The biggest thing Grok Bot in the Tesla urgently needs now is callbacks. Some tasks take too long to complete, and having to sit awkwardly in the open Grok window while it waits for a callback from your bot is rough UX. Need a little silent pop up on the screen when grok gets the answer back from your bot. Would be game changing for the exp.

We’ve reached a point in AI development where I find myself choosing an inferior model inside of a far better product over a better model inside of a shittier product. All models have gotten good enough to handle the majority of things the average person wants to do with them. All criticism from here is really relative. Grok 4.7 is amazing in absolute terms, it’s just not great relative to recent shipments from competitors. But Grok Bot is still the main product I want to use in my everyday AI life

I was very surprised to see this last night. I do NOT have my Grok account linked to Cursor in any way. My X account uses a different email than my Cursor account. There is no direct link between my Grok and Cursor accounts. They must be name matching or something. I honestly don’t know how they were able to do this, but I’m happy they did 😂

I’m driving from Raleigh NC to Philly tomorrow afternoon. Should I build and deploy a website with my voice by talking to my car on the way back while I chill? Can’t believe this is the current moment

I can’t imagine being bullish on Netflix unless they’re working on something entirely new. Their streaming service is a total fucking ripoff and it makes zero sense to hold a subscription through time. In the rare event that they produce something I want to watch, I will just subscribe and cancel 5 seconds later so I have a month to watch whatever I want to watch. But you’re totally out of your mind if you think I’m gonna pay 30 a month or whatever indefinitely.

This moment we are all living through together right now is truly something special. Seriously. We will look back to these times with insane nostalgia. The great tech revolution of our time. Don’t forget to enjoy it.

Grok @bot in Tesla is wild - having the car driving itself around, while having conversations with my bots doing stuff is surreal! Only wish I have for @Tesla - allow callbacks from Grok so it can activate itself. This way, I just get notified when there are updates, and I don`t have to wait in silence.

Today we announced the Claude-led discovery of a molecular machine that we suspect could represent a new gene editing mechanism. Its precise function, biotechnological utility (if any), or level of significance is not yet clear, but at minimum it is work I would have been proud to do as a PhD student. The work was done mostly, though not entirely, by Claude: our life sciences team suggested a broad area of research, Claude read through the literature and a bunch of genome data and discovered something interesting, then Claude proposed experiments to verify the discovery and our team carried them out. It’s easy to dismiss this as a one-off or curiosity, but we’ve repeatedly seen a pattern where AI performance in new intellectual domains goes from weak to superhuman in a matter of a few years. In 2023 models struggled to do math at the level of an average high-school student. In 2024 they started to do well on math competitions for the best high-schoolers in the country, in 2025 they started to solve minor open problems, in early 2026 more significant open problems, and in late 2026 they are beginning to solve the top few open problems in all of mathematics. We believe AI for biology is on a similar exponential trend. The main difference between biology and mathematics, of course, is that math can be done purely theoretically, while biology requires experimentation. Some have used this to draw the conclusion that AI’s utility in biology will be limited. We think this is wrong. As we’ve demonstrated today, humans can collaborate with AI to perform the experiments, validate key results in a few weeks and, if necessary, work with the AI to iterate on what they find. Eventually it may even be possible for Claude itself to safely perform the experiments by autonomously controlling lab equipment, with appropriate safeguards in place, but we aren’t doing that today (our lab is also a BSL1/BSL2 facility that doesn't handle materials dangerous to humans). More broadly, biomedical advancement has many stages — from fundamental biology discoveries, to translational research, to drug discovery, clinical trials, and finally the actual delivery of medicines and health care to patients. We are also interested in these later stages, but even simply accelerating the first stage of fundamental biological discoveries has the potential to speed up and broaden the entire pipeline. Improving our understanding of biology and sharpening biologists’ tools can drive forward all of the later stages, for example by identifying new drug targets, finding new therapeutic modalities, allowing for more precise measurement, and speeding up the experimental loop which itself further accelerates our understanding of biology. This will not in itself speed up clinical trial times, but if it succeeds it could greatly increase the number of promising candidates that go into the pipeline — an increase in throughput even though latency remains. In Machines of Loving Grace, I wrote about AI’s potential to “cure most diseases in 5-10 years” — a goal that sounds impossible, but one I believe is just barely possible if AI is applied to every stage of the pipeline. The first step is showing that AI can first help with, and then drive, biological discoveries. Claude’s discovery is the latest in a line of related prior work that goes back decades, beginning with systems like CRISPR, and continuing with discoveries like the bridge recombinase and VIPR in the past few years. Recently, there has been heightened interest in systems based on reverse transcriptase (RT) enzymes, the enzyme underlying the system Claude identified. And most recently, a Stanford team working independently described a novel RT system with an associated non-coding array that is in some ways similar to the one Claude found, though they are distinct systems that evolved independently from each other. I believe that we’re at the very beginning of finding such systems and developing them into powerful tools for biotechnology. I’m proud of the resources Anthropic has invested in accelerating the public benefits of AI through the life sciences, and we’re aiming both to grow our life sciences team and to work with other scientists to extend this approach to a broad range of problems. If you have a proposal for a research collaboration or are interested in joining our life sciences team, please reach out.