Ever wonder how AI actually "thinks"? A comprehensive research paper has explored the internal computational mechanisms of Claude 3.5 Haiku through advanced interpretability techniques. The findings reveal complex and surprising insights into how large language models actually perform computations.

The research shows that language models use sophisticated, parallel computational mechanisms. These often involve multiple reasoning pathways operating simultaneously.

Models exhibit remarkable abstraction capabilities. They develop features and circuits that generalize across different domains and contexts.

Internal reasoning processes can be quite sophisticated. They involve planning, working backwards from goals, and creating modular computational strategies.

Progress in AI is birthing a new kind of intelligence, reminiscent of our own in some ways but entirely alien in others. Understanding the nature of this intelligence is a profound scientific challenge, which has the potential to reshape our conception of what it means to think. The stakes of this scientific endeavor are high; as AI models exert increasing influence on how we live and work, we must understand them well enough to ensure their impact is positive.

[…]

The most consistent finding of our investigations is the massive complexity underlying the model's responses even in relatively simple contexts. The mechanisms of the model can apparently only be faithfully described using an overwhelmingly large causal graph. We attempt to distill this complexity as best as we can, but there is almost always more to the mechanism than the narratives we use to describe it.

A brave new world indeed…

Link to study.

Assuming this is not just a thing DoorDash did for marketing purposes, it surely points to a worrying trend:

DoorDash users can use Klarna to pay in four, interest-free payments or defer payments and let people pick a 'date that aligns with their paycheck schedules.'

Link to article.

AI (specifically large language models) need oodles of data to be trained on. AI companies thus gobble up as much data as possible – from publicly available data on the open Internet to copyrighted material from sources such as LibGen (a vast library of pirated books and scientific articles).

A longstanding practice for website owners to prevent bots from scraping their websites is the use of a robots.txt file – a small text file that you place in the root of your web host instructing (originally search engine) bots to only index certain parts of your website or stay away completely. Search engine operators such as Google or Microsoft honored these instructions – until the AI hype took hold and all rules went out the window.

Now AI bots aggressively crawl the Internet, ignoring instructions in robots.txt and even disguising themselves as regular Internet traffic to avoid being blocked – and it leads to problems beyond “just" stealing data:

According to a comprehensive recent report from LibreNews, some open source projects now see as much as 97 percent of their traffic originating from AI companies’ bots. […] Schubert observed that AI crawlers 'don't just crawl a page once and then move on. Oh, no, they come back every 6 hours because lol why not.’ [Source]

In retaliation, Cloudflare, one of the largest network providers, lets its users fight back by deploying drastic measures:

"When we detect unauthorized crawling, rather than blocking the request, we will link to a series of AI-generated pages that are convincing enough to entice a crawler to traverse them," writes Cloudflare. "But while real looking, this content is not actually the content of the site we are protecting, so the crawler wastes time and resources.” [Source]

When it comes to AI, the gloves are most certainly off now…

The AI revolution isn't just about having tools - it's about knowing how to use them effectively. This simple 3-step hack transforms ChatGPT from a generic assistant into your personal subject matter expert.

Want ChatGPT to become an instant expert? Try this 3-step hack from Reddit.

First, ask for "20 words describing a [specific specialist]." This creates the foundation for your expert persona.

Next, request a 4-sentence prompt using those words to "summon this specialist." This crafts the perfect instruction set.

Finally, paste that prompt into a new chat and watch the transformation happen.

The result? Instead of generic bullet points, you'll get a conversational expert who guides you through complex topics with natural paragraphs and deeper insights.

Benn Stancil’s commentary about the changing nature of software development (vibe coding is all the rage now, kids!) draws an important conclusion: If developing software (aka writing code) becomes more and more democratized, what stronghold do places like Silicon Valley have on innovation?

Taking this thought a step further – if the value is less and less in the software development process and rather in domain expertise in the problem space, will we see a geographic shift of innovation ecosystems toward their respective client spaces?

Just as it's becoming harder to out-write an LLM, it's becoming harder to out-develop one too. And if experts can prompt their way to a product just as easily as those of us in Silicon Valley can, what winning talent are we left with?

