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There are many factors at play here but if I had to pick one... an open-source company has to find product market fit twice: first for the OSS project and again for a commercial product. The AI market moves very quickly so it's easy to take a step in the wrong direction and fall behind.

I might publish a long-form reflection when the dust settles.

Mostly salaries to support a small team.

We are returning the remaining capital to investors.

See my sibling comment

The story of literally 95% of tech startups

Turns out I was wrong :)

Things change, situation change. Life is not constant. In business, it is even more Tumultuous.

I would applaud that the fact that found took bold decision to think out of the box and take action towards it.

LinkedIn is the worst platform to get accurate news as everyone is only incentivized to hype themselves up. Lots of examples abound in r/LinkedInLunatics

$7m actually isn't a whole lot, especially if they hired a (larger) engineering team. Assuming their cali based, that's easily 150-200k per engineer, a team of 20 easily eats through that. Idk the specifics, but I don't the organization was fradulent, it could also be that they're going commercial and no longer want to maintain their oss stack

We raised in 2024 and only burned through ~$3m of it, mostly on salaries to support a small team.

And AI tokens

Those Claude tokens are not cheap you know /s

Were the thousands of commits and hundreds of feature branches over the last 9 months just to keep up appearances, then? Were the 850 people who forked it in on the scheme, too?

Sometimes things just fail.

You can call it a bait but where is VCs due diligence for this. Most VCs where out there defending their infra layers investment. Just look at YC batches and see the inflated number of infra startups.

Do you understand that when you raise money it doesn't go into your personal account? Its not like you can move this money in your retirement account and sail into the sunset.

> The calculus in “buy or build” has shifted for me over the last six months especially. If I can make an agent build it, I get the version that’s tailored for me.

I feel like this is really going to change the software industry moving forwards. Historically it was tedious and time consuming to actually develop tailored dev tools which is why so many organizations relied on third party solutions. When nowadays you can easily half bake something in a few hours and get it working, tailored _specifically_ to your needs.

> Perhaps in the future big tech projects will be delivered with a common “core” and the expectation that agents fill in the use-specific stuff.

I suspect so, the headless / "api/cli only" tools like CRM are pretty big right now and I don't think we've seen the end of that trend, probably more like just beginning.

First of all, I respect your decision. I apologize for speaking too hastily about your choices. What I was trying to do was simply talk about how incredibly fast AI infrastructure changes. I also understand your respect for investors looking for the next Databricks. But the reason I wrote what I did is because the confidence expressed in the README ended so early. That said, such confidence isn't necessarily a bad thing. Isn't it said that victory belongs to challengers like you, not cynical people like me? I feel bad about that part. Still, I have no intention of withdrawing my skeptical view about whether AI infrastructure can succeed as an open source startup. I'm very sorry it came across as if I was mocking your failure. That was not my intent. I was simply trying to leave a comment saying that AI infrastructure has a different direction from traditional infrastructure.

Due to the echo chamber effect, our opinions get reinforced, which can lead to biased conclusions, so it gives me pause. But your comment is so eerily similar to my own thoughts that I'm writing this reply.

I agree that most people misunderstand the concept of a 'moat' and become obsessed with that misunderstanding. People tend to think that only technical 'coding skills' which they can easily understand constitute a moat. But in reality, the moat is the entire workflow across the product's lifecycle, including coing skills. In that sense, infrastructure workflows are nothing more than 'the most easily replaceable consumables.' The essential purpose of infrastructure is to pursue 'standardization,' which paradoxically means a state of 'zero switching costs' where customers (app developers) can switch at any time to a better API or a big tech built in feature. Pure technology that doesn't latch onto the messy real world domains of customers will inevitably be absorbed without resistance by massive capital.

In some ways, customer lock in at the application layer, or even the fan culture around a product, creates emotional lock in. The end user app that provides a specific workflow integrated into users' daily routines can overcome even technical inferiority through 'experience' and 'emotion.' Technology can be copied, but the user identity attached to a tool is what I think a real moat is.(That is also the reason I love Windows.)

The example you gave, Cursor's Composer, is exactly the case I'm talking about. I think Cursor is inferior, and I don't think its Composer model feature is all that great either. But Cursor has a passionate fan base, and users who choose Composer as the best value for money no longer care about absolute technical performance or benchmark scores. They are captivated by the 'speed of experience' of code being completed quickly as they intended, and the 'frictionless workflow' the tool provides.it's not the company that builds the best AI model that wins, but the company that wraps 'good enough technology' in 'great UX' and dominates users' habits. That is how apps dominate infrastructure, and that's the moat you and I are thinking about.

That said, this conclusion is probably too hasty and has many flaws. Still, your thoughts are so similar to mine that I'm leaving this reply. Thanks for the great comment. Have a good day

Anyway, I wasn't trying to mock your profession. Here's what I think. Most VCs and investors have their own success formula. There will be VCs who succeeded by investing in infrastructure. But the question is whether that same success formula applies to AI startups right now. Of course, from your perspective, it might look like 'this clueless kid is just being cynical without knowing anything.' I partly agree. But that's not the core of my argument.

What I'm trying to say is that those success formulas themselves need to be reconsidered.An insider from up there came out and talked about the next 'Databricks,' believing that's the kind of potential they're looking for. All of them do. Everyone wants to be the first investor in a goldmine. I don't think this is just about greed

The question is whether the traditional infrastructure investment logic holds here. I think most current AI infrastructure tools are closer to 'temporary patches' that exist before the functionality gets internalized.

Let's say infrastructure is like a concrete building. Traditional IT infrastructure basically has a standards committee, and once that committee sets things, changes are extremely rare. It's a kind of 'lake.' But AI infrastructure right now is different from one to another; even the ecosystems differ—the Chinese ecosystem is different from the US ecosystem. It's a flowing 'river.' I just think the question is whether the old grammar can be applied in this situation.

You probably have more money, more investment experience, and more success than I do. I only have a lot of failure. But apart from that, the issue is simply that 'potential' in growth potential ends up being data measured against past examples, and the question is whether that data still holds up now. Anyway, I might have been slightly sarcastic earlier, so I apologize for that. Someone as successful as you, please bear with it a little.

Sometimes people don't realize that 'professional' ideals and 'reality' are different.

What you're talking about seems like 'ideal' investing, not real world investing at all. Of course, the VCs in your country and the VCs in my country are different.

It's like in software, where everyone says you should write maintainable code within the norms, but in reality, most people don't do that

that investing in 'potential' is the basic principle of VCs. They call it the power law. But when you look at actual investment portfolios, it seems quite rare for people to follow only that principle. I guess you don't think so. Of course, I agree that ideal venture investing follows the power law. But in real world investing, there are pragmatic investors who operate somewhere between the ideal and reality. We always project ourselves onto the 'ideal,' but I don't think there are only people who are immersed in that ideal. Of course, no VC would invest in someone like me. I've met with VCs three times in my career, but they all turned me down. Haha.

You're right. Looking at recent indicators, there are more stable investments than I thought. But please understand that, as a human, I haven't achieved ROI in terms of marriage, relationships, a stable job, etc., so my perspective might be mixed with a bit of envy

Thank you, appreciate the response. It’s a great part of the HN community that there’s almost always someone around with the first-hand experience and facts.

Best of luck with whatever you do next!

ceil(market_share)

And new Github profile too.

https://github.com/TensorOne

That was funny, I'm not even mad. Upvote for you.

"I was assigned sucker at birth"