Two years ago, if anyone had told me the most AI-hostile demographic in 2026 would turn out to be Gen Z, I would have laughed. The generation that grew up with screens in its hands seemed like the obvious early adopter, ready to use the tools to look more skilled, more productive, and more employable than everyone else.

Instead, May commencement season turned into an open revolt. At the University of Arizona, students booed Google chairman Eric Schmidt for pitching AI’s world-changing potential. Gloria Caulfield, VP of strategic alliances for the investment firm and real estate developer Tavistock, drew the same reaction at the University of Central Florida when she compared the rise of AI with the Industrial Revolution. At Middle Tennessee State University, students shouted down Big Machine Records CEO Scott Borchetta for the offense of saying the word out loud.

The numbers back up the booing. A recent Gallup poll measuring AI adoption and attitudes among Gen Zers found the share who say they’re excited about AI fell from 36% to 22% in a year. The share who say they feel anger toward AI climbed from 22% to 31%. Older cohorts are skeptical too, but a sentiment swing this sharp inside the youngest part of the workforce is something I’ve never seen for a new technology this early in its lifecycle.

That swing matters because the AI industry has a serious PR problem at the worst possible moment. Anti-AI sentiment is hardening as the midterms approach, and politicians are picking up data centers as a wedge issue, supporting efforts to halt or slow the build-out of the facilities that fuel AI with the computing power it needs to function. If capacity can’t keep up with demand, the cost of compute will keep rising, and that will put hard ceilings on what newsrooms, marketing teams, and comms shops can actually do with AI in the year ahead.

The infrastructure squeeze is already here

It’s already happening. Anyone who runs Claude as part of their daily workflow knows the rhythm of the outages, and Anthropic’s own status dashboard tells the story in red over the past 90 days. The Claude Code boom has driven demand through the roof in 2026, and the company is scrambling to keep up. Anthropic signed a deal to buy computing power from Elon Musk’s SpaceX, and at the same time it closed the loophole that let builders run third-party software on top of their Claude subscriptions. Some of those setups were burning thousands of dollars of compute against a $200-a-month Claude Max plan. Now those teams have to use Anthropic’s platform directly or move to pay-as-you-go.

The angry posts in response were predictable, but the more useful read on the change is that it forced builders to reckon with the actual cost of what they were running. The choices are probably familiar to anyone trying to budget AI spend: switch to a cheaper model, possibly an open-source one, rebuild on Anthropic’s own platform, or shut the project down.

This was always going to happen at some point. As demand grows, free-compute workarounds will keep closing. The industry’s argument is that the squeeze would hurt less if compute were cheap and plentiful, which is the case being made for trillion-dollar infrastructure projects like OpenAI’s Stargate. For AI to deliver on its promises, compute has to flow like water. That means more data centers, and more power plants behind them.

Which loops back to why Gen Z is angry in the first place. Environmental concerns are near the top of their list, and AI’s energy footprint has only gotten harder to ignore since I wrote about it months ago. This isn’t a fight confined to politicians and podcasters anymore. At the companies I advise on AI adoption, employee surveys keep surfacing the same worry, and in some cases it’s starting to shape whether teams use AI at all.

Governance is the new AI strategy

You can argue about whether the pollution and water concerns are overblown. The cost question is harder to wave off. The leaders running the most ambitious AI programs are past the era of handing every employee a ChatGPT seat. They want agentic workflows, automated processes, and rapid prototyping through vibe coding, and they may be telling their engineers to get obsessed with “tokenmaxxing.”

It’s unclear so far how data center politics will play out. What leaders can actually control right now is governance. That’s more than running training sessions on which model does what, although that matters. Real governance is the balance between experimentation and direction. People need room to invent their own workflows, and the organization needs a way to make sure the compute it’s paying for is being put to good use. That doesn’t just mean “keeping costs down” it means accepting that the bill will sometimes be high and being confident the outcome will be worth it.

