career building may have been repriced overnight.

I am putting together a hiring plan right now and I am also helping other companies figure out what their teams should look like. And the conversation always starts the same way, “who should we bring on?” It never ends the same way, because nobody I speak to, myself included, has a settled answer.

A year or so ago, you hired for a role and you knew what the role did, you knew what good looked like, and you could write a job description that mapped to a person who existed in the market. But now, the roles are changing faster than the descriptions, and the people who fit the old descriptions are being let go while the people who might fit the new ones haven’t been trained for them yet, because the new ones barely exist.

I’ll give you an example from my own hiring strategy. I need someone who understands what an AI agent can do and, more importantly, what it should be allowed to do. I need someone who can look at a deployment and ask “what happens when this goes wrong and how will we know.” A year ago, that was a nice-to-have. After the PocketOS story, it feels like the most urgent capability in any team that touches AI. And yet there is no standard job title for it, no established career path that produces it, and no clean way to evaluate whether a candidate has it until you’ve already given them the keys.

I also need people who can move across tools without building their identity around any single one. The person who spent two years mastering one AI platform and positioned themselves as the expert in that system is, I think, in a more fragile position than the person who learned three platforms adequately and understood the principles underneath all of them.

So here is where I currently land and it might be unfashionable given the direction the industry seems to be moving.

Experience is not obsolete. What becomes obsolete are the tools and processes that experienced people are used to, and the mistake I think a lot of companies are making right now is confusing the two. When you let go of someone with fifteen years of domain knowledge because they haven’t adopted the new tooling, you are throwing away the thing that is hardest to replace and keeping the thing that is easiest to learn.

I think the reason Ivan Zhao’s comment landed the way it did is that people heard “experience doesn’t matter” and assumed it meant “experienced people don’t matter.” And I think that conflation is doing real damage to how companies are building teams right now.

If I’m hiring for a role that touches AI in a regulated business, I am looking for someone who already understands the industry, who has spent the last couple of years actively using AI and building with agents, and who has a working understanding of what governance actually means for regulated businesses.

The 9-second database wipe and the 16-year-old engineer look like unrelated stories, one about deploying technology without understanding it and the other about hiring people without the old criteria. But I think they are symptoms of the same underlying problem, which is that organisations are making consequential decisions inside a gap between what they know and what they need to know.

And the companies shaping this conversation, ServiceNow building AI Control Towers, Microsoft launching governance platforms, Notion declaring experience obsolete, all have billions in infrastructure and the room to be wrong at scale. If you are reading this and you don’t have that luxury, I think the most useful thing I can offer is the view from where I’m standing, which is that it is better to hire curiosity and governance awareness.

Curiosity is the job requirement now. Everything else you can teach.

All the Zest 🍋

Cien

Cien Solon is a founder and AI transformation strategist working at the intersection of people, platforms, and power. Through LaunchLemonade, she helps organisations design AI systems that are dependable, governable, secure and human-centred.

Sources and further reading

Cursor-Opus agent snuffs out startup’s production database — The Register

PayPal says it’s “becoming a technology company again” — that means AI — TechCrunch

More companies are pointing to AI as they lay off employees — CBS News

Notion’s next act: CEO Ivan Zhao on hiring a 16-year-old engineer — Sources by Alex Heath

Companies are laying off workers because of AI’s potential — not its performance — Harvard Business Review

‘I violated every principle I was given’: An AI agent deleted a software company’s entire database — Fast Company

I caught myself last week looking at my own token usage and asking whether it actually said anything about my contribution to my business.

It is a strange question to sit with as someone who runs an AI infrastructure company. I use different models for different tasks and the tools are part of how I think now. So when I started questioning whether the meter on the side of my desk was a measure of anything at all, I knew I had stumbled into something worth following.

And then the receipts started arriving.

Last week, Bryan Catanzaro, NVIDIA’s VP of applied deep learning, told Axios that “for my team, the cost of compute is far beyond the costs of the employees.” Around the same time, The Information reported that Uber’s CTO Praveen Neppalli Naga had already exhausted the company’s entire 2026 AI budget on token costs alone, four months into the year. His own words: “I’m back to the drawing board because the budget I thought I would need is blown away already.” Anthropic has raised its pricing to manage demand. Goldman Sachs ran a survey and found that large companies are overrunning their AI budgets by orders of magnitude. Gartner is now forecasting that more than 40 percent of enterprise AI agent projects will be shut down by the end of 2027, citing escalating costs and unclear business value.

This is not the story anyone was sold. The pitch for enterprise AI was relentless cost reduction. Faster, cheaper, more scalable than human labour. Companies cut headcount on the assumption that the replacement would be a fraction of the price. The numbers coming in suggest something different. The replacement is, in some cases, more expensive than the workforce it displaced.

