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Watch: Tesla's Cybertruck recall, layoffs set the stage for its Q1 earnings | TechCrunch

Tesla is not having a good start to the week. In its defense, it didn’t have a very good end to last week, either.

Today the news is that recent price cuts have irked Tesla investors, who sent its shares off around 4% in early trading today. Those losses have extended Tesla’s total share-price declines to around 43% for the year. Which is, as they say, a lot.

But those price cuts are hardly the only issues needling the U.S.-based EV company. Tesla’s last week saw the company slash its staffing, including high-performers. With the company reporting earnings tomorrow, its actions at the moment are under even greater scrutiny than usual.

The backdrop to all of this is the company’s apparent move away from a basement-priced EV, and towards a robotaxi effort that some consider to be technologically premature. Regardless, Tesla’s price cuts, pivots, and mass-recall of its Cybertruck vehicle are not the recipe for content investors. Hit play, and let’s have some fun.

After we recorded this clip, Bloomberg posted a fascinating dig into the company’s current form that we recommend as further reading.

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Robotic Automations

Watch: TikTok and Meta's latest moves signal a more commodified internet | TechCrunch

The internet’s mega-platforms are slowly merging into a great blob of sameness, and even the hottest companies in the world are not immune from the trend. TikTok’s winning strategy to focus on short-form, vertical video has found fans amongst other internet platforms, and now TikTok is taking a page from its rival, books, reportedly borrowing from what made them popular.

TikTok is working toward launching a new app called TikTok Notes that will allow users to post images in an apparent bid to rival Instagram, a service best known for its static-photo-sharing feature. Instagram, of course, has expanded into video and stories itself, taking pieces of other services and incorporating them into its own product.

Instagram’s parent company Meta’s other services are frequent borrowers as well. As is nearly every social service you can imagine. Recall that great Stories Boom that led to everyone from Line to Spotify to Instagram to LinkedIn trying out the popular sharing format. If it works for one social media service, expect the rest to follow in some manner at some point — probably sooner rather than later.

There’s good logic behind the effort. The answer is why X wants to become a super app; the more a service can offer its userbase to do, the more time they may spend inside the app’s walls. Expanding a feature set can bolster engaged time, and therefore how much revenue a social media service can earn. At the same time, bloat is a real issue that can dilute a user experience and render an app, well, Facebook in time.

This theme — the slow commodification of digital services via sameification — is similar to why we’re seeing LinkedIn try to ape The New York Times’ gaming might, and to some degree why major platform companies in tech wind up trying to be good at everything: the never-ending need to grow revenue. Perhaps this is why your favorite app always feels more and more like an alien world as time passes. It will evolve away from what made it special, and unique, because sticking to those guns is not the way to create a service that the maximum number of people will use. For that, you need to become Facebook.

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Robotic Automations

Watch: Meta's new Llama 3 models give open-source AI a boost

New AI models from Meta are making waves in technology circles. The two new models, part of the Facebook parent company’s Llama line of artificial intelligence tools, are both open-source, helping them stand apart from competing offerings from OpenAI and other well-known names.

Meta’s new Llama models have differently sized underlying datasets, with the Llama 3 8B model featuring eight billion parameters, and the Llama 3 70B model some seventy billion parameters. The more parameters, the more powerful the model, but not every AI task needs the largest possible dataset.

The company’s new models, which were trained on 24,000 GPU clusters, perform well across benchmarks that Meta put them up against, besting some rivals’ models that were already in the market. What matters for those of us not competing to build and release the most capable, or largest AI models, what we care about is that they are still getting better with time. And work. And a lot of compute.

While Meta takes an open-source approach to AI work, its competitors are often prefer more closed-source work. OpenAI, despite its name and history, offers access to its models, but not their source code. There’s a healthy debate in the world of AI regarding which approach is better, for both speed of development and also safety. After all, some technologists — and some computing doomers, to be clear — are worried that AI tech is developing too fast and could prove dangerous to democracies and more.

For now, Meta is keeping the AI fires alight, offering a new challenge to its peers and rivals to best their latest. Hit play, and let’s talk about it!

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Watch: NASA needs your help to bring rocks back from Mars

NASA’s decision to scrap its $11 billion, 15-year mission to Mars to bring back samples could create a startup feeding frenzy, TechCrunch reports. Describing its plans as too slow, and too expensive, NASA is going back to the drawing board, with an eye on getting the space industry to help. Sure, you might worry that NASA can’t manage its own mission on a timeline and budget that it deems acceptable, but the chance for a deluge of dollars to engulf the startups working on making space more accessible could prove a massive boon.

Startups are not all social media apps, enterprise software and NFT-based online games. There are a good number focused on the bits-and-atoms side of the technology fence, even if the idea of building advanced hardware without a software element is all but unthinkable. Ergo, hardware startups are really working both sides of the digital divide at the same time.

