2) I can't remember a CEO who has complained more about the stock price in particular those shorting the stock. And worst of all then go on to illegally manipulate the market to target them.
3) You have that completely backwards. Musk is the worst at solving problems with the least capital. Look at the way his manufacturing processes work or the decision to build out the Supercharger network/Gigafactory. There are definitely cheaper ways to do those. Better probably not. Cheaper yes.
4) I have worked with more Data Scientists than most and I assure you that they are just like everyone else. They care most about: company reputation, work environment, money, challenging problems etc. It's not just whether there is a great dataset or not.
And the trained network doesn’t matter as much as the training pipeline, which will include hardware and software external to the cars.
A neural net that you can’t update is less useful, and I highly doubt that the cars themselves are doing any learning.
It was about the best AI chip in general not based on some arbitrary efficiency number.
No one cares how many TOPS your 500watt non-redundant monster can run, because it’s not being put in an EV.
Because self driving cars are a multi-year journey and those platforms will likely become more efficient.
Remember we’re talking about systems made of multiple chips. It’s like asking “what’s the best Bitcoin mining rig?” The question is one of hashrate per joule. If someone asked if you should buy a 1000watt box that did 10TH/s versus a 200watt box that did 5TH/s, which is better?
They maxed out their available power envelope (~70 watts) and the resulting performance is about 20x what they were getting from their prior nVidia platform. In other words, they will always make the chip big enough to pull ~70 watts, at the highest efficiency possible.
My limited understanding of the hardware design is that once you have figured out the specific architecture you can scale up and down the power consumption almost linearly with the die size. Obviously plus or minus various hard limits (e.g. oversizing compute for the available memory bandwidth)
Tesla has already been working on their next chip for over a year. This chip they just started shipping finished design 1.5 years ago. This will not be the last AP hardware they ever ship.
I fully expect that as we delve further into “FSD” there will be functionality on the fringes that will need even more horsepower.
It’s easy to think in the present that there’s a logical “end” where development will be “complete” but that’s almost never actually the case. So there will be a Gen4/5/6, specifically what features they will unlock is very hard for me to predict. I think this is actually the type of thing Elon sees right through like it’s obvious.
Like how he sees a path to colonizing Mars which includes launching a global satellite internet company.
2. Musk has investors and the price of his cash financing goes up when people short his stock and there is negative sentiment. It was a major strategic weakness when the viability of tesla depended on short-term cashflow financing and he did what he had to save the company that he poured his life savings into. Not so much now that he's built a system where every additional vehicle sold adds additional value and long-term investors can see that. Musk thinks long-term the markets think short-term but if you run out of cash, there is no more planning and he hates the markets lack of vision and people profiting from negative sentiment effectively killing optimum solutions. The market has a lot of long-term faith in Amazon and Google and Microsoft because of their dominant networks and economies of scale and profit from complex software with zero marginal cost.
3. Solving problems in sub optimal ways isn't solving them it's compromising on them. He solves them optimally with the least capital but admits his mistakes, one of which was not judging the benefit of humans and teamwork on production lines versus robotic manufacturing technology correctly. If you are generating negative externalities you aren't reducing entropy in the system and Tesla learnt this painfully and won't forget it.
4. I'm completely out of my depth on the data scientists employer preference but if I had the choice of solving problems from a constantly growing ocean or from an artificial fishpond and I had a desire to contribute majorly to solving the autonomous driving problem and I had to factor in the probability of earning enough money to buy my time freedom as quickly as possible then the data is suggesting that Tesla at this point in time is a reasonable bet.
The question is whether he can compete with Toyota on mass scale while battery tech comes down in price but he's quite lucky Toyota misbet on fuel cells and don't readily admit their mistakes, it appears he's built a 10 year lead on the autonomous driving and can compete in the high end of the electric market profitably. The first question anyone would ask a driver of an electric BMW is why they didn't choose a Tesla? That's a fairly strong position, I wouldn't be shorting that stock.
The entrenched car lobby have only have two lines of defense left. The first which was to short him so he runs out of access to cash from negative sentiment is virtually broken on Tesla's current revenue and IP acceleration. The last line will be a delaying argument over Safety but he has a much larger dataset.