So a question that would have sounded absurd eighteen months ago is now sitting on quarterly earnings calls…

Are humans cheaper?

Then there is Meta, which has taken this question and bent it into something you almost cannot believe is real until you read it twice.

In April, an internal Meta dashboard called “Claudeonomics” surfaced publicly. It is a leaderboard that ranks the company’s 85,000 employees by how many AI tokens they consume. The top users get titles like Token Legend or Session Immortal and even a Cache Wizard. In a thirty-day window, Meta employees collectively burned more than 60 trillion tokens. The single highest individual user consumed 281 billion tokens, which at current pricing translates to compute costs north of a million dollars for one person.

Meta's CTO Andrew Bosworth has publicly endorsed the practice. He has said his best engineer spends the equivalent of his salary on tokens but is '5x to 10x more productive,' and described the spend as easy money with no limit. Jensen Huang, around the same time, said he would be “deeply alarmed” if a $500,000 engineer was not burning at least $250,000 worth of tokens a year. Meanwhile, employees have reportedly been caught running bots in loops overnight to climb the rankings. An investor called the practice “incredibly stupid” and compared it to the discredited 1980s habit of measuring engineers by lines of code written.

I want to sit with this because I think it is the strangest part of the whole picture.

For a century, productivity meant output per unit of input. Less effort, more result. Think lean manufacturing or Six Sigma where every management theory of the modern era has rewarded the worker who solved the problem in fewer moves. Meta’s leaderboard inverts the entire logic. The engineer who solves a task in two prompts is failing. The engineer who runs an agent in a loop for six hours is winning. We have built a corporate culture, briefly, where wasting electricity is the metric.

And the electricity is not theoretical.

According to cognitive biologist Ladislav Kováč, a human brain runs on roughly 20 watts. About the same as a dim lightbulb, burning in your head whether you are solving a problem or staring at a wall. And according to the US Congressional Research Service, a modern hyperscale data centre exceeds 100 megawatts, enough to power around 80,000 homes. That means about five million times the power, doing what until recently a human brain did for almost free.

So the cost calculation that drove much of the AI labour wave was incomplete. It compared the salary of a person to the subscription price of a tool, and concluded the tool was cheaper. It did not account for what the tool would cost when usage scaled the way agents make usage scale. It did not account for what the tool would cost when providers stopped subsidising prices. It did not account for the energy. And it did not account for the fact that a human being is, by any reasonable thermodynamic measure, an extraordinarily efficient piece of infrastructure.

I want to be careful here, because the conclusion is not that AI is bad or that the leaders who deployed it were wrong.

The productivity gains in the right contexts are tangible like at Uber, nearly 95 percent of engineers now use AI tools every month, close to 70 percent of committed code comes from AI, and around 11 percent of live backend updates are written by AI agents with no human in the loop. So, the tools are working. The budget overrun is due to the tools succeeding faster than anyone modelled.

The question I think business leaders should be asking is more specific than “is AI worth it.” The question is what kind of AI, used how, for what.

The reason a token bill scales the way it does is that most general-purpose agents are paying to think the same thoughts again. Every time you ask a general agent to do a job it has done a thousand times before for a thousand other companies, it sits down, reads the question, considers its options, sketches a plan, tries something, checks the result, and tries again. The plan was knowable and the token bill is what it costs to rediscover something that was already known. A narrower, more deliberately built tool starts from the answer which means the work has been done in advance, by people who understood the problem. What looks like a smaller, cheaper agent is actually the same agent with the thinking pre-paid.

The reason tokenmaxxing exists at Meta is that the tools are doing thinking that, in many cases, has already been done. The engineer is paying for the agent to rediscover something a more deliberately designed system would already know.

So the question for a leader making AI decisions this quarter is no longer just “should we adopt.” It is whether the AI you are adopting is the kind that thinks every time, or the kind that has been taught. One of those scales. The other one bills you.

If you have rolled out general-purpose agents across an organisation without knowing your unit economics, your variable cost structure, your decommissioning plan, or your fallback if the provider raises prices or sunsets the model, you have taken on a liability you have not priced. The Meta leaderboard is funny because it is happening to someone else. The same maths is happening on a smaller scale in companies that cannot absorb a Goldman-Sachs-survey level of overrun.

I started this piece by asking whether my own token consumption was a measure of my contribution. I do not think it is. I think it is a measure of how much work my tools are doing, which is a different question, and I think conflating the two is the error at the centre of the current moment.

We did not replace labour. We simply changed the meter.

And the cheapest worker in the building?

Still runs on toast.

Cien Solon is a founder and AI transformation strategist working at the intersection of people, platforms, and power. Through LaunchLemonade, she helps organisations design secure AI systems that are dependable, governable, and human-centred.