But space startups are not worried about it. Looking at recent TechCrunch space headlines, we can see that Dark Space is working on a way to clear space debris; True Anomaly’s working on landing on the moon; Varda Space’s work to manufacture drugs in space and bring them back to Earth seems to work, so it raised $90 million more; Orbital Fab wants to refuel satellites; the list goes on and on.

So, the NASA money might have a bunch of startup-sized buckets to drip into, and I am here for it. Yes, I am a gigantic science-fiction dweeb, but I am still nothing short of dizzy with hype for our future as a species in space. To that end, if any startup that works with NASA on the Mars rock mission needs a human to send up there to check on the dials and such, I’m your guy. Hit play, let’s have some fun!

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Watch: Google's Gemini Code Assist wants to use AI to help developers

Can AI eat the jobs of the developers who are busy building AI models? The short answer is no, but the longer answer is not yet settled. News this week that Google has a new AI-powered coding tool for developers, straight from the company’s Google Cloud Next 2024 event in Las Vegas, means that competitive pressures between major tech companies to build the best service to help coders write more code, more quickly is still heating up.

Microsoft’s GitHub Copilot service that has similar outlines has been steadily working toward enterprise adoption. Both companies want to eventually build developer-helping tech that can understand a company’s codebase, allowing it to offer up more tailored suggestions and tips.

Startups are in the fight as well, though they tend to focus more tailored solutions than the broader offerings from the largest tech companies; Pythagora, Tusk and Ellipsis from the most recent Y Combinator batch are working on app creation from user prompts, AI agents for bug-squashing and turning GitHub comments into code, respectively.

Everywhere you look, developers are building tools and services to help their own professional cohort.

Developers learning to code today won’t know a world in which they don’t have AI-powered coding helps. Call it the graphic calculator era for software builders. But the risk — or the worry, I suppose — is that in time the AI tools that are ingesting mountains of code to get smarter to help humans do more will eventually be able to do enough that fewer humans are needed to do the work of writing code for companies themselves. And if a company can spend less money and employ fewer people, it will; no job is safe, but some roles are just more difficult to replace at any given moment.

Thankfully, given the complexities of modern software services, ever-present tech debt and an infinite number of edge cases, what big tech and startups are busy building today seem to be very useful coding helps and not something ready to replace or even reduce the number of humans building them. For now. I wouldn’t take the other end of that bet on a multi-decade time frame.

And for those looking for an even deeper dive into what Google revealed this week, you can head here for our complete rundown, including details on exactly how Gemini Code Assist works, and Google’s in-depth developer walkthrough from Cloud Next 2024.

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Google Cloud Next 2024: Watch the keynote on Gemini AI, enterprise reveals right here | TechCrunch

It’s time for Google’s annual look up to the cloud, this time with a big dose of AI.

At 9 a.m. PT Tuesday, Google Cloud CEO Thomas Kurian kicked off the opening keynote for this year’s Google Cloud Next event, and you can watch the archive of their reveals above, or right here.

After this week we’ll know more about Google’s attempts to help the enterprise enter the age of AI. From a deeper dive into Gemini, the company’s AI-powered chatbot, to securing AI products and implementing generative AI into cloud applications, Google will continue to cover a wide range of topics.

We’re also keeping tabs on everything Google’s announcing at Cloud Next 2024, from Google Vids to Gemini Code Assist to Google Workspace updates.

And for those more interested in Google’s details and reveals for developers, their Developer Keynote started off at 11:30am PT Wednesday, and you can catch up on that full stream right here or via the embed below.

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Robotic Automations

Watch: Spotify rolls out an AI-powered playlist feature

Spotify is building on its AI DJ feature, adding a new AI-powered playlist feature. No, this is not merely asking Spotify to spit out, say, metalcore classics from the 2010s, but instead something more of a “my dog is sad and I love the color purple please make me a list of songs sort of thing. You can prompt it, and Spotify will come up with a list of tunes for you. How far you can push it remains to be seen, but I do intend to test its guardrails when I get the chance.

Spotify’s AI work nests into its other efforts to differentiate its service from rivals like Apple Music and offerings from Amazon. The European tech giant has also pushed into audiobooks, podcasting and even edtech in recent years.

Starting in just a few countries, the new AI playlist feature will roll out to more markets over time. How long it will take to reach your hands is not clear, if you, like myself, are not located in the feature’s launch countries. Some Spotify users have complained that the rollout of new products can take longer than they want to reach their home market, it’s worth noting.

The AI wave crashing into the world of music has yet to make artistry obsolete, but it does appear to be working toward finding a place in how we discover and consume art itself. Perhaps that’s a good working compromise.