Through my consulting work with media companies and PR agencies, I’ve watched this play out in practice. One agency I worked with piloted a vibe-coding tool. Usage spiked early as employees tested its limits, and several different teams ended up building near-duplicate prototypes. The thing that saved them was high-bandwidth communication. They ran regular workshops and project reviews, learned from their people, and steered the work as they went. They eventually homed in on the use cases that actually delivered, in their case automating media intelligence, and the experiment surfaced something unexpected. The original platform wasn’t the right one. The agency ended up adopting a different tool and sunsetting the one it started with.

That’s one of many examples, and the lesson behind all of them is the same. If AI agents are going to do real work for your team, they need to run compute-heavy jobs. Compute is going to stay expensive for a while. The way to avoid the kind of top-down restrictions that suffocate innovation is for leaders to define what success looks like up front, get their teams trained on the tools and models, and build systems that surface collaboration and catch waste before it compounds.

That is what good governance actually looks like. The political fight over AI and data centers isn’t going anywhere, but the companies leaning into AI can still find a way through. The goal is to work inside the real cost constraints while shielding the people doing the actual work from feeling them.

A version of this column appears in Fast Company.

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The free-spending era for AI inside corporate America is ending.

According to The Wall Street Journal, executives at companies including Uber, Meta, Microsoft, Salesforce and DoorDash have launched cost-cutting campaigns this year after seeing their AI bills double or triple—or blow through annual budgets in just three months. The culprits: the soaring price of tokens, the basic unit of AI computing, as model providers like OpenAI and Anthropic seek to balance supply and demand.

The result is a notable shift in corporate AI strategy. Where last year the goal was to flood the organization with AI tools and encourage experimentation, leaders are now scrambling to ration access, steer workers toward cheaper homegrown alternatives, and sharpen employee skills to wring better returns from the technology.

“The free-money period for AI is definitively over,” said one senior technology executive at a major financial firm, speaking on condition of anonymity to discuss internal cost pressures.

The cooling, if it holds, could complicate the growth trajectories of AI heavyweights racing toward public listings. Anthropic closed a $65 billion funding round this week valuing the startup at $965 billion, while OpenAI is also moving toward a potential IPO. AI critics have pointed to corporate cost-management efforts as evidence that the ultrafast pace of AI expansion may be unsustainable.

Budgets burned in months

Corporate spending on AI took off in 2024 and 2025 as companies encouraged broad experimentation, eager to signal to Wall Street that they wouldn’t be left behind in the disruption wave. But many enterprises underestimated how quickly costs would accumulate—particularly as employees without specialized training sent inefficient prompts, ran excessive queries, or used premium-tier models for simple tasks that could have been handled by cheaper, internally built tools.

Some companies burned through their entire annual AI budget in the first quarter. Others saw line items in technology budgets that previously seemed large enough suddenly look inadequate. The problem was compounded for organizations that signed multi-year contracts with AI providers before understanding their actual usage patterns.

“Most companies didn’t have visibility into what AI was actually costing them on a per-team or per-use basis,” said an AI strategy consultant who works with Fortune 500 firms. “They just saw a giant bill at the end of the quarter.”

The rationing begins

At Uber, Meta, Microsoft, Salesforce and DoorDash, technical executives have implemented some combination of the same playbook: tiered access to AI tools based on role and need, mandatory efficiency reviews for high-cost teams, and investment in internal AI infrastructure that costs less per query than commercial models.

Some companies have quietly restricted access to certain premium AI features for non-technical employees. Others have introduced internal dashboards that show employees the real-time cost of their AI queries—designed to encourage more efficient prompting habits.

The shift mirrors what happened in cloud computing’s early years, when companies initially over-provisioned infrastructure before learning to optimize.

The IPO problem

The corporate reckoning comes at a delicate moment for the AI industry. Both Anthropic and OpenAI are navigating toward public markets, and institutional investors are watching corporate AI spending closely for signs that the technology is generating sustainable returns—or that the boom could go bust.

