POLICY

Will the midterms slow AI's data center hopes?

The US midterm elections dealt a significant blow to AI data centers. Tech firms may be reeling as a result.

Across the country, voters largely rebuked AI data centers, casting their votes for both candidates and ballot measures seeking to strongly regulate the construction of these facilities. Since then, tech stocks have tumbled, specifically in the data center and semiconductor sectors, including companies like IREN, AMD, Nvidia, CoreWeave and Core Scientific.  

As the public perception of data centers worsens and momentum builds around regulation, the entities that are building these facilities may be getting nervous about their eye-popping, multi-billion dollar infrastructure investments. 

• According to Bloomberg, Microsoft may be backing away from its deals with neocloud firms like Nebius, IREN and Nscale, worth tens of billions of dollars between them, amid high costs and regulatory uncertainty. Reuters also reported that the company declined an option to more than double the size of its existing agreement with Nscale.

• Meta president Dina Powell McCormick, meanwhile, warned last week that the Democratic wins in the midterm elections could make it difficult for a lot of companies to build data centers, saying at a WSJ Tech conference on Wednesday that the industry's power players have "not done as good a job as we needed to explain the benefits of these investments."

• On Thursday, OpenAI published a blog post pledging to handle its data center deals responsibly, including avoiding non-disclosure agreements, giving residents the right to weigh in on projects and paying for its own electricity. Additionally, it laid out a policy blueprint for lawmakers, recommending things like mandated public reporting on environmental impacts and transparency on tax incentives. 

Additionally, at an event hosted by Nvidia's Jensen Huang at the San Francisco Opera House on Tuesday, Silicon Valley executives expressed concern over the overwhelming disapproval of data centers, according to the Wall Street Journal.

The likely result is that tech companies will slow down their relentless pursuit to build more data centers as regulation adds red tape and raises the cost of getting these projects approved, Valence Howden, advisory fellow at Infotech, told The Deep View. The biggest impact will be at a state level, given how many gubernatorial candidates campaigned on data center construction moratoriums, such as now-Gov. Gina Hinojosa in Texas. Additionally, these projects could face even more opposition on the municipal level. 

"Companies may move projects to friendlier states, but that shift would still take time and slow their plans," Howden said. 

However, Howden noted, while the infrastructure buildout may face delays, it likely won't be stopped. Rather, it will just force these companies to make more concessions and eat more of the costs of standing up their data centers. 

"This looks more like a slowdown with higher costs than a wholesale retreat from infrastructure investment," said Howden. "The pressure would also extend to smaller providers that depend on commitments from big tech companies, which is relevant to the concerns around Microsoft’s neocloud deals."

Whether or not the midterm election results represent a temporary slowdown of this buildout or a meaningful reevaluation of compute needs depends entirely on how effective the forthcoming regulation is at actually forcing these companies to press pause. It's indisputable that these AI giants desperately need the data centers that they are building to power their pursuit of increasingly capable models and widespread adoption, and with so much cash and influence at their disposal, the question remains if these firms can buy their way out of the red tape, whether that be through lobbying or more strategic data center deals. Either way, the costs of this new wave of regulation are going to drive data center prices skyhigh, and could put even more pressure on these companies to extract revenue from their frontier models. 

TOGETHER WITH FINGERPRINT

Can your website tell which AI agents are acting for real customers?

AI agents sign in, browse, and buy for real customers, and some, like Meta's Muse, don't identify themselves. Block them all and you break real experiences. Allow them all and you invite abuse.

Join Fingerprint's live build on Nov 17 at 9AM PT, where Keshia Rose codes a server-side policy layer from scratch and tests it against a real Muse session, a bot script, and a human visitor.

You'll learn how to:

• Tell humans, AI agents, and automation tools apart server-side

• Adjust trust for signed, verified, unknown, and spoofed agents

• Write policies that allow, limit, or block

Save your spot for the live build

CONSUMER

AI agents need to learn when not to buy

The holiday season is around the corner, but letting AI place your orders may still be a ways away, according to a new benchmark from Visa.

Last week, the credit card giant launched its Agentic Commerce Benchmark, claiming to be the first of its kind in testing AI in realistic shopping scenarios and challenges. Visa notes that shopping presents unique challenges, as users don't always know what they are going to buy at the onset. This means that AI models involved in shopping are responsible for distinguishing when they should simply choose to not to buy something, or when it has enough information to justify its decision—a task the benchmark discovered they weren't very good at. 

Though it didn't disclose which models were evaluated, Visa claims that the first round of tests involved 22 different AI models from 12 labs that took on 15 shopping challenges, including buying a TV, laptop or personal care appliance. The results overwhelmingly pointed to one outcome: The models are not quite ready for this task yet. 

Across testing, the selected AI models achieved a benchmark pass rate of 57.2%. Other notable results included:

• None were perfect: The best performing models passed 82.2% of attempts, the worst scored 28.9%.

• Cost wasn't a differentiator: The five models with the highest average cost per run passed 67.1% of runs, compared with 50.2% for the five least expensive.

• Bad decisions: Around half of the failed runs, 54%, still ended in the AI making a purchase.

