Is AI killing jobs or not? The (seemingly) eternal question, which gets answered with yes, no, and “it depends” in equal measures, just received its latest update. In a recent study by Revelio Labs, a workforce data company, the data points to a world where AI already has a direct impact on hiring:

In short, jobs that AI can perform are disappearing from job boards faster than those that AI can't handle. […] Since OpenAI released ChatGPT in 2022... the hiring downturn has been steeper for high-exposure roles (31%) than for low-exposure roles (25%).

The important bit here is the distinction between high- and low-exposure roles – or the ability of an AI to do your job.

Link to study.

Looks like your doctor likes his AI not only in his LinkedIn feed but also in all sorts of medical devices and platforms. According to the FDA, more than 1,000 medical AI products have been cleared for use – with interesting (and potentially troubling) consequences.

"Unlike most other FDA-regulated products, AI tools continue to evolve after approval as they are updated or retrained on new data.”

It gets worse:

"...medical algorithms often perform poorly when applied to populations that differ from the ones they were trained on.”

and:

"...many hospitals are buying AI tools 'off the shelf' and using them without local validation. That is a recipe for disaster.”

Brave new world. Maybe ask your doc next time if his diagnosis was aided by AI…

Link to article in Nature.

Legendary tech analyst Mary Meeker hasn’t released one of her colossal trend reports since 2019. This week she did – a whopping 340 slides on all things AI.

https://www.bondcap.com/reports/tai

A new paper showed that LLMs are now more persuasive than humans when trying to influence others:

LLM persuaders achieved significantly higher compliance with their directional persuasion attempts than incentivized human persuaders, demonstrating superior persuasive capabilities in both truthful (toward correct answers) and deceptive (toward incorrect answers) contexts.

Panicking aside, this is both good and bad news. The bad news is pretty obvious (we already live in a world of semi-constant disinformation; it’s not just hard to distinguish truth from lies, but now AI has the upper hand in persuading us), but there are also some interesting upsides: AI could make us more compliant to take our medications (a huge problem in the healthcare industry), save for retirement (another massive problem in the financial services industry), or work out more regularly…

But there is a very real danger:

Human persuasion is naturally constrained by effort and opportunity, but AI-generated persuasion can operate continuously and at scale, influencing vast audiences simultaneously.

Link to study.

Take this with a grain of salt – as it comes from the very people who would greatly benefit from this statement being true (venture capital investors) – but this sentence in the article stood out:

Tech companies, in particular, will see "significantly reduced hiring," he added.

Ironically, the very people building all this stuff might be the ones most affected by it (on a per-sector/industry level; maybe not on the individual level… AI engineers will be fine for a while).

Link to article.

New study about the use of AI in German offices shows that Germans don’t use AI in their workplaces all that much and that they are increasingly wary and mistrusting of AI compared to their international peers. That being said, even with those caveats, a solid 42% of white-collar workers are already using AI in their jobs. Given that ChatGPT is just a little over two years old, this is an impressive penetration number.

Link to article and study.

Wondering what the world of “work from home” truly looks like? Despite the rhetoric that it's over, the average (!) American spends 1.6 days per week working from home…

Source: Apollo research​

On the heels of my last post about Google co-founder Sergey Brin’s rather questionable approach to using AI to replace managers (or, at least, replace a lot of what they do), here is new research on the impact AI is having on the workplace.

From the study “The Dark Side of Artificial Intelligence Adoption: Linking Artificial Intelligence Adoption to Employee Depression via Psychological Safety and Ethical Leadership” published in Nature:

“While the adoption of AI brings numerous benefits, it also introduces negative aspects that may adversely affect employee well-being, including psychological distress and depression.”

The study goes on to say that

“With AI adoption, there is a real risk that employees will perceive threats to crucial resources like job stability, independence in their work, and professional competencies.”

In summary (and very much contrary to Mr. Brin’s approach):

“With the continuous advancement and integration of AI technology in our workplaces, it is imperative for organizations and leaders to prioritize the welfare of their employees, foster a supportive and inclusive working environment, and embrace an ethical approach that prioritizes people when incorporating AI.”

Link to study.

