Yashjain413
If you look at how our AI budget has changed, a good example is when we were looking to hire a part-time motion designer. We were actively hiring for the role, but then we started seeing videos of Opus 5.5 creating these crazy videos, and we suddenly decided to stop hiring for that position.
What ended up happening was that our entire team of 14 people started making these 1–1.5 minute videos themselves. Within a couple of days, our two Claude subscriptions were completely maxed out and exhausted in under 2 days, even though they were supposed to last us an entire week.
So I think that gives you a pretty good sense of how quickly our AI usage—and consequently our AI spend—can change.
Especially if you’re selling a business for under $1M, I’d consider this route. You could also look for companies that are actively trying to expand into new markets or diversify their product portfolio, as there could be a natural fit.
In your case, though, the context is a little different. In our case, the acquiring company was preparing to go public and wanted to diversify its product portfolio, so they acquired us. It was pretty straightforward.
Ultimately, it depends a lot on the context and the kind of buyer you’re looking for.
And yet, the valuations are still staggering, valued at over $1 trillion while collectively burning close to a quarter trillion dollars in the coming years.
I think this is the part that gets missed when people say you don’t need a technical co-founder anymore because of Claude Code. I agree to an extent. For building V1? Sure, you probably don’t need one. Even for getting your first 20–30 customers, you might be able to get pretty far without one.
But eventually, there’s a point where you genuinely need someone technical, and I think that point depends heavily on the industry you’re building in.
I work in the attribution space, and there’s just no way I believe Claude can build and maintain an attribution platform as reliably as a strong human engineer can. Once you’re dealing with 5, 10, 15 different systems all interacting with each other — Meta, Google, attribution, postbacks, APIs, SDKs, etc., things get complicated very quickly.
AI coding agents are insanely good at building things, but when you have to maintain the intersection of a bunch of systems reliably over a long period of time, I still think you need a really strong technical person who understands what’s happening underneath it all.
That’s where I think a technical co-founder becomes really important.
I have a specific ChatGPT chat that rewrites my posts really well every time; I usually just give it a rough draft and let it clean things up. But it was pretty interesting to realize how good these LLMs are at actually finding mistakes rather than just rewriting things. The point about not letting the model influence your voice was also very, very relevant.
One of the most useful use cases for me, especially since I work in GTM, is keeping track of new ways to get replies from prospects, whether through cold outbound or things like SEO/GEO optimization. I read at least 2-3 articles a day on this, and it genuinely helps me figure out which ideas are worth trying because I can now keep track of everything I’ve read.
Customers often know the outcome they want to achieve, but have no idea what steps they need to take to get there. For example, they might want to launch a campaign, but don’t know where to start or what they need to configure.
Having something like an AI assistant that understands the product and guides them through every step of the process could be really helpful. It could essentially take the user from the outcome they want to achieve and walk them through the entire workflow.
With coding agents, what I’ve noticed is that a simple task can often be handled with a fairly simple harness. But the hidden cost is really around context. One of the more interesting things I’ve seen is that two different harnesses can make a similar number of model calls while consuming a very different amount of context.
I think I recently came across a paper comparing Claude Code and Pi that touched on this. More context, more tooling, focused context, simpler loops, all of these can lead to very different costs and performance, even when the number of model calls looks similar.
More and more non-technical people can now build things they’ve always wanted to build, including their own websites.
As a non-technical person myself, I built my own website simply because I’ve always loved building things, but never really had the medium or technical skills to do it.
Now I do and the medium is Claude code.