Marian Sander

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[Elon Musk @elonmusk]

FSD approved in Denmark

[Tesla Europe, Middle East & Africa @teslaeurope]

In the last 2 months, FSD Supervised has been over 3x safer than manual driving on Dutch roads

[Anthony Pompliano 🌪 @APompliano]

I have changed my mind on how AI will impact jobs in America.

Previously, I believed AI would replace many entry level roles typically filled by young employees. The technology would then work its way up the organization and eventually reduce the total number of jobs in a company.

The data is saying something different, so when I get new information I am willing to change my mind.

The number of software engineers being hired has been increasing. The number of open software engineer roles is growing.

The number of new college grads who get hired has increased 5.6% over the last 12 months. The unemployment level for people aged 20-24 years old who have a college degree has fallen from nearly 9% to almost 5% as well.

The Wall Street Journal recently wrote “AI created 640,000 jobs between 2023 and 2025 in the U.S., according to an analysis by LinkedIn of job posting data, including new white-collar positions such as Head of AI and AI engineer.”

And I am starting to see companies throughout our portfolio aggressively hiring to keep up with the demand for their products and services.

If AI can make employees more productive, which is widely accepted as fact, then companies are going to want as many productive units of labor as possible. This is a key reason why I am changing my mind.

AI appears to be a magical technology that will make companies more productive and more profitable. The net result will be more corporations, more startups, and more jobs.

All three are big, positive wins for the American economy.

[Joscha Bach @Plinz]

@207Nasher Wisdom is the ability to recognize what should be done and entails understanding and relating to the largest picture. Conversely, intelligence is the ability to solve puzzles

[Tesla @Tesla]

FSD Supervised approved in the Netherlands 🇳🇱

[Tesla Europe, Middle East & Africa @teslaeurope]

De toekomst van mobiliteit is aangebroken

FSD Supervised has been approved in the Netherlands 🇳🇱 & will begin rolling out in the country shortly! 

Trained on billions of kilometers of real-world driving data, it can drive you almost anywhere under your supervision – from residential roads to city streets & highways

No other vehicle can do this. 

We're excited to bring FSD Supervised to more European countries soon

[NASA @NASA]

We see our home planet as a whole, lit up in spectacular blues and browns. A green aurora even lights up the atmosphere. That's us, together, watching as our astronauts make their journey to the Moon.

[Tesla Europe, Middle East & Africa @teslaeurope]

Together with RDW, we have officially completed the final vehicle testing phase for Full Self-Driving (Supervised) and have submitted all documentation required for the UN R-171 approval + Article 39 exemptions. The RDW team is now reviewing the documentation and test results package internally. They have communicated the expected approval for Netherlands date of 4/10, shifting from 3/20 previously and we look forward to successful completion of this cooperation. 

Following the Netherlands’ approval, European countries will be able to recognize this approval nationally. We are anticipating a possible EU-wide approval during the summer.

Over the past 18 months, this approval has involved a series of intense documentation, development, testing, research & audits. Including but certainly not limited to:

– 1,600,000+ km of FSD (Supervised) testing on EU roads
– 13,000+ customer sales ride-alongs
– 4,500+ track test scenario executions
– Thousands of pages of written documentation for 400+ compliance requirements
– Dozens of research studies into safety performance/results

We're extremely proud of the work conducted with the RDW team up until this point.

We very much look forward to the approval in April, and sharing FSD (Supervised) with our patient EU customers!

[Nic Cruz Patane @niccruzpatane]

Just a reminder why electric vehicles are better than gas cars:

[Yun-Ta Tsai @yunta_tsai]

I wonder how many humans can pass Turing Test today. Their dialogue trees are more limited than a chatbot.

[Marian Sander @MarianSander]

@yunta_tsai Loving my ‘24 Refresh Model 3 in 🇩🇪! But adaptive headlights still blind truckers on highways – high cabs over median barriers not detected well (headlights blocked), leading to flash-backs. Potentially problematic: Auto-on with FSD.
Also, going thru small EU villages at night with full beams scares poor inhabitants out of bed 😵‍💫
Most annoying: manual off causes full flash before dimming – very irritating for other drivers! Any tweaks coming for EU?

[André Thierig @AndrThie]

Today marks 1 YEAR without any process-wastewater being discharged into the public sewer - achieved by the incredible Nina Turtles and our advanced wastewater treatment facility.
Making a sustainable product matters a lot but doing it sustainably is just as important!

[Tesla Europe, Middle East & Africa @teslaeurope]

Tesla has been working hard toward shipping Full Self-Driving (Supervised) in Europe for over 12 months now. We have given FSD demos to regulators of almost every EU country. We have requested early access, pilot release programs or exemptions where possible.