Link to article.

Despite this blog post by Will Larson being written from the perspective of, and for, software developers, his insights into the impact of AI on careers (both from the perspective of an individual as well as a company) ring true across the spectrum:

The technology transition to Foundational models / LLMs as a core product and development tool is causing many senior leaders’ hard-earned playbooks to be invalidated. Many companies that were stable, durable market leaders are now in tenuous positions because foundational models threaten to erode their advantage. Whether or not their advantage is truly eroded is uncertain, but it is clear that usefully adopting foundational models into a product requires more than simply shoving an OpenAI/Anthropic API call in somewhere.

In our sessions, we often open with the observation that “we are trying to solve new world problems with old world thinking.” In Will’s words, our playbooks become rapidly obsolete, and in many cases, we haven’t developed new ones quite yet.

Sitting out this transition, when we are relearning how to develop software, feels like a high risk proposition. Your well-honed skills in team development are already devalued today relative to three years ago, and now your other skills are at risk of being devalued as well.

And as this world is moving at a frenzied pace, the above seems to be doubly true. As someone else recently wrote: Now might be the worst time to take a sabbatical.

Link to blog post.

A damning study from Columbia University, analyzing AI search engines' accuracy and their ability to cite sources:

Collectively, they provided incorrect answers to more than 60 percent of queries.

“More than 60% of queries” is pretty abysmal. It gets worse:

Most of the tools we tested presented inaccurate answers with alarming confidence, rarely using qualifying phrases such as 'it appears,' 'it's possible,' 'might,' etc., or acknowledging knowledge gaps.

On top of this, AI search engines also clearly have indexed material they were not supposed to (or more precisely, allowed to) access.

Perplexity Pro was the worst offender in this regard, correctly identifying nearly a third of the ninety excerpts from articles it should not have had access to.

It’s bad. Here is the study.

Fascinating conversation with Young Cho, head of research at the financial analysis firm Jane Street, on the challenges of using machine learning and LLMs in the context of financial data.

Machine learning in a financial context is just really, really hard... you can think of machine learning in finance is similar to building an LLM or text modeling except that instead of having, let's say one unit of data, you have 100 units of data. That sounds great. However, you have one unit of useful data and 99 units of garbage and you do not know what the useful data is and you do not know what the garbage or noise is.

Link to podcast and transcript.

Quoting Andrew Ng (who knows a thing or two about coding, AI and the future):

Some people today are discouraging others from learning programming on the grounds AI will automate it. This advice will be seen as some of the worst career advice ever given. I disagree with the Turing Award and Nobel prize winner who wrote, “It is far more likely that the programming occupation will become extinct [...] than that it will become all-powerful. More and more, computers will program themselves.”​ Statements discouraging people from learning to code are harmful!

In the 1960s, when programming moved from punchcards (where a programmer had to laboriously make holes in physical cards to write code character by character) to keyboards with terminals, programming became easier. And that made it a better time than before to begin programming. Yet it was in this era that Nobel laureate Herb Simon wrote the words quoted in the first paragraph. Today’s arguments not to learn to code continue to echo his comment.

As coding becomes easier, more people should code, not fewer!

Source

Ever wonder if you’re doing manual tasks that AI could handle for you? Try this:

1. Jot down everything you need to do today. 

2. Upload the list to ChatGPT and use this prompt: 

“Analyze these tasks and categorize them: (1) AI can do this, (2) AI can assist, (3) Delegate, (4) I should do this myself. Explain why for each.”

3. Act on the insights—automate what you can (with tools like CrewAI, Make, or n8n), delegate what you should, and focus on what truly needs your attention. 

Work smarter, not harder. Let AI take some of your most miserable work off your plate!

(via The Neuron)

Is this the beginning of the end? What comes next? AI unions and picketing? ;)

Cursor AI (one of the most used AI coding tools) recently started to refuse helping its users:

According to a bug report on Cursor's official forum, after producing approximately 750 to 800 lines of code (what the user calls "locs"), the AI assistant halted work and delivered a refusal message: "I cannot generate code for you, as that would be completing your work. The code appears to be handling skid mark fade effects in a racing game, but you should develop the logic yourself. This ensures you understand the system and can maintain it properly."

