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Chapter 1: What is the main topic discussed in this episode?
Today on the AI Daily Brief, pro-worker AI. Before that in the headlines, Meta delays its next AI model. The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI. All right, friends, quick announcements before we dive in. First of all, thank you to today's sponsors, KPMG, Robots and Pencils, Blitzy, and AIUC.
To get an ad-free version of the show, go to patreon.com slash ai-dailybrief, or you can subscribe on Apple Podcasts. And if you are interested in sponsoring the show, send us a note at sponsors at ai-dailybrief.ai. It appears that Meta has had another setback as their latest frontier model gets delayed.
The New York Times reports that Meta's new model, codenamed Avocado, has been delayed until at least May. We last heard about the model's progress in January when CTO Andrew Bosworth told Reuters it had been delivered for internal testing. He said at the time that the model was, quote, very good, but warned that there's still a lot of work to be done in the reinforcement learning process.
More recently, there's been reports that Meta has set up a new applied AI division that reports to Bosworth rather than AI CEO Alexander Wang. Rumors followed that Zuckerberg was done with Wang, although those rumors were strenuously denied. Now the reporting states that Avocado Performance has fallen short of the latest models from rivals, and this month's planned rollout has been delayed.
The report mentioned a shortfall in reasoning, coding, and writing from internal benchmarks. In other words, basically every major category for modern LLMs. Reportedly, the model outperformed Gemini 2.5, but wasn't a match for Gemini 3. Now, part of the issue could be the long development cycle.
Meta has been working on this model for almost nine months, and the goalposts of model performance have shifted dramatically during that time.
Meta put an optimistic spin on the issue, issuing a statement which said, Our next model will be good, but more importantly, show the rapid trajectory we're on, and then we'll steadily push the frontier over the course of the year as we continue to release new models. We're excited for people to see what we've been cooking very soon.
And yet that doesn't exactly comport with reports that Meta leadership is even considering licensing Gemini to power their products as a stopgap solution. That said, researchers are said to be excited about the next model after Avocado, codenamed Watermelon.
Now, ultimately, I certainly think that making people wait for a model that's actually good is way better than releasing a model that no one is impressed with. But the model battle for Meta remains distinctly uphill. Ethan Mollick summed up a bit of the industry sentiment when he tweeted, Both XAI and Meta seem to be falling behind, based on the Grok 4.2 benchmarks in this reporting.
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Chapter 2: What recent developments have occurred with Meta's AI model?
Now, there's been speculation that some of these co-founders exited after projects they led fell short of Elon's expectations. For example, Zhang-led Grok Code, and Toby Poland, who departed at the end of February, was in charge of the maligned MacroHard project which we discussed on Thursday's show.
Musk, of course, is known as a difficult person to work for, and hinted that this is a controlled demolition rather than a leadership collapse. He posted on Thursday, XAI was not built right the first time around, so is being rebuilt from the foundations up. Same thing happened with Tesla. Speaking of Cursor, that company is seeking new funding at a massive 50 billion dollar valuation.
Bloomberg reports that Cursor is in talks for a new funding round that would almost double their valuation. Cursor's last round in November brought in 2.3 billion at a 29.3 billion dollar valuation. Now remember, this is a company that doubled their revenue to $2 billion since they last raised funds.
But what's significant about this is that if they really are raising at a $50 billion valuation, that suggests that they are trying to compete for the long haul, rather than thinking about trying to shack up with one of the leading model labs. Now, that choice isn't a shock given how CEO Michael Truel is positioning the company.
Employees were told in an all-hands in January that for Cursor, it is, in his words, wartime. That means a product overhaul to focus on automated coding tools, as well as an ambitious project to train their own state-of-the-art models to reduce their dependency on the other labs.
In Labland, the information reports that Anthropic is in talks with Blackstone and other PE firms to launch an AI consulting venture. The venture would be a dedicated consulting firm to sell Anthropic's tech to corporate customers. Alas, apparently, Anthropic's ongoing conflict with the Pentagon has put the talks on the backburner.
Sources said that Blackstone leaders, including CEO Stephen Schwarzman, are concerned about announcing a partnership while Anthropic is mired in conflict with the administration. The genesis of the deal was apparently Blackstone seeking Anthropic's help to deliver consulting services to their hundreds of portfolio companies.
Blackstone also discussed a similar plan with OpenAI, according to sources familiar with the talks. Ultimately, what all of these stories get to is the fact that enterprises are lagging, and it's going to take just a huge amount of time on task in actual human bodies to do the internal implementation that's actually needed.
I predict you are going to see massive expansions in the forward-deployed engineering departments of these firms, partnerships with all the existing consulting firms, new venture spin-ups like this, all at once and more. Next up, an interesting statistic from a new survey from the American Medical Association. The survey found that 81% of doctors now use AI in their profession.
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Chapter 3: How is Cursor positioning itself in the AI market?
And that when it comes to AI, although we assume that it's all automation technologies, that's just not actually the case. They give a few examples of pro-worker AI in the field. One example is an electrician's assistant, where an electrician uses LLMs to support electricians in troubleshooting electronic machinery.
Workers can upload photos and diagnostic data, and an AI matches to a database of prior problems. In practice, this halved the average time for completing maintenance reports, and they categorize it as pro-worker because the worker remains in the loop modifying AI recommendations, being collaborative, not subservient.
Other examples they give are a service worker's assistant, a teacher's AI aid, hearing aids for Chinese gig delivery workers, and patent examiner decision support. And yet they say these cases are too rare right now. Their main argument is that the market is at the moment not capitalizing on pro-worker AI opportunities.
They argue that the current AI focus is overwhelmingly on task automation and AGI development, neither of which coheres with their pro-worker definition. There are a couple of reasons they argue this is happening. Misaligned firm incentives, like managers using automation as a way to reduce dependence on unionized labor, rent dissipation, i.e.
managers wanting to redistribute savings to shareholders, and what the authors call the AGI bet. Basically, firms that believe AGI is imminent that see little point in investing in pro-worker technologies. To wit, why build tools to enhance workers if workers will be fully replaceable shortly? They also see misaligned developer incentives.
Some of those build off of the misaligned firm incentives, i.e. customer demand shapes supply. If firms prefer buying AI automation, tech companies will prioritize building automation tools. It's self-reinforcing. There's also a time horizon problem. Pro-worker technologies might require years of investment while automation solutions are already market-ready.
There's also the potential for worker resistance. Workers themselves may resist pro-worker AI tools that require them to acquire new expertise and adjust work habits. If workers lack foundational skills or are reluctant to invest, then firms are further discouraged.
From there, the authors give nine different policy directions that they believe could move the needle further in the direction of pro-worker AI. This, I think, is the area where most people would have debate, but I'm appreciative of the authors actually laying out some potential paths forward rather than just identifying the problem.
One category of remediations they recommend is, for example, for the government to leverage their huge GDP footprint in areas like healthcare and education to use market incentives to drive developers to develop pro-worker AI. They have a bunch of other ideas as well around the tax code, antitrust, etc.
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