POLICY

Can AI outbuild its political backlash?

Data centers' bad reputation has come for the ballot box. 

AI infrastructure has emerged as one of the most politically-charged issues of the November midterm elections, with candidates leaning into issues that US voters across the political spectrum feel strongly about. In an NBC News exit poll, 56% of voters said data centers were a “crucial or major factor” in their vote for Congress, while only 13% said the issue played no role at all.

And anti-data center sentiment showed up strongly in the results. Time reported that Democratic candidates spent more than $100 million on broadcast advertising that painted Republican opponents as pro-Big Tech and data centers, allowing these candidates to win key races in Ohio, Kansas and Iowa.   

Beyond the candidates themselves, dozens of local ballot measures across the country asked whether to limit or permit data center development in their communities. Redbud Consulting, a polling and data strategy firm, tracks these measures in its Data Centers on the Ballot 2026 Results. 

Because the questions were worded differently for each municipality, Redbud's tracker sorts each outcome by its effect: “Restrict” for results that limit data centers and “Allow” for results that permit them. As of the time of publishing this article: 

• "Restrict" results had won 38 of the 39 measures with complete counts; "Allow" measures had only won one.

• While votes are still being counted, six elections have "restrict" results leading and two elections have posted no results as of yet, "Restrict" results have 77% of the vote. 

Several bills to restrict the building of these facilities were already in place prior to the election, such as New York Gov. Kathy Hochul, who has since been re-elected, implementing the nation's first statewide moratorium on hyperscale data centers of 50 megawatts or more for a year back in July. The results of the midterms could turn that trickle of regulation in the US into a flood. For instance, Texas's first Democratic governor in more than 30 years Gina Hinojosa campaigned on a statewide data center moratorium. 

That, in turn, could stymie the multi-billion-dollar buildout that frontier model labs are counting on to achieve the bombastic growth targets they've promised to their investors.

As it stands, the frontier AI labs are likely questioning where they go from here. The reality is, with so much on the line ahead of major IPOs, these labs aren't going to stop their relentless pursuit of compute, even as many of them call to pace frontier development. And despite the public abhorrence of these facilities, as well as the political and regulatory threats, these labs don't necessarily need to stop. Rather, they just have to become more strategic. For one, domestic data center buildouts could simply be made smaller to fly under the radar of regulators, as evidenced by reports that OpenAI and Anthropic are eyeing smaller data center facilities, such as 20 to 30 megawatt facilities. These companies can also start building out more compute overseas, such as Google's $15 billion initiative to build out data centers in Malaysia. And of course, with access to the mountains of investor cash that these companies have, don't be surprised to see an increase in pro-data center lobbying as we approach the 2028 election, which is already an industry practice. 

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HARDWARE

OpenAI's hardware ambitions get a new engine

OpenAI is applying the power of its frontier models to its long-awaited hardware efforts. 

On Wednesday, OpenAI published a blog post detailing an experiment in which the company used GPT-6 Astra to design a new device where users can talk to Codex. OpenAI explained that the model designed every component without direct human editing, including the functional requirements, enclosure, the electrical components and printed circuit board, the user interface, and software. 

Two team members designed the test device with Astra as a side project in under a week, and a contract manufacturer built a small batch of prototypes in under a month. The lab notes that getting it ready for manufacturing still required rounds of review and interaction, as Astra did not account for details such as the manufacturer's production limits, but it is using the identified gaps to improve its frontier model. 

OpenAI also claims its hardware and infrastructure teams use Codex and ChatGPT in many capacities, including technical tasks like creating and revising computer-aided design models, and creative ones, such as preparing manufacturing drawings spanning both robotics and infrastructure. And the results have started to show: 

• Since February, model usage on OpenAI's hardware teams has grown 23-fold to 212 million tokens per employee per day.

• On BenchCAD 2.0, a 3D modeling benchmark, a newer internal model achieved an average score of 0.875, compared with 0.594 for GPT‑6 Astra at its highest reasoning setting. 

The company is putting its engineering capabilities to the test by working with a small group of industry collaborators to evaluate them on real engineering problems. OpenAI announced an expanded collaboration with Chip Ganassi Racing and a new research collaboration with Mazda to explore how OpenAI models can help with new vehicle design. The lab also has a program for companies or individuals who want to participate in its physical engineering research efforts. 