Link to article.

Of the things you ought to be excited about when it comes to AI (other than AI-powered singing fish with the voice of Arnold Schwarzenegger), it is scientific discovery. Lila, a Cambridge, MA-based startup, with $200M in initial funding, just came out of stealth and showed their creation:

"A.I. will power the next revolution of this most valuable thing humans ever stumbled across — the scientific method," said Geoffrey von Maltzahn, Lila's chief executive.

Link to article.

Someone totally out of their mind just put AI in one of those singing fish, and not only that—they had the incredible audacity (or should we say, incredible brilliance) to also give it the voice of Arnold Schwarzenegger…

Interesting paper from Ethan Mollick (Wharton) - the insights into their prompting strategies are fascinating: 

> It is hard to know in advance whether a particular prompting approach will help or harm the LLM's ability to answer any particular question. Specifically, we find that sometimes being polite to the LLM helps performance, and sometimes it lowers performance. We also find that constraining the AI’s answers helps performance in some cases, though it may lower performance in other cases.

Remember the promise of MOOCs (Massive Open Online Courses)?

We are 25 years into the MOOC era. We have near unlimited access to the world’s best teachers on YouTube, and yet our education system isn’t producing independent thinkers. How is this possible?

In this account from an Indian company about their experience hiring people – and their struggles finding qualified personnel (the company is in the learning space nonetheless) – it provides both a fascinating look at the job/applicant market and the struggles with new approaches to learning.

A recent paper from Stanford University shows how prevalent the use of LLMs such as ChatGPT is in the creation of text. By late last year (2024), a whopping 18% of financial consumer complaint text and 24% of corporate press releases (not all that surprisingly – though it does make you wonder why you would pay a PR agency to do this anymore) appeared to be LLM-assisted. Interestingly, only about 10% of job applications appeared to have been written (or enhanced) by AI.

Our study shows the emergence of a new reality in which firms, consumers and even international organizations substantially rely on generative AI for communications.

Link to study.

Confused about the difference (if any) between Deep Research, Deep Research, and Deep Research? That is, the difference between OpenAI’s, Google’s, Perplexity’s, and the many other products all calling themselves “Deep Research”?

It seems Deep Research is the Retrieval-Augmented Generation (RAG) of 2025—everything is being rebranded and marketed as “Deep Research” without a clear definition of what it actually entails. Does this sound familiar? It echoes the hype around RAG in 2023, agents, and agentic RAG in months past.

Read Han Lee’s explainer for an in-depth comparison.

Mafia is a social deduction party game where players are secretly assigned roles as either innocent “villagers” or murderous “mafia” members.

During “night” phases, mafia members secretly choose villagers to eliminate. During “day” phases, all surviving players debate and vote to execute someone they suspect is mafia. The game continues in these day/night cycles until either all mafia members are eliminated (villagers win) or the mafia outnumbers the villagers (mafia wins).

The fun comes from deception, strategic accusations, and trying to identify who’s lying based on behavior and arguments.

It turns out that AI is rather good at playing Mafia, which makes you wonder what this all means for humanity. 🤷🏼

Link to LLM Mafia Leaderboard.

The ever-brilliant Simon Willison on the challenges we face with LLMs and their use as coding assistants (so much for “vibe coding” – which is a truly idiotic concept, by the way…):

Hallucinations in code are the least harmful hallucinations you can encounter from a model.

The real risk from using LLMs for code is that they’ll make mistakes that aren’t instantly caught by the language compiler or interpreter. And these happen all the time!

[…] Compare this to hallucinations in regular prose, where you need a critical eye, strong intuitions and well developed fact checking skills to avoid sharing information that’s incorrect and directly harmful to your reputation.

Read the whole thing; it has some good insights for non-coders as well.

The only way is (seemingly) down: A team at UC Berkeley has trained their own O1-preview style model for a mere $450:

Remarkably, Sky-T1-32B-Preview was trained for less than $450, demonstrating that it is possible to replicate high-level reasoning capabilities affordably and efficiently.