Google cofounder Sergey Brin recently rambled his way through a conversation on management in the age of AI:

"Management is like the easiest thing to do with AI," Brin said.

Apparently, management, for Brin, consists of summarizing meetings and assigning to-dos:

"It could suck down a whole chat space and then answer pretty complicated questions," he said. "I was like: 'OK, summarize this for me. OK, now assign something for everyone to work on.’”

So far, so bad. Where it gets really fun is when he lets AI make promotion decisions:

"It actually picked out this young woman engineer who I didn't even notice; she wasn't very vocal," he said. "I talked to the manager, actually, and he was like, 'Yeah, you know what? You're right. Like she's been working really hard, did all these things.’”

And as he clearly has outsourced his management to an AI, he doesn’t even really know if this all has happened or not:

"I think that ended up happening, actually," Brin said of the promotion.

All in all, it’s a pretty bleak vision for the future.

Link to article.

A recent (Q1/2025) report from Artificial Analysis delves into the capabilities and emerging trends of frontier AI models. The report identifies six key themes:

Continued AI progress across all major labs, the widespread adoption of reasoning models that "think" before answering, increased efficiency through Mixture of Experts architectures, the rise of Chinese AI labs rivaling US capabilities, the growth of autonomous AI agents, and advances in multimodal AI across image, video, and speech.

There is a lot to unpack in the report, but the key chart might be this:

All frontier models are converging on the same set of capabilities and the quality of those, which means that we will (and are already seeing) a brutal race to maintain their position in the peloton with further increased price pressure. This brings up the question of how these companies might justify their insane valuations…

Link to Report.

Just this last week saw the announcement of new, sophisticated AI research tools from all the frontier labs, claiming exceptional results. Headlines such as “U. researchers unveil AI-powered tool for disease prediction with ‘unprecedented accuracy’” or “Microsoft's new AI platform to revolutionize scientific research” gush about these new tools’ abilities.

Meanwhile, Nick McGreivy, a physics and machine learning PhD, shared his own experience with the use of LLMs in scientific discovery – and his story reads very differently:

“I've come to believe that AI has generally been less successful and revolutionary in science than it appears to be.”

He elaborates:

“When I compared these AI methods on equal footing to state-of-the-art numerical methods, whatever narrowly defined advantage AI had usually disappeared. […] 60 out of the 76 papers (79 percent) that claimed to outperform a standard numerical method had used a weak baseline. […] Papers with large speedups all compared to weak baselines, suggesting that the more impressive the result, the more likely the paper had made an unfair comparison.”

And in summary:

"I expect AI to be much more a normal tool of incremental, uneven scientific progress than a revolutionary one.”

And the discussion about what is hype and what is reality in AI continues…

Link to his blog post.

Science fiction author Neal Stephenson, who popularized the concept and term “metaverse” in his seminal book Snow Crash (1992), recently spoke at a conference in New Zealand on the promise and peril of AI.

His (brief but razor-sharp) remarks are well worth reading in full, but this quote stood out:

“Speaking of the effects of technology on individuals and society as a whole, Marshall McLuhan wrote that every augmentation is also an amputation. […] This is the main thing I worry about currently as far as AI is concerned. I follow conversations among professional educators who all report the same phenomenon, which is that their students use ChatGPT for everything, and in consequence learn nothing. We may end up with at least one generation of people who are like the Eloi in H.G. Wells’s The Time Machine, in that they are mental weaklings utterly dependent on technologies that they don’t understand and that they could never rebuild from scratch were they to break down.”

Link to his remarks.

Remember that MIT paper which showed that researchers leveraging AI are significantly more productive (as in: a higher number of discoveries), yet are less satisfied with their work (as they are relegated to drudgery while AI does all the hard, challenging, and exciting work)?

Well, it turns out that this was another case of “too good to be true.” MIT just recalled the paper, the researcher who published the paper isn’t affiliated with MIT anymore, and MIT states it “has no confidence in the provenance, reliability, or validity of the data and has no confidence in the veracity of the research contained in the paper.”

Ouch.

Link to report.