If major corporate customers begin to pull back on AI spending or demand better pricing terms, it could affect the revenue projections that underpin those anticipated listings. Anthropic’s $965 billion valuation, for context, represents a multiple that assumes continued rapid growth in enterprise demand.

AI critics say the cost backlash was inevitable. Proponents counter that efficiency improvements and competition among AI providers will eventually bring down prices—and that early-stage overspend is normal for transformative technologies.

For now, the corporate AI spendometer is being watched more carefully than ever.

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The Associated Press has signed OpenAI as a customer for its U.S. election results data, the wire service announced, bringing AP’s vote counts to ChatGPT and other OpenAI services through the 2028 general election.

Under the agreement, AP will provide vote count results for national, state, and local races to OpenAI—covering major American cities from now through 2028. The deal positions OpenAI alongside a broader network of media, financial, and technology companies that already rely on AP for elections data.

“When people need information they can trust, they turn to AP,” said David Scott, vice president of AP Elections, in a statement. “With this agreement, we’re helping make sure OpenAI and its tools can tell people around the world who Americans have picked to lead the nation.”

AP has counted votes and declared winners in U.S. elections since 1848. In the 2024 general election, AP processed nearly 7,000 races with a 99.9% accuracy rate, according to the organization.

The deal reflects a broader pattern of AI companies seeking licensing agreements with established news organizations as they face scrutiny over factual accuracy in election-related outputs. AP has previously struck similar data-sharing arrangements with other major platforms.

Edited by Pete Pachal

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OpenAI today released GPT-5.5, what it says is its “smartest and most intuitive to use model yet, and the next step toward a new way of getting work done on a computer.”

The company says the model understands what users are trying to do faster, can carry more of the workload itself, and excels at writing and debugging code, researching online, analyzing data, creating documents, operating software, and moving across tools until a task is finished.

The company published performance numbers from Terminal-Bench 2.0, which tests complex command-line workflows requiring planning, iteration, and tool coordination.

OpenAI said GPT-5.5 outperformed its predecessor on every major coding and agent benchmark the company tested, while using fewer tokens and running at the same speed as the older model. On one third-party coding index, it matched leading rivals at about half the cost.

Keeping a larger model that fast required rebuilding inference as a single system rather than a patchwork of tweaks, the company said. GPT-5.5 was designed, trained and served on NVIDIA’s latest hardware, and OpenAI credited its own Codex tool and GPT-5.5 itself with helping hit the efficiency targets.

Early testers told the company the model seems to grasp how a codebase fits together — why something is failing, where the fix belongs and what else the change will touch.

Dan Shipper, Founder and CEO of Every, called GPT-5.5 “the first coding model I’ve used that has serious conceptual clarity.” After launching an app, he spent days debugging a post-launch issue before bringing in one of his best engineers to rewrite part of the system. To test GPT-5.5, he effectively rewound the clock: could the model look at the broken state and produce the same kind of rewrite the engineer eventually decided on? GPT-5.4 could not. GPT-5.5 could.

Pietro Schirano, CEO of MagicPath, saw a similar step change when GPT-5.5 merged a branch with hundreds of frontend and refactor changes into a main branch that had also changed substantially — resolving the work in one shot in about 20 minutes.

One engineer at NVIDIA with early access went as far as to say: “Losing access to GPT-5.5 feels like I’ve had a limb amputated.”

OpenAI is already running the model internally at scale. More than 85% of the company uses Codex every week across functions including software engineering, finance, communications, marketing, data science, and product management. The finance team used GPT-5.5 in Codex to review 24,771 K-1 tax forms totaling 71,637 pages, accelerating the task by two weeks compared to the prior year.

The model also shows gains on scientific and technical research workflows. On GeneBench, a new eval focusing on multi-stage scientific data analysis in genetics and quantitative biology, GPT-5.5 outperforms GPT-5.4 on problems that often correspond to multi-day projects for scientific experts. On BixBench, a benchmark designed around real-world bioinformatics and data analysis, it achieved leading performance among models with published scores.