Geico falls when Tesla proves safe autonomy. There is a lot at stake here and Musk has focused primarily on the physics which is the right thing to focus on for humanity.
Does that make it the most advanced AI chip in the world? No! In fact, frankly, you're trying to have a technical discussion about a bullshit term. Tesla isn't producing an AI chip, they're producing a scalar product chip. It does a 96x96 Mult-add and that's basically it.
How does it do on any of the standard AI benchmarks? Oh it can't run any of them because it's not got support for any of them because it lacks many of the basic features of what any normal person would consider a chip capable of doing AI work.
It is the best chip to run the convolutions and pooling of 1 neural net that Tesla thinks it will want to run. It's the best 'AI' chip in the same way that the chip Nokia produces for it's mobile backhaul is the best 'neworking' chip.
The point is that the chip they're trying to compare their custom ASIC to is capable of running several AI frameworks none of which the ASIC can. Hell it can't even run single precision floating point operations.
Tesla has the best chip in the world for the one neural network they want to run. I bet you good money their custom ASIC sucks at running Waymo's AI workload.
"Tesla has the best chip in the world for the one neural network they want to run" is what I wanted to convey but don't actually have the technical background to do it.
"Frankly you're trying to have a technical discussion about a bullshit term"
You are right. I apologise for diluting the discussion and will go back to reading hacker news not creating it.
I love this site and don't want to dilute it.
This is an oft-repeated trope, but one look at Tesla's reports will show it's not the case. Last year, approaching $100M (I want to say certainly greater than $80M) spent on "Advertising and marketing".
They have a highly effective, modestly funded, non-traditional advertising strategy.
I think whoever develops level 4 FSD tech that works for most Americans daily car commute is going to be a trillion dollar company. It can even require me to be in the driver's seat, not work in rain/snow, and be geofenced if it gives me at least a 5+ seconds heads up for taking over as long as it is enough time to switch context from working/whatever to drive. Think about how much time Americans spend driving for which we could free ourselves from if our cars had FSD tech that worked most of the time. If/when I have a decent commute that benefits from a car over public transportation, I would pay thousands of dollars extra for a car if it was able to free myself from driving 75% of the time.
Tesla now has got hundreds of thousands of cars that people use in level 3 mode which is then used to train their L4/L5 FSD tech while also retaining all of that real world data to train their models. I don't work for self driving tech, but given the massive amount of real world data they can collect, I think they are now most likely to hit the point where they can solve basic commutes in geofenced areas with L4 over everyone else and I think people will pay out the nose for that (with the added benefit of people buying electric cars too).
- sufficiency of compute;
- software architecture;
- complexity budget;
- sensor suite;
- datacenter.
Tesla is the most hyped and least proved contender. They moved the conversation around the AI chip but that doesn't matter compared to the algorithms that runs on it! Waymo and Cruise are years in front but mask managed to hype you and make you drink koolaid in such a way that you know think they are the leaders.
This is laughable.
How difficult is it for BYD to clone his autopilot chip and will that stop the Chinese from exporting knockoffs because I'm certain they can close the data and software gap instantly by swapping out a harddrive or 10.
I think he's got 10 years before the chinese chip fabs catch up.
Musk's question how did Tesla build the best AI chip for driving in the world without being a chip designer was the far more interesting yet overlooked question.
Musk has a talent for framing problems appropriately so that optimum solutions emerge with a minimum deployment of capital. And he's not secretive about it, he applies his training in physics and phenomenal knowledge to make good bets and eventually identifies and acknowledges his mistakes.
Tesla doesn't have an advertising budget. Musk couldn't care less about it's shareprice but I think he will be a bit perturbed if identical technology emerges faster than he foresaw as was Apple when the Android ecosystem emerged.
As it stands with his chip and data it is game set and match for autonomous driving which is a software game and the leader takes all the data and a fully autonomous vehicle is worth significantly more than non-autonomous vehicle and if you are data scientist you probably want to work for the company with the best data-set and highest remuneration.
The data is the gold and Tesla owns it and no-one can steal it and use it for at least 10 years by which point battery vehicles will be cheaper than petrol vehicles which is the major problem with the model 3. It is currently uneconomic against a toyota corolla but gains ground with every piece of battery and energy storage research that musk does not pay for.