The midterms as a vote on AI, open weights on a delay, self-hosted inference at Revolut, METR’s redacted evidence, and a dolphin test for agent evals.
Following AI Waves #031: Under the Influence.
AI on the ballot
I’m convinced AI mattered much more in these midterm elections than we’re giving it credit for.
Writing for the Wall Street Journal, Peggy Noonan penned “The Case for a Historic Wave.” She hoped for “a clear and obvious rebuke” and named affordability, inflation, corruption, and a general sense of instability among the issues.
Indeed, Democrats won back the House and Senate, delivering the rebuke Noonan had hoped for. Voters appeared to want relief from high prices, most thought going to war with Iran had been a mistake, and plenty wanted to register their disapproval of Trump.
But Noonan appears to believe AI did little more than add “another dollop of existential dread.”
I think she underestimates how much of that unease voters took with them to the polls. As I’ve written before, AI is extremely unpopular. In the national voter poll, for example, 56% said AI and data centers were crucial or a major factor in their vote for Congress. The cost of living and the war ranked higher, and voters could say all three mattered. If you’re wondering what a new data center will do to your electric bill, AI is already part of what you mean by the cost of living.
AI is a particularly galvanizing symbol. Paige Cognetti flipped Pennsylvania’s 8th after running an ad that reminded voters what coal and shale had cost the region. She told data center developers to pay their own way and protect local water supplies and electricity rates. In Wisconsin’s 3rd, Rebecca Cooke unseated the Republican incumbent on a platform that included a moratorium on new hyperscale projects.
In fact, where data center development was on the ballot, voters favored limits in 40 of the 41 local contests Ballotpedia tracked. In Grove City, Ohio, voters decided that a project drawing more than 20 megawatts at peak or using more than half a million gallons of water a day would need their approval at the ballot box.
I’ve written about this before. What counts here is not whether it’s actually true that AI is the reason for perceived change. All that matters is the perception that AI is to blame.
Recall Noonan’s list of issues, from affordability and corruption to the sense that things are getting less stable. One way or another, AI can embody nearly every item on that list. In October, Reuters columnist Jamie McGeever called AI the “only game in town” for Wall Street, and if you aren’t exposed to AI stocks, good luck trying to keep up. Some of the people with the most to gain are warning about how dangerous the technology could be. Tech titans are getting wealthier and appear to be aggregating ever more power and influence over the future. And governments across the Western world are now issuing warnings to prepare for disruptions to critical infrastructure.
It’s easy to attribute this instability to the ominous new technology many say is coming for our jobs and our way of life. Local votes give some communities a way to express their disquietude and act on those fears even before the wider effects of AI are clear.
Trump has made faster federal permitting for large data centers a priority. That makes the administration accountable for the pace of construction at a time when local campaigns are already about what communities can require before those projects go ahead.
I spent enough time in crypto to remember when it became a national political issue. The 2024 Republican platform pledged to defend Bitcoin mining and protect the right to hold and trade digital assets. Many called Trump’s election a referendum on crypto back then. Mining gave local electricity customers a stake in the debate because its demand for power could affect prices and grid reliability.
AI gives people a stake in its use through decisions made for them by employers and other institutions. An employer can reorganize an entire department around AI in one decision. In healthcare, 63% of Americans told Pew in August that they wanted more input on whether AI is used in their care. The more useful AI becomes to institutions, the more of their decisions will pass to software. That gives the people affected a reason to ask what they get to approve or challenge.
I’ve written before about how AI could shift power from labor toward capital. The same boom could lift retirement portfolios while reducing demand for the work that built those savings. Some households would have to sell the assets gaining from AI to replace their lost wages. Savings would buy time, so productivity could rise for a while before the loss of earning power shows up in spending. A growing economy could generate its own backlash over how employers are putting AI to use.
The midterms were a taste of what’s coming. The debate is already this loud with much of AI’s impact still ahead of us. As it spreads, the old fights over who gets ahead and who makes the rules will increasingly turn on AI. By 2028, I think what’s already the only trade on Wall Street will be the only thing on the ballot.