Typically, when people think about AI and hardware, they think of consumer devices being polluted by unnecessary AI features or bloatware. However, this paper highlights another possibility: AI can actually help accelerate the buildout of entirely new hardware. It is particularly interesting to hear how its own hardware teams are using it to build, as there haven't been many updates since the company originally announced it was collaborating with Apple's Jony Ive on a product, other than the Apple lawsuit alleging that the iPhone maker's employees systematically defected with trade secrets during and after being hired by OpenAI. When OpenAI finally releases its device, it will be interesting to learn how much AI assisted the development. Ideally, incorporating AI models into the development process would help OpenAI push the envelope beyond what would have been possible without them. Ultimately, I don't think there's a better endorsement for AI models in engineering than OpenAI developing a truly cutting-edge device and then openly sharing how AI made that possible. 

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RESEARCH

Anthropic wants AI to prove its identity

As security becomes a major focal point of the AI industry, Anthropic has added a new tool to its defense arsenal. 

This week, the AI lab unveiled FlashProve, a system built for "provable inference" to ensure that an AI output comes from the model a system claims it does. This open-source system uses cryptography to verify that generated output is from a given model, rather than from one that a model router swapped in. 

"A user querying a model today cannot check that the responses came from the specific model that a developer claims is responding, as opposed to one secretly substituted in by a routing service for cost reasons, or even by an attacker who has tampered with the token stream," Anthropic wrote in its blog post. 

While the current state of AI forces users to trust that model providers and routing services are telling the truth about which models they serve, FlashProve removes that ambiguity by bringing zero-trust principles to the process. 

• To do this, Anthropic uses a cryptographic technique called "zkSNARK," or zero-knowledge succinct argument of knowledge, which lets a model provider create a fingerprint of a model without revealing its proprietary information, such as its weights. 

• Then, along with a user's output, FlashProve will generate a short proof that a user can verify using software "in seconds on a CPU." If the response deviates from the specified model, the user's check fails. 

• Along with keeping the model's original weights private, Anthropic touted that the proofs generated by this system are "small and cheap to check," allowing users to verify the authenticity of outputs "without access to elaborate infrastructure." 

Anthropic said that its motivation to develop provable inference is twofold: To increase customer trust, and to pace frontier development by giving auditors the ability to monitor the outputs from frontier models without the need for expensive hardware. 

"The goal of provable inference (PI) is to enable a user with limited computational resources and without any information about the weights to verify that a response came from the requested model," Anthropic wrote.

FlashProve seeks to solve one vector of a much larger problem that the AI industry is facing: trust. While this provides a solution to ensure that an output comes from the AI model that it says it does, preventing tampering and output spoofing, it's only one piece of the puzzle. For instance, hallucination is a never-ending battle that AI developers are fighting, creating a layer of mistrust with the perception that users can't trust the accuracy of the outputs. Additionally, users also face the quandary of whether or not to trust these model providers with their data at all, given that, if it's used in training, it can be extracted via prompt injection attacks. While FlashProve certainly represents a step forward, there is far more work to be done to make these models trustworthy. 

LINKS

• Claude helps create first 3D structure of a vault particle in cells

• Oracle scraps $7 billion chip leasing deal with Tencent under US pressure

• Qualcomm to supply hardware for SpaceX's 5G terrestrial network

• Amid backlash, Google clarifies educational services announcements

• OpenAI acquires Pierre Computer to integrate its Code Storage into Codex

• Apple cuts engineering team's internal AI usage to reduce spending

• Google invests $15 billion in building Malaysia data centers

• Grok Bot: New shopping experience 

• Hy4: Tencent's strongest open-source model

• Kling 4.0 Flash: Now fully available 

• Gemini 2.5 Pro + Gemini 2.5 Flash: Will be deprecated and auto-redirected

• IBM: Applied AI Specialist-Application Operations

• OpenAI: Applied AI Architect

• WorkOS: Applied AI Engineer

• Silna: AI Deployment Analyst

GAMES

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