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Over the weekend, I found myself in an hour-long conversation during my drive with an AI.

We jumped from discussing Cooper Flagg’s basketball stats at Duke to comparing Carlo Rovelli and Brian Greene’s competing theories of physics to talking about the history of San Francisco. In a daring feat of economic analysis, I asked it to calculate if the after-tax returns of two ETFs were statistically significant & to compare the energy portfolios using the 13-Fs of a few hedge funds for investment ideas.

The major areas of AI innovation automate white-collar work. Reviewing the BLS’ data on employment for white collar work, I aggregated the data to these categories.

It’s striking that most of them already have a significant number of AI startups pursuing their ambitions to change workflows.

Software engineers were the first to benefit with Copilot. Today, there’s a panoply of different kinds of AI software for developers, including test generation, code refactoring, code generation, & security analysis.

“AWS’ AI business is a multibillion-dollar revenue run rate business that continues to grow at a triple-digit year-over-year percentage and is growing more than 3x faster at this stage of its evolution as AWS itself grew, and we felt like AWS grew pretty quickly.”

“Our AI business is on track to surpass an annual revenue run rate of $10 billion next quarter, which will make it the fastest business in our history to reach this milestone.”

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.