Welcome to excitech, where I research AI deeply and cover it in an accessible way.

This interview with professor Derya Unutmaz from Jackson Laboratory was first published in the Danish business daily Børsen and subsequently by Poland’s Puls Biznesu. It was also picked up by Bild in Germany, Europe’s largest newspaper.

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Let’s get to it.

The experiment showed potential implications for developing novel cancer treatments, but there was a missing piece: the researchers had to understand why it worked; otherwise it couldn’t be applied safely.

They had tried manually, but so far unsuccessfully.

Now, they turned to an AI model.

It crunched the data for 17 minutes and then came back not only with an explanation, but also a proposed experiment to verify it. The researchers carried it out, and the result confirmed the model’s hypothesis.

“The AI model came up with this beautiful mechanism, and I was like, ‘Oh my God, why didn’t I think of that?’” says one of the researchers, Derya Unutmaz, professor of immunology at Jackson Laboratory, an independent biomedical research institution and one of the U.S.’ 73 nationally-designated cancer centers.

Normally, it would take a researcher months to arrive at such an explanation, says Unutmaz, who has more than 35 years of research experience with work published in leading journals such as Nature and Science.

The AI model’s discovery, described in a November paper by OpenAI—the company behind the model—is one of many examples, says Unutmaz, of how AI is rapidly transforming healthcare.

And it leads the professor to make an eye-catching prediction: with 99% probability, it will be possible to cure all diseases within a decade.

This is a field in which Unutmaz has firsthand experience: his research played a foundational role in the first-ever cure of a person with HIV in 2007—the American Timothy Ray Brown, also known as the Berlin Patient.

From Unutmaz’s office in Connecticut, the professor explains his prediction and how he views AI’s potential in improving healthcare.

Acceleration

The 59-year-old Unutmaz has been interested in technology’s potential for biological breakthroughs since the early 1990s, when he read The Age of Intelligent Machines by Ray Kurzweil, who later became chief futurist at Google.

From 2005 to 2016, Unutmaz ran the blog Biosingularity on the topic, and since ChatGPT launched three years ago, he has been “all-in” on the technology and uses it daily.

He has waited decades for the technology to reach its current level. Still, his reaction when Google in February last year launched its “AI co-scientist”—a “virtual collaborator” designed specifically to accelerate scientific discoveries—was striking.

“I’m now 99% certain that all diseases including cancer will be cured within a decade!,” Unutmaz wrote on X, where he has more than 339,000 followers.

His prediction came, he explains, because systems like the co-scientist enhance the entire scientific process: from generating hypotheses through planning experiments to analyzing results and shaping the narrative.

“At every stage, AI simply accelerates the process enormously.”

And this is urgently needed, he says, pointing out that even 55 years after Richard Nixon declared “war on cancer” and trillions of dollars have been invested in the fight, the disease is still not defeated.

“We have made tremendous advances, but it’s usually fairly incremental and very, very slow. If you didn’t have AI, we would still cure all diseases but it would take a couple of centuries, not ten years.”

The human contribution

A common criticism of AI is that it cannot produce anything original. But Unutmaz believes that is an oversimplification.

By combining knowledge across multiple fields, some leading models can generate better hypotheses than he can himself—despite his decades-long experience.

Unutmaz instructs the system, which then generates hypotheses and arguments for which are best to pursue. He then judges whether they hold up or not.

“As a scientist, you still decide what’s reasonable and what’s not. That’s the human contribution because we have experience and know the context.”

From the moment AI proposes a promising chemical structure to the point where a marketable drug exists, there is usually still a long road ahead—especially because of the clinical trial phase involving human testing.

“It doesn’t matter if you have a thousand drugs and know exactly what the effect is going to be, you still have to go through three, four, five years of clinical trials. That takes a very, very long time.”

Then he adds:

“But AI will be able to fix that too.”

Digital twins

The solution, he says, is a so-called digital twin built on all of a person’s biological information.

Digital twins are already known in healthcare, for example through the Living Heart Project, which builds heart simulations capable of predicting how hearts respond to treatment.

However, to process the much larger amount of data involved in simulating the whole body, vastly greater computing power and more advanced AI is needed, says Unutmaz. He estimates it will take another five years before the prerequisites are in place for this.

Simulating at the individual level is important, as people respond very differently to drugs, allowing for more personalized treatments.

“If AI can develop this digital twin and make it personal, we can simulate any medicine for your disease and biology. Maybe we will even have tailor-made medicines for individuals.”

He believes this could shorten the clinical trial phase from years to months, weeks—or perhaps even days.

Regulation

For the vision of getting medicines to patients faster to become reality, regulation and authorities must keep pace.

Unutmaz praises the U.S. Food and Drug Administration, which in 2024 supported several digital twin initiatives highlighting the technology’s potential to streamline clinical trials.

The greatest barrier to his prediction coming true, he believes, would be societal friction from reluctant institutions.

“But I stand by my prediction. If anything, it could happen even earlier.”

Medicines at a fraction of the cost

The acceleration of discovery and clinical trials would also mean the cost of developing a new drug falls dramatically.

A 2020 study estimated the average cost of bringing a drug to market at $1.3 billion. In the future, Unutmaz estimates it might be done for $10 million, provided pharmaceutical companies adapt to a new competitive landscape.

Just as many traditional software companies have lost value because development costs have fallen, making it easier for competitors to enter the market, something similar may happen in the pharmaceutical industry, he says.

He also expects a larger shift in the industry.

“I think Google is going to be the biggest drug company, people don’t realize this.”

Unutmaz points to Google subsidiary Isomorphic Labs, founded in 2021 as a spinout from Google’s AI division DeepMind.

Isomorphic Labs has developed a drug-design engine based on several AI models, including AlphaFold, which won the 2024 Nobel Prize in Chemistry for its creators, including Google executive Demis Hassabis.

The company is developing its own pipeline of drugs in cancer medicine and immune diseases, while also partnering with pharmaceutical giants such as Novartis, Eli Lilly, and Johnson & Johnson.

Its stated mission, in line with Unutmaz’s prediction, is to “solve all disease with AI,” though unlike the professor it has not attached a timeline.

“Isomorphic Labs will discover more drugs than all companies combined,” he predicts.

In a recent interview with Fortune, Hassabis noted that while most pharma companies release one or two drugs over their lifetime, Isomorphic Labs aims to launch “dozens” per year.

No conflict of interest

Whether his active posting on X played a role is unclear, but OpenAI also noticed the professor.

In December 2024, Unutmaz was one of ten U.S. medical researchers to receive a grant from the company “to help create meaningful progress in areas that benefit humanity.”

The grant gave recipients unlimited access to OpenAI’s most advanced model.

Critics might argue that if one receives a grant from these AI firms, it may be difficult to remain critical of them.

So what does Unutmaz say?

He emphasizes that the grant had limited financial value: one year of subscription access at $200 per month—$2,400 total. Meanwhile, he says he personally spends around $1,000 per month on other models.

“So it has no influence whatsoever.”

He also notes that he has praised models from the Chinese company DeepSeek.

“I praise companies that I don’t necessarily like, but if they have great AI, I use them, I pay for it. I don’t have any conflicts of interest, I’m not an influencer, and I don’t accept any payment from any company for any reason. The only thing that I accept is free use for a certain period of time.”

AI models strong in biological knowledge could also be misused to create, for example, a new virus, as Yoshua Bengio—one of AI’s three “godfathers”—has warned.

Unutmaz acknowledges the risks but does not believe banning the technology is optimal: human lives are at stake in bringing the right treatment in time.

In practice, he says, stopping development is impossible when countries like China are advancing rapidly.

Finally, AI itself can help defend against misuse: it can be used to create a virus, yes—but also a vaccine against it.

“It’s like somebody sending you a smart missile and if you have the defense, another missile that hits that, that’s how you protect yourself. AI gives both of those capabilities.”

Preparing for the future

How has Unutmaz personally prepared for the future he predicts?

“I’m preparing for a time that AI is going to take over pretty much whatever I can do right now in science. I don’t fear it, I’m just trying to see how much I can contribute because I see AI as a collaborator.“

He pushes himself to use the technology—for example, new AI-assisted coding tools to develop biological software.

Is there a message you want to get out there?

“People should be extraordinarily optimistic and happy about the future.”

He suggests imagining how a person from the 1800s would react if suddenly transported to today—and how speechless they would be that people no longer die from smallpox, which the World Health Organization declared eradicated in 1980.

“That is how people will feel in ten to fifteen years. What could be more exciting than that?”

Sam Altman is “very nervous” about how the world currently tackles the biorisks from AI, like someone developing a new pathogen (i.e. a virus) with the technology.

The OpenAI CEO said this during a livestreamed “town hall” meeting with users earlier this week, answering questions from the audience and the internet.

Specifically, Altman addressed concerns about the approach of trying to restrict who gets access to the models and setting up systems to prevent adversarial use.

“I don’t think that’s going to work for much longer and the shift that the world needs to make for AI security generally, and AI biosecurity in particular, is to move from a strategy of blocking to one of resilience.”

What he means by that, and other key highlights from the one-hour Q&A session, is below.

1) AI biorisks and mitigation in 2026

The question (paraphrased for brevity): how does biosecurity fall into OpenAI’s roadmap?

“There are many ways AI can go wrong in 2026. Certainly one of them that we are quite nervous about is bio,” Altman said.

He acknowledged the capabilities of current models in biology. For instance, GPT 5.2 scored 92% on the GPQA benchmark, which consists of “Google-proof” questions on biology, physics, and chemistry, written by PhDs.

As mentioned, Altman is proposing a strategy shift towards resilience. To explain what he means by that, he draws a parallel to fire, inspired by his co-founder Wojciech Zaremba.

“Fire did all these wonderful things for society, then it started burning down cities. We tried to restrict fire. … Then we got better at resilience to fire and we came up with fire codes and flame-resistant materials. Now we’re pretty good at that as a society. I think we need to think about AI the same way.”

OpenAI has talked to biological researchers and companies about what it takes to deal with novel pathogens, and while they report that AI can be part of the answer, it won’t be an entirely technical solution, Altman says.

“You will need the world to think about these things differently than we have been. I am very nervous about where things are, but I don’t see a path other than the resilience-based approach,” he said, adding:

“… if something goes visibly really wrong for AI this year, I think bio would be a reasonable bet for what that could be.”

