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A little more conversation: Company executives can't stop talking about AI

A little more conversation: Company executives can't stop talking about AI

David Crowther

A little more conversation

‍Interestingly, executives at top companies seem to be doing the exact opposite — they can’t stop talking about AI, perhaps hoping that if they position their companies as “AI forward” they may be more attractive to investors (see Nvidia becoming the latest company to join the $1 trillion club).

A little more conversation: Company executives can't stop talking about AI

Data collated by Goldman Sachs reveals that artificial intelligence has been cropping up more and more in the quarterly earnings calls of Russell 3000 companies, as businesses like Mark Zuckerberg’s Meta line up to explain to investors how AI will play a pivotal role in the wider world and within their organizations… in the future at least.

‍Tried and tested

‍That’s a lot of corporate chatter, but how much are companies actually using AI? The answer depends somewhat on your perspective.

‍McKinsey’s latest report into AI, published in early August, reveals that 79% of people surveyed have had at least some exposure to the tools, although only 22% of respondents say that they personally are regularly using it in their own work. For tools that (mostly) haven’t even celebrated a first birthday yet, that feels like quite a lot.

The people using it most are your pals in marketing and sales, with 14% of respondents reporting that their organizations were regularly using generative AI in that function, more than any other.

A little more conversation: Company executives can't stop talking about AI

The most common uses cited in the survey were for creating first drafts of text, personalizing marketing materials, identifying trends or communicating with customers with chatbots. AI isn't quite doing iRobot stuff yet, but taking the sting out of some of the more "boring" corporate tasks will always have its place.

‍We aren’t in Kansas anymore

‍Clearly, the answer to the question “have we had peak AI?” is a resounding, definitive “no”. The buzz around ChatGPT specifically may have slightly diminished, but that’s only because its mind boggling initial success has birthed — literally — thousands of competitors.

One website, accurately called theresanaiforthat.com, currently tracks more than 6,900 different tools. There’s AI to summarize your emails, edit your videos, build your website, design your house, write your dating profile, plan your vacation, edit your code, test new hairstyles on you, create personalized stories for children, be your personal assistant… or even just be your friend who is “always on your side”. There’s even one that creates a chatbot so you can "talk" to a PDF document(?), and there's quite a few to make charts and analyze data. Luckily for us, they aren’t very good... yet.

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Jon Keegan

DeepSeek releases new V4 series models highlighting efficiency and long context

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 Unveils Flagship AI Model a Year After Breakthrough

DeepSeek Unveils Flagship AI Model a Year After Breakthrough

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.

$28.5T
Rani Molla

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.

tech
Rani Molla

Tesla Cybercab production has begun

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.

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.

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.”

tech
Rani Molla

Meta signs deal to use Amazon Graviton chips

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.

Meta Partners With AWS on Graviton Chips to Power Agentic AI

Meta Partners With AWS on Graviton Chips to Power Agentic AI

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.

tech
Rani Molla

Oracle rises after Wedbush’s Dan Ives calls the stock a buy with 25% upside

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.

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