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In yesterday’s post, I calculated the profitability of public software companies. To calculate these figures, I built a little Rube Goldberg machine.

I didn’t download the data into Excel. Instead, I complexified things by sending the analysis to 4 AIs to see if they would agree.

The inspiration : many companies have used Amazon’s Mechanical Turk to crowdsource tasks, & pick a consensus answer across three workers to improve accuracy.

Why not try this across 4 AI workers instead?

On Monday, at TC Disrupt Colin Zima CEO of Omni, Jordan Tigani CEO of Motherduck, Daniel Svnova CEO of Superlinked & Toby Mao CTO of Tobiko Data who are leading the evolution of the Post Modern Data Stack discussed the trends they are seeing.

Here are some of the themes & predictions from the group.

If AI continues on its current trajectory or accelerates, what will change in your business?

We’ve been asking leaders of companies & departments this question & the answers aren’t clear.

In a few years, consensus agrees that rote work of BDRs & paralegals & software engineering will be somewhat to mostly automated. But determining the timing of that impact is much more difficult : it depends on the accuracy of the AI.

When I was a novice product manager, I remember hearing that acronym for the first time : PRD. The Product Requirements Document.

The PRD contains the output of a conversation between product & engineering - what is to be built within a few leaves of digital paper.

Perhaps similar to the conversation one might have with a chatbot about a product feature. 😏

Over the weekend, a Reddit user asked the Meta AI model powering WhatsApp to reveal its system prompt - the instructions Meta engineers provide to an AI as part of every query.

Since July, have you noticed how much better your AI model has become? Measuring them is hard to do. All we can do is quantify the vibe : is this one better than that one?

Elo is a score that measures how often one model wins against another, as judged by a human. Which model answers the prompt : “Describe the differences in texture between a Pink Lady and a Macoun apple” better? The one with the higher Elo score.¹

Budget cuts followed interest rate hikes in 2022. By late 2023, more than a year of financial scrutiny had challenged many publicly traded software companies.

However, 2024 has been a tough year again.

Net dollar retention for publicly traded software companies fell from 113% on average to 108%, a five percentage point drop.¹ 80% of publicly traded companies saw their NDR wilt again in 2024. Overall account expansion remains roughly 5 percentage points above inflation.

Klarna, the Swedish fintech giant, is making waves by churning from industry-standard software like Salesforce and Workday in favor of building its own internal systems with AI.

After their success with AI customer support automation which manages 2/3 of their customer inquiries, Klarna is now doubling down on this strategy.

Klarna is betting AI-enabled software is the future of internal tools. The corollary : the overall cost of building internal software with AI is lower than buying off-the-shelf solutions.

Recently, Thomas Laffont of Coatue highlighted a non-obvious impact of the M&A slowdown after this preamble :

“Ironically and I think somewhat perversely one of the byproducts of constraining big companies from buying small companies is it hurts small companies.

This point is clear & straightforward. Less demand for an asset decreases price. 2023 & 2024 are two of the three lowest in the last 12 years in both activity & value across the ten largest strategic software buyers. ¹

Visa announced their plans to launch a stablecoin with BBVA today.

Throughout the quietness in crypto created by the wake of the FTX collapse & the AI boom, stablecoins have become a large & very fast growing part of web3.

Users clock 3m stablecoin transactions per day at an approximate average value of $5,000.

But, the use of stablecoins extends far beyond crypto trading.

Stablecoins own more US Treasuries than South Korea & Germany & have become an important buyer of US goverment debt.

Morgan Stanley surveyed a group of CIOS to understand the sources of AI budget. If hyperscalers deploy $100b in capex on AI this year & legions of software vendors hawk AI solutions, the money to justify it must come from somewhere.

41% of CIOs said net new spend. 35% said existing software spend. Only 6% said it would come from professional services.