Everyone's Replacing Engineers With AI – No One's Replacing CTOs

01 /The Concept

The founders of today's startup have both served as CTOs – including at a company valued at $400 million. They kept running into the same problem: a CTO typically can't see trouble brewing until something actually breaks – meaning a live production outage or a deadline missed in a way that's already visible to leadership.

The irony is that AI coding tools now exist that can make a typical engineer 10× more productive. Yet, as the founders point out, there's still no tool that makes the CTO 10× more effective.

So they built Mesmer – intended to be exactly that.

In early-stage companies, the CTO is often an individual contributor: writing code, talking to users, shipping product. But once a product gains traction and a team forms around it, the CTO's job becomes something entirely different: tracking what everyone else is building.

The new reality is a calendar full of syncs with engineers, product managers, and designers; a constant stream of status updates and incoming requests; and a steady flow of fires to put out across a half-dozen messaging channels.

Mesmer starts by giving the CTO an AI engineering manager that generates weekly project reports: what's been completed, what's on track, and what has stalled unexpectedly.

These reports aren't built solely from what engineers self-report. The AI also independently analyzes the current state of the code repository to cross-check. When a CTO wants to dig deeper, the AI can answer follow-up questions about the specifics of any project.

The first version of this AI manager is already in use at 10 companies with a combined valuation exceeding $3 billion.

What makes the system more than a reporting tool is its ability to suggest solutions. Spot a delay on one workstream? The AI might recommend temporarily shifting engineers from a task that's on track to the one that's stuck. It can even name specific candidates for the reassignment, based on which engineers have experience most relevant to accelerating the blocked task.

Mesmer is currently in the Y Combinator accelerator and published its platform launch on the YC site a few days ago.

02 /Why It Matters

The founders slightly overstated their case when they claimed nobody else is building productivity tools for CTOs.

In March, a startup called Tometo ([related review](/review/tebe-nadoeli-obeshhanija-programmistov-kotorye-oni-ne-nikogda-vypolnjajut)) surfaced on Product Hunt with a platform doing very similar things to Mesmer's current version. Tometo's stated audience is startup founders rather than CTOs – giving them visibility into whether engineers are hitting launch timelines.

And back in 2021, a startup called Zenhub ([related review](/review/edinstvennyj-pravdivyj-istochnik)) built an engineering project management platform with GitHub repositories as the primary source of truth. Zenhub had already raised $4.7 million at the time, and by 2023 had added an AI repository analysis layer and raised another $10 million.

There are at least a couple more in the space attacking adjacent CTO concerns.

One Techstars graduate has raised over $7 million for a platform that maps technical spending to business outcomes – how much engineering effort is going into which business objectives, and how that correlates with performance. This is genuinely valuable: a CTO needs to understand not just what the team is building but how the investment in engineering is translating into business results.

And Bilanc ([related review](/review/kak-dobitsja-chtoby-programmisty-rabotali-ne-rukami-a-golovoj)) addresses a specific CTO concern that's increasingly relevant: it produces reports showing which AI coding tools engineers are actually using and how that usage is affecting their output metrics.

03 /Opportunities

Some bold claims are circulating that AI will write 80% of production code by the end of this year. That may be optimistic. But the direction is clear – whether it happens in 12 months or 24, the trend isn't going away.

Does that make the CTO's job easier? Of course not. It doesn't matter much whether you're managing human engineers or AI ones – the complexity of the platform, the product decisions, and the organizational coordination stay the same.

Consider a hypothetical ride-sharing platform. Does the engineering challenge change when the drivers become autonomous vehicles instead of humans? Not really – the platform's core problems are identical. If anything, the platform becomes more valuable as the headaches of recruiting, managing, and retaining a driver workforce disappear.

Something similar is coming for engineering leadership. As AI takes over a larger share of routine coding work, the CTO's role becomes more strategically valuable, not less – and expectations will rise accordingly. Performance will depend less on the throughput of individual engineers and more on how well the CTO allocates resources toward outcomes that actually move the business.

That raises the value of platforms that help CTOs manage processes and results. Building those platforms is already a compelling direction – and it's getting more compelling fast.

The individual puzzle pieces are already visible in the examples covered here. The clearest entry point is what Mesmer and Tometo have started: repository-aware project intelligence that tells leadership whether the engineering team is actually shipping what it claims. That's the trust gap most CTOs feel acutely and the one customers will pay to close. From there, the intensive path leads toward connecting code output to business outcomes – which is where the real defensible value lies.