Tuesday, June 10, 2025

Benedict's Newsletter: No. 595

NO. 595   FREE EDITION   SUNDAY 8 JUN 2025
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My work

A new presentation: AI eats the world

Every year, I produce a big presentation exploring macro and strategic trends in the tech industry. New in May 2025, 'AI eats the world'. LINK

AI's metrics question

With every platform shift, we want to measure the growth but we're confused about what to measure. That's partly a problem of data and definitions, but it's really a question about what this is going to be. LINK

News

OpenAI moves up the stack 

OpenAI added tools to connect to Google Docs, Teams and a few other third-party services, including the ability to record, take notes and summarise for Teams calls. This is a bunch of startups' entire product (remember 'thin GPT wrappers') - but of course, when you build on a platform you should always be building at right-angles to its provisions (remember 'why won't Google do this?'). LINK

Conversely, Anthropic caused a fuss by cutting access to its models for the coding tool Windsurf, because of rumours that OpenAI might be buying it. This is probably not the best way to persuade API buyers that you're a reliable partner. LINK

Advertising automation 

Mark Zuckerberg has been talking about AI ad-creation for a while now, and the WSJ says it's now working on a fully automated creation system, in which many variants of ads would be dynamically generated, complete with all the creative, and tested and refined in real time.  As Zuck said, tell us the aim and give us a budget and we'll do the rest. This seems a little simplistic about the aims of marketers, but Meta would certainly like to lower the barriers to entry and optimisation for its SME advertisers, which make up about half of revenue. LINK

Meanwhile, WPP has launched its own model for generating segmentation, creative and media planning (people in advertising talk a lot about the scope to automate all of the media buying, which is still very manual). LINK

Data licensing 

Bloomberg reports that three major labels are in talks to license work to Udio and Suno, which can generate new music 'in the style of…' This is an interesting point on a matrix of copyright issues vis-à-vis training data: these companies' outputs are not piracy per se but they are competitive and substitutional to the training data, as  opposed to (say) using a LLM trained on novels to automate accounting systems. LINK

Conversely, Reddit is suing Anthropic for scraping its forums for general and specific training data despite being told not to. LINK

And third, as part of the NY Times lawsuit against OpenAI, the NYT speculated that people might be getting verbatim chunks of its news stories in responses to ChatGPT prompts, so it got a judge to order OpenAI to retain all logs of all users' outputs, which seems like a huge overreach and potential privacy problem. LINK

The week in AI

The FT reports that Apple plans to use Alibaba's models for 'Apple Intelligence' in China, but it's stuck in the approval process due to Trump's trade wars. There's a narrative here that Apple is losing share in China to Android with a blizzard of new AI features from the local players (though see below). LINK

Meta, like Microsoft a while ago, has signed a deal to buy electricity for data centres from a nuclear power plant. LINK

Unsurprisingly: Amazon is testing humanoid robots for delivery. LINK

About
What matters in tech? What's going on, what might it mean, and what will happen next?

I've spent 25 years analysing mobile, media and technology, and worked in equity research, strategy, consulting and venture capital. I'm now an independent analyst, and I speak and consult on strategy and technology for companies around the world.

Ideas

This week's viral AI research paper comes from a team at Apple, arguing that the new wave of 'reasoning' LLMs or 'RLMs' can be made to collapse in predictable ways under test conditions, indicating that they're not actually 'reasoning' at all. This is of course the general question about whether LLMs can become general intelligence: are they actually 'intelligent' (whatever that word means) or do they just look like it, and if they give good enough results does it matter? LINK

Steven Sinofsky on the paper above: don't anthropomorphise LLMs. LINK

Another interesting research paper, trying to calculate precisely how much of the training data is explicitly encoded inside an LLN. LINK

The 'big four' accounting firms all want business auditing models and products. LINK

North Korea hacks the US with the help of witting or unwitting Americans, just trying to get by. LINK

A fascinating little New York Times profile of a Twitter politics troll. He gets a cut of revenue from Twitter, but not actually that much - about $160k since 2023 despite being prominent enough to get an invitation to the White House. LINK

Hailey Bieber (me neither) sold her cosmetics brand Rhode to Elf this week for $1bn. That sounds like a headline from the great D2C boom of the late 2010s, but it's still possible. LINK

Reflecting the above, Bain's annual report on new D2C brands. LINK

How Expedia is talking about LLMs replacing search. LINK

Outside interests

RIP Bill Atkinson. LINK

Demis Hassabis and Darren Aronofsky discuss AI film-making tools. LINK

Data

Epoch AI released a dataset on AI training… what do we call them? Datacentres? Factories? LINK

MrBeast now has 400m subscribers. LINK

Unsurprisingly, it looks like Temu's US usage has halved since the tariff changes. LINK

Preview from the Premium edition

Does AI kill Apple?

Ten years ago, an idea went around the tech industry that machine learning (which we then called 'AI') was an existential threat to Apple. This was clearly the New Thing, it depended on data, and Apple deliberately didn't collect user data ('privacy'). Apple was bad at services. Apple's culture of secrecy would make it hard to hire researchers. Google would use machine learning to turn Android into an iPhone-killer. 

That isn't what happened. It turned out that machine learning was a technology, not a product: Apple used it to build new features, without needing user data (or leaving user data on your device), and meanwhile everyone from Google to Snap to TikTok used machine learning to build new things that you ran on your iPhone. Indeed, a lot of machine learning features were driven by the camera, which meant a high-end phone, which mostly meant an iPhone (especially in the USA). 

Today LLMs bring the same kinds of worry for Apple, but with a much sharper focus. Machine learning was never really a product that a consumer would use (any more than SQL was) - it had to be wrapped in a product. But there is a very widespread view that LLMs themselves area product and can replace large chunks of existing software use-cases and create lots of new ones as well. If you can ask ChatGPT to book you... 

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