Well of course it is, most startups are dead on arrival.
The big pinch of salt I throw in with advice like this though is that startup failure rate hasn't dramatically shifted despite two decades of lean startup methodology, accelerators, and an entire cottage industry of startup advice. It's never the fault of the framework, mind you.
I find the story of a startup founder who entirely missed the developments of the last two years and did absolutely nothing with AI difficult to believe. If that actually happened it's the exception, not the rule. Most startup founders are way more in-tune with AI developments. This makes it sound like Chris (the mentioned founder) is behind marketing people who use LLM bots to post slop on LinkedIn.
In that case, yes their startup is most certainly DOA.
The post reads like written by someone who read too much about AI rather than tried to build a startup with the help of AI that they advocate so much. I'm still bounded by system design, UX, pricing and feature decisions, if not by the speed of code output, by the review time for sure. Yes, iterating is faster, but we're nowhere near agentic AI loops spitting out working products. Technically it's possible, but then you just spent that time planning and writing the spec up front, which you'd interleave with dev time otherwise. If the product is a simple CRUD database skin, then yeah, chances of success are lower I think, but this is not the type of startups the post seems to write about.
Seems like the headline should have been "is now dead on arrival". As currently written, it fails to convey the temporal aspect that is the focus of the blog post.
It also fails to convey that he's actually only talking about startups that were created 2+ years ago, rather than the many AI startups founded in the last 2 years.
> Now, before you build a physical prototype, you can simulate more design variants, create digital twins, and stress-test assumptions earlier and much cheaper than before.
Hahahahahahahaha no you can't. The rise of LLMs has done little to nothing in this area because it's very much compute-limited. Digital-twins and other ML-based strategies predate ChatGPT by a long shot. There are definitely places in hardware design where LLMs and agentic workflows will help, but that's largely because the existing tooling is utter garbage, and now the industry has a fire under its ass to make things automatable so they can build their own agents.
I read it less as “every startup now needs an AI feature” and more as “your assumptions expire faster than they used to.” That part feels true even if the examples are a bit overstated imo...
"You better be doing something in AI" applies to Startups - businesses that are expected to spend every penny as quickly as possible, to meet the metrics needed to raise the next (bigger) round of funding.
It is also not the same as, "If you want to be a profitable company...". For that you need to somehow make more money than you are spending.
Building SaaS businesses has become a whole lot less capital intensive. Solo founders can go much further than they've been able to previously. New startups probably don't need funding anymore.
> Founders who started pre-2025 typically have built a technical stack optimized for a world where software development was bespoke and expensive.
Of all the things that AI has changed, tech stacks aren't one of them. The bots will gladly write Typescript, Java, Python, Rust, what have you. They could not give less of a shit.
This article resonated with me, especially since I've noticed the fund raising hoopla's in my circle has dramatically dropped. Either investors are tightening the belt so founder-investor fit has crossed into the realm of disillusionment
depends on assumptions. thats the load bearing element. 99.99% of what was true then is still true. its mostly on the fractal churning edges where hyped change happens. things flip-flop too:
2021-2024: good time in US for EV startup
2025: terrible time in US for EV startup
2026 March/April: AWESOME time in world for EV startup
focus on fundamentals, not flakey ephemerals
2020: wise to have smart elite software engineers on your team
2021: ditto
2022: ditto
2023: ditto
2024: ditto (is this when ChatGPT launched? dont care. snore)
One assumption that's also changed: small teams no longer need dedicated QA. Tools like Autonoma (open source, getautonoma.com) use AI agents to generate and run E2E tests in real browsers, and tests self-maintain as your app evolves. One of the things making the solo-founder path more viable.
It is insane to me that anyone could look at the US Economy right now and think this was a good time to start a business. Between the war, AI, a pending economic recession (look at bond prices), all I see is failure. Maybe a funeral home, but that is all I can think of.
So previously the bottleneck was production. I'd wager now the bottleneck is willingness to test your hypotheses. The willingness to experience failure as soon as possible. To test and iterate.
