When building a company, finding progress in the failures is a struggle. Hindsight is 20/20, and it can takes years to see it.
Writing has helped me see progress. Going to share stories that helped me, hopefully it will help others that are building too.👇
@davidalvarezdlt@PabloGrueso Si tu cliente target está en España, Reddit tampoco es el sitio.
Si que sale en algunos búsquedas de Google, pero normalmente son posts traducidos de inglés.
En nuestro experiencia Los LLMs tampoco citan tanto las coses de Reddit si preguntes en castellano.
Search funds don’t need an AI thesis.
They need a good deal.
Buy the business you’d still want to own if the AI upside took twice as long and delivered half as much.
Then use AI to make it better.
Every @patrick_oshag guest in 2026:
“Tokens are heavily subsidized, a shortage is coming.”
Me: “Claude, how do I get my kid to eat broccoli?”
Claude: sure, let me write you a doctoral thesis citing the American Academy of Pediatrics, and three studies on food neophobia.
the real moat in AI isn't the model. it's the data you feed it and the workflows you build around it. everyone has access to the same foundation models. not everyone has proprietary context.
Apparently “Top Lawyer” badges were not vanity after all.
They were early GEO infrastructure.
Legal spent decades teaching the internet which directories mattered.
AI showed up and said: “Great, I’ll use those.”
Your SaaS category is building the same layer right now.
the companies that will win with AI aren't the ones building AI. they're the ones quietly using it to do the same work with half the overhead while their competitors are still scheduling "AI strategy workshops."
Don't buy a business where AI is a feature.
Buy a business where AI removes friction from work that already has demand.
Calls.
Quotes.
Dispatch.
Follow-up.
Compliance.
Collections.
The boring workflows are where that AI money is unlocked.
Eli Goldratt's book, The Goal, was famous for its (then unpopular argument) that keeping every machine running 24 hours a day, the metric most plant managers cared about, was actively making factories worse. I suspect we're seeing the same fallacy in how many people are using AI agents.
Goldratt's point was that machine utilization isn't throughput. What you want from a manufacturing plants is making good widgets as cost-effectively as possible.
It doesn't necessarily follow that running your machines all the times optimizes that.
Picture a three-station assembly line. Stations 1 and 2 each crank out 200 widgets an hour. Station 3 can only handle 100. Running stations 1 and 2 around the clock doesn't ship more product. It just piles up half-finished widgets in front of station 3, ties up cash in inventory, and creates more work managing the pile.
He developed the Theory of Constraints to point out that what matters is solving the bottleneck in the system, not increasing machine utilization.
I suspect a lot of agent usage right now is the same fallacy at higher resolution. Running 20 Claude Code sessions in parallel can feel productive because something is always happening. But, if the bottleneck in your work is judgment about what's worth doing, more agents just generate more output for you to wade through.
This is not to say there aren't workflows running 20 agents in parallel very effectively, I'm sure there are. And, I suspect there's a general retraining we all need to do around evolving historical workflows. But....
The constraint for most knowledge work is deciding what's worth executing and no one is task switching between 20 things at the same time effectively I don't think. I find I can run maybe 2 or 3 things in parallel with maybe 1 or 2 admin-y type things on the side and that is only if I'm very locked in.