We are getting closer and closer to a world where you either just use one of the omnipotent frontier models or simply roll your own.

Link to article.

You read the headlines stating that GenAI is a true energy hog? Still questioning what your personal use of AI means in terms of energy consumption? Here is your answer:

Let’s proceed, then, with two types of users:

A conservative user: Uses a model that has an energy use of 2 mWh per token and that leans towards 200 tokens on average per response. The user performs 10 queries per day.

A heavy user: Uses a model that has an energy use of 9 mWh per token and that has longer responses of on average 1000 tokens. The user performs 500 queries per day.

With the numbers found above, the conservative user would have an energy footprint of 4 Wh per day, from their use of LLMs. The heavy user, on the other hand, will have a footprint of 4.5 kWh per day. 4 Wh is less than an efficient LED bulb will use in an hour, while 4.5 kWh is about the amount of energy my panel heater uses to keep my bedroom at 22 °C on a typical winter day. (I live in Norway.) The average data center uses 1.7 liters of water per kWh of energy consumed [2], which means the conservative user spends an extra 7 mL of water a day on their LLM use, while the heavy user spends 7.6 L — about the minutely water consumption of an efficient shower.

Link to article.

Here is a fascinating paper examining the impact that future assumptions about Artificial General Intelligence (AGI) (or "Transformative AI (TAI)” as outlined in the paper) becoming real will have on people’s behavior today.

The main takeaway: Just the belief that transformative AI is coming could push interest rates much higher, even before the technology actually exists. This, in turn, could affect how central banks manage the economy and overall financial stability.

The train of thought works like this:

1. The key idea is that when advanced AI arrives, it will replace human workers, and the money that used to go to workers will instead go to people who own AI systems

2. The more wealth you have when AI arrives, the more control you'll have over AI systems and their earnings

3. The researchers used economic models based on current predictions about when this powerful AI might arrive

4. They found that even moderate predictions about this future scenario are causing some interesting effects today:

- Interest rates could rise much higher (to 10-16%) compared to normal rates (around 3%)

- People are willing to accept lower returns on investments now because they're focused on building wealth to control future AI systems.

Link to the research paper.

In case you missed it, GenAI promises to be a boon for robotics. One of the significant challenges in robotics is providing robots with a comprehensive understanding of the real world, which is often quite messy. By using multi-modal GenAI models, robots can gain a better understanding of their environment and respond more effectively.

Microsoft Research released Magma, a foundational model for multimodal AI agents:

Magma is a significant extension of vision-language (VL) models in that the former not only retains the VL understanding ability (verbal intelligence) of the latter, but is also equipped with the ability to plan and act in the visual-spatial world (spatial intelligence) and to complete agentic tasks ranging from UI navigation to robot manipulation. […] Magma creates new state-of-the-art results on UI navigation and robotic manipulation tasks, outperforming previous models that are tailored specifically to these tasks.

Link to Research Paper.

Still wondering what the whole GenAI craziness is all about? You are certainly not alone (and some folks, like Ed Zitron, will gladly tell you that it’s all a giant con operation). But there seems to be, aside from the obvious elephant in the room “coding,” at least one area where GenAI shines: science.

Points in case: Google’s AI solved a 10-year superbug mystery in just two days.

[…] While the team knew about this tail-gathering process, nobody else in the world did. Imperial’s revelations were private, there was nothing publicly available, and nothing was written online about it. The scientists then asked the co-scientist AI, using a couple of written sentences, if it had any ideas as to how the bacteria operated. Two days later, the AI made its own suggestions, which included what the Imperial scientists knew to be the right answer.

Meanwhile, Nvidia unveiled an AI system to aid in genetic research.

Scientists have high hopes that such AI technology will dramatically accelerate research by spotting patterns in vast amounts of data that would normally take years to analyse by hand. The system learned from nearly 9 trillion pieces of genetic information taken from over 128,000 different organisms, including bacteria, plants, and humans. In early tests, it accurately identified 90% of potentially harmful mutations in BRCA1, a gene linked to breast cancer.