Want to get better at writing prompts (it’s a worthwhile investment of time – the difference in results from a mediocre prompt to a good prompt can be vast)? Here are a couple of excellent sources – straight from the horse’s mouth:

OpenAI has a whole site dedicated to their “AI Cookbook” - which includes, for example a ChatGPT 4.1 Prompt Guide.

Google has an excellent resource on prompt design strategies on their "AI for Developers" site.

And Anthropic (Claude) has something similar on their own developer documentation site – which even includes a prompt generator tool.

Happy prompt engineering!

Here is an interesting question: If AI is truly so good at creating code, why don’t we see many more code contributions in Open Source repositories made with the help of AI?

Here is Satya Nadella, CEO of Microsoft:

“Maybe 20 to 30 percent of the code that is inside our repos today in some of our projects is probably all written by software.”

That’s a lot of “maybe” and “probably”… And we just can’t know, as Microsoft’s code repository is (of course) private. But Open Source code lives in public places like GitHub and thus is inspectable. And when you look closely, you will find very little evidence that code in those repositories is written by AI.

Admittedly, a lot of Open Source projects aren’t particularly excited about AI-generated pull requests:

“It’s true that a lot of open source projects really hate AI code. … the biggest one is that users who don’t understand their own lack of competence spam the projects with time-wasting AI garbage.”

But that aside, when you look at the data, it’s just not there:

“TL/DR: a lot of noise, a lot of bad advice, and not enough signal, so we switched it off again.”

In many ways, AI keeps furthering the skill gap:

“The general comments … were that experienced developers can use AI for coding with positive results because they know what they’re doing. But AI coding gives awful results when it’s used by an inexperienced developer.”

Overall, a good reminder to look past the marketing and hype…

Here is the full article.

On one hand, we have OpenAI announcing HealthBench, a physician-designed benchmark that rigorously evaluates AI models on 5,000 realistic health conversations across diverse scenarios, thus preparing to pave the way for Doc ChatGPT to become a reality. On the other hand, you have LLM-skeptic Gary Marcus trying something as trivial as having ChatGPT draw a map to hilarious effect:

It was very good at giving me bullshit, my dog-ate-my-homework excuses, offering me a bar graph after I asked for a map, falsely claiming that it didn’t know how to make maps. A minute later, as I turned to a different question, I discovered that it turns out ChatGPT does know how to draw maps. Just not very well.

After quite the saga (it’s worth reading Gary’s article in full), he concludes:

How are you suppose do data analysis with 'intelligent' software that can’t nail something so basic? Surely this is not what we always meant by AGI.

Think AIs will come for your CFO and his team? Not quite, not yet… Vals.ai’s Finance Agent Benchmark clearly shows we are some ways off from the professions caving in to our LLM-powered overlords:

“The foundation models are currently ill-suited to perform open-ended questions expected of entry-level finance analysts”

But fret not — not all is lost:

“Models on average performed best in the simple quantitative (37.57% average accuracy) and qualitative retrieval (30.79% average accuracy) tasks. These tasks are easy but time-intensive for finance analysts.”

Link to benchmark and analysis.

Talk about a nail in the coffin: Minecraft, the proto-3D world-building game played by hundreds of millions, is shutting down its VR and Mixed Reality support.

If there ever was a question of whether the Metaverse is dead (though calling it ‘dead’ assumes it was once alive, which is debatable), this might be the definitive answer.

“Microsoft’s latest update to Minecraft‘s Bedrock Edition, version 1.21.80, removes support for both virtual reality and Mixed Reality, Microsoft’s own take on AR that it killed off years ago. Now, Minecraft remains as a game that can be played on multiple consoles and platforms — just not VR.”

Of course, it is not just Minecraft—after losing oodles of money, Meta (the company which conveniently rebranded itself to indicate it is the king of VR) is shuttering large parts of its VR efforts for quite a while.

Makes me wonder what happened to all the Chief Metaverse Officers (the “new” CMOs)? And yes, THAT was indeed a thing for a hot minute…

A good reminder to keep applying Chris Yeh’s brilliant “Does it have utility?” framework: Frequency / Density / Friction: How often do you encounter the problem? How much time/energy do you spend in the problem space? How much pain does it cause you? If those factors are too low, you simply don’t have a problem worth solving. Metaverse - Never/None/Zero. Q.E.D.