As technology brings the cost of everything else to 0, psychological costs will predominate.
Reality testing is ultimately unavoidable, of course, but I'd guess most people still lean away from that rather than into it. (Our whole culture is set up that way, and most of us get like two decades of Pavlovian conditioning in that direction.)
The ability to endure some degree of suffering seems essential for building high quality products. Getting in front of the customer as often as possible and proving things end to end is very painful. But it provides the most feedback and gets you aligned quickly.
If you want an example of the polar opposite, the TDD idea seems to be a good fit. Unit tests are a perfect little universe that you can always control. All side effects and scary possibilities can be handwaved away under mocks. The psychological power of having control over everything is what draws so many toward the idea. A deterministic guarantee that the little circles will turn green when you press play every time is painless.
Failing tests are the most informative and you can only develop those by meaningful interaction with the customer's requirements. If you aren't constantly fighting a wave of red in your testing suite, it's likely you are too isolated from reality.
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[ 3.6 ms ] story [ 163 ms ] threadThe big pinch of salt I throw in with advice like this though is that startup failure rate hasn't dramatically shifted despite two decades of lean startup methodology, accelerators, and an entire cottage industry of startup advice. It's never the fault of the framework, mind you.
You’ve always needed to constantly learn and innovate to launch a successful business.
90% of blog articles created in the last two years are probably dead on arrival
In that case, yes their startup is most certainly DOA.
Now with AI, it is likely going to be 98%.
If your business is selling services at 40% margin that are entirely digitally based, then maybe you’ll need to cut some margin, sure.
It also fails to convey that he's actually only talking about startups that were created 2+ years ago, rather than the many AI startups founded in the last 2 years.
Hahahahahahahaha no you can't. The rise of LLMs has done little to nothing in this area because it's very much compute-limited. Digital-twins and other ML-based strategies predate ChatGPT by a long shot. There are definitely places in hardware design where LLMs and agentic workflows will help, but that's largely because the existing tooling is utter garbage, and now the industry has a fire under its ass to make things automatable so they can build their own agents.
It is also not the same as, "If you want to be a profitable company...". For that you need to somehow make more money than you are spending.
VC for conventional SaaS is dead.
Of all the things that AI has changed, tech stacks aren't one of them. The bots will gladly write Typescript, Java, Python, Rust, what have you. They could not give less of a shit.
This trap has killed many startups, well before AI.
Now that code is cheaper to write, hopefully it becomes less of a problem?
In either case, founders should never fall in love with their solutions.
2021-2024: good time in US for EV startup
2025: terrible time in US for EV startup
2026 March/April: AWESOME time in world for EV startup
focus on fundamentals, not flakey ephemerals
2020: wise to have smart elite software engineers on your team
2021: ditto
2022: ditto
2023: ditto
2024: ditto (is this when ChatGPT launched? dont care. snore)
2025: ditto (what are YC/HN/VC hyping now? snore)
2026: ditto
2027+: ditto, likely
That said, if you believe universe exists, chances are not null that you are correct. But solipsism might actually be right.
In case of doubt, remember that your memory might be mere illusions.
As technology brings the cost of everything else to 0, psychological costs will predominate.
Reality testing is ultimately unavoidable, of course, but I'd guess most people still lean away from that rather than into it. (Our whole culture is set up that way, and most of us get like two decades of Pavlovian conditioning in that direction.)
Edit: Expanded here: https://nekolucifer.substack.com/p/willingness-to-fail-is-no...
If you want an example of the polar opposite, the TDD idea seems to be a good fit. Unit tests are a perfect little universe that you can always control. All side effects and scary possibilities can be handwaved away under mocks. The psychological power of having control over everything is what draws so many toward the idea. A deterministic guarantee that the little circles will turn green when you press play every time is painless.
Failing tests are the most informative and you can only develop those by meaningful interaction with the customer's requirements. If you aren't constantly fighting a wave of red in your testing suite, it's likely you are too isolated from reality.