AI agent development is what Diffco does now, but the company is 18 years old — it started as web development in 2008, moved through mobile, and arrived here. In Episode 7 of the NoRobots Podcast, Vlad asks founder Vadim Peskov what clients actually pay for when agents write all the code, why project costs fell while rates went up, and who is liable when AI-generated code leaks data.
Eighteen years of changing the answer
What a client needed in 2008 has nothing to do with what they need now, and Vadim credits the company’s longevity to changing processes repeatedly rather than committing to one mantra. Asked whether there was a master plan, his answer is dry: they were stupid enough to start in 2008 and continued by mistake.
The AI work goes back further than most people assume — computer vision and ML projects from 2016. Selling it then was mostly education, because nobody understood what the technology could do, and some clients expected 100% recognition accuracy. Back then 92 to 95% was a success; now 99-plus is normal, better than the humans it replaces. The bigger shift is that they used to train models and invent things that didn’t exist. Today there are thousands of models to test in parallel, and it’s a question of budget rather than invention.
One hundred percent
Asked what share of delivered code is written by AI, he doesn’t hedge: for projects delivered in the last six months, 100%.
The exceptions are specific. Compliance-heavy systems where the stakes are high stay closer to manual with AI assist. Pixel-perfect front-end work isn’t there yet — if the design has to match exactly, full autonomy is out. He expects 90%-plus even on compliance projects by year end, with human oversight layered on top of the AI oversight. His line on where that matters: you don’t want to ship a banking system moving millions of dollars a minute with AI.
So what is the invoice for?
Nobody ever paid Diffco per line of code, and he points out that “you wrote another 500 lines, here’s money” was never how the business worked.
What clients pay for is the system that turns an idea into a working product: the specification, the agent engineering, the memory management, and knowing what the technology can’t do. Ask an AI to deploy on AWS in the best fashion and it will do something — if you don’t understand what it did, that something is useless.
The part he emphasises most is partnership: being the firm that occasionally says this is a bad idea and here’s a better way. He points at a long graveyard of products built over thousands of hours by founders who never tested the concept with a paying customer, and only discovered afterwards that what they’d built wasn’t what anyone wanted.
Rates up, client costs down three times
Diffco raised its rates substantially. Client costs still fell roughly threefold over three years: a project that would have cost a million dollars now lands under three hundred thousand.
The gap between what’s possible and what’s permitted shows up inside a single enterprise client. One department does pixel-perfect work, manual with AI assist. Another department of the same company adapts prototypes and ships fast. The delivery rate in the second is 20 to 30 times the first. Same company, desks next to each other, different appetite for risk.
Clients arrive with prototypes now
Roughly 70 to 80% of incoming projects come with a prototype, often built in Lovable. Vadim welcomes it, especially when the prototype actually launched and gathered real usage data, because what surfaces then isn’t bugs but user-flow problems — and those experiments cost the client almost nothing.
Their approach is guardrails rather than gatekeeping. Clients can change copy, images, and flows themselves. Touch the database structure and a human or a more capable agent reviews it first. By his estimate, 90% of what founders and marketing teams want to try can be handled that way.
Which moved the bottleneck somewhere unexpected. Agents now produce work faster than humans can approve it, so inside enterprises the approval flow — not engineering capacity — is the constraint.
What vibe coding doesn’t solve
His objection isn’t to vibe coding but to the belief that half a page of specification yields a complex product. A brief that short means many conversations before anything gets designed; on some projects they’ve had literally hundreds of calls with a client to pin down requirements, because the decisions are complex and the regulation is real.
Build-versus-buy has genuinely changed, though. A tool Diffco uses announced a move to an enterprise plan that would take their bill from a few hundred dollars to roughly fifty thousand a year. They found an open-source alternative within minutes and are migrating within days, and may fork it into their own harness and contribute back. Five years ago he would have asked why anyone would bother.
But he doesn’t extend that to everything. For another tool — effectively several products stitched together — rebuilding is possible and pointless, because maintenance would cost more than the subscription. He mentions a company that rebuilt about 5% of Salesforce in two months with three developers and saved a million a year in licences; it works for them, and it’s still a distraction for most. His framing: software is never finished, and if you stop at version one you die. Price the maintenance, not the build.
Who is liable when it leaks
On AI-generated code and security, the legal answer is that the client ships the product and carries that risk, while Diffco insists on at least some security testing — a request clients rarely refuse.
Then the reality check. No system is ever fully secure; if you want one, don’t launch and put the computer in a safe. Across 18 years, he attributes roughly 95% of incidents to employees doing something they shouldn’t have and the remaining 5% to systems clients declined to update. And the multiplier people don’t price in: if AI writes code a thousand times faster, the surface for mistakes grows accordingly.
His practical floor is cheaper than most expect. A standard pen test runs a few thousand dollars, broader security testing perhaps five, and doing either puts you ahead of most teams shipping today. With no budget at all, pointing a capable model with the right setup at your own code and network will still catch the majority of it. The rest — disaster recovery, backup strategy, incident response — is a company question, not a developer question.
The junior problem, unanswered
Junior hiring is down 30 to 50%, and asked how anyone becomes senior in five years if nobody hires juniors, Vadim says honestly that he doesn’t have a good answer.
What he can describe is what replaced the role. Diffco doesn’t hire developers; it hires engineers who design systems that build themselves. The interview filter is abrupt — introduce yourself as a front-end developer, or a back-end developer, and they’re not hiring. Their technical product managers build more complex systems than many developers do, because they write better specifications. He’s equally blunt about seniority: fifteen years of experience counts for nothing if you aren’t working this way now.
His advice for someone starting out is to experiment relentlessly and never deploy blindly. If you ask an AI for an architecture and it returns Docker swarms across multiple clouds for an application with a hundred customers, something is wrong — a single server is fine. Knowing enough to challenge the machine is the job.
Growing in a different direction
Diffco has fewer developers than it used to and is growing faster than ever. Vadim expects the developer headcount to keep shrinking in favour of architects, while the work that remains is understanding what a client actually needs and walking them through the requirements.
Meanwhile the agent count goes the other way: 30 to 40 running across their systems today, heading toward hundreds or thousands. Humans are still needed to decide where to point them — without that, he says, it’s a journey into nowhere.
Find Vadim on LinkedIn or at diffco.us, and watch the full episode on YouTube. Diffco also posts open roles on our job board. For another take on where AI stops and humans pick up, see our episode with James Zammit of Roark.