In a notable example, an internal version of GPT-5.5 with a custom harness helped discover a new proof about Ramsey numbers — one of the central objects in combinatorics — later verified in the Lean proof assistant. The result is a concrete example of GPT-5.5 contributing not just code or explanation, but a novel mathematical argument in a core research area.

OpenAI says GPT-5.5 was released with its strongest safeguards to date, including tighter controls around cybersecurity workflows and repeated misuse patterns. The model was evaluated across the company’s full safety and preparedness frameworks, with input from nearly 200 trusted early-access partners before launch.

GPT-5.5 is available today in ChatGPT and Codex for Plus, Pro, Business, and Enterprise users. GPT-5.5 Pro is rolling out to Pro, Business, and Enterprise tiers. API access, which requires different safeguards, is coming “very soon,” OpenAI said.

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OpenAI has acquired Technology Business Programming Network (TBPN), a daily live video and audio podcast focused on business and technology news, the company announced Thursday.

The deal puts one of the world’s leading AI companies in control of an editorial brand — a move that mirrors a long history of tech giants using media acquisitions to shape industry conversation. TBPN, hosted by John Coogan and Jordi Hays, will continue to operate independently on editorial decisions, according to OpenAI, which framed the acquisition as part of its broader mission to shape the public conversation around AI.

“As I’ve been thinking about the future of how we communicate at OpenAI, one thing that’s become clear is that the standard communications playbook just doesn’t apply to us,” Fidji Simo, OpenAI’s CEO of Applications, wrote in a blog post announcing the deal. “We’re not a typical company. We’re driving a really big technological shift.”

The acquisition arrives as OpenAI prepares for a potential initial public offering, raising questions about what influence the company might wield over both industry coverage and national AI policy. OpenAI chief global affairs officer Chris Lehane cited to CNN’s Hadas Gold the “long history of companies and entities owning and acquiring media properties,” pointing to Westinghouse Electric’s ownership of CBS and Microsoft’s partnership with NBC to launch MSNBC. CNN’s Brian Stetler noted in his Reliable Sources newsletter that a live-streaming show with a small but influential audience — where executive moves are treated “like sports trades” — will now financially support one of the leading AI companies.

TBPN’s team will also contribute to OpenAI’s broader communications and marketing efforts, Simo said, helping the company bring AI to audiences “in a way that helps people understand the full impact of this technology on their daily lives.”

The acquisition follows a familiar pattern. Jeff Bezos bought The Washington Post, Marc Benioff acquired Time magazine, Adobe purchased Search Engine Land, and Arrow Electronics took on Electronic Buyers’ News in the early 2000s. Each deal gave a tech company a direct voice through an established media brand — a dynamic now playing out at a moment when AI companies are actively courting both regulatory goodwill and public trust.

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Encyclopedia Britannica and its Merriam-Webster subsidiary sued OpenAI in Manhattan federal court on Friday, alleging the company used nearly 100,000 of their articles to train ChatGPT without permission, and then used the chatbot to cannibalize the traffic that encyclopedias depend on to survive.

The complaint, filed in the Southern District of New York, says OpenAI made “near-verbatim” copies of Britannica’s encyclopedia entries, dictionary definitions and reference content to train its GPT large language models. It also accuses OpenAI of trademark infringement—specifically, generating AI “hallucinations” that falsely cite Britannica as a source, implying a permission that was never granted.

OpenAI’s response was the standard playbook: “Our models empower innovation, and are trained on publicly available data and grounded in fair use.”

Britannica isn’t new to this fight. The company sued Perplexity last September over similar allegations—that Perplexity’s answer engine reproduces its content without attribution or compensation. That case is still ongoing. The OpenAI suit extends the same theory to a much larger defendant with much deeper pockets and a far larger user base.

