I think there are maybe 5 business books out there. I’m not sure how exactly I’d define the 5 different business books, but I think of you read 10-15 business books you’ve pretty much read them all. After a while, they all start boiling down to the same few points with differences in narrative content. If I were to take an unconsidered stab at a few of them: hard work + luck is about the closest formula anyone has found for success if applied over long time periods; you have to be disagreeable and believe in yourself, but not so disagreeable that you can’t get along with anyone; people are important, and treating them well leads to better businesses (over long time periods); sometimes you get dealt a bad hand.

I sent outputs to experts (friends )in those fields who provided me with some specific topic that is common to their niche. In general their feedback all aligned with: “yeah, this is pretty decent, but anyone who was a professional would know about <some hole in the output>”.

I think you’re fixating on the specific example that I cited rather than imagining the possibilities of the technology. We now have: zero-shot classification capabilities for basically any task, performance that is consistent enough for independent LLMs to collaboratively produce robust long-form responses, the ability to produce almost any web UI element on command with functional hookups to an API. And that’s just LLMs. SAM2 has good performance for realtime object detection in video.

Perhaps a more fruitful line of inquiry could be: what would the internet look like if every web application implemented support for A2A?

I am surprised at the negativity and cynicism in this thread. I suppose the pendulum of hype has high amplitude.

Just because AI isn’t going to be some kind of all-knowing sci-fi AGI doesn’t make it all a sham. The recent research models are miracles of technology - we can now get in ~15 minutes an undergraduate-level report (that would take an undergrad days or weeks) on almost any topic. That’s incredible! The capability of an AI model is approximately junior level in the fields I’ve tested it in (programming, law, philosophy, neuroscience). If you’d don’t see any possible uses for the technology, keep thinking about it.

I think we are saying slightly different things. COGS are composed of many smaller capital allocations. According to this untested, pet thesis, putting on a report that $250M was spent on capex is just fine; but if you go to a single vendor and sign a $250M contract, you have wasted money by not being more careful about how that capital is allocated. $100M is _a lot_ of capital, and I think it’s easy to lose sight of how much stuff you can do with that much money when applied to industries that don’t pay tech salaries for speculative growth. As examples: how many pounds of food could you grow for 100M? How many doctors could we train for 100M?

I think the thesis is thought provoking. Not sure yet if it’s worth anything, but it also doesn’t preclude businesses from having massive cashflow.