You Are Not Pre-ChatGPT. You Are Just Building the Wrong Thing.
The CNBC story about stranded startups got the diagnosis wrong. Timing did not kill those companies. The wrong business model did. And most founders reading that story today are making the identical bet.
CNBC ran a story last week about hundreds of pre-ChatGPT startups that are now stranded. Cut off from funding. Valued near zero. The framing was sympathetic: bad timing, wrong era, caught in the transition.
That framing is wrong. And if you are a founder reading that story and feeling relieved you are post-ChatGPT, you are making the same mistake the stranded cohort made.
Most of those companies were not killed by timing. They were workflow companies wearing SaaS pricing. They built dashboards, automations, reports, and integrations. The value proposition was organizing human labor through software. AI did not disrupt that. It revealed that the underlying problem was never that hard. The workflow was the product, and the workflow is now free.
Here is the uncomfortable version of this story for founders today: AI-assisted is just pre-ChatGPT on a two-year delay.
I watch founders add AI features and call it a transformation. Smarter search. Auto-summarize. Draft this email. Suggest next steps. These are fine features. They are not a moat. They are table stakes in six months and commodities in twelve. Every SaaS product will have them. The question is not whether you have AI features. The question is whether your product still makes sense if AI handles the underlying workflow for free tomorrow.
The test is brutally simple. Take your product. Now imagine the workflow it automates is handled perfectly and for free by a general AI model. Does your product still have a reason to exist? If the answer is no, or if you have to squint to get to yes, that is your real roadmap.
The companies that survive this decade are not the ones who added AI the fastest. They are the ones who identified a problem that AI makes harder to solve, not easier. Problems that require proprietary data accumulated over years. Problems embedded in physical reality that no language model can see. Problems where the trust and relationships in a specific niche are the actual product.
The CNBC cohort lost because they were selling the interface between a person and a task. The task got automated. The interface became worthless.
Founders who ship AI-assisted features without asking this question are making the identical bet. Not because they are building bad products. Because they are building the right products for the wrong timeline. In two years, the feature they are proud of is the baseline. The moat they thought they had is the product category their AI provider offers for free.
The non-obvious read from the stranded startup story is not that you need to move faster or pick better models. It is that the founders who win are the ones who find problems the AI cannot solve without them. Domain knowledge that is not in any training set. Customer relationships that require years to build. Physical context that no API can access.
If you can describe your company without mentioning any of those things, start asking harder questions now. The timing may feel different. The structural problem is the same.