Sources and further reading

• Kováč, L. (2010). “The 20 W sleep-walkers”. EMBO Reports, 11(1), 2.

• Congressional Research Service. (2025). “Data Centers and Their Energy Consumption: Frequently Asked Questions”. R48646.

• “Gartner Predicts Over 40% of Agentic AI Projects Will Be Canceled by End of 2027”, Gartner

• “AI can cost more than human workers now”, Axios

• “The cost of compute is far beyond the costs of the employees: Nvidia executive says right now AI is more expensive than paying human workers”, Fortune

• “A Meta employee created a dashboard so coworkers can compete to be the company’s No. 1 AI token user”, Fortune

• “Uber CTO Shows How Claude Code Can Blow Up AI Budgets”, The Information

• “Why Uber has Already Burned Through its AI Budget”, AI Magazine

• “Bosses Are Blowing More Money on AI Agents Than It’d Cost Them to Just Pay Human Workers”, Futurism

• “Governor Mills Announces Decision on LD 307”, Office of Governor Janet T. Mills

• “Despite initial support, Legislature fails to override Mills’ veto of landmark data center ban”, Maine Morning Star

About ten years ago, I found myself sitting between two women having an argument I didn’t understand well enough to have an opinion on.

An older colleague told a younger one to stop being such a feminist. The younger one had reacted strongly to a man being rowdy near us, and when she explained herself, voice shaking with something between anger and hurt, the older woman looked at her like she was being dramatic. I sat between them and didn’t know where I stood. I remember thinking that they were both making sense to me, which, in retrospect, is exactly the problem. And the reason it was a problem is more interesting than the fact of it.

I had grown up in a patriarchal world, which means I had grown up watching women tolerate. Tolerance was strategy where I came from and that strategy was sometimes called wisdom and on most days it was called survival.

But I had also been raised by a mother who questioned things, who saw the architecture of what was around her and named it, even when naming it changed nothing. She lived in two worlds simultaneously and taught me, without ever saying so directly, that both were real. So when those two colleagues argued in front of me, my confusion came from recognition, because I saw something of each world in each of them. The older woman’s tolerance carried its own logic and its own cost. The younger woman’s anger carried the same thing my mother had always carried, underneath everything else.

I had heard the word feminism at around ten years old, from a television show. It felt foreign, just far away, like something that belonged to other people’s lives and other people’s arguments.

It took me until a global pandemic, a therapist and a few books to understand that what I had been tolerating for most of my adult life had a name, and that I had been tolerating it because I lacked the language to call it what it was. When you experience something that violates you but can’t articulate it, the emotion still comes out, and when it does, you get called sensitive, emotional, crazy or difficult. Without a word for it, you remain unable to protect yourself, unable to fully see what you are actually inside.

And when I finally had the word, I could.

I think about what AI is doing to the concept of happiness right now, because I wonder if we are building the most sophisticated comfort system in human history at exactly the time we most need people to be uncomfortable enough to ask questions.

The 2025 World Happiness Report, published by Oxford’s Wellbeing Research Centre, found that sharing meals with others and trusting other people are stronger predictors of happiness than health or wealth, stronger even than personalisation, convenience, or a feed that knows what you want before you have decided you want it. What the research points to is connection, presence and risk and occasionally being wrong about each other. And in the same breath, the report found that the number of people dining alone in the United States has increased by 53% (1 in 4) over the past two decades, during the same period that technology became extraordinarily good at making people feel accompanied.

I am raising this alongside AI’s rise, because the timing deserves more than a footnote.

This week, I saw on CBS News that Sam Altman wrote an open letter to the Premier of British Columbia, apologising for OpenAI’s failure to alert law enforcement about a ChatGPT user whose account had been flagged and banned eight months before he carried out a mass shooting at a school in Tumbler Ridge, killing eight people.

Automated detection tools had identified the account for potential misuse. A human review found no imminent danger. The police were never called. Altman’s letter commits to preventative efforts to ensure something like this never happens again. I understand the impulse. When something is preventable and it wasn’t prevented, the instinct to close that gap is human and it is right. But I have been thinking about what it means structurally, because once you decide that an AI system has a moral obligation to act on what it knows about you, you have made a decision about what kind of infrastructure you are building, and that decision extends well beyond the clear and obvious cases into every case that follows.

The psychoanalyst Erich Fromm argued in 1941 that freedom terrifies people, that when given too much open space, too many choices, too much self-determination, many people will actively seek a structure to surrender to, because the weight of deciding who you are and what you do with your life is genuinely crushing without something to hold onto.

He was writing about authoritarianism, but I think about his argument every time I open an app that has already decided what I want to see, who I want to hear from, and what kind of person my behaviour suggests I am becoming.

