Same AI, Four Different Jobs: Startup, Mid-Size, Big Tech, IT Services

In March 2025, Y Combinator managing partner Jared Friedman said that a quarter of the W25 batch had codebases about 95% generated by AI.

That same year, in Stack Overflow's survey, 28% of developers agreed that their company's IT or InfoSec team has strict rules that don't allow them to use AI agent tools.

Same year, same models, same Twitter timeline, and completely different working lives.

This is part 4 of a series on how engineers who learned to build before AI see the AI era. Part 3 looked at mandates. This part looks at where you work, because I'd argue it now shapes your relationship with AI more than your stack, your seniority, or your opinions do.

I looked for a large study that cleanly splits engineer AI sentiment by company size. I didn't find one I'd trust.

What exists is scattered: a startup accelerator's figures, enterprise-only surveys, one company's culture, and industry reporting on IT services. What follows stitches those together into an argument. The data points are real and sourced. The pattern connecting them is my theory. Push back in the comments if your experience says otherwise.

Friedman was clear that the 95% figure was not about non-technical founders who vibe-coded something: "Every one of these people is highly technical, completely capable of building their own products from scratch. A year ago, they would have built their product from scratch — but now 95% of it is built by an AI."

MIT's NANDA report found startups doing well with generative AI too. Lead author Aditya Challapally described young founders whose revenue went from zero to $20 million in a year "because they pick one pain point, execute well, and partner smartly."

For a pre-AI engineer who joins a startup like that, the job changes a lot:

What a startup engineer gets from AI: speed and ownership. What they risk: being the only person who knows where the bodies are buried.

Honeycomb is a useful example of the middle ground.

Emily Nakashima, Honeycomb's SVP of Engineering, told LeadDev the company sent a top-down founder memo saying everyone should try to 2x their impact with AI over the next year. "The first question we got from engineers was 'how are we going to measure this?'"

Their answer was to deliberately put less weight on formal measurement and use self-reporting instead. Nakashima said what engineers report "aligns pretty well with what's seen in their work," and then named the condition: "you have to have a relatively high-trust organization."

LeadDev's own reporting adds the caveat: that approach needs a level of organizational trust that many large companies don't have.

This is where my theory comes in. Mid-size companies may be in the best position for a pre-AI engineer. There's enough process that AI saves real toil, and the company is small enough that leadership knows your name and believes you when you say what worked.

What a mid-size engineer gets: a voice in how AI is judged. What they risk: that trust going away the moment the company gets big enough to want a dashboard.

At scale, trust gets replaced by instrumentation.

Meta's "Claudeonomics" leaderboard ranked token usage across 85,000+ employees until it came down in April 2026. According to Fortune, citing The Information, Meta also runs a separate official token dashboard aimed at software engineers.

Harness's 2026 survey of 700 practitioners and managers at large enterprises shows what that feels like from the inside:

Big organizations also have more friction between wanting AI and being allowed to use it. That 28% InfoSec figure lives here. MIT found that enterprises "almost everywhere" were trying to build their own AI tools, even though internal builds succeeded only about a third as often as purchased ones. WRITER's 2026 survey, covering companies from 100 to more than 10,000 employees, found 55% of executives describing AI use at their company as "a chaotic free-for-all."

What an enterprise engineer gets: the best tools money can buy, eventually. What they risk: being judged on the metric instead of the work.

This setting gets the least attention in Western tech media, and it may be the most important one for a lot of engineers.

In August 2026, Reuters reported on what AI is doing to India's $315 billion IT services industry, which was built on billable hours:

Think about what that means for the engineer. In a product company, if AI makes you twice as fast, you ship twice as much. In a billable-hours services firm, if AI makes you twice as fast, your employer may bill half as much. Your productivity becomes your company's revenue deflation.

One more data point, and I want to be careful with it. In Stack Overflow's 2025 survey, developers in India reported the highest trust in AI tools of the top ten responding countries: 56%, compared with 28% in the US and 22% in Germany. I don't know why, and the survey doesn't say. It's worth asking whether trust is highest where the economic pressure to adopt is strongest.

What a services engineer gets: very high demand for AI skills. What they risk: the business model their career was built on.

Here's my theory in one line: how an engineer experiences AI depends on who absorbs the consequences of AI's output.

DORA's finding that AI acts as "a mirror and a multiplier" fits all four. The tool is the same everywhere. What it reflects and amplifies is different.

The model is the same everywhere. The job isn't. Before you argue with someone about whether AI "works," ask them where they work.

Final part of the series: the mid-career squeeze. What happens to engineers who learned the hard way, when the next generation may not get the chance to.

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