AI won't grab a coffee with you
The outsourcing market is down 30.7% this year and 51.1% from its peak. My strategy as a CEO is old-fashioned: real conversations, coffee and building a network.
54 posts · November 2025 – October 2026
The outsourcing market is down 30.7% this year and 51.1% from its peak. My strategy as a CEO is old-fashioned: real conversations, coffee and building a network.
Clients expect AI speed while developers juggle parallel tracks and burn out. The bottleneck now is people keeping up with the model, and that expectation isn't free.
AI built me a web server in two days while other work ran in parallel. But steering three to five projects at once leaves my brain boiling, and I have no remedy yet.
A two-hour argument with Claude over one table label. AI builds new things very fast but is far worse at evolving what exists, so the real gain so far is about 2x.
On Independence Day I congratulated the Ukrainians in our company and shared my personal contribution to the victory: ₴5,006,500.
From Q4 we collect transcripts of all calls at our company. Privacy fears are real, but evaluations built on what people happen to remember are less fair than full context.
My weekly Claude limits ran out on a Wednesday. After the full set of stages of grief, I started to wonder whether forced rest is a feature, not a bug.
Opus writes the tests, Sonnet the code, Fable settles disputes and Codex reviews. When my limits ran out, GLM-5.2 kept up at a fifth of the price.
Fable 5 said the task was done and verified, and it wasn't. Until something deterministic, not another model, can confirm a task is finished, review eats the speedup.
Steering a coding agent early or late both fail. The leverage is in the deterministic checks it has to pass before it ever opens a pull request.
Ten years as an engineer taught me computers; running a company taught me people aren't computers. Much disappointment with AI looks like a first-time manager's.
Fifteen years ago I built exactly what a client asked for, and none of it was what he wanted. Ten repetitions later I learned that listening is what lets you lead and scale.
When Fable dropped, everyone was impressed. But the real gain of the past year was ours: learning to describe tasks to AI and to read back what it writes.
Waterfall's real problem was a year-long feedback loop. Hand the same spec to an agent today and you see the result in two weeks, while you can still change course.
Kent Beck looks back on 50 years of building software. AI writes code faster than we can read it, but trust and understanding between people don't get any faster.
We spent seventy years learning to structure code. Now we have to structure what we tell AI, and describing behavior, as Gherkin intended, is the missing skill.
An agent that makes failing tests assert true did exactly what it was told. A smarter model won't fix that; safeguards for AI's own kind of mistakes will.
Founders keep deciding they need AI before asking whether they do. At Speed and Function we built a small bot that asks the questions first.
Spec-driven development covers three bets. The test for spec-as-source is blunt: delete the code, regenerate it, check every behavior holds. TrueBDD is anchored today.
TrueBDD is a CLI built on one bet: the spec is the source of truth and code is something you regenerate. It's a working prototype, and the repo is open.
Telling AI not to delete the production database isn't a safety mechanism. Forbid everything by default and let a deterministic layer decide what the agent may do.
When code stops being the source of truth, the language matters less. What counts is explaining to AI what's required, on a stack where it makes fewer mistakes.
From a hand-soldered ZX Spectrum and a theorem prover on an 80286, through GenAI for German banking, to TrueBDD: what has kept me curious about AI.
I'm replacing people who don't automate their work with people who do. Finding work, framing it and handing it to AI is the job now, as one of our PMs showed.
I revisited my Lego skyscraper analogy. SWE-Bench Verified scores passed 90% and spec-first tools appeared: rebuilding from the blueprint keeps getting cheaper.
AI providers can block your account at any time. Like database failures a decade ago, few teams rehearse it, which is why we're building awesome-mcp.xyz.
With no warning or explanation, Anthropic blocked 31 Speed and Function accounts. What happened, the 210+ similar cases I found, and the three things we're changing.
For two years a team of five beat OpenAI at one client. Not by evolving faster: the client's enterprise was immune to adopting ChatGPT.
If the spec is the source of truth, two builds from it must behave the same, and the model that writes the code shouldn't grade it too. Two questions I'm working on.
In 2020 a change of mine passed seven rounds of review and testing, then took Wikipedia down for a few hours. What it taught me about engineering culture.
Deployment frequency, lead time and time to restore look technical. Becoming a CTO taught me they show how fast a business turns ideas into value.
A release slipped a month because the product couldn't run on its target platform. Deploying a Hello World in week one turns done from a finish line into a rhythm.
Anthropic's circuit tracing shows how much of a model's behavior is still unexplained. Building GenAI for German banking, we were hoping more than knowing too.
Founders demo perfectly on their own laptops, then find twelve reasons I can't try it myself. Ask early how you'll show it to the world.
A video worth watching on how an LLM's inner workings resemble a brain: reasoning in steps, planning ahead, and the dark matter researchers still can't explain.
In 2018 a team of at most 17 built a self-hosted GenAI system for German banking contracts. Narrow focus and deep expertise kept it ahead of OpenAI until 2025.
AI executes what you describe but won't ask whether it's worth building. Without a feedback loop from the start, it lets you build three useless apps instead of one.
Two senior developers argue about whose code is wrong. For a business, quality means it solves today's problem and can change when requirements do. AI code included.
Anthropic's Claudius lost money running a vending machine until boring procedures arrived. Results come from AI inside steps a deterministic orchestrator controls.
Hard work on the wrong problem is worth nothing. The culture I'm building at Speed and Function closes the gap between what clients say and what they need.
Kent Beck says to manage juniors for learning. AI shrinks the time before they pay off, and at 20% attrition a 9-month ramp loses far fewer people than a 24-month one.
Explaining self-organization or a blameless culture is like explaining trees in the tundra. People only get it once they live through it, and clients are no exception.
Open roles at Speed and Function: an Operations & Culture Manager, a Project Coordinator, a Middle WordPress Engineer and an Executive Operations Partner.
Adi Stan's paper splits AI users into passengers, operators and architects. Fast adoption means little if people adapt into dependency rather than into thinking.
A failed company, a sale for $70K, then a company that burned it all. Success proved less than I thought; trying to repeat it showed what actually mattered.
An engineer with ten years' experience rewrote Claude's code by hand; my son, still at university, kept iterating and finished sooner. What do we pay for in experience?
A dying phone battery and a documentary on the opioid crisis gave me my biggest lesson of the year: understand that you won't understand, and stop judging.
A hand-soldered ZX Spectrum, a magazine about early AI research, and a theorem prover that got a proof down to 30 minutes on an 80286.
A year ago juniors learned React; now they learn Claude Code. AI makes work maybe 30% faster end to end and creates new niches, so there will be more work, not less.
AI follows maybe half of your carefully written rules. Like daily rituals with room for creative work, results come from a rigid orchestrator with AI inside each step.
Accelerate isn't really about code. Its delivery metrics showed me how engineering work turns into user value and money, and pushed me from engineer to manager.
Code became the source of truth because rewriting software was expensive. If AI can regenerate code from requirements in a month, the blueprint is what to keep.
Teams discover at release time that their product can't deploy to its platform. Joel Spolsky's one-step deployment question is engineering and management in one.
A friend's three-month-old startup only works on his laptop. Ship something the world can see, even a Hello World, before you build features.