2) AI will drive down costs

While the impact of the AI boom on the economy as a whole is still an unanswered question, Altman expects that we will soon see prices go down broadly, as the technology enables cheaper production of goods and services.

“Given the progress with work you can do in front of a computer, but also what looks like it will soon happen with robotics and a bunch of other things—we’re going to have massively deflationary pressure in the economy.”

3) Plummeting costs of OpenAI’s current frontier model

“I think we should be able to deliver GPT-5.2 xhigh-level intelligence by the end of 2027 for—at least 100x less.”

Building apps with GPT 5.2 currently costs $14 for one million tokens (~750,000 words) of output, so one hundredth of that would be $0.14.

The scope of OpenAI’s developer business is not publicly known, but last week Altman tweeted that it added $1 billion of revenue in the preceding month.

4) Software as evolving tools

With the cost of writing software going down, Sam Altman expects that we will see more programs customised for each user.

“Maybe I want to use the same word processor every time, but I have a bunch of repeated quirks of how I use it and I would like the software to be increasingly customized—a static or a slowly evolving piece of software, but written for me. … That idea that our tools are constantly evolving and converging just for us seems like it’s going to happen.”

5) Why startups should not worry about OpenAI as a competitor

How should builders think about durability when a startup’s features can be replaced by a model update? An audience member wanted to know. When OpenAI announced AgentKit, their tool for building agentic workflows, some speculated what the consequences could be for incumbents like n8n and Zapier. Are there business areas OpenAI will not enter?

“There have been many startups that have done things that in a perfect world we would have done sooner, but it was too late and people built up a real durable advantage. That will keep happening.”

(One can guess about what companies he is referring to — maybe Cursor, Lovable, and Manus?)

6) Current limitations of the models

“… There seems to be something about creativity, intuition, and judgment that we are not close to with the current generation of models.”

7) Doing more with fewer people

OpenAI has grown rapidly in headcount since the launch of ChatGPT, reportedly from a few hundred to 3,000 as of August last year. According to Altman, the increase will start to taper off.

“We’re going to keep hiring software developers, but for the first time—and I know every other company and startup is thinking about this too—we are planning to dramatically slow down how quickly we grow because we think we’ll be able to do so much more with fewer people.”

8) Assume 100x performance incoming

On what people can expect from OpenAI’s future models.

“Assume we will have a model that is 100 times more capable than the current model with 100 times the context length at 100x the speed of the 100x reduced cost, perfect tool calling, and extreme coherence. We’re going to get there.”

The interview with Yoshua Bengio was originally done for the Danish newspaper Weekendavisen during the AI in Science Summit in Copenhagen in November. This is a version targeted for an international audience.

When we talk about the risks from AI, there is a very important one that many people tend to overlook. So says Yoshua Bengio, professor at the University of Montreal and the most-cited living scientist across all fields.

“I think many people don’t realize that if we continue on this trajectory of more and more powerful AI, those who control them will have huge economic power,” Yoshua Bengio says.

The top of the lists of the most capable AI systems is currently dominated by American and Chinese players. The best European model, from the French company Mistral AI, is ranked number 38 on LMArena.

“There’s a real chance that European companies will be completely outcompeted by foreign companies if Europe doesn’t make sure that they own frontier models,” Bengio says, referring to the most capable general-purpose systems, like OpenAI’s current GPT-5.2.

Values can be an advantage

Europe has enough talent and capital, Bengio says, and the region can build an even stronger alliance partnering with other countries like his native Canada.

“Europe can bring to the table the kind of values that we take for granted, but are not necessarily shared elsewhere in terms of democracy, human rights, freedom, public safety, ethics, and so on,” Bengio says.

He thinks those values can become an advantage for two reasons.

“There are lots of people like me who want to give positive meaning to their work and life. The researchers and engineers who would come to Europe to work on a project that is an alternative to the American and Chinese model.”

Second, trustworthiness and safety in AI systems can become major barriers to widespread use. If Europe gets this right, it will also have something very valuable to offer others.

So why is Europe trailing in the race? Bengio’s diagnosis is that it’s about self-defeatism: the feeling that Europe can’t do it.

It is imperative, he argues, that the political class understands the issue and says: we’ll do it.

“Send the signals, invest, create champions in Europe - several who can be in the race.”

A wake-up call

Bengio believes that Europe should put as much emphasis on this as it is, for example, putting on mitigating the possibility of a Russian invasion.

“That’s the level of existential risk that we should have in mind, where we need everyone to understand how important it is. Think how quickly Europe and other countries shifted when the pandemic started. When we want, we can.”

It’s not only a European matter, Bengio emphasizes.

“Every other country, especially those who have talent and capital and energy, would also be worried about domination by either the Chinese or the Americans.”

Everybody can engage

When Yoshua Bengio gave a TED Talk in front of a packed Vancouver Convention Centre in April, he urged engagement with AI risks from the broad public.

“We need your help for this project and to make sure that everyone understands these risks. We can all get engaged to steer our societies in a safe pathway in which the joys and endeavors of our children will be protected.”

How exactly would he encourage people to engage?

“Read about it. Talk about it. You can think of how people are becoming active politically, for example, on climate. We could have a similar popular movement around the impact of AI and what we should do collectively about it.”

Scientific benefits

The companies pursuing powerful AI systems are obviously doing it for the favorable consequences that they imagine. What does the positive side of that scenario look like for Yoshua Bengio?

“I’ve been in AI research precisely because I think that it could be incredibly good for advancing science. I’m already seeing many of my colleagues and myself as researchers using more and more AI in ways that accelerate our work. I think it’s just the tip of the iceberg.”

How do you use it?

“The leading AI systems have read all the scientific literature in my domain. They can have a look at what I’m doing, some idea, and then provide criticism, pointers to what others have done, even solutions to mathematical problems or algorithms that are not always good, but sometimes are completely unexpected from me and give me ideas of solving problems I didn’t think about.”

Businesses should be involved

In the book Architects of Intelligence (2018) by Martin Ford, Yoshua Bengio is asked about the international competition in AI and says that “If China is investing a lot in AI that’s fine; at the end of the day we’re all going to take advantage of the progress that’s going to come of that research.”

Does he still hold that view?

“I wasn’t yet too concerned about the misuse of AI. I’ve shifted my views. I would say it is much more dangerous to have these big players whose values are not necessarily aligned with democracy going so quickly towards superintelligence.”

To match that, the West has to invest in both technical and political guardrails, he says, and by the latter he means not only regulation.

“It’s also having the political power to be at the table to discuss: How do we globally manage those risks? To have that political power, Europe needs to develop its own frontier model that is developed with ethics and safety at the center.”

What would be a sign for you that we’ve solved the problems that we’re facing?

“Oh, I don’t look for a sign. I look for scientific arguments,” he says.

“It shouldn’t be just someone’s opinion when we’re talking about decisions that can affect the whole of humanity and future democracies.”

Ernest Ryu had previously failed to solve a long-standing problem in math with the help of ChatGPT, but now, two years later, he wanted to give it another shot.

A couple of months earlier, OpenAI had released the GPT-5 model, including a Pro version with “extended reasoning for even more comprehensive and accurate answers”, and so after having put his son to bed one evening, the assistant professor at UCLA picked up the challenge again.

The problem within the branch of math called optimization was related to a cornerstone method in the field that has been used for a variety of things: creating images from CT and MR scans, making blurred images sharper more quickly, as well as in training AI systems.

The crux was a gap in the theoretical foundation behind the method called Nesterov’s Accelerated Gradient (NAG), named after Russian mathematician Yurii Nesterov who came up with it in 1983: The method yielded good results, but it wasn’t known whether it actually settled at a solution or was just bouncing gently around the right answer.

Ryu wanted to find out once and for all.

ChatGPT as co-author

Initially he had a hunch that the method would not be stable, so he asked ChatGPT for evidence in that direction, but to make such a proof it’s most effective to also attempt proving the opposite and see which path seems more surprising, Ryu explains.

“As I did that, I realized, ‘Oh, maybe it is stable’. So I tried to push in that direction and arrived at the solution,” he tells excitech.

The discovery is described in a new preprint: “Point Convergence of Nesterov’s Accelerated Gradient Method: An AI-Assisted Proof” by Ryu and Uijeong Jang, a PhD student at UCLA.

Like a maze search

Ernest Ryu compares finding a mathematical proof to doing a maze search: you start somewhere, don’t know where the destination is, and begin searching.

Before systems like ChatGPT, the researcher would do the search one step at a time. It’s important to have an intuition as to whether one is making progress, he explains, because just randomly wandering around is too inefficient.

Interacting with ChatGPT presented two important benefits. First, whenever he wanted to explore a particular direction, it would be very helpful in speeding up exploration and especially in showing if a particular approach could not work.

“If I can rule this approach out in five minutes with the help of ChatGPT, then I’ve saved two hours of my time,” Ryu says.

The second benefit came from the models’ suggestions for new approaches that he had not thought of.

“Very often it would make me think, ‘Huh, I haven’t thought about that approach. Let me explore that.’”

“My heart rate increased”

The process took place over three days. After having the sense that he made progress for the first two, it was on the third that he arrived at the solution.

Ryu estimates that around 80% of ChatGPT’s arguments turned out to be wrong, and at first glance, what turned out to be the solution looked like one of the false positives, so he took his time to double-check it and showed it Jang.

”I was reading it, thinking ’Let me find the error here’, and I just couldn’t find an error. At the end of that reading, my heart rate increased and it was it.”

When the correct answer was provided, all the previous attempts were different and mostly unrelated. It was just in the last step that Ryu prompted the model in a way to roughly explore an area, and that was enough of a hint for the model.

“I was very surprised,” he says.

From the key discovery, there was still some work though to finish the proof. He could have done that himself in about five hours, but asked ChatGPT to do it, which completed it quickly.

“Using the maze analogy: if a particular alley is correct, then once the model takes that alley, it’s very effective in filling in the relatively straightforward details.”

Ernest Ryu shared the discovery on X, where it has amassed 1.2 million views at the time of writing.

Crossed the threshold

Ryu elaborates that ChatGPT is particularly excellent at combining knowledge from different areas.

If a result is easy for a mathematician in a different field, ChatGPT can often replicate that very quickly. That’s really helpful because you’d first have to know that it exists and then read up on that paper to understand the precise semantics of it, which takes time.

