This is a turbulent time for software engineering organizations. There’s a huge amount of fear, uncertainty, and doubt everywhere about AI. It’s also a tremendously exciting time: we might not have “intelligence too cheap to meter” yet, but we are honestly awash in tools that seemed like pipe dreams 24 months ago.

With how fast things are changing, I wanted to write down my thoughts on the trends I’m seeing today! Let’s see how this ages.

EPD is flattening

The boundaries between engineering, product, and design are dissolving. AI enables product managers and designers to prototype work that previously required engineering resources.

I talked to Michelle Bu from Stripe on a panel recently, and she pointed out that she’s seen these roles converging at larger tech companies as well as startups. The gatekeeping function engineers once provided is rapidly eroding. This makes product-knowledgeable engineers more critical than ever. The engineers who thrive aren’t just technically skilled—they understand user needs, business constraints, and product strategy.

Ownership has always been the top thing I’ve looked for when hiring engineers at startups. But I think this profile is becoming the standard, not the exception, and I expect this to continue.

Hands-on leadership

Engineering leadership needs to be hands-on now. It used to be that hiring and scaling large organizations was the key senior leadership skill, but that’s changed rapidly.

Will Larson captures this shift well in his recent writing. I think that although the trend started with the end of ZIRP, it has only accelerated. At this point, AI capabilities are changing so rapidly and the implications for engineering strategy are significant. That means leaders have to be very good at understanding exactly what is possible at any given moment, and that means digging deep enought to understand the difference between a demo and

On the bright side, AI tools do make parts of this transition easier. Orienting yourself in an unfamiliar codebase and achieving productivity happens much faster now.

This challenges engineering leaders who’ve drifted away from technical work, but I honestly think this is a positive trend overall.

The bottleneck isn’t writing code…

Code generation is dramatically faster, but that’s only one part of building software. Michelle observed this happening at Stripe too: implementing solutions has accelerated, but other parts of the engineering process haven’t kept pace.

Consider the complete cycle of fixing a bug: understanding the task, deciding on a solution, implementing the solution, testing, and deploying. AI tools have transformed the implementation step, but the others remain largely unchanged.

This reminds me of Amdahl’s law – a 2x improvement in implementation speed might only yield a small improvement in overall velocity if the other stages haven’t been optimized. It also implies that accelerating the rest of the process is where startups should focus next.

…or getting a demo running.

Accelerating prototyping has huge implications for early-stage startups, though. A VC friend recently noted that the bar for Series A funding has risen dramatically. Startups that used to raise with just a landing page now need working MVPs and paying customers.

AI tools make it possible for small teams to build working products faster than ever. Even if they’re hacky under the hood, features that once required weeks can now go live almost immediately. User interfaces that demanded dedicated design resources can be created by technical founders with AI assistance.

These changes haven’t yet propagated up the stack to later funding rounds – as far as I can tell, the dominant trend there is still the “zombiecorn” as VCs move away from funding non-AI-native startups. I’ll be interested in seeing which companies that weren’t originally AI-native manage to make the leap.