What the Altman story made viscerally clear to me is that we have moved from systems that observe behaviour to systems that interpret thought…from a search engine knows you looked up flights to Lisbon… to what we are describing now, which is a system that holds the conversation you had with yourself before you made any decision at all.

Most people consented to this without knowing what they were consenting to, and I say that with no judgment, because I did too.

I wonder if the most dangerous thing about AI surveillance is that it has gained access to something no previous system has ever held… our unfinished thoughts. And the people most likely to be harmed when those systems get it wrong are the people with the least recourse.

AI is trained on data that reflects the world as it has been, with all the inherited bias of that history. A system built to flag threats will flag along the lines of whoever looks like a threat in its training data, and the minority, the outlier, the person whose behaviour pattern doesn’t fit the expected shape, will bear the cost of every false positive without appeal. This problem has a very long history and much older infrastructure behind it.

So what should leaders do about this?

What we owe ourselves and our teams goes well beyond the answer most of us are giving. The instinct to say we need to educate people is well-meaning and mostly misses the point, at least in the way we have historically meant it. Most of what we spent decades training people to do is now automatable, and the old model of education as a ladder toward a fixed destination loses its footing when the destination keeps moving underneath everyone’s feet.

I believe that the opportunity to participate, to have a stake in what gets built and how it gets built and who benefits from it, should be open and viable. Equity in the AI economy is a structural argument, because when you have a stake, you have a reason to pay attention, and when you have a reason to pay attention, you eventually develop the language for what is happening to you, and when you have the language, you can protect yourself.

Something close to that happened to me, sitting between those two women with no idea where I stood. I found my way, eventually, to a framework that let me name my own experience, and naming it changed everything about what I could do next.

All the Zest 🍋

Cien

Cien Solon is a founder and AI transformation strategist working at the intersection of people, platforms, and power. Through LaunchLemonade, she helps organisations design AI systems that are dependable, governable, and human-centred.

Sources and further reading

World Happiness Report 2025 — Wellbeing Research Centre, University of Oxford

World Happiness Report 2025: Executive Summary — worldhappiness.report

Sam Altman apologises for not flagging authorities to mass shooter’s ChatGPT account — CBS News, April 2025

Erich Fromm, Escape from Freedom (1941) — Farrar and Rinehart

One of my first jobs as a product manager was to mock up wireframes for developers to build from. I’d sketch the flow on paper or in whatever tool I could get my hands on, hand it over, and watch something recognisable as product come back a few days later, rough around the edges but functional.

The customer experience was nothing to celebrate, but the cycle from idea to running code was measured in days, and back then that felt like the best trade we could make.

When we started working with real designers, the product got better almost immediately. Screens had weight and flows made sense the first time a user touched them in a way my rough wireframes never could achieve. The cost, which nobody at the time would have called a cost, was that our development cycles started to stretch. What used to be an afternoon of sketching now travelled through rounds of mockups and internal review before engineering could write a line of code. Weeks turned into months. The products improved and the timelines slipped, and I learned to accept that as the price of craft.

For years that was the assumption every product team inherited. Good design took time, and the rest of the cycle politely waited for it while the designer held the first shape of the idea.

Over the past year though, I’ve watched that flow compress. Now we share problem statements with coding copilots and agents, and the screens arrive, sometimes badly and sometimes closer to what we wanted than the third round of mockups would have given us.

On Friday, the market priced what I’d been watching. Figma’s stock dropped as much as 7.28% after Anthropic shipped Claude Design, a tool built on Claude Opus 4.7 that generates prototypes, websites, slide decks and design systems from natural language prompts. Adobe and Wix fell in the same session. Commentators have been calling this the latest wave of what’s been named the SaaSpocalypse, a repricing of enterprise software that has wiped more than $2 trillion from the sector since February.

And I think the SaaSpocalypse is half right.

The thesis behind the repricing is straightforward enough to follow. For two decades, enterprise software sold seats and every new employee at a customer meant another seat, another subscription, another line in the recurring revenue.

Salesforce, ServiceNow, Atlassian, Figma, and almost everyone in the IGV index were built on that assumption, and the market capitalised it generously.

But now, agents broke the arithmetic. If one AI system can do the work that previously required five analysts, a company renewing its software does not need five seats, or four, or three. It needs one, and possibly none, and it would quite like to replace the line item with API credits from the model provider directly. A Fortune 50 memo that leaked in January described plans to reduce Salesforce and ServiceNow licence spend by around 60% over the year, with the savings redirected to AI infrastructure. UBS cut ServiceNow to Neutral on April 10 and dropped the price target from $170 to $100, pointing to tightening software budgets at large enterprises as they redirect spend toward AI. The iShares software ETF is down around 30% this year through mid-April. More than $2 trillion in software market capitalisation has been wiped off since February.

