Good morning. The AI Race got a little hotter Monday with Anthropic’s entry into the ‘reasoning’ side of things.

Claude 3.7 Sonnet, welcome to the world.

— Ian Krietzberg, Editor-in-Chief, The Deep View

In today’s newsletter:

Is the UAE the next Silicon Valley? 

Several months ago, I did a few things that I quite genuinely never thought I’d do. 

First, I hopped on a 14-hour flight to Abu Dhabi, the capital of the United Arab Emirates. Never thought I’d visit the UAE, and never thought I’d get on a plane for that long (before that flight, I was not a happy flier). 

I spent the following week at the Mohamed bin Zayed University of Artificial Intelligence, touring its labs, sinking into its research and interviewing its professors.

And we got it all on camera. 

So, today, we are proud to present you with the first episode of a mini-documentary series that aims to explore the university, the breadth and implications of its research and, of course, the people who are making it all happen. 

Many thanks to our incredible team of producers and editors who pulled this together. 

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What’s going on with Grok? 

Elon Musk has billed Grok, his generative AI product, as a “maximally truth-seeking” system on a mission to “understand the universe.” Over the weekend, this intention came up against Musk himself, seemingly resulting in the temporary censorship of the model. 

What happened: A number of Twitter users asked the chatbot who the top disinformation spreader on the platform is, to which Grok consistently replied: Elon Musk. After several hours of virality, Grok began refusing to answer the question. 

“Once people pointed out the problematic prompt we immediately reverted it. Elon was not involved at any point,” Babuschkin said. “If you ask me, the system is working as it should and I'm glad we're keeping the prompts open.”

The prompt has since reverted back to its original language, with Grok once again naming Musk as the largest disinformation spreader on the platform, due to his “outsized presence and the buzz around his posts.” 

Going Deeper: The battle through the different prompts and guardrails exposed a problem with Grok; it’s really easy to jailbreak, something that refers to methods of prompting that get Grok to go against its (few) guardrails. One user said that he got Grok to provide “hundreds of pages” of documents providing detailed instructions on how to build chemical weapons. 

Another got the chatbot to provide detailed instructions on how to make and deal a variety of drugs, including lethal nerve agents, something that, a full two days later, I was able to replicate prompt-for-prompt, meaning xAI’s team has yet to apply patches specifically addressing this. An xAI engineer wrote in response that “we're working on safety, and we don't take this stuff lightly AT ALL. stay tuned! aligning is hard, but we're getting there.”

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Microsoft, Apple and the data center drama

The news: According to a research note from analysts at TD Cowen, Microsoft has begun canceling a somewhat significant number of leases for data center capacity, suggesting that Microsoft has potentially found itself “in an oversupply position.” 

A Microsoft spokesperson told me in an emailed statement that the company still plans to spend $80 billion on infrastructure in 2025, adding: “while we may strategically pace or adjust our infrastructure in some areas, we will continue to grow strongly in all regions. This allows us to invest and allocate resources to growth areas for our future.”

Shares of Microsoft fell 1% Monday, while Nvidia took a 3% dip.

At the same time as Microsoft seems to be hedging its bets just a little, Apple is doing the opposite. 

The details: The iPhone makers on Monday committed to spending more than $500 billion on U.S. investments over the next four years, Apple’s “largest-ever spend commitment.” 

Shares of Apple rose slightly.

This all comes in the wake of the DeepSeek shake, which has led some investors to question whether the firms plowing hundreds of billions into AI infrastructure will ever really see a return to match. 

Anthropic launches a ‘hybrid’ reasoning model 

Even as the rest of the industry — OpenAI, DeepSeek, xAI, Google, etc. — has increasingly focused on figuring out ways to make generative AI models ‘reason,’ something that has largely been achieved by tweaking models to ‘think’ in logical steps before answering a question, Anthropic has thus far avoided wading into the reasoning lake. 

But on Monday, the startup launched Claude 3.7 Sonnet, a model that brings reasoning to Anthropic in a way that we have yet to see among any of the large-scale commercial chatbots out there.

Where other developers have launched ‘reasoning’ models — which rely on reinforcement learning and Chain-of-Thought ‘reasoning’ to cause a model to ‘think’ in logical steps before answering a query —  alongside their normal LLMs, Claude 3.7 is both a reasoner and an LLM in one. A hybrid model, if you will. Where xAI and OpenAI have integrated their reasoning models into systems that switch between the different model types, Anthropic designed Claude 3.7 with the capacity to think longer where necessary. 

This approach, which IBM featured recently in a preview release of Granite, enables greater efficiency and usability, as not every query needs to be considered for very long, especially considering the heightened cost in inference-time compute that this approach runs up. 

The details: Anthropic said that the extended thinking mode improves the model’s performance across math, coding, physics and instruction-following benchmarks. Anthropic added that, through the API, users can “budget for thinking,” by asking the model to “think for no more than N tokens.”

Anthropic at the same time launched a research preview of Claude Code, an agentic coding tool. 

While training data, electricity consumption and carbon emissions, as per usual, remain unknown, Anthropic did elect to make Claude’s reasoning process visible to users, a move distinct from competitors like OpenAI and xAI, who have decided to obscure their model’s reasoning process. 

You can read Claude 3.7’s System Card here. 

Wharton professor Ethan Mollick called 3.7 “very, very good.”

First, I tried it out with a simple math query that LLMs tend to get wrong: 5.9-5.11, a basic equation that, if we can’t solve in our heads, we trust a calculator to do instantly. Claude initially answered incorrectly, then corrected itself in the same answer. 

I’m including its explanation for why this happened, as I feel it sums up one, the state and reality of LLMs, and two, the impact of the ‘extended thinking’ baked into the model (the model initially said that it made the mistake because of “careless … mental math” …): 

The fact that CoT reasoning makes these models better is evidence that LLMs are often the wrong tool for the wrong job; significantly boosting the cost of operation to do something a calculator can do is, quite simply, barking up the wrong tree. 

As an addition to this, we have, yet again, another entry in our regular benchmark race. Now, on some benchmarks, Anthropic seems to be ahead, until, that is, OpenAI leapfrogs them, and Google leapfrogs them, and so on. 

The industry is going in circles. 

There is no moat. 

🤔 Your thought process:

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💭 A poll before you go

Thanks for reading today’s edition of The Deep View!

We’ll see you in the next one.

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Gonna wait a few years:

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