By now you may be a little tired of hearing about AI all day, every day. Not that there’s anything wrong with AI news per se; lots of tech companies are working hard to infuse new AI tech into their products and services. It’s a big business story at a minimum. Then there’s the consumer angle, where AI comes closer to our daily lives. But for those of us who aren’t mega-ChatGPT users, AI can seem ever so slightly remote from our regular existence. Tools like Spotify’s latest can bring AI more into how we do our regular, mundane tasks like queueing up new tunes. Or not-so-new tunes, at least according to some users who view Spotify’s playlist work as part of a recurring effort to promote the same songs time and again.

Hit play, let’s have some fun!

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Robotic Automations

Watch: New Atlas robot stuns experts in first reveal from Boston Dynamics

This week Boston Dynamics retired its well-known Atlas robot that was powered by hydraulics. Then today it unveiled its new Atlas robot, which is powered by electricity.

The change might not seem like much, but TechCrunch’s Brian Heater told the TechCrunch Minute that the now-deprecated hydraulics system was out of date. It’s not hard to spot why Boston Dynamics, owned by Hyundai, wanted to go electric. Its new Atlas robot is leaner, and appears to have improved range-of-motion. Size and ability to contort and maneuver are not cosmetic elements to a humanoid robot — they can unlock new use cases and possible work environments.

The new Atlas is not incredibly well-defined today, which is not a massive surprise given that it’s still a work in progress. Still, we do know that it will first head to Hyundai factories before hitting the market more generally down the road.

Happily for those of us who want a domestic robot to handle household chores and hold our hands whilst we cry, there are other startups working on the humanoid robot project. Figure, Agility, Tesla, there are too many companies vying for the same prize to note in this short post. Which has me incredibly excited — more people working on the problem means quicker progress, and hopefully faster completion of a general purpose humanoid robot that can learn.

On that last bit, it’s worth keeping in mind that AI is set to play a large role in how robots go from being great at set, repetitive tasks to being able to learn, and do a great deal more without direct programming. While it will take time for LLMs’ ability to ingest language, write code, and the like to connect to robots under development today, you can spot the future if you squint far enough into the distance. To which all I can say is, faster, please!

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Robotic Automations

Watch: Why Tesla’s big layoffs happened, and what comes next

Tesla’s layoffs and executive departures took a bite out of its share price this week. The well-known electric vehicle company shed around 10% of its staff, impacting an estimated 14,000 people or more. Two well-known executives also decided it was time to move on.

In response to the news, shares of Tesla lost ground. The company’s value has eroded this year, falling 35% through the end of trading yesterday.

The year has not been kind to Tesla. It missed delivery estimates for the first quarter, has reportedly reduced hours for the production line of its Cybertruck and is seeing rivals in China stack market share with low-priced EVs. Tesla, in other words, helped foster the global electric vehicle market but is losing some of its primacy in that same market.

Which may be a bigger risk than it seems. The global auto market is large, complicated and replete with different manufacturers and badges competing for share. What’s the risk of being a bit smaller than expected? For Tesla, a lot. The company is currently valued at a price/sales multiple of 6.2x, per Yahoo Finance. GM? It’s worth 0.34x. Ford? An even more modest 0.29x.

In human terms, for every dollar of car that Tesla sells, it generates far more company worth than its rivals. Why? Because many investors are betting that Tesla is not only going to keep growing its EV business that became a profit center in recent years, but also that its work in energy, energy storage and related industries will generate a company that is far larger, and more valuable over time. If Tesla was to trade at a GM or Ford-style revenue multiple, it would erase most of its worth.

And with price cuts, falling deliveries, increasingly sophisticated competition and now mass layoffs, Tesla is starting to look more like a traditional company than a company that can avoid traditional business rules and trade like its peers. Hit play, let’s chat!

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Robotic Automations

Watch: How Anthropic found a trick to get AI to give you answers it's not supposed to

If you build it, people will try to break it. Sometimes even the people building stuff are the ones breaking it. Such is the case with Anthropic and its latest research which demonstrates an interesting vulnerability in current LLM technology. More or less if you keep at a question, you can break guardrails and wind up with large language models telling you stuff that they are designed not to. Like how to build a bomb.

Of course given progress in open-source AI technology, you can spin up your own LLM locally and just ask it whatever you want, but for more consumer-grade stuff this is an issue worth pondering. What’s fun about AI today is the quick pace it is advancing, and how well — or not — we’re doing as a species to better understand what we’re building.

If you’ll allow me the thought, I wonder if we’re going to see more questions and issues of the type that Anthropic outlines as LLMs and other new AI model types get smarter, and larger. Which is perhaps repeating myself. But the closer we get to more generalized AI intelligence, the more it should resemble a thinking entity, and not a computer that we can program, right? If so, we might have a harder time nailing down edge cases to the point when that work becomes unfeasible? Anyway, let’s talk about what Anthropic recently shared.

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