The five models that were able to understand a customer's shopping intent, or "hidden buying factors," through conversation flows tended to perform better, scoring an average pass rate of 70.2%. Yet this was inconsistent, as the same models often failed when made to repeat a scenario they had already passed. 

Given how far the technology still has to go, the obvious question is when agentic commerce will actually be viable. According to Rubail Birwadker, Visa’s global head of growth, the biggest hurdle may not be technical at all, but whether consumers will trust it. That gap is already visible in Visa’s data: the company’s Trust Index, released in September, found that while 72% of U.S. consumers have used an AI assistant, only 23% trust AI to make payments on their behalf.

“The models are moving fast, and agents are already shopping," said Birwadker. "But the real milestone is when consumers trust them to pay consistently and securely on their behalf. That’s the bar we’re focused on at Visa, and I think we’ll get there sooner than people expect."

Out of all the AI application trends that have emerged over the last couple of years, the one that seems the most difficult to bridge is agentic commerce, particularly because it involves giving AI the power to access your finances, which, naturally, people will be reluctant to. It doesn't help that even in controlled environments, AI agents are going rogue, taking actions that are unintended and not permitted, and not giving users the confidence needed to hand over their credit cards. Additionally, a lot of purchases reflect personal taste, something I'm not entirely sure I would ever trust AI to just handle on its own. However, something AI is very good at is working as an idea partner and brainstorming ideas. I think this is a safer, more productive approach to try for embedding AI into commerce, especially as the holiday season quickly approaches. 

TOGETHER WITH TRIPLE WHALE

Here Are 50 LLM Prompts To Crush Black Friday

Anyone who’s anyone knows that BFCM (Black Friday Cyber Monday) is a BFD (big f****’ deal). As the biggest sales day of the year for many businesses, it brings an immense amount of opportunity – and stress. But this year Triple Whale, the AI operating system for commerce, has your back with these 50 prompts built specifically to maximize your BFCM efforts.

Each prompt will help your AI assistants analyze your data, identify crucial patterns, and make the kind of strategic on-the-fly recommendations that can make (or break) your business’ BFCM goals. Whether you’re ahead of the curve and already planning, or simply want to have some moves in your back pocket should anything go awry, Triple Whale’s has you covered.

Download the entire guide for free right here.

ENTERPRISE

Code is cheap now. Trust is the new bottleneck

AI is making it easier for everyone to create software. The harder challenge is building the infrastructure, security and skills to turn that explosion of code into useful work.

In this episode of The Deep View Conversations, we sit down with Mario Rodriguez, GitHub's chief product officer, at GitHub Universe 2026 to explore how AI agents are changing the platform, the community, and the future of software creation.

Rodriguez explains why GitHub is rebuilding its foundations for machine-speed development, how its move to Azure supports that effort, and why writing code faster only helps if the software is secure and reliable. He also responds to Anthropic's decision to build Scion, its own version control system, and makes the case for Git's continued role as AI accelerates development.

The conversation goes beyond the technology to a bigger question: who gets to build? As people without technical backgrounds start creating software, Rodriguez argues that lowering the barriers can bring more people into the craft and some will naturally become professional developers. His framework: lower the floor, raise the ceiling, and widen the walls. This episode explains what he means.

Topics covered:

• GitHub's vision for collaboration between people and AI agents
• AI scanning, code review, and cybersecurity upgrades
• Copilot projects, agent squads and automations that learn from their results
• Keeping work moving across mobile, the web, Slack, and Teams
• Why AI demand accelerated GitHub's move to Azure
• GitHub's ambition to reach a billion developers
• Why easier entry into coding can strengthen open source

If you want to understand where professional software development, vibe coding, and open-source AI are heading, don't miss this one.

📺 Watch on YouTube

🎧 Listen in your favorite podcast player

Subscribe to Deep View Conversations for interviews with the leaders shaping the future of AI, business, and technology: https://tdv.transistor.fm/ 

And don't forget to sign up for The Deep View daily newsletter. We don’t just cover AI, we decode it. In a world flooded with hype, we deliver sharp, no-nonsense insights to keep you ahead of the curve and help you put AI to work every day: subscribe.thedeepview.com

LINKS

• OpenAI closes $11 billion funding round at $1.25 trillion valuation

• Thinking Machines Lab debuts Inkling 2 Small, a 594B MoE model

• Anthropic IPO valuation faces skepticism as investors question $1.5 trillion

• Nvidia requires Southeast Asian cloud partners to track chip locations

• House Dems eye Apple, Meta, Musk firms as early investigation targets

• Microsoft workers worry if they're among 60,000 offered buyouts

• AI firm PSI says Anthropic model proved decades-old quantum conjecture

• Perplexity Teammates: personal agents for work, rolling out to Max users on the web 

• Google Antigravity: Has a new interface with a new look and feel 

• Gemini Omni 1.2 Flash: debuts at #1 in both Text-to-Video and Image-to-Video

• Motion: Now has AI editing for Premiere Pro

• Fanatics: Applied AI Engineer

• Snowflake: AI Associate, Office of the CEO

• Obin AI: Agentic AI Engineer

• Percepta: Applied AI Engineer

GAMES

A QUICK POLL BEFORE YOU GO