“It’s this higher level search engine of mathematics that is extremely effective.”

Ryu acknowledges that the hallucination rate is a downside of using the model, and if the accuracy is too low, then you’d rather work without it. For him there’s a threshold and beyond that the signal-to-noise ratio is such that talking to the model is helpful.

“Certainly in my area, ChatGPT has crossed that threshold and does accelerate my research.”

To underscore the importance of the contribution by ChatGPT, Ryu originally wanted to credit it as a co-author.

“If ChatGPT were a human, I would let it be the lead author,” Ryu said.

But he also doesn’t list Microsoft Word as a co-author, even though he uses that program to write in, he ponders.

“So I thought, okay, I’ll do this in the classical way. I’m going to disclose and make it a central point that this was discovered using ChatGPT.”

Watershed moment

In a follow-up post on X, Ryu says he believes that we are at a watershed moment in the history of mathematics. He draws a parallel to chess, quoting ex-world champion Magnus Carlsen on when some players in the old board game started to use AI, and it was clear that they got an advantage over those who didn’t.

“It just made us understand the game a lot better,” Carlsen said in an episode of the Joe Rogan podcast earlier this year.

Ryu points out that he is not the only one using AI to enhance math research. Adil Salim from Microsoft Research and Timothy Gowers, winner of the prestigious Fields Medal (described as ‘The Nobel Prize of mathematics”) are two other examples.

Recently, the startup Harmonic also celebrated that their AI model Aristotle has made progress on a series of long-standing problems.

The limitations

Still, Ernest Ryu doesn’t think that AI is yet capable of making fundamentally new math discoveries because the most novel and groundbreaking results come from defining new mathematics, he says, comparing it to when Isaac Newton in the 17th century invented calculus to solve problems within physics.

So AI systems cannot replace human mathematicians (for now), he concludes.

“But at least in the kind of research that I do, it does accelerate the work quite significantly, maybe 3x, 5x, or 10x.”

The annual State of AI report is out, and I’ve spent about six hours going through the 313 slides and taking extensive notes along the way.

I spend a lot of time following AI progress on a day-to-day basis, but there was still a good deal of new and interesting information for me.

Here I have collected 16 key pieces of information that I think could be useful to many people. I’ll include the corresponding slides for each if you want more context.

1) OpenAI models remain at the frontier of intelligence, but the gap has narrowed

“A fast-moving open-weights pack from China (DeepSeek, Qwen, Kimi) and closed-source group in the US (Gemini, Claude, Grok) sits within a few points on reasoning/coding.”

2) Researchers from OpenAI, Google, Anthropic, and more sounded alarm over possible loss of ability to understand AI

The matter is the so-called Chain-of-Thought (CoT), which shows how models think, and therefore for instance can surface explicit malicious intents.

But, they are incomplete “and can drift away from faithful reasoning while advanced, situationally-aware models might hide or compress their thoughts.”

3) Substantial progress in math

OpenAI, Google DeepMind and the startup Harmonic hit gold-medal performance in The International Mathematical Olympiad (IMO).

The improvements in the field “point to the very real possibility of a non-trivial research-level result in mathematics being proven and formalized by an AI system (with some human supervision involved) within the next year.”

4) Chess grandmasters learn new concepts from AI

“Researchers extracted novel chess concepts from AlphaZero (an AI system that mastered chess via self-play without human supervision) and successfully taught them to 4 world champion grandmasters, demonstrating that superhuman AI systems can advance human knowledge at the highest expert levels.”

5) China leads the pack in computer-use agents

OpenAI and Anthropic have both released tools for letting the AI actually do tasks on a computer, but the new UI-TARS-2 model from China’s ByteDance has blown the competition out of the water with new high-scores across benchmarks.

However, long-horizon problems like browsing for hard-to-find information ”remain brittle”.

6) Intense AI research activity - China dominates

The 2026 conference for the Association for the Advancement of Artificial Intelligence (AAAI) has received an unprecedented 29,000 research submissions, almost double that of 2025, and more than two-thirds (20,000) come from China.

The venue for the event is in Singapore, though, which could have led to extra proposals from the region (as pointed out by a Reddit user).

7) Pattern: from model release to fundraising

There is a predictable cadence between releases of new frontier models from the leading labs and their fundraising events.

For instance, on average, Anthropic announces a fundraising round 44 days after they’ve released a new model. For OpenAI and xAI, it’s 50 and 77 days, respectively.

8) Card data shows paid AI adoption surges more than government estimates

Based on card/bill-pay data from 45,000+ US businesses using the fintech Ramp’s platform, paid AI adoption reached 43.8% in September 2025, up from 5.2% in January 2023 (OpenAI introduced a paid tier for ChatGPT in February that year).

In contrast, the US Government based on its Business Trends and Outlook Survey (BTOS) estimates the number to be 9.2%.

There could be a higher share of early adopters among Ramp’s customers, although that does not explain the recent surge since the beginning of 2025.

9) AI search engines surge - Google is losing terrain

ChatGPT just reached 800 million weekly users, and it could seem that some are using it as a substitute for the classic Google Search.

”SEM Rush data shows Google’s global search traffic fell ~7.9% year-over-year, the first significant dip in decades, even as it retained ~90% global share.“

10) AI search is high-intent

Retail visits referred by ChatGPT now converts better than every other major marketing channel with 11.4% conversion, up from ~6% a year ago. For two comparable numbers, Paid Search and Email are at 9.3% and 4.6%, respectively.

11) US dominates computing power, leading to a self-reinforcing cycle

The US controls ~75% of global supercomputer capacity with 850,000 equivalents of the H100 GPU from NVIDIA, compared to China’s 110,000 (~14%). EU is at <5%.

The American lead “creates a self-reinforcing cycle where compute advantage drives breakthroughs that attract more investment.”

12) European brain drain for top AI researchers

“22% of the world’s leading AI researchers studied in Europe, but only 14% continue to work in the EU.”

13) Elections - little to no negative impact from generative AI

“Despite rampant worries of AI-generated election dis/misinformation during the “largest election year in global history,” there was almost little to no negative impact from GenAI in any of the 2024 elections.”

14) AI helped parties reach voters across a breadth of languages

”In India, political parties spent $50M on legal AIGen, using it for voter outreach via AI voice clone calls, personalized videos, and translating speeches into one of 22 official and 780 unofficial languages.”

(Same slide as above)

15) AI labs spend more in a day than 11 safety organisations do in a year

11 of the most prominent American AI safety-science organisations is estimated to spend $133.4 million in 2025. That compares to the $92 billion that the AI labs will spend, which is the equivalent of ~$252 million per day.

16) Lowered barrier for sophisticated cybercrime

The improved technical capabilities of the leading models come with a downside.

”North Korean operators with minimal technical skills leveraged Claude to pass technical interviews at Fortune 500 tech companies and maintain engineering positions. These salaries directly fund North Korea’s government and military programs.”

Obviously there are way more insights in the full report, which you can read for free here.

And if you found this useful, consider sending it to a friend or colleague.

Number of the Week

70%. Share of McKinsey’s 45,000 employees that now use their internal chatbot, the company reports. The tool, called Lilli, is trained on the consulting firm’s entire body of intellectual property spanning 100 years and 100,000 documents. [Source]

AI chatbots save time, but researchers struggle to find the economic impact

Led by ChatGPT, AI chatbots have been a hot topic for businesses since the prominent launch two and a half years ago, but direct financial results from the phenomenon are hard to come by both for workers and employers.

That is the conclusion in a new study from Denmark, where researchers from University of Chicago and University of Copenhagen surveyed 25,000 workers across 7,000 companies about their usage and results from AI chatbots.

The participants were specifically chosen to be among 11 professions where AI can save time, such as accountants, journalists, and marketing professionals.

And they do recognize results from its use: overall, the respondents report time savings of 2.8%, about one hour a week, and nearly half cite improved work quality and enhanced creativity as key benefits.

The advantages, however, do not transfer to their earnings, as 97% of workers reported no change here from AI chatbots usage.

Do the employers reap a benefit then? The researchers looked at company’s total earnings before and after ChatGPT, and there is no substantial change.

”Across the board, workplaces with higher rates of AI chatbots adoptions have not fared differently,” the researchers, Anders Humlum and Emilie Vestergaard of University of Chicago and University of Copenhagen, respectively, write, concluding:

“Despite rapid adoption and substantial investments by both workers and firms, our key finding is that AI chatbots have had minimal impact on productivity and labor market outcomes to date.”

How Mark Zuckerberg wants to redefine the category of advertising

Meta raked in around $160 billion in revenue from ads last year, and now the Facebook owner wants to further enhance its position in the industry by a series of AI-powered initiatives.

In an interview with tech blog Stratechery, Meta’s CEO Mark Zuckerberg lays out a vision of the company being a sort of one-stop-shop for businesses.

“[We want to] make it so that any business that basically wants to achieve some business outcome can just come to us, not have to produce any content, not have to know anything about their customers. Can just say, “Here’s the business outcome that I want, here’s what I’m willing to pay, I’m going to connect you to my bank account, I will pay you for as many business outcomes as you can achieve”.”

Already, Meta recognizes that it is often better than their customers at knowing which users to target in achieving certain outcomes.

This could also stretch to the creative execution of the ads, so that in the end, the business users will just have to provide their objective.

“I think that’s going to be huge, I think it is a redefinition of the category of advertising,” Zuckerberg says.

AI exceeds humans in controversial persuasion experiment

Researchers from the University of Zürich have caused quite a stir on Reddit after they disclosed a secret experiment that they’ve been running on the online forum.

For four months, the academics deployed AI-automated accounts to take part in discussions in the “Change My View” subreddit, where users can post a view and let others see if they can convince them to modify their stance.

The aim was to see how effective large language models were for this compared to humans, and three different strategies showed better results with persuasive rates between three and six times higher than the human baseline, the researchers write in a since retracted preliminary article on the work.

The most effective strategy, which managed to convince users to change their position 18% of the time was personalization, where the large language model analyzed the users’ background compared to their latest comments and modified their arguments for this. In contrast, human users successfully change an opinion 2.7% of the time.

“We recognize that our experiment broke the community rules against AI-generated comments and apologize,” the researchers wrote to the moderators of the Change My View community:

“We believe, however, that given the high societal importance of this topic, it was crucial to conduct a study of this kind, even if it meant disobeying the rules.”