We have developed & shared detailed safety evidence for FSD, now public in our latest Safety Report. And we have driven over 1 million kilometers safely on EU roads across 17 different countries (internal testing). 

Our main path to success is partnering with the Dutch approval authority RDW to gain exemption for the feature. This involves proving compliance with existing regulations (UN-R-171 DCAS) + filing an exemption (EU Article 39) for yet-to-be-regulated behaviors like Level 2 systems off-highway, system-initiated lane changes with hands-off the wheel etc. 

Some of these regulations are outdated and rules-based, which makes FSD illegal in its current form. Changing FSD to be compliant with these rules would make it unsafe and unusable in many cases. While we have changed FSD to be maximally compliant where it is logical and reasonable, we won't sacrifice the safety of a proven system or materially deteriorate customer usability. 

As a result, we are gathering evidence to get exemptions on a specific rule-by-rule basis. Unfortunately, the real world fleet-proven safety wins alone are considered insufficient. 

Currently, RDW has committed to granting Netherlands National approval in February 2026. Please contact them via link below to express your excitement & thank them for making this happen as soon as possible. Upon NL National approval, other EU countries can immediately recognize the exemption and also allow rollout within their country. Then we will bring it to a TCMV vote for official EU-wide approval. 

We're excited to bring FSD to our owners in Europe soon!

https://t.co/FhoK0xX81r

[Marian Sander @MarianSander]

@yunta_tsai @elonmusk Can’t you put this insight in giant letters on one of the outside walls of Giga Berlin @AndrThie 👀

[Marian Sander @MarianSander]

@yunta_tsai @RichardSndor A big, heartfelt thank you @yunta_tsai
Please don’t give up on us in Europe 😔

[Joe Tegtmeyer 🚀 🤠🛸😎 @JoeTegtmeyer]

Let’s help @Tesla get FSD to our European friends!

Here’s is a quick and easy way you can support this effort!

[Tesla Welt - german Podcast about Tesla @teslawelt]

Falls ihr es noch nicht gesehen habt...

[Andrej Karpathy @karpathy]

My pleasure to come on Dwarkesh last week, I thought the questions and conversation were really good.

I re-watched the pod just now too. First of all, yes I know, and I'm sorry that I speak so fast :). It's to my detriment because sometimes my speaking thread out-executes my thinking thread, so I think I botched a few explanations due to that, and sometimes I was also nervous that I'm going too much on a tangent or too deep into something relatively spurious. Anyway, a few notes/pointers:

AGI timelines. My comments on AGI timelines looks to be the most trending part of the early response. This is the "decade of agents" is a reference to this earlier tweet https://t.co/NiSn6jftqq Basically my AI timelines are about 5-10X pessimistic w.r.t. what you'll find in your neighborhood SF AI house party or on your twitter timeline, but still quite optimistic w.r.t. a rising tide of AI deniers and skeptics. The apparent conflict is not: imo we simultaneously 1) saw a huge amount of progress in recent years with LLMs while 2) there is still a lot of work remaining (grunt work, integration work, sensors and actuators to the physical world, societal work, safety and security work (jailbreaks, poisoning, etc.)) and also research to get done before we have an entity that you'd prefer to hire over a person for an arbitrary job in the world. I think that overall, 10 years should otherwise be a very bullish timeline for AGI, it's only in contrast to present hype that it doesn't feel that way.

Animals vs Ghosts. My earlier writeup on Sutton's podcast https://t.co/rSp1noyGBr . I am suspicious that there is a single simple algorithm you can let loose on the world and it learns everything from scratch. If someone builds such a thing, I will be wrong and it will be the most incredible breakthrough in AI. In my mind, animals are not an example of this at all - they are prepackaged with a ton of intelligence by evolution and the learning they do is quite minimal overall (example: Zebra at birth). Putting our engineering hats on, we're not going to redo evolution. But with LLMs we have stumbled by an alternative approach to "prepackage" a ton of intelligence in a neural network - not by evolution, but by predicting the next token over the internet. This approach leads to a different kind of entity in the intelligence space. Distinct from animals, more like ghosts or spirits. But we can (and should) make them more animal like over time and in some ways that's what a lot of frontier work is about.

