# Software after software, *and the record so far.*

[Notes](https://www.orfloat.com/notes) · Field note · 27 May 2026

"Software After Software" is a short manifesto we have been working from inside the studio for the last few months. Twelve numbered propositions, in the Tractatus tradition, on what software becomes when intelligence is abundant, continuous, and cheap. We had treated it as a working thesis until the last six months. The empirical record has now overtaken it. The manifesto is not speculative anymore. Every load-bearing claim has a corresponding line in the data — and the operators who treat this as future-of-work reading are already a quarter behind.

## The capability curve, refreshed

[The capability overhang](https://www.orfloat.com/notes/capability-overhang) — the gap between what frontier models can already do and what operating businesses are actually using them for — was the spine of an earlier note in this archive. Three things have moved in the six months since.

First, agent autonomy is now measurable, and it is climbing. In a research piece published this year, [Anthropic reported](https://www.anthropic.com/research/measuring-agent-autonomy) that the 99.9th-percentile turn duration for Claude — the elapsed time between an agent starting work and stopping — nearly doubled in roughly three months, from under twenty-five minutes in late September 2025 to over forty-five minutes by early January 2026. The language Anthropic chose for the piece is itself striking: there is a *deployment overhang* — the autonomy the models are capable of handling exceeds what they exercise in practice. Capability is now running ahead of habit, not the other way around.

Second, the practitioners with the largest individual footprint on Claude have made the shift in name as well as in practice. In February 2026, Andrej Karpathy — who coined "vibe coding" exactly one year earlier — declared the term effectively over. The new default, in his framing, is *agentic engineering*: you are not writing the code directly 99% of the time, you are orchestrating agents who do and acting as oversight. The vocabulary has moved from convenience to discipline.

Third, the supply side has put very large numbers behind all of this. The four largest hyperscalers — Microsoft, Google, Amazon, Meta — collectively plan [$725 billion in capital expenditure in 2026](https://www.tomshardware.com/tech-industry/big-tech/big-techs-ai-spending-plans-reach-725-billion), up 77% from last year's record $410 billion. Microsoft alone is at $190 billion, well above the $152 billion analysts had been modelling. The composition has shifted too: more than 60% of that spend is now going to power infrastructure rather than chips — a structural change in where the binding constraint actually sits.

## The bottleneck moved

The manifesto's third and fourth theses are about where the constraint sits inside a software organization. The summary: writing valid code is trivial now. What remains are errors of engineering — priorities, sequencing, tradeoffs — and these are the errors that matter.

> You are not writing the code directly 99% of the time. You are orchestrating agents who do.

Karpathy's framing maps almost word-for-word onto the manifesto's claim that the unit of work becomes the delegated task, not the code to be written. The implication for review is concrete: review shifts from code to decisions. The pull request you read is no longer "is this implementation correct" — that question is now settled before it reaches you. The pull request you read is "did the agent pick the right thing to build, in the right order, with the right tradeoffs against the cost of building the other thing." That is a different skill, and it is the only skill that compounds from here.

## The old assumptions break

Two of the manifesto's harder claims: software, as a *profession*, was built on the assumption that writing code is hard and error-prone. Software, as an *industry*, was built on the assumption that code is scarce. Both assumptions no longer hold.

When code stops being scarce, the value migration is mechanical. Software whose only job is to encode a workflow loses value the moment an agent can perform the workflow directly. The moat for thousands of mid-market SaaS vendors — "customers cannot justify building this themselves" — becomes a clearance sale. What gains value, in this picture, is everything an agent cannot produce on demand: proprietary data, distribution and customer relationships, regulatory position, physical assets, trust, and the permissions to operate in a regulated space.

The hyperscaler capex composition is the same migration in a different register. The line item that grew fastest in 2026 was not chips — it was power, and the physical infrastructure that delivers it. Compute is being commoditized as fast as the supply can be built; what remains scarce is the substrate underneath it.

## Organize around the models

The manifesto's ninth and tenth theses are the ones that hit operating businesses hardest. It is not enough to fit models into existing systems, org charts, and processes. The winners are those who organize *around* the models. A small team with strong judgment and many agents will outrun a large team trying to fit AI into processes designed before the transformation.

Inside the studio, our four operating disciplines apply to this directly. We call them the 4D framework, and they map cleanly onto the manifesto's organizational claim:

- **Delegation.** Decide what each agent owns, and what it does not. An agent forced to work like a human is a wasted agent — the manifesto says it; our engagements measure it.
- **Description.** Tell the agent how to use the tools at its disposal, in writing, the way you would brief a thoughtful new colleague. The model's behaviour is your specification.
- **Discernment.** Read the agent's decisions the way you would read a junior engineer's pull request — for taste, sequencing, tradeoff selection. The code is incidental.
- **Diligence.** Audit the surface area before granting access. Re-audit every eight weeks, because the capability curve will have moved.

For a GCC operator, none of this is abstract. A family business in Muscat that is running its hospitality group, its clinic, or its retail operation through a layer of agents in 2027 — and many will — must have started reorganizing around the models in 2026. Bolting AI onto the old way of working is a category error, not an integration project.

## What to do with this

The manifesto closes with five short "Therefore" lines. The one that is hardest to argue with: we do not wait for the end state to become obvious, because the best move is to play the game. The end state is not yet apparent — but the curve is, and the curve is now enough to act on.

For an Omani business in May 2026, the concrete move is to seat a small team — three or four people, judgment-heavy, low on process — close enough to your real systems, real data, and real consequences to discover the new way of working inside your specific operation. That team's job is not to add AI to anything. Its job is to find the new shape of the work and pull the rest of the organization toward it. Its output, as the manifesto says, is not only software but also people and practices.

That kind of small autonomous team is exactly what a Discovery Phase is. If you would like one inside your business, [start a conversation](https://www.orfloat.com/contact).

— Akram Ahmed, CTO · Orfloat · Muscat

## References

1. Anthropic. *Measuring agent autonomy.* 2026. [anthropic.com/research/measuring-agent-autonomy](https://www.anthropic.com/research/measuring-agent-autonomy)
2. The New Stack. *Vibe coding is passé. Karpathy has a new name for the future of software.* 2026. [thenewstack.io/vibe-coding-is-passe](https://thenewstack.io/vibe-coding-is-passe/)
3. Tom's Hardware. *Google, Microsoft, Meta, and Amazon capex spending to hit $725 billion in 2026, up 77% from last year.* 2026. [tomshardware.com/tech-industry/big-tech/big-techs-ai-spending-plans-reach-725-billion](https://www.tomshardware.com/tech-industry/big-tech/big-techs-ai-spending-plans-reach-725-billion)
4. CNBC. *Tech AI spending approaches $700 billion in 2026, cash taking big hit.* 6 February 2026. [cnbc.com/2026/02/06/google-microsoft-meta-amazon-ai-cash.html](https://www.cnbc.com/2026/02/06/google-microsoft-meta-amazon-ai-cash.html)
5. Orfloat. *The capability overhang is no longer theoretical.* 22 May 2026. [orfloat.com/notes/capability-overhang](https://www.orfloat.com/notes/capability-overhang)

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