Nvidia is planning on spending $26 billion to train its own AI open-weight models, according to a 2025 financial filing. Wired was first to report the information. Nvidia has released several of its own AI models, including the Nemotron reasoning model, as well as specialized ones for specific tasks.
Nvidia making its own large frontier models could allow the company to go head-to-head against some of its biggest AI customers.
Chinese AI lab DeepSeek has released a major new version of its eponymous open-source AI models that are nipping at the heels of leading frontier models in some areas.
The most significant DeepSeek-V4 Pro and DeepSeek-V4 Flash both have a 1 million-token context — the amount of information the model can actively work with in a single session — which is a crucial feature for complex, long-running coding tasks.
DeepSeek rebuilt how the models process information under the hood, making them substantially more efficient — and that efficiency is what makes the large context window actually usable.
Also, the new models’ coding skills have closed the gap with the major frontier models from Anthropic, OpenAI, and Google.
The authors of the model acknowledge some of V4’s shortcomings, such as its lower scores on reasoning benchmarks, saying that V4 “trails state-of-the-art frontier models by approximately 3 to 6 months.”
As open-weight models, V4 can be run on any user’s own hardware, making the V4 models among the top-performing open-source models out there. V4’s large context and token efficiency are especially significant among open-source models.
But like with earlier DeepSeek models, don’t ask it about Tiananmen Square.
DeepSeek rebuilt how the models process information under the hood, making them substantially more efficient — and that efficiency is what makes the large context window actually usable.
Also, the new models’ coding skills have closed the gap with the major frontier models from Anthropic, OpenAI, and Google.
The authors of the model acknowledge some of V4’s shortcomings, such as its lower scores on reasoning benchmarks, saying that V4 “trails state-of-the-art frontier models by approximately 3 to 6 months.”
As open-weight models, V4 can be run on any user’s own hardware, making the V4 models among the top-performing open-source models out there. V4’s large context and token efficiency are especially significant among open-source models.
But like with earlier DeepSeek models, don’t ask it about Tiananmen Square.
SpaceX thinks its total addressable market (TAM) is a whopping $28.5 trillion for its businesses, according to an S-1 filing for its upcoming IPO reviewed by Reuters. And most of that market isn’t rockets. The company says roughly 90% could come from AI — largely selling artificial intelligence tools to businesses.
“We believe that our enterprise strategy, which is focused on serving the digital needs of the world’s largest industries with Al solutions, positions us competitively to pursue this rapidly growing opportunity,” SpaceX said in the filing. “We believe we have identified the largest actionable total addressable market in human history.”
TAM, of course, assumes capturing every possible customer. But even a small slice of a $28.5 trillion market would be enormous.
On Tesla’s earnings call earlier this week, CEO Elon Musk said production of the company’s steering-wheel-less Cybercab had begun. Since then, Musk and Tesla have posted videos showing the gold two-seater rolling off the line at its Texas Gigafactory and onto the road.
Cybercab has started production pic.twitter.com/MAeswanf96
— Elon Musk (@elonmusk) April 24, 2026
The Cybercab — meant both for consumers and Tesla’s Robotaxi network — is widely seen as central to the company’s future. “The future of the company is fundamentally based on large-scale autonomous cars and large scale and large volume, vast numbers of autonomous humanoid robots,” Musk said last year.
Whether these cars actually make it to consumers is another question. For now, regulations generally require steering wheels, and Tesla still has to prove the vehicles can reliably drive themselves.
In formation pic.twitter.com/7qA0SluL8J
— Tesla Robotaxi (@robotaxi) April 24, 2026
On the earnings call, Musk said production would be “very slow” but would ramp up and go “kind of exponential towards the end of the year and certainly next year.”
Meta said it will deploy “tens of millions” of Amazon Web Services Graviton CPU cores to power so-called “agentic” AI systems — tools that can reason, plan, and act on their own. The move makes Meta one of the largest customers of Amazon’s in-house chips.
The deal also underscores a broader shift in AI infrastructure, as companies move beyond Nvidia GPUs and use different chips for different tasks.
Meta, which is working on its own custom inference chips, also has chip deals with Advanced Micro Devices and Nvidia.
The deal also underscores a broader shift in AI infrastructure, as companies move beyond Nvidia GPUs and use different chips for different tasks.
Meta, which is working on its own custom inference chips, also has chip deals with Advanced Micro Devices and Nvidia.
Oracle extended its premarket gains Friday after Wedbush Securities’ Dan Ives initiated coverage with an “outperform” rating and a $225 price target — about 25% upside to its pre-initiation level — calling the enterprise software and cloud infrastructure company a “foundational infrastructure provider for the AI revolution.”
Ives argues investors are misreading Oracle’s heavy capital spending and negative free cash flow as risky, despite being backed by a massive $553 billion backlog of contracted demand. He says the company’s “secret sauce” is a two-part strategy: building high-performance cloud infrastructure for AI workloads while connecting those models directly to companies’ own data.
“We believe Oracle is in the early innings of a significant repositioning as it executes on this generational opportunity,” Ives wrote.