On RL. I've critiqued RL a few times already, e.g. https://t.co/mYrMFVdVDW . First, you're "sucking supervision through a straw", so I think the signal/flop is very bad. RL is also very noisy because a completion might have lots of errors that might get encourages (if you happen to stumble to the right answer), and conversely brilliant insight tokens that might get discouraged (if you happen to screw up later). Process supervision and LLM judges have issues too. I think we'll see alternative learning paradigms. I am long "agentic interaction" but short "reinforcement learning" https://t.co/2L7FiaoKsw. I've seen a number of papers pop up recently that are imo barking up the right tree along the lines of what I called "system prompt learning" https://t.co/df5mJDdN3C , but I think there is also a gap between ideas on arxiv and actual, at scale implementation at an LLM frontier lab that works in a general way. I am overall quite optimistic that we'll see good progress on this dimension of remaining work quite soon, and e.g. I'd even say ChatGPT memory and so on are primordial deployed examples of new learning paradigms.

Cognitive core. My earlier post on "cognitive core": https://t.co/q2s1ihGy0T , the idea of stripping down LLMs, of making it harder for them to memorize, or actively stripping away their memory, to make them better at generalization. Otherwise they lean too hard on what they've memorized. Humans can't memorize so easily, which now looks more like a feature than a bug by contrast. Maybe the inability to memorize is a kind of regularization. Also my post from a while back on how the trend in model size is "backwards" and why "the models have to first get larger before they can get smaller" https://t.co/6k0FZRGXsb

Time travel to Yann LeCun 1989. This is the post that I did a very hasty/bad job of describing on the pod: https://t.co/fQgqaXPyp6 . Basically - how much could you improve Yann LeCun's results with the knowledge of 33 years of algorithmic progress? How constrained were the results by each of algorithms, data, and compute? Case study there of.

nanochat. My end-to-end implementation of the ChatGPT training/inference pipeline (the bare essentials) https://t.co/SIetgyoKWN

On LLM agents. My critique of the industry is more in overshooting the tooling w.r.t. present capability. I live in what I view as an intermediate world where I want to collaborate with LLMs and where our pros/cons are matched up. The industry lives in a future where fully autonomous entities collaborate in parallel to write all the code and humans are useless. For example, I don't want an Agent that goes off for 20 minutes and comes back with 1,000 lines of code. I certainly don't feel ready to supervise a team of 10 of them. I'd like to go in chunks that I can keep in my head, where an LLM explains the code that it is writing. I'd like it to prove to me that what it did is correct, I want it to pull the API docs and show me that it used things correctly. I want it to make fewer assumptions and ask/collaborate with me when not sure about something. I want to learn along the way and become better as a programmer, not just get served mountains of code that I'm told works. I just think the tools should be more realistic w.r.t. their capability and how they fit into the industry today, and I fear that if this isn't done well we might end up with mountains of slop accumulating across software, and an increase in vulnerabilities, security breaches and etc. https://t.co/8556ESSpyY

Job automation. How the radiologists are doing great https://t.co/FVUI872dkD and what jobs are more susceptible to automation and why.

Physics. Children should learn physics in early education not because they go on to do physics, but because it is the subject that best boots up a brain. Physicists are the intellectual embryonic stem cell https://t.co/p72Elk8lPV I have a longer post that has been half-written in my drafts for ~year, which I hope to finish soon.

Thanks again Dwarkesh for having me over!

[Andrej Karpathy @karpathy]

"AI isn't replacing radiologists" good article

Expectation: rapid progress in image recognition AI will delete radiology jobs (e.g. as famously predicted by Geoff Hinton now almost a decade ago). Reality: radiology is doing great and is growing.

There are a lot of imo naive predictions out there on the imminent impact of AI on the job market. E.g. a ~year ago, I was asked by someone who should know better if I think there will be any software engineers still today. (Spoiler: I think we're going to make it). This is happening too broadly.

The post goes into detail on why it's not that simple, using the example of radiology:

- the benchmarks are nowhere near broad enough to reflect actual, real scenarios.
- the job is a lot more multifaceted than just image recognition.
- deployment realities: regulatory, insurance and liability, diffusion and institutional inertia.
- Jevons paradox: if radiologists are sped up via AI as a tool, a lot more demand shows up.

I will say that radiology was imo not among the best examples to pick on in 2016 - it's too multi-faceted, too high risk, too regulated. When looking for jobs that will change a lot due to AI on shorter time scales, I'd look in other places - jobs that look like repetition of one rote task, each task being relatively independent, closed (not requiring too much context), short (in time), forgiving (the cost of mistake is low), and of course automatable giving current (and digital) capability. Even then, I'd expect to see AI adopted as a tool at first, where jobs change and refactor (e.g. more monitoring or supervising than manual doing, etc). Maybe coming up, we'll find better and broader set of examples of how this is all playing out across the industry.

About 6 months ago, I was also asked to vote if we will have less or more software engineers in 5 years. Exercise left for the reader.

Full post (the whole The Works in Progress Newsletter is quite good):
https://t.co/ON3GwlI3mi

[Tesla @Tesla]

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