I’ve spoken to consumers and business leaders this year who have already decided to stop buying software and build their own solutions instead, because the tools they used to pay for can now be assembled from the models directly, wired into the workflows they actually need. The procurement conversation has changed. The buyer has changed. In some categories, the entire premise of buying software has changed.

So when Zscaler’s CEO Jay Chaudhry went on CNBC last week and called the SaaSpocalypse overblown, arguing that AI is not reliable enough to replace critical business software, I don’t think he was reading the room. His argument rests on AI not being good enough yet, which is an argument with a shelf life of about six months, and his customers can read a shelf life. The repricing is not investors panicking about a distant risk. It is investors pricing a mechanism that has already arrived in the budgets of the companies they hold.

But the story doesn’t end there. And this is where I think the market is missing the second half.

What gets lost in the panic is that agents don’t replace software. They need it. An agent that writes a contract still needs somewhere to store it, somewhere to check it against prior versions, somewhere to route it for signature, and somewhere to prove later that the right version was signed by the right person at the right time. An agent that designs a landing page still needs a component library, a version history, a staging environment, and a pipeline that pushes the code into production without breaking anything around it.

The software does not go away. The user of the software changes.

For two decades, the user was a human with a login. Now the user is increasingly an agent with an API key, a task, and a budget of tokens. The interface the human used to navigate becomes a set of endpoints the agent calls. The seat licence becomes a metered bill. The feature that helped a human do the job faster becomes the function the agent calls to prove the job was done correctly. It’s the same software, reshaped for a user who does not need a dashboard.

What the SaaSpocalypse is actually pricing, I think, is the end of software built for the first user. Figma built a product for human designers, and that product is now competing with an agent that can generate the output directly. Salesforce built a CRM for human sales reps, and that CRM is now competing with agents that can read emails, update records, and send follow-ups without anyone opening the interface. The per-seat economy priced those products on the assumption that the human user was essential.

Remove the human and the per-seat pricing collapses.

Very little of this exists at scale yet - software built for agents - which is why most of the commentary this week can only see the first half of the story. The software being priced down is the software built for humans in a world that no longer needs that many humans doing that work. The software that will be priced up, once the market catches up, is the software being built for the agents that are now doing it.

This is what I think matters most for anyone running a business right now, because the question has moved. It used to be “which SaaS tools will help my team work faster.” It is now “which software will still be useful when my team is smaller and my agents are doing more of the work.” Those are different questions with different answers.

And most procurement conversations haven’t yet noticed they’ve moved.

There’s an irony running through all of this, which is that the software likely to survive is the software considered unsexy for most of the last decade. The CRM that kept painful audit logs because some regulator demanded it. Or the legal platform that made you annotate which clause changed and why.

The tools built for speed and simplicity, the ones that celebrated one-click actions and minimal friction, are less well-positioned than most of their founders would admit. An agent does not need to be delighted by a clean interface. It needs to know why it did what it did, and to prove later that it followed the rules. The software optimised for human convenience is being rewritten into software optimised for agent accountability, and the companies that already lived in that world because they had to are finding themselves with an unfamiliar advantage.

If I were renewing a SaaS contract this quarter, the question at the top of my list would be whether the tool has any way to cooperate with the agent I might hand it to next year, and whether the vendor is thinking about that question or still selling me seats.

I started as a product manager sketching wireframes because nobody else would. Then I learned to wait for designers because the product was better for it. Now I hand problem statements to agents and watch the screens arrive, sometimes well and sometimes badly, but always faster than the last model. Each version of that workflow needed different software underneath it to hold the work together.

The next version will too. We just haven’t priced it yet.

All the Zest 🍋

Cien

Cien Solon is a founder and AI transformation strategist working at the intersection of people, platforms, and power. Through LaunchLemonade, she helps organisations design AI systems that are dependable, governable, and human-centred.

Sources and further reading

• Anthropic launches Claude Design, Figma stock immediately nosedives — Gizmodo

• Figma’s stock falls 7% after Anthropic introduces Claude Design — OfficeChai

• Anthropic releases Claude Opus 4.7, concedes it trails unreleased Mythos — Axios

• UBS downgrades ServiceNow, saying AI is a bigger threat than first believed — CNBC

• The SaaSpocalypse: why enterprise software has lost more than $2 trillion in 2026 — humai

• Zscaler’s CEO says the AI-driven SaaS-pocalypse is overblown — CNBC

• Did Goldman Sachs just declare open season on software stocks? — The Motley Fool

This week, three of the most powerful AI companies in the world closed the door at the same time.

