Everyone's writing about how AI is shrinking product teams and killing product jobs. In my case, I'm fighting Cost Control to hire more. The dominant narrative is clear by now: Brian Chesky (Airbnb) says pure people managers have no future. Tomer Cohen killed LinkedIn's APM program and replaced it with Full Stack Builders. Tobias Lütke told Shopify to prove AI can't do the job before asking for more headcount. All real. All worth taking seriously. Proving AI can't do it before asking for headcount is already part of Revolut's process ✅. But here's what AI actually did to my teams in Crypto at Revolut: it didn't reduce my need for people. It exposed how badly under-resourced my ambition was. We're running 5 new big bets in parallel right now. If I had one more strong PO tomorrow, I'd start a 6th and a 7th. The constraint was never "do we have enough work to keep teams busy" — it was always "do we have enough strong POs to point at the next opportunity." What AI actually changed: 📈 PRD prep / review / iteration — 5x faster 🔍 Regulatory research — every PO can now dig into the depth of each regime and navigate the product accordingly ⚡ Coding — talented engineers shipping 10x faster, no comments needed 🤖 Automations — nearly in every workflow → less routine nonsense 🎯 Concrete output — our MCP server for Revolut X as a side project. Without AI-driven release capacity and simple building tools, it wouldn't have shipped. 🤔 What we haven't cracked yet — governance. And this is non-negotiable in a regulated business like Revolut. Anyone selling you "AI replaces compliance review" is selling you a future lawsuit. So, my take from the seat: → AI fluency is now the baseline. Agree with Tobias Lütke here — stagnation is slow-motion failure. 70% of skills in tech jobs will change by 2030, no argument there. → But the narrative that AI = smaller teams is mostly a story from consumer SaaS and dev tools. From where I sit, AI is doing something different: it's enabling top talent to operate at 3-5x capacity, which means I can finally chase the bets that were previously beyond reach. → We still have 18 months of strong items in the backlog. I'm all in on AI to compress that to 3 months — so we can move on to brainstorming what comes next and drive the next revolution in the space. 💡 The real question isn't "how many people will AI replace?" It's: how big is your ambition, and do you have the builders to match it?
Really strong framing — the “ambition ceiling” lens is much more honest than the headcount-reduction narrative dominating most of these conversations. The governance point is the one I keep coming back to. You’re right that it’s non-negotiable in regulated environments, but I’d push it further: it’s the unsolved problem across all enterprise AI deployment, not just fintech. The speed gains are real, but most organisations have no reliable way to know what their agents actually did, on whose authority, and whether it was within policy — until something goes wrong. That’s the problem I’m building Vortalis to solve. It’s an AI agent governance platform — a proxy layer that sits between your orchestration stack and your tools, giving you field-level audit trails, principal chain tracking, kill switches, and EU AI Act compliance attestation out of the box. The thesis is simple: the teams shipping fastest with AI are going to be the ones who can prove they’re shipping safely. Governance isn’t the brake on ambition — it’s what makes the ambition defensible. Would be curious how you’re currently handling audit and accountability for agent actions in the Revolut X stack. Feels like exactly the environment where this matters most.
Tomer Cohen
3d
Thanks for sharing, Leonid. I very much agree that AI doesn’t necessarily mean fewer people (it might be the opposite); but it does mean raising the ambition for the company/product/roadmap, raising the bar on expectations from builders, and re-shaping the structure of the org and team to support those changes. With a full stack builder model, you are better positioned to create smaller teams which brings many clear benefits: quicker setup, faster decision-making, more accountability, and less coordination. Ideally, you'll be able to tackle emerging priorities with greater speed and higher sense of urgency. All are critical right now.On your last point, as you'll run through your 18 months backlog in 3 months, you'll find that now the new and real bottleneck are great ideas (vs. just picking up lower priorities from the list). Still a great place to be vs. many others are at right now...
Anu Verma
4d
The under-discussed part is that AI does not remove the need for strong product people, it raises the cost of weak product judgment. When PRDs, research and prototypes get 5x faster, the bottleneck moves to prioritization, risk ownership and knowing which bets should not ship. In regulated teams, that judgment layer becomes even more valuable because speed without accountability just creates faster compliance debt.
Nadia Babich
3d
"AI exposed how badly under-resourced my ambition was" — this is the reframe the whole narrative needs. The teams shrinking story makes sense when ambition is fixed. When ambition scales with capability, you need more strong people, not fewer. The governance point is the honest part everyone skips.
I think the reason governance is still uncracked is because it does not have live measurement underneath it yet. Running AI-driven workflows without that is a bit like flying while looking at the blueprints and trying to work out your airspeed. The design may be excellent. The team may be excellent. But neither tells you what the aircraft is doing right now. At human speed, review after the fact is likely survivable. At machine speed, without live measurement??? The distance between “good automation” and “future lawsuit” gets very short indeed.
This is a useful counterpoint to the “AI means smaller teams” narrative. In complex domains, AI does not just remove work. It also reveals more opportunities to pursue and more decisions to make. The bottleneck becomes less about producing output and more about having strong people who know where to point that new capacity.
Leonid Bashlykov Really enjoyed this perspective. Feels like a lot of the AI conversation online is still centred around reducing headcount, when in reality many of the stronger teams seem to be using AI to increase execution speed and go after bigger opportunities. The point around ambition being the constraint not workload genuinely stood out. Also completely agree on governance. Moving faster is one thing, but operating safely and responsibly in regulated environments is a very different challenge altogether. Interesting post and refreshing to hear a more practical/operator-led view of where AI is actually creating impact.
This matches my reality almost exactly. AI didn't thin out my team, it highlighted the gap between our ambition and capacity. And I'd push your "strong POs are the constraint" one step further: the real scarce resource is judgment. Which is why governance stays human. You can compress the backlog from 18 months to 3. You can't compress the accountability for getting a licensing read wrong. Great post.
"How big is your ambition, and do you have the builders to match it?" — that's the question most orgs aren't asking. Most of the "AI shrinks teams" narrative comes from orgs where the constraint was always headcount. In high-ambition orgs the constraint was always strong judgment at the point of the next bet. AI doesn't change that — it just makes the judgment gap more visible. The governance point you flagged and moved past quickly is actually the whole game. Not just in regulated industries. Anywhere the cost of a wrong decision compounds — a bad bet, a broken trust signal with users, a product that ships fast in the wrong direction — you still need someone who can read that risk before the data catches up.