Game over.

And so it (finally, maybe, hopefully) begins: after we all get used to Waymo’s robotaxis on our roads (at least if you live in a place like San Francisco), Aurora is now bringing Level 4 autonomous driving to trucks. And just like Waymo, which is retrofitting existing cars, Aurora’s tech is designed to be integrated into OEM trucks.

“[…] starting with deliveries between Dallas and Houston... aims to expand to El Paso, Texas, and Phoenix, Arizona by the end of the year.”

And the OEM aspect:

“[…] the company has already partnered with Volvo and PACCAR (which makes trucks with Kenworth and Peterbilt badges) to bake its hardware and software into their freight vehicles.”

Link to article.

The AI revolution is powering the robotics revolution—and now you can play!

Hugging Face, the “GitHub for AI models,” just announced a programmable, 3D-printable robotic arm that can pick up and place objects, starting at a mere $100. The arm features improved motors that reduce friction while allowing the arm to sustain its own weight and can be trained via an AI technique called reinforcement learning.

Time to get the order in and start playing…

Link to article and instructions.

A new study by the Booth School of Business at the University of Chicago shows that the use of GenAI, at least so far, hasn’t had any measurable impact on jobs and wages:

“AI chatbots have had no significant impact on earnings or recorded hours in any occupation," the authors state in their paper.

One might consider this a reason for celebration (“no, AI won’t make you unemployed”), but it has a very important implication:

“The adoption of these chatbots has been remarkably fast," Humlum told The Register. "Most workers in the exposed occupations have now adopted these chatbots. Employers are also shifting gears and actively encouraging it. But then when we look at the economic outcomes, it really has not moved the needle.”

Despite the billions of dollars being poured into AI, the economic impact (again, so far) seems to have been minimal…

Link to article and study.

Ever heard of “vegetative electron microscopy”? It is a term that has been popping up in AI responses throughout the earlier part of the year—one that is completely nonsensical, originating from a translation error dating back to the 1950s. Alas, AI doesn’t know anything—it's all tokens to AI, and thus we now see the term appearing in AIs—something called “digital fossils.”

Like biological fossils trapped in rock, these digital artefacts may become permanent fixtures in our information ecosystem. […] Digital fossils reveal not just the technical challenge of monitoring massive datasets, but the fundamental challenge of maintaining reliable knowledge in systems where errors can become self-perpetuating.

Link to article here and here.

The subhead of this Business Insider piece says it all:

Carnegie Mellon staffed a fake company with AI agents. It was a total disaster.

Some more gems:

It's relatively easy to teach them to be nice conversational partners; it's harder to teach them to do everything a human employee can.

And in conclusion:

Instead of being replaced by robots, we're all slowly turning into cyborgs.

As always in (tech) life: All that glitters is not gold. (for now at least)

Maybe not so good…

OpenAI's latest reasoning models, o3 and o4-mini, hallucinate more frequently than the company's previous AI systems, according to both internal testing and third-party research. On OpenAI's PersonQA benchmark, o3 hallucinated 33% of the time -- double the rate of older models o1 (16%) and o3-mini (14.8%). The o4-mini performed even worse, hallucinating 48% of the time. Nonprofit AI lab Transluce discovered o3 fabricating processes it claimed to use, including running code on a 2021 MacBook Pro "outside of ChatGPT." Stanford adjunct professor Kian Katanforoosh noted his team found o3 frequently generates broken website links.

Source.

Despite the seemingly negative headline, this is an incredible feat of engineering.

More than 20 two-legged robots competed in the world’s first humanoid half-marathon in China on Saturday […] The first robot across the finish line, Tiangong Ultra – created by the Beijing Humanoid Robot Innovation Center – finished the route in two hours and 40 minutes.

That last statement is much more important—not the attention-grabbing headline that the robots didn’t win against their human opponents:

Alan Fern, professor of computer sciences, AI and robotics at Oregon State University, told CNN he was 'actually very impressed' that the robots managed within the time limit, saying he 'would have bet that none of them would finish.'

Link to article and video.