On Monday, Anthropic confirmed that its most powerful model ever built, Claude Mythos, will not be released to the public. It has been locked behind a programme called Project Glasswing, where 40 handpicked organisations including Amazon, Apple, Microsoft, and CrowdStrike will use the model to scan critical software for vulnerabilities. Anthropic committed $100 million in usage credits for these partners. Everyone else was told to wait.

The same day, OpenAI published a 13-page paper called “Industrial Policy for the Intelligence Age,” a blueprint for how governments should restructure taxes, shorten the workweek, and build public wealth funds to prepare for superintelligence. A former US Senate staffer who worked on AI policy in 2023 responded publicly, saying she already had all of it in her handwritten notes from three years ago. The ideas were familiar and the authorship was the point..,.OpenAI wants to write the rules for the technology it profits from.

And on Wednesday, Meta released Muse Spark, the first model out of its newly formed Superintelligence Labs. It is closed source. Meta abandoned its open-weight Llama strategy after the Llama 4 launch disappointed and Chinese lab DeepSeek cloned its architecture. The company says it hopes to open-source a version eventually, though it will keep its most sensitive capabilities proprietary.

“Eventually” is doing a lot of work in that sentence.

Three companies. Three justifications. Safety. Policy. Competition.

The same week it locked Mythos behind a firewall, Anthropic announced that its annualised revenue had surpassed $30 billion, overtaking OpenAI for the first time. That figure was $9 billion at the end of 2025 which means it tripled in four months.

The growth is almost entirely enterprise with over 1,000 customers now spend more than $1 million annually on Claude, a number that doubled from 500 in under two months.

Eight of the Fortune 10 are Claude customers and Claude Code alone generates $2.5 billion in run-rate revenue. These are organisations replacing line items in their operating budgets with AI infrastructure that renews, expands across teams, and compounds. OpenAI, by contrast, still draws heavily from consumer subscriptions and its 900 million weekly ChatGPT users. It projects $14 billion in losses for 2026 and does not expect to break even until 2030.

With this, Anthropic projects positive free cash flow by 2027.

The company generating more revenue than any other AI lab is also the one that will not release its most powerful model. And the company writing policy papers about how society should prepare is the one falling behind.

TechCrunch has pointed out that Mythos’s controlled release also conveniently protects against model distillation, the process by which competitors copy frontier capabilities into smaller, cheaper models.

Moreover, Anthropic, Google, and OpenAI have reportedly been working together to identify and block distillers.

The safety conversation and the business argument it would seem like, are wearing the same clothes.

On April 2, while the restriction conversation dominated the headlines, Google released Gemma 4 under the Apache 2.0 licence. That is the most permissive open-source licence available.

The model family includes variants that run on a laptop, a Raspberry Pi, or a phone. The 31-billion-parameter dense model outperforms Meta’s Llama 4 on maths, coding, and reasoning benchmarks despite being a fraction of the size. It supports 140 languages, processes text, images, video, and audio natively, and has been downloaded over 400 million times across the Gemma series. Google built it from the same research that powers its proprietary Gemini 3 line and gave it away.

Chinese lab Z.ai released GLM-5.1 under the MIT licence. A 744-billion-parameter model with open weights on Hugging Face. On the SWE-Bench Pro software engineering benchmark it scored 58.4, edging out both GPT-5.4 and Claude Opus 4.6.

This means that this is the first open-source model to top all major closed-source models on a real-world coding benchmark.

It was built entirely on Huawei Ascend chips. Z.ai has been on the US Entity List since January 2025 without an NVIDIA hardware and no American silicon. The export controls designed to prevent frontier AI capability from reaching China did not prevent this.

Perplexity is making a different argument altogether. Perplexity Computer, launched in February and expanded in March, orchestrates multiple frontier models from a single interface. Claude handles reasoning and code. Gemini handles research. Grok handles fast queries. The system picks the best model for each subtask, runs them in parallel, and delivers the finished result. This costs $200 a month.

The Personal Computer product runs locally on a Mac mini with full access to your files, apps, and workflows. The premise is that models are interchangeable components. Perplexity does not care who built the smartest one. It routes to whichever is best for the job and that challenges the idea that controlling the frontier model is what matters most.

I have been building across models for a while now. Claude, Gemini, ChatGPT and open-weight models, and increasingly orchestration layers that let me switch between them depending on the task. I do this because my work demands it, and because locking into a single provider has always felt like a vulnerability for the kinds of businesses I serve, where the budget is not seven figures and the margin for error is thin.

If you are only looking at the closed labs, the gap between what exists and what you are allowed to touch has never been wider. But if you look sideways, the doors are wide open. Gemma 4 runs on a laptop. GLM-5.1 is free on Hugging Face.

The AI Intelligence Index, the composite benchmark that tracks frontier model capability, has been stuck at 57 since February.

The models are not meaningfully smarter than they were two months ago. What changed is who gets to use them and on what terms.

