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The Capability Curve Has No Memory

Vektor Memory

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And we keep building anyway. What choice do we really have?

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Anthropic published a progress report last week that I have not been able to stop thinking about.<br>https://www.anthropic.com/institute/recursive-self-improvement

Not because of the headline numbers, though those are striking enough. Claude authored over 80% of the code merged into Anthropic’s own codebase, and so are other frontier companies now. Engineers are shipping eight times more output per quarter than they did two years ago. An agent completing tasks that would take a skilled human sixteen hours, working continuously, without being redirected once.<br>What got me was the graph showing lines of code per engineer over time. Flat for four years. Then a sharp bend upward in 2025 when Claude started running code rather than just suggesting it, the ouroboros, a binary Gödel machine feeding code back into itself. Then steeper again in 2026 when agents started working autonomously over longer horizons.<br>Smart cookies, Anthropic. In just a few years they managed to get the moola, 1 trillion, in fact. Purchasing strategic infrastructure like Vercept, Bun, Coefficient Biohealth, Fractionless AI, and Stainless, the SDK experts, for whom Anthropic was one of their first larger clients, makes sense strategically.<br>I looked at that graph and felt two things at the same time. Genuinely impressed. I really like Anthropic, and, if I’m honest, I'm a little concerned.<br>Pretty much all of the brains and infrastructure in AI will be consolidated into a handful of companies, reminiscent of the 80's when Microsoft made deals with all the hardware manufacturers so Windows was the only licensed OS allowed. That's why Linux was smart to pivot to servers and retained 60% of market share to this day, Ubuntu is great; it works and very rarely has any reliability issues, along with Red Hat and Debian.

The Inflection Point Nobody Has a Map For<br>Here is what I think is actually happening and why the idea is more rational rather than alarmist.<br>We are approaching a threshold. Not gradually, but in the way the frog in water approaches boiling with nothing much visible, then everything all at once. An agent can reliably replicate its own development cycle and sustain above 90% code accuracy on open-ended tasks, the nature of human work does not just change. It restructures from the ground up.<br>The Anthropic article is careful to frame this as a positive development, and they are not wrong. More code shipped faster, bugs caught before production, research that would have taken humans months to years was completed in weeks. Real gains for real problems.<br>Press enter or click to view image in full size

But here is what it means on the ground for the people doing the work. The volume of what needs to get done does not decrease. It multiplies . What changes is the type of work. Manual execution gives way to high-level direction. Writing code gives way to reviewing it, shaping it, and deciding what strategic problems it should be solving. The human role becomes a layer of high-level authorisation above an autonomous system that is already capable of most of the execution.<br>That is not a smaller job. That is a harder job, more cerebral, and also requires multidisciplinary experience and detective skills. Ten times the output means ten times the decisions, ten times the context to hold, ten times the responsibility for what ships.<br>Being the head HITL is not easy; stuff moves so quickly. Did you read 20 pages of code from 20 different projects and text instantly on your mobile phone and approve all?

You Already Need to Know 100 Things<br>I feel this shift personally, and I feel it constantly.<br>Building VEKTOR as a solo developer means I am a developer, a product manager, a security engineer, a devops engineer, a content writer, a growth person, a customer support function, and a business owner, all at once.<br>AI has made each of those roles individually more accessible and even possible. It has also made it possible to run all of them simultaneously in a way that was not realistic before. If you go back in time, I remember we had 5 systems at work: Oracle Unix green screen (it never crashed once), which was fast but needed mental repetition to learn; one database; Outlook; Intranet; then Salesforce came along and 20 other apps bolted on.<br>The result is not less work. It is more work, spread across more domains and more systems with logins and M2FA, with higher stakes at each one.<br>This week I was mid-session debugging a certbot renewal failure on the VPS when it became clear the issue was a credentials format mismatch between an old apt-installed certbot version and a Cloudflare API token that expected a...