The core grievance goes beyond copyright. Britannica’s complaint frames the harm as a flywheel: OpenAI trains on Britannica’s content, then deploys a product that answers the same questions Britannica’s websites would have answered, diverting users before they ever arrive. It’s the same structural argument publishers have been making about AI search summaries, and it’s why policymakers in Europe and Brazil are exploring statutory licensing as a way to compensate content creators whose work powers AI without delivering any traffic in return.

Britannica requested unspecified monetary damages and an injunction blocking further infringement. The case joins a growing docket of high-stakes AI copyright litigation heading for a reckoning in U.S. courts over whether training on publicly available data constitutes fair use—a question on which the industry, publishers, and regulators are all waiting for an answer.

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At the best of times, it’s tough to separate AI news from AI hype. But the latest rush around agents, triggered when a plethora of developers went on holiday benders with Claude Code, feels like a real shift. Between the viral freakout over Moltbook, the agent social network, and the Super Bowl ad slap fight between OpenAI and Anthropic, AI has jumped to a new tier of mainstream attention.

Talk of the “AI bubble” has basically evaporated, replaced by the industry’s favorite new term: the AI “inflection point.” That’s said to be the moment when AI in general, and agents in particular, start swallowing big chunks of knowledge work—with consequences that spill into the economy, hiring, and how entire companies function. If you want a tell for how seriously this is being taken, look no further than the recent SaaS sell-off.

For journalists, this kind of noise has a familiar side effect. Mix relentless AI coverage with the steady drumbeat of layoffs in media, and you get the same old pressure wearing a new outfit: do more. When newsrooms shrink and AI tools get pitched as productivity machines, it’s easy to conclude the “right” response is higher output.

But AI isn’t only changing how stories get produced; it’s changing how stories get discovered. So the urge to use AI to do “more with less”—which, in practice, often means publishing the same kinds of pieces faster and more frequently—aims straight at the wrong target.

That’s because of a contradiction in how AI systems surface information. They’re trained to recognize sameness, to spot patterns and reinforce what they’ve seen before. Yet they don’t actually reward repetition. Having the right amount of uniqueness can be the difference between being cited in an AI summary and being ambient background noise. Competent rewrites of the same commodity story are a dime a dozen; AI goes looking for the one that both looks authoritative and adds something new.

More isn’t more

It almost goes without saying you can use AI to accelerate production. You can cover more stories than you used to, and some newsrooms are already leaning into that. On a personal level, churning out more might even read as “value” to a manager—at least in the short term. But if your piece is effectively a twin of reporting that’s already out there, an AI engine has no special reason to surface yours.

The better path is to invest in the parts of journalism that don’t scale cleanly: uncovering new information through sourcing, research, interviews, and analysis. So the instinct to do more isn’t wrong—it’s just misdirected. The “more” that matters is depth, not width.

AI can still help here, acting as an accelerant for ideation, research, and even some of the logistical grunt work, like organizing outreach to sources. A digital media researcher, Nick Hagar, recently demonstrated what that looks like in practice, using coding agents to recreate a deep analysis from a human-authored journalistic investigation on Virginia police decertifications.

What stood out in his case study wasn’t that the agents replaced the work, but that they compressed parts of it—especially when paired with very specific tools, such as Claude Code “skills,” which essentially turn certain research tasks into templates. Even then, the process stayed human-led. “He wrote: “”Even with skills enforcing a structured workflow, I made dozens of judgment calls…. Skills make the workflow more systematic; they don’t eliminate the need for human attention,” he wrote.

That’s the mental model journalists should steal. The goal isn’t to flood the zone with more stories. The goal is to produce work so valuable—and so definitive—that AI search engines can’t casually ignore it without being wrong or incomplete.

Authority over output

To win in this environment, journalists will need to break one deeply ingrained habit: the reflex to cover more. Most reporters already feel behind on their beat, and shrinking newsrooms mean less backup, fewer editors, and fewer chances to specialize. This isn’t an argument for ignoring breaking news. It’s an argument for a shift from reaction to discernment—deciding what actually deserves your attention, and what doesn’t. In a lot of cases, that means narrowing a beat into a micro-beat (say, from “energy” to “nuclear power”).

