Picture Donald Knuth. Eighty-eight years old, the man who wrote The Art of Computer Programming by hand in TeX, which he invented himself to be able to write his own books. The father of algorithmic analysis. The most laudeated living computer scientist. A legend, and a well-known AI skeptic.

Now picture him reading a printed chat log between a fellow colleague and Claude Code. Not skimming. Reading it in detail, because there is something genuinely baffling about it.

… Most accounts that open with this scene take one of two off-ramps. The first off-ramp is the replacement narrative. The second off-ramp is the stochastic-parrot dismissal.  — Read More

Everyone can vibe code now. That’s not an exaggeration. Product managers are prototyping. Engineers are creating UIs. Non-designers are producing interfaces that look pretty reasonable (only at first glance!).

So the pressure on designers to keep up is real. Move faster. Use AI more. Show your value through speed and output. Be the person who executes quickest.

I understand the instinct. But it’s pointing in exactly the wrong direction!!!

Design was never supposed to win on execution.

… What’s not democratised, what AI genuinely cannot do, is understanding. Knowing which problem is actually worth solving. Knowing how users think about a product, where they get stuck, and what they’re really trying to do underneath the surface request. Knowing why a solution will land or fall flat before you’ve spent two weeks building it.

That’s the job. It has always been. — Read More

AI labs often treat the ability for AI to work autonomously as the model’s most important capability. As a result, today’s models and interfaces aren’t optimized for humans to remain in the loop.

Autonomous interfaces are valuable, but in most real work, users can’t fully specify their requirements upfront and walk away—good results benefit from a collaborative process where the human stays in the loop, clarifying and giving feedback along the way. However, humans increasingly get pushed out not because the work doesn’t need them, but because the interface has no room for them. Instead, people are most effective when they can collaborate with AI the same way we do with other people: messaging, talking, listening, seeing, showing, and interjecting as needed—and for the model to do the same.

In order to resolve this, we need to move beyond the current turn-based interface for the models.  — Read More

Health systems worldwide are striving for better outcomes, lower costs, and an improved experience for both patients and clinicians. However, progress is constrained by a global shortage of clinical experts, with the World Health Organization predicting a shortfall of more than 10 million health workers by 2030.

While AI is often seen as the key to bridging this gap, it has not yet been able to fully meet the needs of clinicians and patients. That’s why, today, we are announcing our AI co-clinician research initiative, to explore how AI could better amplify doctors’ expertise and deliver higher quality care to patients.

At Google DeepMind, our journey in medical AI has evolved from mastering examination-style tests of medical knowledge with MedPaLM, to matching physician performance in text-based simulated medical consultations with AMIE, including in real-world feasibility trial settings. We also have a long history of studying how clinicians and AI systems might work together. — Read More

It can be hard to tell what’s real these days between the productivity/token maxxing and robot apocalypse – terrorizing our eyeballs with messages that the machine is either perfect or complete garbage. While technology is moving faster than I have ever seen in my lifetime, I can’t help but think we are applying to solve our non-technical problems.

The cracks were always there. AI just made them visible.

At Monki Gras 2026, Laura Tacho called out that what holds us back are our human and systems-level constraints. Not the technology – Us and the ways in which we organize and communicate (or don’t).  — Read More

Every week I meet with founders building in the agent space. And lately, I keep hearing the same concept come up over and over – digital twins (or some version of this). When a concept starts showing up as frequently as this one, my ears generally perk up. Digital twins are the thing perking up my ears! And I think they’re about to become one of the most important concepts in AI. I think they could become a layer that helps scales AI to the masses (and consumption of AI).

So what actually is a digital twin? The term originally comes from manufacturing. You’d build a digital replica of a physical asset (a jet engine, a factory floor) to simulate and monitor it. With AI it’s the same core concept, but with a totally new application. In the AI era, a digital twin is just representing knowledge (from any source, in any form) digitally, so an agent can act on it. That knowledge could live in a person’s head, across a dozen siloed systems, in years of company history, or in the collective behavior of your customers. The twin is just the bridge between that knowledge and the agent that needs it to do work.

… This is where I think the job displacement narrative gets it wrong. Everyone asks “will AI take my job?” But the better question is “can I build a digital twin of myself before someone else does it for me?” The people who win in this world are generally the ones who move fastest to adopt new technologies. — Read More

Textbooks are a cornerstone of education, but they have a fundamental limitation: they are a one-size-fits-all medium. Any new material or alternative representation requires arduous human effort, so that textbooks cannot be adapted in a scalable manner. We present an approach for transforming and augmenting textbooks using generative AI, adding layers of multiple representations and personalization while maintaining content integrity and quality. We refer to the system built with this approach as Learn Your Way. We report pedagogical evaluations of the different transformations and augmentations, and present the results of a a randomized control trial, highlighting the advantages of learning with Learn Your Way over regular textbook usage. — Read More

#augmented-intelligence

Rather than fear AI, writers should learn how to use them properly. While this tech is transforming many sectors, and creative writing is no exception, it boils down to how unique a written content.

To this end, the Microsoft research team joined hands with the University of Southern California to experiment on whether generative AI boosts or weakens a writer’s uniqueness.

The study, titled “It Was 80% Me, 20% AI”, included 19 fiction writers, 30 readers, and AI-generated suggestions using OpenAI’s GPT-4. … Lead researcher Angel Hsing-Chi Hwang explained that for an author or writer, the value of someone’s work is what it means to be authentic. In this regard, co-writing with AI might destroy this purpose. — Read More

Performance of large language models (LLMs) on medical tasks has traditionally been evaluated using multiple choice question benchmarks. However, such benchmarks are highly constrained, saturated with repeated impressive performance by LLMs, and have an unclear relationship to performance in real clinical scenarios. Clinical reasoning, the process by which physicians employ critical thinking to gather and synthesize clinical data to diagnose and manage medical problems, remains an attractive benchmark for model performance. Prior LLMs have shown promise in outperforming clinicians in routine and complex diagnostic scenarios. We sought to evaluate OpenAI’s o1-preview model, a model developed to increase run-time via chain of thought processes prior to generating a response. We characterize the performance of o1-preview with five experiments including differential diagnosis generation, display of diagnostic reasoning, triage differential diagnosis, probabilistic reasoning, and management reasoning, adjudicated by physician experts with validated psychometrics. Our primary outcome was comparison of the o1-preview output to identical prior experiments that have historical human controls and benchmarks of previous LLMs. Significant improvements were observed with differential diagnosis generation and quality of diagnostic and management reasoning. No improvements were observed with probabilistic reasoning or triage differential diagnosis. This study highlights o1-preview’s ability to perform strongly on tasks that require complex critical thinking such as diagnosis and management while its performance on probabilistic reasoning tasks was similar to past models. New robust benchmarks and scalable evaluation of LLM capabilities compared to human physicians are needed along with trials evaluating AI in real clinical settings. — Read More

#augmented-intelligence

TL;DR: Today we’re releasing a new episode of our podcast AI & I. I go in depth with Steph Smith, a16z Podcast host and internet creator. We dive into how AI is reshaping the world that internet creators live in. Watch on X or YouTube, or listen on Spotify or Apple Podcasts.

Steph Smith is the ultimate internet explorer. 

I spent an hour talking to her about the future of creating on the internet in the age of AI. We had a wide-ranging discussion about:

— How AI narrows the gap between ideas and execution 
— How AI changes what humans perceive as valuable in art and creativity
— The type of AI tools that are poised for success   — Read More