Tech
Ford turns to F1 and bounties to build a $30,000 electric truck
Ford is promising to deliver an EV truck next year that starts at $30,000 and can compete with Chinese automakers without undermining profit margins. A combination of 3D-printed Lego-like parts, Formula 1 thinking, and a bounty program will help the company hit that target, Ford said Tuesday.
It will have to. Ford took a $19.5 billion hit in December and ended production of its battery-electric F-150 Lightning. It can’t afford for this new EV business strategy to fall flat.
Ford’s bet on a line of affordable EVs began several years ago with a skunkworks team led by Alan Clarke, a 12-year Tesla veteran. Pieces of its plan were revealed last August, when Ford said it would ditch its traditional moving assembly line and invest $2 billion in its Louisville factory to adopt a new production system that promises to speed up manufacturing by 15%.
The company said at the time that its line of EVs would be built on a universal platform with single-piece aluminum unicastings — large components cast as one piece to eliminate parts and allow for faster assembly — and lithium iron phosphate batteries with tech licensed from China’s CATL.
Now Ford is sharing more specifics in a flurry of blog and social media posts on how it will fulfill its promise of a desirable EV truck that will be $20,000 cheaper than the average new vehicle while still generating profits. Ford didn’t share specs like the range, features, or charging times of this future EV. But it did reveal how it plans to build lighter, cheaper, more efficient EVs made with fewer parts.
It all starts with the universal EV platform, or UEV. The platform will underpin a midsized truck first, then could support a sedan, crossover, three-row SUV, and even small commercial vans, according to Clarke. The UEV is Ford’s first “clean sheet” EV built from the ground up — a strategic shift for the company, which built its Mustang Mach-E and the Lightning EVs using existing infrastructure and manufacturing practices.
“It’s a platform that is built around efficiency,” Clarke said in a briefing with the media. “It’s built around affordability to be able to make long-range electric vehicle travel affordable to more people.”
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To achieve that, Clarke set out to create a new culture seeded by talent from Formula 1 and companies like Apple, Lucid Motors, Rivian, and Tesla, as well as Auto Motive Power, a startup acquired by Ford in 2023. The team, which includes about 450 people at its base in Long Beach, California, and 200 people in an office in Palo Alto, also adopted a bounty program to help engineers understand how their day-to-day decisions impact the customer and the end product, Clarke said in an interview with reporters.
The focus of the bounty program was efficiency. Numerical metrics were assigned to every aspect of the UEV, including vehicle mass, aerodynamic drag, and even specific vehicle parts. In practice, this meant Ford might decide to use a more expensive part if it helped decrease the weight of the EV, thereby making it more efficient and cost-effective.
“We’ve been very focused on making sure that the cost that we’re moving from the product doesn’t remove value,” Clarke said. One example is that even the base trim of the EV truck will have a power-folding mirror, a premium feature on most vehicles, because it decreases aerodynamic drag, according to Clarke. The company saved money by using one motor, instead of two, to handle the mirror adjustment and the folding.

That obsession with efficiency included a team of ex-Formula 1 engineers who worked closely with Ford’s design team. The result, according to Ford, is a midsized EV truck that is 15% more aerodynamically efficient than any other pickup truck on the market today.
This team of former F1 engineers used 3D-printed and machined parts to create a Lego-like build for its test vehicle. Thousands of 3D-printed components, which are accurate within fractions of a millimeter of Ford’s simulations and could be swapped out in minutes, were used to measure aerodynamics. These Lego-like prototypes were used in wind tunnel testing early on — and often — to measure aerodynamics, a process that Ford traditionally used only when the design of a vehicle was nearly complete.
A natural place to focus was on the battery, which can account for about 40% of a vehicle’s total expense. A lighter, more efficient vehicle allows Ford to use a smaller battery, which reduces cost. The end result, according to Clarke, will be an EV truck with about 15% more range, or 50 miles, than an equivalent pickup powered by gas.
The efficiency push also led the team to adopt manufacturing tactics adopted and popularized by Tesla, including the use of aluminum unicastings and moving from a 12-volt system to a 48-volt power system that will be used for some vehicle functions.
Ford also upended the electric vehicle architecture of the UEV, taking a zonal approach similar to Tesla and Rivian. Instead of scattering dozens of electronic control units (ECUs), or computers, throughout the vehicle, Ford has integrated multiple vehicle functions into five main modules. This reduces complexity, cost, and copper usage and helped make the EV truck’s wire harness 4,000 feet shorter and 22 pounds lighter than one of its first-generation electric vehicles, according to Luccas Di Tullio, a software engineer at Ford who previously worked at Auto Motive Power.
Di Tullio said the company carried the same philosophy to the power electronic components, finding ways to share components and reduce parts with a single module that manages power distribution and battery management and provides AC power back to a customer’s home during an outage.
Ford also developed its own software for those five main ECUs, down to the application layer, according to Clarke. Because Ford owns the software — to the lowest level — it becomes very portable, Clarke said.
“Other than being able to control the infotainment, what shows up on the screens, [and] how you interact with the vehicle, all of the body controls then are directly coupled,” he said. “So you can imagine that many of the experiences that can only be created by coupling all the different sensors around the vehicle are now at our fingertips and under our own control.”