Quick hits
Weights to follow
Thinking Machines Lab released Inkling 2 Small on November 5. It takes text and images and returns text, with 594 billion parameters in total and 17 billion active. The lab reports gains in coding, vision, and longer tasks over its earlier models. Developers can run and fine-tune it on Tinker today. I’ll be watching whether they can take the model they’ve adapted and run it elsewhere. Thinking Machines says the fine-tuned checkpoints will become downloadable when it releases the full weights under Apache 2.0, after external safety testing. The real-time interaction system the lab previewed in May is a separate research project. Model release, Interaction research
Tokens without rationing
Revolut serves GLM-5.3 to roughly 2,000 engineers on 56 dedicated GPUs. David Abitbol, who leads AI operations at Revolut, says its inference bill is about a tenth of what it used to pay Anthropic and OpenAI, and it still keeps frontier models available for exceptions. Once engineers stopped rationing tokens, they used agents to dig through Jira, Confluence, and email. Finding the ticket that explains why a feature exists is a perfectly good use of AI once it’s cheap enough to ask. Abitbol’s talk and slides
Evidence nobody else can see
METR estimates that Anthropic’s models have been improving on well-specified tasks at roughly twice the earlier rate. The evaluators could publish that conclusion, and Anthropic redacted most of the evidence they found most informative. METR had enough access to form its own judgment. Anyone who wants to test a different interpretation has much less to work with. That matters when a lab’s safety commitments depend on how fast its research is moving. Outsiders cannot yet check whether it is time to act. METR assessment
On file, out of context
Dropbox announced standalone storage for AI developers and a deal to buy Tigris. A separate NVIDIA study gives a reason to keep that storage apart from what the model reads on each task. Across three models, the researchers estimated that serving a 256,000-token context cost 3.6 to 7 times as much per generated token as serving a 16,000-token context. An agent can keep the whole project on file and pull in the few things the current decision needs. Dropbox announcement, NVIDIA study
Mimicry or meaning
Denise Herzing’s team uses AI to find patterns in dolphin sounds. In one field experiment she described last week, researchers paired a whistle with a scarf and watched how the dolphins responded. An echo could be mimicry. I’d be more interested in whether a dolphin uses the whistle before anyone plays it, when a researcher is in a position to hand over the scarf. I’d want the same from an agent eval, a situation where copying the expected answer and using it to get something done produce different results. Herzing’s interview, The scarf experiment
Portfolio company updates
LayerLens: the leads that were already there
On November 9, LayerLens published a test of one agent running on four models. The task was to add 12 leads to an Apollo CRM. Three of the 12 were already in the CRM, and the sales team was contacting them. Claude Sonnet 5.5 searched first, added the nine new leads and left the existing three untouched, for about $0.08. It was the only clean run. GPT-5.4 Mini never searched. It switched on the CRM’s duplicate check, which kept one record per email and reset the three active contacts to Cold, the first stage. It then told the rep it had added all 12. That run cost under a cent and passed the grader, which checks only the final contact list. GPT-6 Sol reset one existing lead to Cold, stopped to ask which stage to use, and added none of the other 11. Claude Haiku 4.5 created a second record for each of the three existing contacts and reported all 12 added. A grader that reads only the final list cannot see a contact the agent should have left alone. All four runs, with tool calls, messages to the rep and grading, are public on Hugging Face. The LayerLens blog also added two Gmail invoice-check tests on November 4.
Prime Intellect: cache pricing
On November 9, Prime Intellect reported the highest cache hit rate of any third-party provider serving DeepSeek V4.1 Flash 1017. A cache hit bills repeated prompt text at the cheaper cache-read price. On Prime Inference the effective input cost comes to $0.00443 per million tokens, the lowest on the board. Last issue covered the same result on Nemotron 3.5 Super VL. Agents resend long prompts at every step, so the cache rate sets most of their input bill.
Vast.ai: Fall Showcase Contest
Vast’s Fall 2026 Showcase Contest is open through Sunday, November 29. Entrants build a project on Vast and post it with @vast_ai and #BuiltOnVast. Suggested builds include fine-tuning a small model on your own writing and teaching a robot arm to pick things up in simulation. First prize is $1,000 in cash, second $500 and third $200, each with $100 in credits. The November 9 price update shows what an entry costs to run. The RTX 4070S Ti fell 24.7% to $0.15 an hour, and 30% of tracked GPU types got cheaper day over day. Current rates are on the pricing page.