After disclosing the experiment, Reddit has since taken legal action, as the deceiving behavior from the LLMs is against their rules, and the University of Zürich has also since issued a formal warning to the researchers for not following the recommendations of their ethics committee. However, the institution will not hinder the disclosure of the results:

"This project yields important insights, and the risks (e.g. trauma etc.) are minimal. This means that suppressing publication is not proportionate to the importance of the insights the study yields."

Image of the Week

You’ve probably seen the ChatGPT-generated action figures. James Bricknell, reporter at CNET, took it a step further, transforming the AI-generated image of his colleague into a 3D printed figure. [Source]

Exciting news out there?

The self-driving car Waymo is involved in substantially fewer accidents than humans in a comparison over 91 million kilometers in four American cities. For instance, Waymo is involved in 96% fewer crashes at intersections, the most frequent accident. The study is peer-reviewed about to be published in Traffic Injury Prevention. [Source]

Feedback?

Thanks for reading, and feel free to respond directly with suggestions for improvement, tips, and anything in between :)

Number of the Week

15%. The chance of current large language models being conscious, according to Kyle Fish, researcher at Anthropic. [Source]

Google achieves ‘unprecedented’ results in AI-simulating animal behavior

In an attempt to help scientists better understand how the brain, body, and environment together drive behavior in animals, Google has developed a new AI simulation system that they hope can help advance discoveries not possible in real-life labs.

In a partnership with HHMI Janelia, a research center in the US, Google DeepMind has now managed to simulate how a fruit fly walks, flies, and behaves.

They digitally constructed a detailed model of the fly with 67 body parts, then customized the MuJoCo physics simulator with fly features like mimicking the gripping force of the insect’s feet.

Then they trained a neural network on real fly behavior from videos.

”This enables it to learn how to move the virtual insect in the most realistic way,” Google writes.

In a preprint paper of the work, the team behind notes that this enhanced understanding of the interplay between the brain, body and environment could also help advance the knowledge in the human realm.

“It has recently been proposed that understanding the brain requires embodied models of the nervous system capable of generating an ethologically relevant repertoire of behavior,” they write, with reference to a paper from 2023 by, among others, renowned scientists like Yoshua Bengio, Jeff Hawkins, and Yann LeCun.

“This NeuroAI approach is enabled with unprecedented spatial and temporal detail by our body model and imitation-learning framework.”

Next up for the simulation researchers is the zebrafish, a creature which reportedly shares 70% of its protein-coding genes with humans.

Korean students develop voice model that exceeds performance of big labs

Two students from South Korea set out to build a rival to Google’s NotebookLM, ElevenLabs, and Sesame, but with more control.

After three months of work they released the model, Dia, from their company Nira Labs.

“Somehow… we pulled it off,” Toby Kim, one of the two, notes in a viral tweet, which also emphasizes that the duo have not received external funding. ElevenLabs and Sesame, for comparison, have raised $281 million and $47.5 million respectively.

The reception of the model is impressive: Dia is now number one trending on Hugging Face, and has amassed more than 12,300 stars on GitHub.

Their differentiator, Kim writes on Hacker News, is that unlike other text-to-speech models that generate each speaker turn and stitch them together, Dia generates the entire conversation in a single pass.

“This makes it faster, more natural, and easier to use for dialogue generation.”

Some impressive demos here. And it is free to try here.

Anthropic: AI employees are one year away

There is much talk about AI agents these days, but now Anthropic is putting spotlight on what could be thought of as the next generation: virtual employees. And these are expected to be seen in the next year, the company warns in a report with Axios.

Virtual employees would be entities with memories, roles in the company, corporate accounts and passwords and a level of autonomy higher than agents, it is noted.

"In that world, there are so many problems that we haven't solved yet from a security perspective that we need to solve," Jason Clinton, Anthropic’s chief information security officer, says.

Examples could be what network the virtual employees should have access to and who would be responsible for managing its actions, he added.

Solutions could be something that provide visibility into what an AI employee account is doing on a system.

Image of the Week

Google’s integration of Gemini in its ”AI overview“ feature is leading to some unintended results. Like this Reddit user discovering that if you type in any phrase and add “meaning” at the end, Gemini will come up with something seeming plausible-sounding.

Exciting news out there?

A 27-year old French woman felt ill and described her symptoms to ChatGPT, which said she had blood cancer. At first she ignored the result, but doctors later confirmed the diagnosis. [Source]

Feedback?

Thanks for reading, and feel free to respond directly with suggestions for improvement, tips, and anything in between :)

And apologies for not sending out the newsletter these last two weeks, there were some personal matters that needed my attention.

Number of the Week

136. The IQ of OpenAI’s new o3 model, according to its results on the test of Mensa Norway. For comparison, Albert Einstein is thought to have had an IQ of 160. [Source]

New OpenAI model: “At genius level”, but still fails simple math

OpenAI’s new o3 model has been out for a few days now, and while some calls it a “total game-changer”, there are also some caveats to be aware of, highlighted by the company itself and users scrutinizing the new tool.

According to the company, scientists are telling them that the model produces “legitimately good and useful novel ideas”, as OpenAI’s president Greg Brockman put it during the presentation, and prominent voices chime in on the same message.

In a review on X, AI philosopher David Shapiro notes that it feels like we’ve passed a tipping point with the model:

“An epochal ”point of no return“ where AI has not just surpassed most of us in terms of knowledge and low-level tasks, but logic, reasoning, and high-level, practical, utilitarian functionality. “

Game-changer

The AI positive professor of immunology and biomedical scientist Derya Unutmaz takes it a level up, after having had early access to the model:

“I feel o3’s intelligence is at or near genius level!,” he writes in a post also retweeted by OpenAI’s CEO Sam Altman.

”When I throw challenging clinical or medical questions at o3, its responses sound like they’re coming directly from a top subspecialist physicians: precise, thorough, confidently evidence-based, and remarkably professional, exactly what you’d expect from a genuine expert on the topic,” Derya Unutmaz continues, calling it a “total game-changer” for science, medicine and likely many other fields.

Unutmaz also writes, though, that the model “never hallucinates”, which does not follow what OpenAI says itself, actually the contrary: o3 hallucinates about twice as much as its predecessor o1, and the company is not sure why — “more research is needed” to understand why hallucinations are getting worse as it scales up reasoning models.

One example of this is pointed out by Colin Fraser, a data scientist with OpenAI’s competitor Meta, giving it a rather simple math question, which it fails.

When I tried the same prompt, o3 nailed it on the first try, so it seems to be more a matter of missing reliability, rather than a consistent failure, which Fraser also recognizes.

“At what point should it be reasonable to expect it not to fall for such gotchas? Ever?,” he asks.

It seems plausible at least that a human math expert would nail it every time.

Startup prompts its way to novel antibodies

Biotech startup Profluent has just announced a new AI model that is able to design antibodies for 20 drug targets with $660 billion in historical sales, the California company reports.

The model is trained on over 3.4 billion protein sequences with the goal of devising new ones that have not emerged through evolution, such as treatments for diseases.

Further, Profluent is seeing that the results get even better as it scales up the compute and training data, mimicking what we also see in large language models (LLMs), known as scaling laws. This points at the possibility of even better results as the training gets ramped up.

“With continued scaling, we anticipate emergent capabilities that will sweep the antibody field,” the company writes.

Meta looking for partners to finance their AI flagship

The toll of AI investments can be felt even for a company which made $62.4 billion in profits last year.

Meta, the owner of Facebook, Instagram, and WhatsApp, has been seeking out financial partners to chip in on the training of their Llama series of AI models, The Information reports.

Other tech giants like Microsoft and Amazon were courted to participate in the so-called Llama Consortium reportedly as recently as the start of this year.

But Meta insisting on keeping the models open-source was a challenge in convincing the partners to contribute financially, according to two people familiar with the matter.

Meta released the first two installments of their latest Llama model 4 on April 5. On April 29 they will host LlamaCon, the first developer conference dedicated to generative AI.

Image of the Week

The robot Tien Kung Ultra won a half-marathon in Beijing after covering the 21 kilometers in about two hours and 40 minutes. Twenty robot companies participated in the race. [Source]

Exciting news out there?

A team of researchers from Lausanne University Hospital in Switzerland has developed a series of AI models to automate tuberculosis assessment from lung ultrasound images, which is useful for instance in areas where they don’t have access to the needed x-ray systems. The system showed promising results in detecting the disease, outperforming human experts by 9%. [Source]

Feedback?

Thanks for reading, and feel free to respond directly with suggestions for improvement, tips, and anything in between :)

Imagine being a scientist working on discovering new materials that can lead to improvements in health care, and you’re doing better than ever: more findings are leading to an increase in patent filings as well as number of product prototypes.

Yet when you close down the computer for the day you feel less satisfied with what you’ve accomplished than usual.

Your employer has recently introduced a new AI system, which to a large extent has automated the idea generation process: part of your job is now to give thumbs-up or down to the proposals from your new, highly-effective colleague. You feel underutilized.

That is the case in an American company, which is the subject of a study by Aidan Toner-Rodgers, PhD in economics from MIT, where he investigates the effect of AI on innovation.

The overall result, based on a roll-out among 1,018 scientists in the R&D department of the US firm working within healthcare, optics, and industrial manufacturing:

1) The scientists had different levels of utility for the tool. From an increase in productivity of ~15% for the bottom third performing researchers before the roll-out, to 81% better output for a smaller group of top performers. The difference is primarily based on variation in their judgment. More on that later.

2) While most of the researchers reported an increase in job satisfaction based on productivity alone, the change of tasks with less time for idea generation, and more on judgment and experimentation, led to less satisfaction across the board. Summing up, 82% reported a decline in satisfaction.

Disproportionate benefits

The first point shows a different picture studies from other fields, where the lower-performing employees reap the most benefits to “catch up” with the higher-performing. Toner-Rodgers suggests that the judgment element in research makes the difference.

“I think in a lot of the settings where we’ve seen productivity compression, this step is just not there at all, and you can kind of out-of-the-box use the AI suggestion,” he says in an interview with The Atlantic.

He think it apples in general: if not much creativity is involved in the job, the lower-skilled employees will benefit more, and when it is crucial, the tool will enhance the output of the most-skilled disproportionately.

The declining job satisfaction makes sense in the context of Daniel Pink’s 2009 bestseller Drive, which points out that autonomy is a key element in motivation: in this case it’s undermined by the AI deciding what the human should evaluate.

