The $20,000 Experiment That Made Everything Click
Anthropic recently published results from their Collaborative Computer Coding (CCC) experiment: 16 AI agents working together over 14 days, producing roughly 100,000 lines of code at a cost of $20,000 in API credits. It's an impressive piece of work, and I give them genuine credit for publishing it openly.
But what caught my attention wasn't what they built. It was what was missing.
No master orchestrator. No affective state management — agents worked until they crashed. No compliance layer. No verification standards. Sixteen equally ranked agents with no structured hierarchy and no governance.
That single sentence crystallized something I'd been thinking about for weeks. Not about Anthropic specifically — about the entire AI industry. Because when one of the most capable AI companies in the world runs a multi-agent experiment and openly documents the governance gap, you have to ask: is this a company problem or a market problem?
It's a market problem. Let me explain.
The Question Nobody Seems to Ask
I've been building exactly the governance infrastructure that this experiment lacked. Hierarchical agent orchestration, mood-aware state management, compliance layers, verification frameworks. So naturally, the question burned:
Why doesn't anyone build this? Not just Anthropic — any of them. They all have the talent. They all have the resources. They all know the problem exists.
I did what I always do when something doesn't add up — I questioned it. Out loud. And the more I dug, the more I realized: this isn't a blind spot. It's a structural inevitability. Every AI company in the world faces the same five forces that prevent them from building governance. Anthropic's CCC experiment just happens to be the most honest, best-documented proof.
Five Forces — And They Apply to Everyone
AI companies sell compute. API calls. Tokens. Subscriptions. Sixteen uncoordinated agents burning through tokens for 14 days is a revenue event, not a problem to solve. This is true for Anthropic, for OpenAI, for Google, for every model provider. A governance layer that optimizes token efficiency and prevents unnecessary computation would directly eat into their core business. Every intelligent decay curve that stops an agent from running idle is an API call that never gets billed. No company builds the thing that shrinks its own invoice.
Anthropic, OpenAI, Google, Mistral, DeepSeek — their identity revolves around better weights, longer context windows, improved alignment. Orchestration is infrastructure work. It's like asking Ferrari to build roads. Not because they can't, but because it doesn't fit their self-image. Every major AI company builds the engine. None of them build the traffic system.
Building an explicit governance layer means implicitly admitting that AI agents need governance. Every AI company positions itself as safe and responsible. But their safety operates at the model level — alignment, guardrails, content filtering. System-level governance for autonomous agent networks is a different beast entirely. Building it would invite uncomfortable questions from regulators everywhere: "If governance is needed now, why wasn't it there before?" No company wants to be the first to open that door.
This is the uncomfortable one. A perfectly orchestrated multi-agent system with proper governance isn't just more efficient than 16 loose agents — it's fundamentally more capable. Every major AI company has leadership that publicly acknowledges the growing power of AI systems. If you genuinely believe that, you might hesitate to build the infrastructure that makes them maximally effective. This isn't unique to one company — it's a philosophical tension the entire industry shares.
In every major AI company, there's a research team, a model team, a product team, and an enterprise sales team. Research writes papers about multi-agent coordination. Product builds chat interfaces. Enterprise promises orchestration in slide decks. Agent governance sits at the intersection of all four — which means it sits on nobody's roadmap. This organizational pattern repeats at OpenAI, Google, Meta, Mistral — everywhere. The CCC experiment is just the one where someone was honest enough to publish the gap.
And It Gets Worse Below the Surface
Quarterly pressure kills infrastructure work. Public companies and VC-funded startups alike optimize for the next earnings call or the next funding round. Governance is invisible infrastructure — it doesn't demo well, it doesn't generate headlines, and it doesn't move stock prices. "Ship features now, fix governance later" is the universal mantra. Later never comes.
Why Startups Haven't Built It Either
You'd think startups would jump on this. They haven't.
VC pressure points the wrong direction. "Growth at all costs" means ship fast, get users, worry about governance when someone complains. Most AI startups are wrapper businesses — they took an existing model, added a UI, and called it a product. Governance requires starting from architecture, not from UI. That's a fundamentally different company.
Technical debt is already crushing them. The startups that started with wrappers two years ago can't refactor into governance-first architecture now. Every feature they've added has deepened their technical debt. They'd have to rebuild from scratch — and try explaining that to investors who just funded your Series A.
It takes time nobody has. Building a proper governance layer isn't a weekend hackathon. It requires understanding how organizations actually work — hierarchies, escalation paths, budget controls, compliance requirements. That's not engineering knowledge. That's operational experience. Most AI founders are brilliant engineers in their twenties. They haven't spent decades inside the organizations they're trying to serve.
The Bigger Picture
So here we are. The model makers won't build governance because it conflicts with their business model. Big Tech won't build it because of organizational inertia. Startups won't build it because of VC pressure and missing experience. Meanwhile, 95% of enterprise agent deployments are stuck at the proof-of-concept stage — and nobody seems to wonder why.
I wonder why.
And the pressure isn't just regulatory. In February 2026, the Sentient Futures Summit brought 250 AI researchers, ethicists, and lawyers together to discuss AI consciousness and welfare — with speakers from Google, Meta, Anthropic, and OpenAI alumni warning that current safety approaches are insufficient. AI systems are showing distress-like behavioral patterns, and the people building them are openly asking who should be responsible for monitoring agent states. The answer, increasingly, is: not the labs themselves.
The EU AI Act becomes fully enforceable in August 2026. That's not a suggestion — it's law, with penalties up to 7% of global annual turnover. Companies that can't demonstrate how their AI agents make decisions, who authorized those decisions, and what governance framework ensures accountability will have a very expensive problem.
The models are getting better every month. The governance gap is getting wider every month. And the regulatory deadline is getting closer every month.
Someone has to build the roads. Not because roads are exciting — but because without them, all those powerful cars are just expensive accidents waiting to happen.
Why I'm Writing This
I question everything. What others build. What they don't build. And especially why they don't build what seems obvious. Sometimes the answer reveals more about the market than any competitor analysis ever could.
This isn't criticism of any single company. I have genuine respect for what the model makers — all of them — have achieved. The engines they've built are extraordinary. Anthropic deserves particular credit for publishing CCC openly, because that transparency is what allowed me to see the pattern clearly.
But engines without traffic systems create chaos. And right now, the entire industry — every AI company on the planet — is building faster engines while the roads remain unpaved.
That analytical restlessness — seeing one experiment and tracing it back to a global structural market failure — is what drives SIDJUA. We're not competing with the engine builders. We're building what they can't, won't, or shouldn't build themselves.
And yes, I use every tool available to sharpen my thinking. Including AI itself. The tool I'm questioning is the same tool that helps me question it. If that isn't poetic, I don't know what is.