Jack Welding

My AI investment thesis is that every AI application startup is likely to be crushed by rapid expansion of the foundational model providers. App functionality will be added to the foundational models' offerings, because the big players aren't slow incumbents (it is wrong to apply the analogy of "fast startup, slow incumbent" here), they are just big. Far more so than with any other prior new technology, there is a massive and fast-moving wave that obsoletes every new app almost as fast as it can be invented. There is almost no time to build a company and scale it. There are two ways AI application startup founders can make money: - Make a flash-in-the-pan app that generates a ton of cash and bank the cash (my estimate is that you have about 12-18 months cashflow generation) - Make a good enough app that you get acquired by one of the big players for sufficient equity The situation is highly unstable - we don't know if it's going to crash or go to the moon but both scenarios make it very unlikely that any AI application startup will independently become a generational supercompany (baseline odds are low to begin with). The best odds are finding an application niche in a highly specialized field with extremely unique and specific data barriers, ideally ones relating to real atoms (hardware or world-related) data and not software/finance.

There is something very important happening in finance. An entire generation of young people who grew up on the internet are now ascending to positions of power and influence. They have access to capital and they have technology at their fingertips. The speed at which they receive information, analyze insights, and make decisions is different than those who came before them. The appetite for risk, the pursuit of volatility, and the addiction to playing the game 24/7 gives them a distinct advantage. Add in the fact that many of them have a megaphone with reach previously thought impossible. Faster, bigger, louder, more concentrated. The internet generation has arrived to capital markets and things will never be the same.

Can someone please explain to me how Benchmark International lets Class of 2022 undergrads run point on $3M EBITDA deals

It's sometimes hard to grasp the significance of the reasoning and logic updates that are starting to emerge in powerful models, like GPT-5. Here's a *very simple* example of how powerful these models are getting. I took a recent NVIDIA earnings call transcript document that came in at 23 pages long and had 7,800 words. I took part of the sentence "and gross margin will improve and return to the mid-70s" and modified "mid-70s" to "mid-60s". For a remotely tuned-in financial analyst, this would look out of place, because the margins wouldn't "improve and return" to a lower number than the one described as a higher number elsewhere. But probably 95% of people reading this press release would not have spotted the modification because it easily fits right into the other 7,800 words that are mentioned. With Box AI, testing a variety of AI models, I then asked a series of models "Are there any logical errors in this document? Please provide a one sentence answer." GPT-4.1, GPT4.1 mini, and a handful of other models that were state of the art just ~6 months ago generally came back and returned that there were no logical errors in the document. For these models, the document probably seems coherent and follows what it would expect an earnings transcript to look like, so nothing really stands out for them on what to pay attention to - sort of a reverse hallucination. GPT-5, on the other hand, quickly discovered the issue and responded with: "Yes — the document contains an internal inconsistency about gross-margin guidance, at one point saying margins will “return to the mid-60s” and later saying they will be “in the mid-70s” later this year." Amazingly, this happened with GPT-5, GPT-5 mini, and, remarkably, *even* GPT-5 nano. Bear in mind, the output tokens of GPT-5 nano are priced at 1/20th of GPT-4.1's tokens. So, more intelligent (at this use-case) for 5% the cost. Now, while doing error reviews on business documents isn't often a daily occurrence for every knowledge worker, these types of issues show up in a variety of ways when dealing with large unstructured data sets, like financial documents, contracts, transcripts, reports, and more. It can be finding a fact, figuring out a logical fallacy, running a hypothetical, or requiring sophisticated deductive reasoning. And the ability to apply more logic and reasoning to enterprise data becomes especially critical when deploying AI Agents in the enterprise. So, it's amazing to see the advancements in this space right now, and this is going to open up a ton more use-cases for businesses.

Interesting to see the way IBs are combating PE recruiting. Basically if you recruit and disclose, you’ll be moved to a diff business segment. Kinda defeats the point of PE funds recruiting early and expecting analysts to gain deal experience. Still seems like the best move for analysts is to simply recruit and stfu about it. No other way around it