My first law of enterprise AI: if you end up having two robots talk with each other to complete a task, that task was bullshit in the first place, and you should probably eliminate it rather than automate it.

For example, if AI is used in both sides of B2B procurement (enterprise software sales), then much of the process is probably bullshit in the first place. There is so much weird and ancient in procurement, on both sides, that it’s clearly a poorly done process and part of enterprise IT culture.

Nobody likes this, and we all know there’s a high degree of waste to it:

The average software sourcing process involves 28 stakeholders and takes six months. That’s six months of manual research, vendor meetings, demos, internal debates, and ultimately, a decision that still may not be fully informed.

Several years ago, Luke Kanies outlines his frustration and experience with that culture. When the buyers and the users are different people, and deal size goes up, beware: you run the risk of sailing in a sea of bullshit. Those selling (vendors) can bullshit a lot, but those buying can bullshit a lot too. The perfect example of using law one of enterprise AI use to remove waste.

“Agentic AI” just means “AI middleware”

Related: this is a great industry analyst overview of the enterprise IT category “agentic AI,” from Jason Andersen:

Conceptually, “AI Development Framework” is a type of middleware technology. To be more specific, it's a layer of shared services that provides a set of APIs and integrations for practitioners (not just developers) to build AI applications, particularly agents. The benefit of this is twofold. First, developers don't have to sacrifice too much flexibility while gaining the potential to work more efficiently. Second, the enterprise also gets a more uniform set of standards to drive better governance and sustainability.

It’s middleware, platform, and operations stuff. all the usual develop, operate, optimize. What’s slightly missing is day two operations, but we’ll all re-discover that soon enough when people try to ship version 2.0 of their (agentic) AI apps.

My advice: from now on, when you hear the phrase “agentic AI” just think “AI middleware used to add AI features to apps.”

Anything else is a bit much. The phrase is fine, just keep a realistic and pragmatic definition for enterprise AI in your head.

There is a distinction between “simple AI” and “agentic AI,” but once you poke at it, agentic AI is doing what you assume AI was doing in the first place. But that won’t last. Once “agentic AI” becomes mainstream (and cheap enough), people won’t really be doing “simple AI” anymore. Eventually (two years from now?) we’ll just drop the “agentic” and go back to calling it AI.

Focus on individual productivity, let the econ-nerds and rich worry about GDP

Here’s a recent Goldman newsletter (PDF) throwing cold water on AI hype-heat:

We first speak with Daron Acemoglu, Institute Professor at MIT, who's skeptical. He estimates that only a quarter of AI-exposed tasks will be cost-effective to automate within the next 10 years, implying that AI will impact less than 5% of all tasks. And he doesn't take much comfort from history that shows technologies improving and becoming less costly over time, arguing that AI model advances likely won't occur nearly as quickly--or be nearly as impressive--as many believe. He also questions whether AI adoption will create new tasks and products, saying these impacts are "not a law of nature." So, he forecasts AI will increase US productivity by only 0.5% and GDP growth by only 0.9% cumulatively over the next decade.

Yes, and…this is why I think individuals will be the ones who benefit from AI usage most.1 Each individual person using even just AI chat apps to get their daily work done.

AI will benefit individuals by reducing the time it takes to do knowledge worker toil, making their work less tedious, and also raising the quality of their work. This means they’ll be able to do their tasks faster, be less bored, and likely get better quality work-product. This gives individuals more time and energy.

You then need to think like a company does: how do you use that extra resource for The Corporation of You?2 You can then choose two strategies:

1. Up their own productivity - do more work, hoping their employers compensate them more - good luck!), or,

2. By working less - getting the same pay, upping their personal productivity profit margin.

Either way, people who use AI for their work will see big benefits.

Wastebook

• “There’s no particular reason 64-year-old alumni should be able go wherever they like. But there’s definitely a different feel.” The dr.

• “The faux cocaine mirrors are so hard to keep - they’re hard to get, and they’re stolen all the time.” #PalmSpringsLife, used to reflect on “luxury beliefs.”

• I missed this when I linked to Charles Betz’s simple definition of a platform, but he mentions that he uses the term “application” instead of the Team Topologies term “stream-aligned” team. “I have not seen the term ‘stream-aligned’ get traction in portfolio management,” he says. Checks out for me.

Relative to your interests

• Lack of AI-Ready Data Puts AI Projects at Risk - If you’ve let your data lakes turn into data swamps your AI projects are going to go poorly. // “Gartner predicts that through 2026, organizations will abandon 60% of AI projects unsupported by AI-ready data.”

• AI Essentials for Tech Executives - Good tables translating AI-tech-speak to business outcomes.

• Paul Millerd on AI and writing - This is the response a very pragmatic writer had to AI.

• 5 Questions to Help Your Team Make Better Decisions - (1) What Would Happen if We Did Nothing? (2) What Could Make Us Regret This Decision? (3) What Alternatives Did We Overlook? (4) How Will We Know If This Was the Right Decision? (5) Is This Decision Reversible?

• Drive Scale And Speed With The Platform Org Model - 59% of respondents have using a platform instead of whole bunch of different platforms as a priority. // Enterprise want the benefits of centralized, standardized IT stacks. Always. DIY platforms and shadow platforms (sprawl od accidental platforms) is often a bad idea. Your platform needs aren’t special, your app needs are. If you focus on platforms, you’ll steal mojo and budget away from those app needs.

• A theory of Elons - If you can get away with breaking regulations and laws, you can gain competitive advantage over those who don’t.

• The big idea: what do we really mean by free speech? - What “freedom of speech” means to the asshats: “what they actually want is freedom from the consequences of broadcasting their views.”

• Vision and Distortion in Cézanne’s ‘Still Life with Plaster Cupid’ - Good example of art criticism, “how to see,” all that.

• Old Media Finally Wakes Up from a Coma - “Hey, guys”-style getting more mainstream. // Also, long form podcasts.

• Dutch people concerned with U.S., Russia, Ukraine developments; More support EU army

• Bill Skarsgård transformed beyond recognition for Robert Eggers’ Nosferatu, a film in which the vampire is still the scariest thing on screen.

• Issues for His Prose Style - Picking the right noun as a deep cut mechanic, to show authenticity, and build up a mythos of yourself, and for yourself. // Hemingway’s letters.

Conferences

Events I’ll either be speaking at or just attending.

VMUG NL, Den Bosch, March 12th, speaking. SREday London, March 27th to 28th, speaking. Monki Gras, London, March 27th to 28th, speaking. CF Day US, Palo Alto, CA, May 14th. NDC Oslo, May 21st to 23rd, speaking.

Discounts: 10% off SREDay London with the code LDN10.

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See y’all next time.