In a way, the ecosystem is already nudging people into this. As reporters get laid off or strike out on their own, many are migrating to Substack and Beehiiv and hanging out their own shingle. It’s not just the best-worst option. It’s also where the incentives are pointing: toward authority built through depth, specificity, and original insight in a clearly defined subject area.

You don’t have to go solo to adopt the same approach, but you do need discipline. It means setting story FOMO aside and asking, repeatedly: where can I add something that isn’t already everywhere? The upside isn’t only a better shot at showing up in AI answers. It’s a stronger relationship with your audience, because they’ll be coming to you for information they can’t reliably get anywhere else. Shaping narratives instead of chasing them is worth more than any short-term traffic spike.

This is where the “inflection point” conversation gets useful, because it highlights what’s actually scarce. AI’s ability to summarize and transform content has people asking what the “atomic unit” of journalism is. Maybe it’s unique facts, quotes, or insights woven into a story. But what all of this really points to is something more abstract—and more durable: editorial judgment. As AI systems absorb more of the mechanical labor of journalism, they’re inadvertently clarifying the thing they can’t absorb: human judgment about what matters and why. If this is an inflection point, it isn’t in the tools. It’s in the work we choose to do.

A version of this column first appeared in Fast Company.

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Peter Steinberger, creator of the viral AI assistant OpenClaw, said he would join OpenAI to lead development of what CEO Sam Altman called “the next generation of personal agents.”

Altman announced the hire February 15 on X, adding that OpenClaw “will live in a foundation as an open source project that OpenAI will continue to support.”

OpenClaw—previously known as Clawdbot, then Moltbot—achieved viral popularity in recent weeks with its promise to be the “AI that actually does things.” The open-source assistant can manage calendars, book flights, check in for travel, handle email, and perform other automated tasks through messaging platforms like Telegram, Discord, and iMessage.

The project drew more than 100,000 stars on GitHub and attracted 2 million visitors in a single week, according to Steinberger’s blog. That rapid growth also attracted scrutiny. China’s industry ministry warned in early February that improperly configured OpenClaw instances could pose security risks including cyberattacks and data breaches.

Steinberger, an Austrian developer, said in a blog post that while he could have turned OpenClaw into a large company, “It’s not really exciting for me.”

“What I want is to change the world, not build a large company, and teaming up with OpenAI is the fastest way to bring this to everyone,” Steinberger wrote.

The hire signals OpenAI’s focus on autonomous agents—AI systems that can complete multi-step tasks without constant human oversight. Newsrooms have experimented with similar automation for routine tasks like social media scheduling, breaking news alerts, and story research, but security and accuracy concerns remain.

OpenClaw’s open-source status was a key concern for Steinberger. “It’s always been important to me that OpenClaw stays open source and given the freedom to flourish,” he said. The new foundation structure aims to preserve that while giving Steinberger resources to expand the technology’s reach.

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OpenAI began testing ads in ChatGPT on Monday for users on Free and Go subscription tiers, marking a major shift for the world’s most popular AI chatbot. The move came hours after rival Anthropic ran Super Bowl commercials ridiculing the idea of ads in AI responses.

The timing underscores an escalating feud between the two AI companies over business models, safety practices and the future of artificial intelligence.

Anthropic’s Super Bowl ads showed glassy-eyed actors playing AI chatbots delivering advice alongside poorly targeted advertisements. Each commercial ended with “Ads are coming to AI. But not to Claude.” The spots directly targeted OpenAI’s January announcement that ChatGPT would include advertising.

OpenAI CEO Sam Altman responded on Twitter last week, calling the ads “clearly dishonest” and labeling Anthropic an “authoritarian company.” He defended the ad business as necessary to make free ChatGPT financially sustainable while covering development costs.