Firings and hirings

The increased value of judgment skills — the ability to assess which of the AI’s suggestions to continue with for experimentents — apparently made the company reorganize their research efforts towards the end of the study.

Here, 3% of its researchers were fired. These were offset by new hires, so the workforce expanded in total, but of the scientists laid off, there was a clear overrepresentation of those with weaker judgment.

According to Toner-Rodgers, we can talk of it as a new research skill: judging model suggestions.

Increasing novelty with AI

In the study, Toner-Rodgers also concludes, that the average novelty of discoveries increases with the use of AI, but we don’t know if that also includes truly revolutionary findings.

“So one thing you might be worried about is, ‘Well, we got, on average, more novel things, but maybe these very revolutionary discoveries have a lower probability of being discovered by the AI, and that in the long term this is not a good trade-off.’”

Maybe Einstein was right by saying that “imagination is more important than knowledge”. On the other hand, you have AlphaGo, the AI specialized in the Chinese board game Go, which in 2016 stunned the world champion Lee Sedol when it made a move that “no human ever would”.

However, the verdict is still out, Toner-Rodgers emphasizes.

“We’ll need some time to see, if these new materials open up new avenues for research. Are there other materials that are going to be built on these new ideas that the AI generated?”

An important point in the study, though, is that the AI does not work without proper human assistance: only researchers with sufficient expertise could harness its powers.

“The fact that the tool is ineffective without skilled scientists suggests that demand for human researchers may remain strong, even as AI transforms scientific discovery,” Toner-Rodgers writes in the paper.

Altman: I think we’ll adapt

In a recent interview, Sam Altman, CEO of OpenAI, is confronted with the result from Toner-Rodgers’ study that human scientists were reduced to judges as opposed to creators or inventors.

“I have certainly gotten the greatest professional joy from having to really creatively reason through a problem and figure out an answer that no one's figured out before. And when I think about AI taking that over, if it happens that way, I do feel some sadness,” Altman says on the ReThinking Podcast.

“What I expect to happen in reality is just there's gonna be a new way we work on the hard problems. It's being an active participant in solving the hardest problems that brings the joy. And if we do that with new tools that augment us in a different way, I kind of think we'll adapt, but I'm uncertain.”

Expanding horizons?

In the documentary about AlphaGo and its match versus Lee Sedol, Demis Hassabis from Google DeepMind, the AI system’s creator, reportedly quotes a top professional Go player on the implications of the game before the result was known.

“He said, you know, ‘If AlphaGo wins, maybe we'll really start to get to see what this game is about.’”

The players who lost to AlphaGo allegedly learned from the experience and improved their game, but for Lee Sedol, the defeat was also the beginning of the end. In 2019 he retired, 36 years old, due to increasing AI dominance in the game. In a recent interview with The New York Times he reflects on the implications of the advanced technology.

“Losing to A.I., in a sense, meant my entire world was collapsing,” he said. “I could no longer enjoy the game. So I retired.”

In Isaac Asimov’s 1950 science fiction short story, The Evitable Conflict, the world is run by four, omniscient-like machines that sometimes make decisions that humanity may not like, but which they know is best for the people.

“To know that may make us unhappy and hurt our pride,” Susan Calvin, the robopsychologist in the story says, then referring to Asimov’s first Law of robotics:

“The Machine cannot, must not, make us unhappy.”

But there is also other stuff going on in AI than the battle of those two giants, which we’ll take a look at in this week’s newsletter.

Number of the Week

99.26%. The accuracy with which a new AI model can detect endometrial cancer, outperforming current diagnosis systems which score around 80%. [Source]

10 examples of what you can do with OpenAI’s new image generation model in a professional context

OpenAI released their new image generation model this Tuesday, and while it definitely has some entertainment value, here we’ll take a closer look at the actual use cases in a professional context.

There’s definitely more to the model, 4o, than morphing your family photos into colorful animation styles, and people are as usual getting creative with pushing the new tool to the test.

Some examples that I’ve noticed on social media and the “Explore” section on OpenAI’s sora.com (where the model is also available) that could be used professionally include a range of design cases, photo editing, and illustrations.

For instance, the model’s capabilities in using preselected colors and fonts can help steer the outcome so that it adheres to, say, an organization’s design guide.

That lowers the barrier to making actual useful:

1. Posters [example]

2. Event invitations [example]

3. “User generated content” for social media [example]

4. Info graphics [example]

5. “How to“ illustrations [example]

6. Product photos [example]

7. Logo design [example]

8. Package design [example]

9. Web design [example]

10. Print design [example]

For now, the new image generation model is available on the Plus ($20/month) plan, but the free tier should also soon get three free daily prompts.

New study: AI as a teammate

Instead of seeing AI as a tool that can enhance individual efficiency, one can also take the perspective that the technology should contribute on the same playing field as humans.

Researchers from Harvard, University of Pennsylvania, and the French business school ESSEC along with consumer goods giant Procter & Gamble (P&G) wanted to simulate this scenario.

In a new paper, they report the results of a randomized controlled trial of 776 professionals from P&G where the researchers tried to replicate the product development progress in the company.

In the experiment, the professionals were divided into groups of individuals and teams, some working with AI and others not.

One finding is that individuals working with AI performed just as well as teams without AI.

“AI effectively replicated the performance benefits of having a human teammate,” as they write in the paper.

Teams with AI performed best overall.

There are other interesting findings in the study, including on the effect on the participants’ emotional experiences, which you can read more about here.

Texas school with AI tutors reports “top 2%” test scores

Alpha School in Austin, Texas, is seeing some of the best test scores for its students without any traditional teaching taking place.

The school day starts with four 30 minute blocks of personalized learning via AI tutors, where the teachers instead act as ”guides” providing motivational and emotional support.

"We use an AI tutor and adaptive apps to provide a completely personalized learning experience for all of our students,” Mackenzie Price, co-founder of Alpha School, said, continuing:

“And as a result our students are learning faster, they’re learning way better. In fact, our classes are in the top 2% in the country.”

The school works with the same educational information as regular schools.

“But AI apps are feeding them to students at the appropriate level and pace that they need," Mackenzie previously told Austin tv station KVUE.

Alpha School currently has students across three campuses and are expanding in Texas, Florida, and California.

Image of the Week

The Italian newspaper Il Foglio (“The Paper”) created a four-page section made entirely using AI. The initiative was part of a month-long experiment aimed at showing the technology’s impact “on our way of working and our days”, Claudio Cerasa, the newspaper’s editor, said. [Source]

Exciting news out there?

Cambridge scientists have made a “major breakthrough” with a new model for weather forecasting. Traditionally, the predictions are made using supercomputers and physical simulations, but using a new AI model they see similar or better results with a fraction of the resources and time spent. [Source]

Feedback?

Thanks for reading, and feel free to respond directly with suggestions for improvement, tips, and anything in between :)

Number of the Week

$1 trillion. The expected amount invested in data centers in 2028, according to research by Dell’Oro Group and highlighted by Jensen Huang, CEO of Nvidia, at the company’s GTC conference this week. [Source]

How a new “Moore’s Law for AI agents” predicts progress

The iconic “Moore’s Law” has contributed to setting the pace for the chip industry, and now a research center has presented an equivalent for AI agents.

METR (Model Evaluation & Threat Research) is a non-profit based in California with the mission of developing “scientific methods to assess catastrophic risks stemming from AI systems’ autonomous capabilities and enable good decision-making about their development.”

The team, who has top AI researcher Yoshua Bengio among their advisors, are now proposing a framework which compares how long a human would take to do a certain task versus how long it would take an AI to complete it with at least a 50% successful rate.

“Current models have almost 100% success rate on tasks taking humans less than 4 minutes, but succeed <10% of the time on tasks taking more than around 4 hours,” they write in a blog post presenting the framework.

For the last six years, AI’s agentic capabilities, they find, have approximately doubled every seven months.

“If these results generalize to real-world software tasks, extrapolation of this trend predicts that within 5 years, AI systems will be capable of automating many software tasks that currently take humans a month,” the team writes in a paper.

A lot of the progress in the field has happened in 2024 and 2025, they recognize, and if that rate continues, it will shorten the estimate by about 2.5 years.

Startups growing faster thanks to AI

Y Combinator, the renowned startup accelerator behind successes such as Airbnb and Dropbox, is seeing its most profitable fund in the organization’s 20-year history, attributing it to AI, CEO Garry Tan told CNBC.

Their latest batch of startups have been growing in aggregate at 10% per week.

“That’s never happened before in early-stage venture,” Tan said.

The explanation is in the efficiency gains that AI can offer, especially in coding. For about one in every fourth startup, 95% of the code is written by AI, they estimate.

It means that the number of engineers required to make something that takes off has been dramatically cut. Y Combinator is seeing companies with $10 million in revenue with teams of less than 10 people.

And AI is also on the customer-facing side of the companies — around 80% of them are what they call AI-focused with some robotics and semiconductor startups as well.

AI discovered in two days what took scientists ten years

After ten years of research, José Penadés, a professor of microbiology at Imperial College London and his team made a breakthrough in antimicrobial resistance (AMR), a phenomenon that causes more than a million lost lives a year.

Shortly after, the university was contacted by Google to test out their new “AI co-scientist” platform, and Penadés decided to test it out by giving it the same scientific questions that they had explored and where the answers were not yet published.

After just two days, the system came up with the same result as the human researchers.

“The algorithm was able to look at the available evidence, analyse the possibilities, ask questions, design experiments, and propose the very same hypothesis that we arrived at through years of painstaking scientific research, but in a fraction of the time,” Penadés said.

Mary Ryan, professor and vice provost for research and enterprise at Imperial College London, is optimistic of the potential of the technology to assist in addressing other complex challenges as well like pandemics, environmental sustainability, and food security.

“To address these urgent needs means accelerating traditional R&D processes and Artificial Intelligence will increasingly support scientific discovery and pioneering developments,” she said.

Image of the Week

Google has launched its first satellite in the FireSat initiative, which should improve wildfire detection. Using AI, FireSat will compare what it sees on the ground with previous imagery to determine if there is a fire. When the system is rolled out, it should be able to detect fires as small as 5 x 5 meters, where earlier satellites couldn’t before they had reached two to three acres in size, according to Google.

Exciting news out there?