Tech
Snapchat launches creator subscriptions in the US
Social network Snapchat announced today it’s launching creator subscriptions in alpha with select people in the U.S. starting on February 23. The company noted that users will be able to buy subscriptions to creators, including Jeremiah Brown, Harry Jowsey, and Skai Jackson. This will allow users to unlock exclusive content while creating monetization opportunities for creators.
Creators can set their own monthly prices for subscription within the app, while Snap will recommend different tiers to them. The subscription will unlock subscriber-only content, priority replies to a creator’s public Stories, and ad-free consumption for that creator’s Stories.
Snap noted that this is a new way for creators to earn more money besides the existing programs.
“Expanding on existing monetization offerings like the Unified Monetization Program and the Snap Star Collab Studio, Creator Subscriptions introduce a premium layer of connection directly into how Snapchatters already engage with creators across Stories, Chat, and replies,” the company said in the blog post.
Snapchat reached 946 million daily active users, according to the company’s Q4 2025 results. The platform noted during its earnings that the number of U.S.-based users posting to Spotlight grew over 47% year-over-year. The company also spun out hardware to a new entity called Specs last month.
The company added that it plans to expand the program to Snap Stars in Canada, the U.K., and France in the coming weeks.
Rival company Meta also allows creators to offer subscriptions on platforms like Instagram and Facebook, which gives users access to exclusive content and badges.
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Tech
Mistral AI buys Koyeb in first acquisition to back its cloud ambitions
Mistral AI, the French company last valued at $13.8 billion, has made its first acquisition. The OpenAI competitor has agreed to buy Koyeb, a Paris-based startup that simplifies AI app deployment at scale and manages the infrastructure behind it.
Mistral has been primarily known for developing large language models (LLMs), but this deal confirms its ambitions to position itself as a full-stack player. In June 2025, it had announced Mistral Compute, an AI cloud infrastructure offering which it now hopes Koyeb will accelerate.
Founded in 2020 by three former employees of French cloud provider Scaleway, Koyeb aimed to help developers process data without worrying about server infrastructure — a concept known as serverless. This approach gained relevance as AI grew more demanding, also inspiring the recent launch of Koyeb Sandboxes, which provide isolated environments to deploy AI agents.
Before the acquisition, Koyeb’s platform already helped users deploy models from Mistral and others. In a blog post, Koyeb said its platform will continue operating. But its team and technology will now also help Mistral deploy models directly on clients’ own hardware (on premises), optimize its use of GPUs, and help scale AI inference — the process of running a trained AI model to generate responses — according to a press release from Mistral.
As part of the deal, Koyeb’s 13 employees and its three co-founders, Yann Léger, Edouard Bonlieu, and Bastien Chatelard (pictured above in 2020), are set to join the engineering team of Mistral, overseen by CTO and co-founder Timothée Lacroix. Under his leadership, Koyeb expects its platform to transition into a “core component” of Mistral Compute over the coming months.
“Koyeb’s product and expertise will accelerate our development on the Compute front, and contribute to building a true AI cloud,” Lacroix wrote in a statement. Mistral has been ramping up its cloud ambitions. Just a few days ago, the company announced a $1.4 billion investment in data centers in Sweden amid growing demand for alternatives to U.S. infrastructure.
Koyeb had raised $8.6 million to date, including a $1.6 million pre-seed round in 2020, followed in 2023 by a $7 million seed round led by Paris-based VC firm Serena, whose principal Floriane de Maupeou celebrated the acquisition. For the firm, this combination will play a key role “in building the foundations of sovereign AI infrastructure in Europe,” she told TechCrunch.
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In part thanks to these geopolitical tailwinds, but also due to its focus on helping enterprises unlock value from AI, Mistral recently passed the milestone of $400 million in annual recurring revenue. Koyeb, too, will be focused on enterprise clients going forward, and new users will no longer be able to sign up for its Starter tier.
Mistral didn’t disclose financial terms of the deal, and it is unknown whether other acquisitions are in the works. But speaking at Stockholm’s Techarena conference last week, CEO Arthur Mensch said Mistral is hiring for infrastructure and other roles, pitching the company to prospective employees as an organization that is “headquartered in Europe, that is doing frontier research in Europe.”
Tech
Anthropic releases Sonnet 4.6
Anthropic has released a new version of its midsized Sonnet model, keeping pace with the company’s four-month update cycle. In a post announcing the new model, Anthropic emphasized improvements in coding, instruction-following, and computer use.
Sonnet 4.6 will be the default model for Free and Pro plan users.
The beta release of Sonnet 4.6 will include a context window of 1 million tokens, twice the size of the largest window previously available for Sonnet. Anthropic described the new context window as “enough to hold entire codebases, lengthy contracts, or dozens of research papers in a single request.”
The release comes just two weeks after the launch of Opus 4.6, with an updated Haiku model likely to follow in the coming weeks.
The launch comes with a new set of record benchmark scores, including OS World for computer use and SWE-Bench for software engineering. But perhaps the most impressive is its 60.4% score on ARC-AGI-2, meant to measure skills specific to human intelligence. The score puts Sonnet 4.6 above most comparable models, although it still trails models like Opus 4.6, Gemini 3 Deep Think, and one refined version of GPT 5.2.