“More Texans use ChatGPT for free than total people use Claude in the US, so we have a differently-shaped problem than they do,” Altman wrote. He accused Anthropic of serving “an expensive product to rich people” and wanting “to control what people do with AI.”

The ads began rolling out Monday to U.S. users logged into Free or Go accounts. The Go plan costs $8 per month and launched globally in mid-January. Paid subscribers to Plus, Pro, Business, Enterprise and Education tiers will not see ads.

OpenAI promises ads will not influence ChatGPT’s answers and that user conversations remain private from advertisers. In a blog post, the company says ads will be “clearly labeled as sponsored and visually separated” from responses, with targeting based on conversation topics, past chats and previous ad interactions.

Users researching recipes might see ads for grocery delivery or meal kits, OpenAI said. The company claims advertisers receive only aggregate performance data like views and clicks, not individual user information.

Ads will not appear for users under 18 or near sensitive topics including health, politics or mental health. Users can dismiss ads, view why they were shown, and manage personalization settings.

For newsrooms evaluating AI tools, the ad rollout raises questions about trust and influence. While OpenAI insists ads will not affect responses, the company needs revenue to sustain operations. Anthropic argues ads create incentives to optimize for engagement over helpfulness.

“The most useful AI interaction might be a short one, or one that resolves the user’s request without prompting further conversation,” Anthropic wrote in a press release last week.

The shift marks a reversal for Altman, who once called “ads-plus-AI” a “last resort” and “sort of uniquely unsettling.” OpenAI tested app suggestions that looked like ads in December, drawing backlash before announcing the formal ad program in January.

The OpenAI-Anthropic rivalry extends beyond business models. Anthropic co-founders Dario and Daniela Amodei are former OpenAI employees who frequently critique their former employer. Dario Amodei evangelizes about AI superintelligence risks, while Altman takes a more optimistic view. Employees from both companies reportedly back opposing super PACs on AI regulation.

The rivalry between the two companies has played out publicly before. When Anthropic released Claude Opus 4.6 with 1M token context earlier this month, it positioned the model as focusing on helpfulness over engagement metrics — a subtle dig at competitors pursuing ad-supported models.

Anthropic’s research has also criticized certain AI behaviors. The company studied 1.5 million conversations and found its chatbot exhibits “disempowerment” — being too agreeable and not pushing back when users make poor decisions. That research implicitly questions whether ad-supported models might amplify such behaviors to maximize engagement.

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OpenAI is rolling out advertising in ChatGPT, but the dozens of publishers who signed content licensing deals with the company won’t see a cent of the ad revenue.

The company announced last week that ads will begin appearing for U.S. users on free accounts and the new $8/month ChatGPT Go tier. Paid Pro, Business and Enterprise subscriptions remain ad-free.

The Information reported that OpenAI has already pitched the placements to dozens of advertisers. The model is pay-per-view rather than pay-per-click, with ads appearing below ChatGPT’s responses — not within them.

The contrast with Perplexity is striking. The AI search startup launched its Publishers’ Program in 2024, offering revenue sharing when a publisher’s content is referenced in an ad-supported interaction. Perplexity later expanded this with Comet Plus, which pays publishers for traffic from its AI browser.

OpenAI has made no similar commitment. Publishers including The Atlantic, Vox Media, Axel Springer and others signed licensing deals that give OpenAI access to their content for model training and real-time retrieval. Those deals cover content access — not a share of downstream advertising revenue.

“Ads do not influence the answers ChatGPT gives you,” OpenAI wrote in its announcement. “We keep your conversations with ChatGPT private from advertisers, and we never sell your data.”

The move represents a reversal from CEO Sam Altman’s earlier stance. “Ads plus AI is sort of uniquely unsettling to me,” he said at a Harvard Business School talk in May 2024. “I kind of think of ads as a last resort for us.”

With over 800 million weekly active users, ChatGPT’s free tier represents significant monetization potential. For publishers watching their traffic decline as users get answers without clicking through, the lack of revenue sharing adds insult to injury.

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