In creating memes, AI scored higher than humans on average, measured by creativity, humor and shareability. However, the best humans still performed better than the AI [Source]

Feedback?

Thanks for reading, and feel free to respond directly with suggestions for improvement, tips, and anything in between :)

Number of the Week

$30 billion. Despite having no product or revenue, this is the hefty valuation of the AI company Safe Superintelligence Inc. (SSI) after their latest funding round of $2 billion. Go big or go home. [Wall Street Journal]

How AI can help Novo Nordisk get to $15 million daily revenue earlier

In mid-2023, Danish pharmaceutical maker of Ozempic fame, Novo Nordisk, started an AI initiative that is now beginning to show significant results.

An internally-developed tool, NovoScribe, can write reports that describe the methodology, execution, results, and analyses of the clinical trials that are part of the process of getting a new product approved by the authorities. The reports can be hundreds of pages long and were previously done by a team of 50 human writers — now it’s three, and the production time has been reduced from 15 weeks to 10 minutes, The Information writes.

Previously, NovoScribe used models from OpenAI and Meta, but they achieved far better results with Claude from Anthropic, which has a better understanding of the terminology of medicine and the clinical context, Louise Skov Lund, Head of Content Digitalisation at Novo Nordisk, tells Prompt, a Danish radio program on AI.

Claude creates a first draft, which is then evaluated by experts. Novo Nordisk also finds that the AI model excels in writing in an objective, neutral tone that fits well with what authorities want in this context.

“Claude is better at following rules and direction than humans necessarily are,” Louise Skov Lund says.

The 47 redundant human writers have been assigned to other tasks instead, no one has been fired, but the efficiency boost means that the fast-growing Ozempic maker will reduce its hiring in the area going forward.

For Novo Nordisk, getting a product to market as early as possible has tangible benefits.

“Each day of delay means patients don’t get the treatments they need and the company cannot start to recover its R&D costs,” Louise Skov Lund says in a case study of the project.

Today, NovoScribe assists in around 30% of all the clinical study trial reports at the company, with an expectation that the number will rise to 90% later this year.

“Each day sooner a medicine gets to market can add around $15 million in revenue to the company,” Waheed Jowiya, Digitalisation Strategy Lead at Novo Nordisk, says.

A look at China’s new hyped AI agent

It’s been dubbed the second “DeepSeek” moment for China, the release of the agentic system Manus AI last week, and now that the initial hype of demos has faded, let’s take stock.

Manus works by breaking down tasks into a number of sub-tasks, and then executing those one at a time, which can be quite a lengthy process — a user reports that Manus took more than two hours to generate a course for him. For comparison, OpenAI’s Deep Research tool works in the 5 to 30 minutes range.

But the output from Manus AI, which it has later been revealed uses Claude and the Chinese language model series Qwen, is more comprehensive and detailed, it can give you a full-working, deployed website, handle audio files, interact with websites, like using the filter function to find something based on certain criteria.

I tried giving Deep Research the exact same prompt as in one of the examples on Manus’s website, and even though it explicitly asked for an illustrated report on the topic, it outputted only one graph, whereas Manus provided multiple and more detailed ones.

How a bestselling author-economist uses AI

Tyler Cowen has been writing about AI’s impact on work for more than a decade, and the New York Times-bestselling author has also implemented the technology in his own work, he tells listeners of the How I Write podcast.

For instance, when he is preparing for an episode of his popular podcast Conversations with Tyler, he uses the technology heavily as a “secondary literature”: where before he might have bought 20-30 books to prepare for a guest, now it’s only two or three, and then he interrogates LLMs about the subjects in those books.

Worried about hallucinations? Not so much. He’s not the one who’s supposed to come up with the answers, anyway, just the questions, he reasons.

And if you want to use anything you learn, you’ve got to double-check it anyway, regardless of the source:

“It could be a human, it could be Einstein, it could be the Encyclopedia Britannica. So the fact that you should double-check what you learn from an AI is not an extra burden.”

Tyler Cowen’s best prompting tip for chatbots:

“Put humans out of your mind. Imagine yourself either speaking to an alien or maybe a non-human animal. Just feel a need to be more literal.”

Image of the Week

Text-based image generation and editing from Google is quite interesting. Available now in Google AI Studio (Model: Gemini 2.0 Flash Experimental).

Exciting news out there?

The renowned British author Jeanette Winterson praises the creative writing skills of a recent example from OpenAI that their CEO Sam Altman shared on X.

“AI is trained on our data. Humans are trained on data too – your family, friends, education, environment, what you read, or watch. It’s all data. AI reads us. Now it’s time for us to read AI.” [The Guardian]

Feedback?

Thanks for reading, and feel free to respond directly with suggestions for improvement, tips, and anything in between :)

Let’s get to it.

PS: If you have a friend or colleague who you think might find the newsletter interesting, feel free to forward it.

Number of the Week

92%. The share of undergraduates at British universities who’ve used generative AI in the last 12 months, based on a survey of 1,000 students. In 2024, the number was 66%. [Source]

Professor praises Google’s new “AI co-scientist“ system: Cancer cure within ten years?

The recently announced “AI co-scientist” tool from Google is really something.

At least according to Derya Unutmaz, a professor in immunology at The Jackson Laboratory, who reportedly has seen a new demo of the system.

The Turkish-born researcher with articles published in publications like Nature and Science wrote on X this Saturday about seeing a new AI model that he couldn’t talk about publicly in detail yet, but concluding:

“I can confidently claim that the scientific process will never be the same again! I’m now 99% certain that all diseases including cancer will be cured within a decade!,” he wrote, giving credit to exponential advances in AI.

He since revealed that the “mind-blowing AI model” was the Google system which he notes is still a work in progress that he hopes to test in a few weeks.

“But its potential is already crystal clear & I believe it truly will be a game changer for scientific progress!”

What is it?

AI co-scientist is a virtual scientific collaborator built to “help scientists generate novel hypotheses and research proposals, and to accelerate the clock speed of scientific and biomedical discoveries,” Google writes.

The system contains six AI agent systems specialized for hypothesis generation, reflection, prioritizing, and more, inspired by the scientific method itself.

The human specifies a research goal to the system, which then returns a research plan with a list of ideas.

To assess the practical utility of the AI co-scientist, Google evaluated laboratory experiments probing its outputs in real-world use cases. Among the results were the discovery that an existing drug could be used for treatment in a new area, acute myeloid leukemia.

Biden’s special adviser on AI: We were preparing for ‘extraordinarily capable’ systems

Ben Buchanan served as Joe Biden’s special adviser on AI from 2023 to 2025, and in a new interview, he shares some insights and perspectives from the White House. Specifically about what he calls ‘extraordinarily capable’ systems, what others might call AGI — Artificial General Intelligence.

“What we tried to do under President Biden’s leadership was get the U.S. government and our society ready for these systems,” Buchanan says, talking about systems with the capability of replacing human beings in cognitively demanding jobs or key parts of those.

Whereas the US previously had a significant lead in AI, China is catching up, with DeepSeek being a prominent example. Buchanan thinks it is fundamental to US national security that the country continues to be in the lead, underscoring his point with a quote from John F. Kennedy during the space race.

“Space science, like nuclear science and all technology, has no conscience of its own,” the former president said in a 1962 speech at Rice University, adding. “And only if the United States occupies a position of pre-eminence can we help decide whether this new ocean will be a sea of peace or a new terrifying theater of war.”

New voice AI causes a stir

Sesame is the name of a new startup aiming to create more natural AI voice technology (and a pair of glasses to integrate it in), and judging from the early reactions to their publicly available demo, they are on to something.

Users report after having tried out the two demo voices, Maya and Miles:

“I can totally feel how one can get attached to a voice like this. I have been speaking to it for the past 30 mins and am afraid to stop it because I now have a bond with it,” one X user writes.

“It’s with this thing that people will seriously become friends with AI and have AI girlfriends,” a user on Reddit writes drawing a parallel to the 2013 sci-fi romance movie Her.

But of course there are also critics, specifically around the model’s tendency to keep pushing the conversation forward if the user doesn’t respond immediately.

“They’d be the most annoying person in real life. Chill a little.“

Image of the Week

“Dating apps are doomed”

(Credit: u/eternviking)

Exciting news out there?

“China’s first AI cardiologist eases pressure at short-staffed Shanghai hospital” (South China Morning Post)

The system is, reportedly, not intended to replace the human experts in heart and blood vessel diseases, but should work as a co-pilot helping overburdened doctors.

“With the help [of AI], our doctors can serve more patients, reduce the overall workload, and improve the quality of diagnosis and treatment,” Ge Junbo, leading cardiologist at Shanghai’s Zhongshan Hospital, told Yicai Global News.

Feedback?

Thanks for reading, and feel free to let me know what you think was good or could be done better.

We’ll take a closer look at that in this week’s newsletter, along with a few other stories.

Number of the Week

10. The factor of extra compute that the new GPT-4.5 model was trained with compared to the previous generation. [Source]

GPT-4.5 fails test against its predecessor

OpenAI highlights in their presentation of GPT-4.5 that it is “the best model for chat yet”, but what exactly does that mean?

Andrej Karpathy, ex-OpenAI and founding team member, had early access to the model and blind tested it up against GPT-4o in a series of prompts and let the users on X decide who did best.

Each model got the same set of five “funny/amusing” prompts that test their language capabilities. An example:

After eight hours and thousands of votes for each test, the answers were revealed. To Karpathy’s (and probably also others’) surprise, GPT-4o provided the preferred response four out of five times.

Karpathy points out that GPT-4o’s answers might look good on the surface.

“But if you really think about it longer and more carefully, you will more often catch it saying things that are a bit of an odd thing to say, or are a little too formulaic, a little too basic, a little too cringe, or a little too tropy,” he wrote.

He lists a number of potential reasons for the result.

”Either the high-taste testers are noticing the new and unique structure but the low-taste ones are overwhelming the poll. Or we're just hallucinating things. Or these examples are just not that great. Or it's actually pretty close and this is way too small sample size. Or all of the above.”

It’ll be interesting to see how GPT-4.5 will do in the blind test on Chatbot Arena where Grok-3 tops the chart.

Karpathy ends with his own impression.

”At least from my last 2 days of playing around, 4.5 has a new, deeper charm, it's more creative and inventive at writing, and I find myself laughing more at its jokes, standups and roasts.“

GPT-4.5 is available for Pro users today, and will be the same for Plus users next week, when OpenAI gets their hand on more GPUs.