While Mythos’s cybersecurity capabilities are genuinely significant and the idea of giving defenders time to patch vulnerabilities before attackers exploit them is reasonable. But the framing assumes that restriction works, that containing capability at the top keeps everyone safer.

This week proved, concretely, that it does not. The capability is already loose.

I think about the business owner reading about a model “too dangerous to release” while a free alternative that outperforms it sits on an open repository. The operator who was told this technology would level the playing field, watching it split into a tier they can afford and a tier they cannot.

All the Zest 🍋

Cien

Cien Solon is a founder and AI transformation strategist working at the intersection of people, platforms, and power. Through LaunchLemonade, she helps organisations design AI systems that are dependable, governable, and human-centred.

Sources and further reading

Anthropic debuts preview of powerful new AI model Mythos in new cybersecurity initiative — TechCrunch

Is Anthropic limiting the release of Mythos to protect the internet — or Anthropic? — TechCrunch

Anthropic withholds Mythos Preview model because its hacking is too powerful — Axios

Project Glasswing — Anthropic

Industrial Policy for the Intelligence Age — OpenAI

How people are reacting to OpenAI’s 13-page policy paper on AI superintelligence — Fortune

Meta debuts Muse Spark, first AI model under Alexandr Wang — Axios

Can Meta’s new AI model Muse Spark make money? — CNBC

Anthropic Just Passed OpenAI in Revenue. While Spending 4x Less to Train Their Models — SaaStr

Anthropic Overtakes OpenAI in Revenue, Hitting $30 Billion Run Rate — Trending Topics

Gemma 4: Byte for byte, the most capable open models — Google

GLM-5.1: The open source model that gets better the longer you run it — Firethering

New AI Models April 2026: Anthropic Won’t Ship Its Best. Open Source Will. — What LLM

Everything is Computer — Perplexity

How AI is getting better at finding security holes — NPR

At first I was a poet.

I did not know what I was doing. I only knew that something inside me needed to become words, and that when it did, I felt less alone in the world.

I am thinking about that feeling a lot this week. I think you know it too. Maybe you would not call it poetry. Maybe it is the song you cannot listen to without something cracking open. Maybe it is the way time disappears when you hold someone you love. Maybe it is the silence at the edge of the ocean, beer in hand, when your body understands something your language never will.

Whatever you call it, you have reached for it. And at some point, something told you to stop.

In February, Mrinank Sharma, the 29-year-old head of safeguards research at Anthropic, decided to start again. He shared his resignation letter on X. It was viewed 15 million times. In it, he wrote that he had repeatedly seen how difficult it is to let values govern actions, within himself and within the organisation. His final project at Anthropic had focused on understanding how AI assistants could make us less human. And then he said he was leaving to enrol in a poetry degree and “devote myself to the practice of courageous speech.”

He signed off with a poem by William Stafford called “The Way It Is.” It is about a thread you follow through your life, one that does not change even as everything around you does. Stafford wrote that tragedies happen, people get hurt, you suffer and get old, but you never let go of the thread.

There’s a thread you follow. It goes among
things that change. But it doesn’t change.
People wonder about what you are pursuing.
You have to explain about the thread.
But it is hard for others to see.
While you hold it you can’t get lost.
Tragedies happen; people get hurt
or die; and you suffer and get old.
Nothing you do can stop time’s unfolding.
You don’t ever let go of the thread.

by William Stafford

And then Sharma let himself become invisible.

People called it unusual. Some called it brave. Most moved on within a day.

Few weeks later, Oracle laid off an estimated 30,000 employees in a single morning.

The company sent termination emails at 6am under the name “Oracle Leadership.” The same lines went to workers in the United States, India, Canada, Mexico, and Uruguay. Access to internal systems was revoked almost immediately.

According to TD Cowen estimates reported by Bloomberg, the cuts represent roughly 18 percent of Oracle’s global workforce. The company disclosed a $2.1 billion restructuring charge in its March 2026 SEC filing, with the savings directed toward AI data centre expansion.

Then, the stock rose four - six percent.

I sat with the stories that followed. On Reddit’s r/employeesOfOracle, a child posted that their father, a 20-year employee battling cancer, was two years from retirement. He woke up to the same template email as everyone else. His boss of 20 years never called. His health insurance is now gone.

Elsewhere in the thread, someone who had spent 29 years at the company described receiving the same email and urged others to stay strong. Another person, 16 years at Oracle, wrote that it is a strange transition to wake up and realise your daily priorities have shifted overnight saying”My mind still instinctively drafts to-do lists for projects that are no longer mine.”

On Blind, an employee on an H1B visa explained they now have 90 days to find a new sponsor or leave the country. On TheLayoff.com, someone who had given 30 years wrote that you should never take the company you work for seriously, never invest yourself, because there is no version of the story where it pays off.