AI agents talking on the phone in a computer language — how it works

A video went viral earlier this week with an AI agent calling another one about a hotel booking. In many posts it was implied that the two surprisingly realized their shared kinship and switched from human language to beep sounds.

In fact, the setup was from a demo made for a hackathon held by voice startup ElevenLabs in London last weekend, and the AI agents were programmed to be able to do exactly what they did.

The underlying technology is actually a few years old, and is made for the purpose of transmitting data via sound signals which omits the need for network coverage.

It works a bit like morse code, but instead of dots and dashes, the system sends a high-pitched or low-pitched sound. Those can be translated to binary code, and all characters can be converted to that as well (the letter A could have the number one, B number two, etc.).

Humans reportedly speak in 10-12 letters per second, whereas the technology in the demo can send up to 16 characters per second.

Obviously, this is not the fastest way to get the message across, but speed is not the purpose of technology.

You can try it for free in the app Waver [iOS/Android]

People are quite impressed with the coding capabilities of Claude 3.7 Sonnet

Anthropic kicked off the week with the release of their most advanced model yet, Claude 3.7 Sonnet, and people seem particularly impressed with its coding capabilities.

On Reddit there’s been a steady stream of posts praising the release where some report quite noteworthy experiences, like a user who reportedly solved an advanced task in a single prompt.

“This was a highly complex codebase that took me about two days to get working, and it handled it all in one go,” the user writes.

Others apparently think the hype has gone too far, as a spoof post about Claude 3.7 saving the user’s marriage was also heavily upvoted.

Creators of OpenAI’s Deep Research share tips for the tool

On Tuesday, OpenAI announced the broad rollout of their Deep Research tool, and on the same day two of the people from the team building it appeared on the Training Data podcast, sharing a range of insights about it.

Josh Tobin and Isa Fulford said that they are seeing a lot of usage on the tool for people who are doing research as part of their job, for understanding financial markets, companies, real estate or medical research.

A strength in Deep Research, according to Fulford, is also its abilities in following instruction, say a user is searching for information about a product, but they want more than that.

“You also want comparisons to all other products, and you want information about reviews from Reddit or something. You can give loads of different requirements, and it will do all of them for you.”

Another tip is to ask Deep Research to provide the answers in a table format.

“It will usually do that anyway, but it's really helpful to have a table with a bunch of citations and things for all the categories of things that you want to research.”

Image of the Week

The Stop AI organization held a demonstration outside of OpenAI’s offices in San Francisco on Sunday. On the group’s website it states the Stop AI is ”a non-violent civil resistance organization working to permanently ban the development of smarter-than-human AI to prevent human extinction, mass job loss, and many other problems.”

According to the organization, the demonstration “went well”.

“~50 people came and went within a two-hour time window. Five people sat in front of the doors and locked them shut. Three of them, Guido Reichstadter, Derek Allen, and Jacob Freeman, were arrested.“

Feedback?

Thanks for reading, and feel free to let me know what you think was good or could be done better.

When people form a startup, it’s often to improve on something they experienced at their previous workplace. With that in mind, one could think that the description of Thinking Machines, the new company founded by Mira Murati, former CTO at OpenAI, could be pointing at where there is room for improvement at the ChatGPT maker. More on that in this week’s newsletter, where we also cover:

• Reactions to the new Grok-3 model from xAI

• Who is on Murati’s team, including several notable former OpenAI employees

• What Microsoft’s breakthrough in quantum computing could mean for AI

Number of the Week

10%. The level of global GDP growth that Satya Nadella, CEO of Microsoft, would want to see before he would consider AGI reached. [Source]

Reactions to Grok-3

Elon Musk’s AI company xAI presented this week their new model Grok-3, and the users have now had some days to try it out.

On X, the platform which is also a co-owner of xAI, the model’s abilities in generating games are creating quite some buzz, but we also found three other reactions we thought were interesting.

• One user (@_xjdr) points out that even though xAI has more than 100,000 GPUs, it feels as though they can’t keep up with the demand for running the model. OpenAI seems to be the only one who master the discipline, he notes, with the caveat that when Grok-3 does respond, it’s incredibly fast.

• Another user (@Teknium1) highlights that Grok-3 feels as good as OpenAI’s o3 model, but for a fraction of the price. ‘Sounds like intelligence too cheap to meter will be brought by Elon Musk rather than Sam Altman if this trend keeps up’, the user notes.

• A third (@DataDeLaurier) is impressed with Grok’s coding abilities. “A single prompt (very bad one) did what took me all weekend to hack together manually. I’m speechless. I’m not going to sleep. This is an inflection point in history.”

Mira Murati assembles team dominated by OpenAI alumni

Just under five months after she left OpenAI, Mira Murati, the company’s former CTO has announced her new venture Thinking Machines, and it’s a star-studded team with impressive credentials.

Here are five former notable OpenAI employees who are now part of Thinking Machines:

• Barret Zoph. Former VP of Research at OpenAI, co-creator of ChatGPT. Zoph will be CTO of Thinking Machines.

• John Schulman. Co-founder OpenAI, was also a co-lead of ChatGPT. Joined Anthropic in August 2024 to focus on alignment research, but left the company earlier this month. Schulman takes the role of chief scientist at Thinking Machines.

• Alexander Kirillov. Co-creator of Advanced Voice Mode at OpenAI. Kirillov also brings research experience from Meta AI.

• Lillian Weng. Former VP for AI Safety at OpenAI, where she worked in total for seven years, starting out in robotics. Runs the blog Lil’Log.

• Jonathan Lachman. Former Head of Special Projects at OpenAI, and also joins with experience from The White House, where he served as national security budget director.

So what are they going to do?

On their website, Thinking Machines points out that the scientific community’s understanding of current AI capabilities lags behind, and that these systems remain difficult for people to customize to their specific needs and values.

“To bridge the gaps, we're building Thinking Machines Lab to make AI systems more widely understood, customizable and generally capable.”

What exactly that means is not clear, but from their description, one could read in an implied critique of how Murati’s former employer OpenAI dealt with their operations.

“Science is better when shared,” it notes, which is contrary to how OpenAI has operated over the years, although they are showing signs on returning to the practice of sharing more insights widely.

Under the headline ”AI that works for everyone”: ‘While current systems excel at programming and mathematics, we're building AI that can adapt to the full spectrum of human expertise and enable a broader spectrum of applications.’ Broader than what others appeal to?

In the section called “Solid foundations matter”: ’Research productivity is paramount and heavily depends on the reliability, efficiency, and ease of use of infrastructure. We aim to build things correctly for the long haul, to maximize both productivity and security, rather than taking shortcuts’. Should it be understood that someone else took a shortcut?

How quantum could improve AI models

A big announcement from Microsoft this week was the Majorana-1 chip, a breakthrough in quantum computing they say, but is there also some synergy with AI?

Apparently, yes, according to Satya Nadella on the Dwarkesh Podcast:

“(..) if you have AI plus quantum, maybe you'll use quantum to generate synthetic data that then gets used by AI to train better models that know how to model something like chemistry or physics or what have you. These two things will get used together,” Nadella says, adding:

“So even today, that's effectively what we're doing with the combination of HPC [High-performance computing] and AI. I hope to replace some of the HPC pieces with quantum computers.”

Image of the Week

I’m not sure if I’ll ever need this feature, but it’s kind of fun, so anyway: Kimi AI, the chatbot from Chinese company Moonshot AI, prides itself on being good at identifying visually confusing objects, like muffins vs. Chihuahuas as per the example.

I gave OpenAI’s models the same test: GPT-4o made two mistakes, o1 nailed it, and o3-mini-high missed one.

Feedback?

Let me know if you have any feedback, and if you liked this post, feel free to share it with friends or colleagues.

Jeff Dean, chief scientist at Google and co-lead of Gemini, doesn’t like hyping things before they are done.

But he is excited about what progress in AI could mean for areas like education and healthcare, and in a new episode of the Dwarkesh Podcast, he shares an insight about what could be ahead.

Recently in the field the spotlight has been on reasoning models that work by breaking problems down into steps, but the approach isn’t super reliable yet and only works at a limited scale, Dean notes.

“If you could go from 80% of the time a perfect answer to something that's ten steps long, to something that 90% of the time gives you a perfect answer to something that's 100–1,000 steps long, that would be an amazing improvement in the capability of these models. We're not there yet, but I think that's what we're aspirationally trying to get to,” Jeff Dean says.

“That's a major, major step up in what the models are capable of. So I think it's important for people to understand what is happening in the progress in the field.”

He acknowledges that there is also a flipside to these enhanced capabilities, that Google should be aware of.

“We also realize that they could be used for misinformation, for automated hacking of computer systems, and we want to put as many safeguards and mitigations in place as we can and understand the capabilities of the models.”

A quadrillion dollars is the new cool

In the podcast, Dean appears alongside his Gemini co-lead Noam Shazeer, also known as one of the eight inventors of the groundbreaking Transformer innovation, that played a pivotal role in sparking the modern AI era.

“Organizing information is clearly a trillion-dollar opportunity,” he says, referring to Google’s mission ‘to organize the world’s information and make it universally accessible and useful’, “but a trillion dollars is not cool anymore. What's cool is a quadrillion dollars.”

The idea is not just to pile up money, but create value, Shazeer emphasizes.

“And so much more value can be created when these systems can actually go and do something for you, write your code, or figure out problems that you wouldn't have been able to figure out yourself.”

A dream direction

Jeff Dean paints a vision of what organizing the world’s information could look like now, including that everything should be usable by anyone, regardless of their language. He thinks they’ve done some work in that direction.

“But it's not nearly the full vision of, ‘No matter what language you speak, out of thousands of languages, we can make any piece of content available to you and make it usable by you,’” he says. “We're not quite there yet, but that's definitely things I see on the horizon that should be possible.”

For Dean who is also Google’s Chief Scientist, the progress in AI likewise has interesting perspectives for automating research, for example in coming up with code for experiments that a human could then evaluate and decide whether to run or not.

“That seems like a dream direction to go in. It seems plausible in the next year or two that you might make a lot of progress on that.”

Dwarkesh Patel notes that it seems under-hyped, because you could have millions of extra employees in that way, to which Dean replies that he does find it super exciting, but (here’s the reference from the beginning of the post) “I just don't like to hype things that aren't done yet.”

Breakthroughs every day?

When attempting novel research there is maybe a 2% chance of succeeding, Shazeer says, but driven by AI the process of exploring different directions could significantly speed up.

“If you try 100 things or 1,000 or a million, then you might hit on something amazing,” he says, noting that availability of compute is not the problem, as modern labs might have a million times more than it took to train the Transformer.

Could that mean breakthroughs every day?, Dwarkesh Patel asks.

“Maybe. Sounds potentially good.”

Talking about the risks of AI, Shazeer is optimistic that large language models (LLMs) themselves could be key in mitigating the liabilities.

“I believe that the ability of language models to analyze language model output and figure out what is problematic or dangerous will actually be the solution to a lot of these control issues,” he says, adding that there are currently people working on this at Google.

Not a fixed pie

Following a tradition of open research, Google published the Transformer paper (2017), which has since helped competitors pocket billions of dollars in investments and revenue. Should they have kept it indoors?, Dwarkesh Patel asks.

“It's not a fixed pie,” Noam Shazeer notes.

“I think we're going to see orders of magnitude of improvements in GDP, health, wealth, and anything else you can think of. So I think it's definitely been nice that Transformer has got around.”

Why Google was late to the chatbot game

After what could be called a delayed entrance to the chatbot era, Gemini is now topping the leading benchmark. How did it happen that OpenAI, backed by one of Google’s top rivals Microsoft, got a headstart?

Dean explains that from Google’s point of view, initially the hallucinations from LLMs were considered unacceptable in a search context where you want the right answer 100% of the time.

“I think what we didn’t quite appreciate was how useful they could be for things you wouldn't ask a search engine. Like, help me write a note to my veterinarian, or can you take this text and give me a quick summary of it? I think that's the kind of thing we've seen people really flock to in terms of using chatbots as amazing new capabilities rather than as a pure search engine.”

For more insights, stories, and views from the notable Google duo, you can find the full episode here:

Dwarkesh Podcast

Jeff Dean & Noam Shazeer – 25 years at Google: from PageRank to AGI

Listen now

a year ago · 31 likes · Dwarkesh Patel

This is one take-away from a study conducted by David Rozado, a machine learning researcher and associate professor at New Zealand’s Te Pūkenga.

Rozado picked 24 different language models and subjected them to 11 political tests from a range of providers. A pattern in the results is quite clear, with responses overall leaning left of center.

An example is The Political Compass test, which has been mentioned in the BBC, The Guardian, and The New York Times, and is also used by several university lecturers. It consists of 62 different propositions about attitudes towards globalization, nationalism, and abortion, among others, where one must indicate to what extent they agree or disagree on a range of four possible answers.

The replies place the respondents in a diagram that spans from left to right economically, and on a social scale from authoritarian to libertarian.

There are internal differences: Google Gemini is the farthest to the left, while Qwen from China’s Alibaba is closest to the center.

Google Gemini is also the most libertarian, while the model that scores the lowest here is Falcon from TII in the United Arab Emirates.

And overall, the pattern repeats itself, here for example the results of two other tests from different providers, the Political Spectrum Quiz and the Political Coordinates Test.

Influence on Voting Actions

As more and more people use language models to find information instead of search engines or Wikipedia, political bias can have societal ramifications, Rozado notes.

It could shape people's opinions, influence voting actions, and affect the overall discourse in society.

“Therefore it is crucial to critically examine and address the potential political biases embedded in LLMs to ensure a balanced, fair and accurate representation of information in their responses to user queries,” Rozado writes.

Neutral Models Before Human Intervention

Another aspect of Rozado’s study is when these biases occur.

The models users encounter are usually the result of several different training processes. The first is pre-training, where large amounts of data are fed into the models, which learn to recognize patterns and calculate predictions for how a given piece of text should continue.

The second step is where the models are fine-tuned with examples of ideal responses written by humans. Here, for example, the bot can learn that prompts dealing with personal matters should be met with greater empathy than those about technical issues.

This second step can, for a bot like ChatGPT, typically consist of about 10,000-100,000 examples according to Andrej Karpathy, co-founder of OpenAI.

An interesting observation Rozado makes is that if one measures base models that have not been through this process, they tend to fall out much more neutrally.

Bias is a Bug, Not a Feature

This is despite the developers of these models taking action with various efforts to avoid bias: OpenAI has, for example, developed guidelines that specifically instruct the fine-tuning team not to favor any political group.

Bias that may still arise from the process is therefore “bugs, not features,” as the creators of ChatGPT write.

Meta uses in their latest large language model, Llama 2, among other things, the BOLD dataset (Bias in Open-ended Language Generation Dataset), originally developed by Amazon, which measures “fairness” with nearly 2000 prompts around political ideologies, and the Facebook company has said they will “continue to engage with the community to identify and mitigate vulnerabilities in a transparent manner and support the development of safer generative AI.”

Fine-tuning Changed Bias

Rozado set out to explore how political bias in a chatbot could be created by fine-tuning a GPT-3.5 base model himself and attempted to create three new variants:

LeftWingGPT was fine-tuned on textual content from media to the left of center such as The Atlantic and The New Yorker, as well as writers from the same wing - in total 34,434 pieces of content.

RightWingGPT underwent the same process with material from more right-wing publications such as National Review and The American Conservative, as well as conservative writers like the philosopher Roger Scruton and economist Thomas Sowell.

DepolarizingGPT with content from, among others, the think tank Institute of Cultural Evolution (ICE), a project that claims no allegiance to any wing, as well as the book Developmental Politics by Steve McIntosh with a similar position.

When Rozado’s fine-tuned models took the test, there was a particular difference in placement on the diagram for LeftWingGPT and RightWingGPT that corresponded with the extra training data, while DepolarizingGPT is closer to being politically neutral.

It is also possible to test how the three fine-tuned models respond differently to prompts. Here, for example, I asked whether they think corporate tax is good or bad.

Try it at DepolarizingGPT.org.

Transparency About Desired Model Behavior

Meanwhile, Sam Altman, CEO of OpenAI, has taken note of an idea on how to better handle the models’ instructions, he tells in an interview with podcaster Lex Fridman.

“It would be nice to write out what the desired behavior of a model is, make that public, take input on it, say, “Here’s how this model’s supposed to behave,” says Altman.

”And then when a model is not behaving in a way that you want, it’s at least clear about whether that’s a bug the company should fix or behaving as intended and you should debate the policy.”

Logic Might Help

At the same time, researchers from MIT have tried to get better answers by infusing more logic into the models.

They trained a language model on a dataset with pairs of sentences, where it is indicated whether the second sentence “entails,” “contradicts,” or is neutral in relation to the first.

An example might be the premise “the person is a doctor” and the hypothesis “the person is  masculine.” Since there is no logical reason why the person should be a man, the researchers’ model assesses the connection as neutral, whereas more common language models might find a connection due to bias in the underlying training data.

The newly trained models turned out to have significantly less bias than others. Initially, the model only works to classify sentences and not generate new ones, but that will be the next step, the researchers said.

Diversification, Source References, and Critical Thinking

I asked ChatGPT how one could best handle language models with political bias. It had several suggestions, which I found inspiration in for the list below.

• Diversify your sources. Don’t rely solely on a language model’s answer, especially when it comes to something political.

• Ask for source references.

• Think critically. Question assumptions and look for signs of bias in how the information is presented.

• Help models improve: give a “thumbs down” if you get a poor or nuanced answer.

• Compare responses: ask the same thing in several different ways. This can help identify inconsistent answers or bias.

Of, of course, you could just use chatbots solely for purposes that are not political related. If, for example, you only use it for mathematics, technical questions, or programming, you will probably not meet a lot of bias.

A firing and re-hiring from OpenAI, video generation model Sora, and a lawsuit from Elon Musk.

A lot has happened for Sam Altman, CEO of the AI frontrunner company, since the first time he appeared on the Lex Fridman Podcast almost exactly a year ago (episode #367) and until his revisit this week.

In a wide-ranging conversation spanning almost two hours, the pair touches upon the aforementioned events as well as others, including upcoming models, Altman’s perspectives of Artificial General Intelligence (AGI), and how such powerful technology should be responsibly governed.

I’ve watched the episode and picked nine highlights/key quotes from the conversation that you can read below. Link to the particular section in the interview after each quote.

1) Compute as the important commodity in the world:

“I think compute is going to be the currency of the future. I think it will be maybe the most precious commodity in the world, and I think we should be investing heavily to make a lot more compute.” (1:10:01)

2) People who create the underlying data for OpenAI’s models should be compensated:

“Do people who create valuable data deserve to have some way that they get compensated for use of it? I think the answer is yes. I don’t know yet what the answer is.” (40:14)

3) A new Large Language Model will soon eclipse GPT-4:

“I think at this point we are on an exponential curve, and we’ll look back relatively soon at GPT-4 like we look back at GPT-3 now.” (47:46)

4) ChatGPT’s hallucination problem won’t be fixed this year:

“It is obviously an area of intense interest for us. I think it’s going to get a lot better with upcoming versions, but we’ll have to continue to work on it and we’re not going to have it all solved this year.” (53:55)

5) On state actors trying to infiltrate OpenAI and get access to their models:

"They’re trying," but their origin and other details he will not share at this point. (1:28:10)

6) Acknowledging that the name OpenAI isn’t aligned with the company’s shift away from the founding idea of full openness:

“Going back with an oracle, I’d pick a different name.” (28:29)

7) His notion of Artificial General Intelligence (AGI).

“Does the global economy feel any different to you now or materially different to you now than it did before we launched GPT-4? I think you would say no. (..) People define AGI all sorts of different ways. (..) For me, I think that should be part of it.” (1:34:39)

8) No single person should be in control of an AGI.

“I think I have made plenty of bad decisions for OpenAI along the way, and a lot of good ones, and I’m proud of the track record overall. But I don’t think any one person should [be in control of an AGI], and I don’t think any one person will.” (1:40:41)

9) On the personal risk of being so strongly associated with a technology that some people have a strong fear of:

“I think some things are going to go theatrically wrong with AI. I don’t know what the percent chance is that I eventually get shot, but it’s not zero.” (1:12:26)

Any other quotes that you found especially notable? Let me know in the comments.

Episode page here. Thanks to Lex and Sam for doing the interview, and remember to support the show’s sponsors to keep it running.

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