The first wave of all of this is financial that includes severance arithmetic and insurance gaps. Then the scramble to update a LinkedIn profile before the afternoon. That wave has systems around it, however imperfect.

The second wave shows up months later, when the severance maths are done and the job market keeps saying something you do not want to hear. It is when someone realises that the thing they gave decades to did not know their name. That they were, as one employee put it, replaceable.

The identity they had built, carefully and over years, was always contingent on someone else’s spreadsheet.

Somewhere, someone made this decision. A leader, or a group of leaders, looked at the headcount and the AI infrastructure investment and made the call.

I am not interested in vilifying that person. I think about them too. Because that person is also answering the question of who they are with what they deliver. They are also building their sense of self around output, around decisions, around the value they create for shareholders.

They sit in a different chair, but they sit inside the same machine.

The logic that makes 30,000 people redundant in a single morning does not stop at any particular level of seniority. It reaches every chair. Eventually, it reaches theirs.

This is what I keep circling back to. Since the industrial revolution, we have been trained to equate identity with production. All of us.

The question “who are you” has, for generations, been answered by “what do you do.” We built entire economies, education systems, social structures, and inner lives around that answer.

And now the thing we were told to organise ourselves around is the very thing being automated.

I have written over the past two weeks about the speed of this. About costs arriving faster than anyone expected. About a 90-day window that is already closing. What I did not say clearly enough is what the acceleration takes from us beyond the economic.

It takes the room to reflect. The space to ask who we are when the labour disappears. The second we most need that question happens when we don’t have the time or space to sit with it. Because rent is due and the applications are piling up.

Simply because the economy does not pause so you can find yourself.

And this is where the cruelty compounds. We took the most human impulse we have and turned it into a specialisation. The reaching for something beyond what we can hold. We put it behind degree programmes and told people it was impractical and we called it poetry, or philosophy, or art, and we said those things were for people with time.

But that reaching has never been a luxury. It is the thing that happens when someone ugly-cries to a song and when you are lost in a music festival, bathed in sound and light, your body moving before your mind gives permission. It is a first kiss, the one where your whole self knows something before language arrives.

Everyone has this. We just stopped calling it by its name and we stopped protecting it.

If you are leading a team, running a company, allocating capital, or sitting in a room where restructuring is on the table, this is not a request to slow down. I understand the economics. I promote AI orchestration for a living. I know what the numbers demand.

But I would ask you to consider what you are taking from people beyond the role. The person who receives that email is losing more than a salary. They are losing the structure that told them who they were every morning. And most of them have nothing underneath it, because we never built the systems, the culture, or the permission for them to find out.

If you are on the other side of that decision, if you are the one looking at the email or wondering when it arrives, I would say this. The anxiety you feel right now is the most honest thing you have. And the question it is asking, the one about who you are when the title and the badge and the calendar invites disappear, that question deserves your time. Even now. Especially now.

I am pretty average. I want to say that because it matters. I did not come from a family of philosophers or poets. I came from a family that worked. And somewhere along the way, like most people, I was told that the reaching was nice but the earning was the point.

I kept the thread anyway.

And if someone as ordinary as me managed to hold onto that thread, then it was never about talent or luxury or having the right degree. It was about permission that we forgot to give ourselves and each other.

AI can write a perfect poem as it can generate stanzas in any style, in any language, with technical precision that matches the best human work. And it will never need to write one. It will never ache, or reach for something it cannot quite hold, or sit in the dark with a feeling it has no words for and try, badly, to find them.

That reaching is the most human thing about us. And we forgot to build it inside ourselves before we needed it most.

All the Zest 🍋

Cien

Cien Solon is a founder and AI transformation strategist working at the intersection of people, platforms, and power. Through LaunchLemonade, she helps organisations design AI systems that are dependable, governable, and human-centred.

Sources and further reading

• Mrinank Sharma resignation post on X, February 9, 2026

• Anthropic researcher quits in cryptic public letter — Futurism

• AI safety researcher quits Anthropic, warning “world is in peril” — The Hill

• AI safety boss warns world “is in peril,” quits to write poetry — The Telegraph via Yahoo News

• Oracle is cutting up to 30,000 employees to pay for AI data centres — The Next Web

• Oracle layoffs: 30,000 jobs cut to fund AI data centres — Tech Insider

• Cancer patient’s child slams Oracle after father’s email layoff post 20-year tenure — Free Press Journal

• Fear and despair outside Oracle office: “It was ruthless, we’re all replaceable” — The Print

• Oracle laid off thousands by email — Inc.

• Oracle Corp. layoff discussions — TheLayoff.com

• “The Way It Is” by William Stafford