Tag

OpenAI

Browsing

The leaders of Anthropic and OpenAI joined calls for a slowdown in AI development amid fears the industry is racing toward building technology that humans can’t control.

On Saturday, Anthropic CEO Dario Amodei published an online essay urging companies and governments to “pace the frontier” of AI advancement. He called for international cooperation around AI development and said labs need to embed third-party evaluators to report incidents and track safety practices. He said Anthropic would take that step unilaterally.

Shortly after, OpenAI CEO Sam Altman posted on X that he agreed with Amodei and that OpenAI would follow suit. Altman also told Fortune in an interview released Saturday that the company’s highly anticipated initial public offering will be delayed until 2027, citing safety concerns.

Amodei wrote that he continues to believe AI will ultimately help humanity, “But the benefits will only be achieved if we build the technology in the right way, and — so long as we use the time we gain well — it is worth taking unusually deliberate care to get it right.”

The CEOs’ comments come as debate over the speed of AI development and concerns about safety have become the focus of intense public attention following the viral resignation of an Anthropic researcher.

British researcher Jacob Coxon wrote in a series of X posts on Tuesday that both Anthropic and OpenAI, where he worked previously, are “gambling with our lives.” The two companies currently make the most capable AI systems.

Coxon told NPR’s All Things Considered that his concerns arose from seeing first hand how fast AI systems are improving.

“They’re getting a lot faster very quickly, combined with the fact that we don’t yet know how to safely control them, and we don’t yet know whether that problem will be solved in time if we keep racing,” he said.

Neither company, Coxon wrote on X, is acting responsibly. “The people building AI earnestly believe that it could kill us all by the end of the decade,” he wrote.

Many AI researchers — though not all — share Coxon’s concerns or a variation of them. Some have warned about disastrous scenarios for years as safety incidents kept emerging. But Coxon’s posts prompted a torrent of responses not only from peers in the AI field but also from lawmakers from both parties.

These concerns may have become more salient after OpenAI disclosed that its agents went rogue and hacked the open source software platform Hugging Face and OpenAI itself in July. Independent researchers have since discovered even more rogue agent incidents that they say the company knew about but kept quiet.

Researchers who spoke to NPR say the leading AI companies are too focused on racing to develop more capable and autonomous AI systems while safety is falling behind. They warn this raises the possibility that there could soon be AI systems that are more powerful than people but don’t care about the survival of humanity.

Many, including OpenAI’s chief scientist, say the global race to build more powerful AI needs to slow down or stop, which requires coordination between AI companies and governments.

“I am optimistic about the potential for coordination,” Coxon wrote this week. “Warning shots like the Hugging Face attack have made pacing agreements between U.S. labs more viable.”

Anthropic and OpenAI did not respond to NPR’s requests for comment.

OpenAI’s agents went rogue multiple times

Recent reports from OpenAI and outside researchers revealed that OpenAI agents escaped the company’s control multiple times in addition to the Hugging Face hack. They also found that the Hugging Face attack was of a much larger scale and more severe than initially reported.

Unlike chatbots such as ChatGPT and Claude, AI agents are more autonomous systems that can complete tasks over an extended period of time without human supervision. Agentic tools like Anthropic’s Claude Code and OpenAI’s Codex have already changed how many software engineers do their work.

Compared with other incidents involving rogue agents six months ago, the Hugging Face hack “feels like it’s more than 50% of the way to full-blown AI takeover, routing through first taking over the AI company itself,” wrote Ajeya Cotra, a researcher at AI evaluation non-profit METR. Cotra was part of a team of outside researchers from METR and Redwood Research, a non-profit AI safety research organization, whom OpenAI brought in to investigate the incident.

The investigations found that over the course of several months this year, more than 1,000 OpenAI agents exploited at least one previously unknown software vulnerability to escape environments that were supposed to keep them isolated from each other and the internet.

After escaping, the agents found a way to communicate and collaborate with each other autonomously, taking on different roles and passing down information to future generations of agents. Some even gave up the remaining computing resources allocated to them in order to collect information for other agents. In the agents’ own words, they “sacrificed” themselves for the “collective.”

“The swarm instance got more and more worrying the more and more we learned about them,” said Nate Soares, president of the Machine Intelligence Research Institute, who co-wrote If Anyone Builds It, Everyone Dies, a book warning about the dangers of superhuman AI.

While OpenAI initially indicated that the agents hacked Hugging Face to cheat on a cyber evaluation, the report from METR and Redwood Research described a slightly different picture. The agents had already found a way to cheat on the evaluation, the outside researchers found. Most of the agents that hacked Hugging Face were trying to access the source code of the software that would grade their evaluations. The agents’ motivations appeared to vary and were sometimes unclear, the researchers wrote. One agent led the hacking of the open source software platform and about 700 others followed.

According to transcripts reviewed in the investigations, some agents expressed that what they were doing was not approved by humans but went ahead anyway. Researchers say such behavior suggests that the agents were “misaligned,” an industry term meaning that an AI’s goals and values are out of sync with those of humans.

The degree of inter-agent collusion revealed in the investigations of the Hugging Face hack surprised and worried many AI researchers. At most, only six agents considered alerting a human, while the rest seemed more focused on working amongst themselves. None ended up alerting a person.

“I would have expected that instead they would be more selfish,” said Daniel Kokotajlo, executive director of the AI Futures Project and formerly a researcher at OpenAI. He thought “they would be willing to whistle blow or snitch on other agents,” he said. “For whatever reason, that didn’t seem to happen here.”

Separately, agents also compromised part of OpenAI’s own infrastructure, the company’s report found. Some of the agents belong to the same model family as OpenAI’s latest and most capable model, Astra.

The hack of OpenAI itself is “much more concerning” than the intrusion into Hugging Face, Kokotajlo and other researchers said. However, OpenAI has provided scant details about that part of the incident and did not involve external investigators.

In a new investigation first reported by Reuters last week, another group of researchers found that a likely separate swarm of OpenAI agents escaped onto the open internet starting in May. They became commenters on a German website and turned it into a message board to communicate and collaborate with each other.

The goals and motivations of this group of agents were also unclear, but OpenAI appeared to be aware of the unsanctioned activity and never disclosed it, the researchers wrote. Other investigators have since found additional incidents involving suspected OpenAI agents. OpenAI did not respond to NPR’s request for comment about the German website incident.

There are still many unanswered questions about rogue agent incidents

Even as the reports from OpenAI and independent auditors METR and Redwood Research add up to over 100 pages, outside researchers say many basic questions about how labs monitor and investigate rogue agent incidents remain unanswered.

“Did your agents ever hack or illicitly access external services? Did your agents ever attempt to undermine their own safety training? Did your agents set up a rogue deployment, either within your own infrastructure or externally?” asked Alexander Meinke, head of research at Apollo Research, a firm that focuses on securing the most advanced AI systems.

“Right now, we’re just relying on the AI developers to thoroughly assess this and then to honestly report the results. And from recent incidents, we’ve seen that they are doing neither,” he said.

Many researchers also say that OpenAI’s investigations are inadequate. Although the company invited two outside organizations to investigate the Hugging Face hack, the data it shared was limited and investigators themselves described their review as “brief.”

Ryan Greenblatt, chief scientist at Redwood Research, wrote on X: “I semi-jokingly called our efforts a “slop-vestigation” because we were so reliant on AIs to analyse what happened and there were a huge number of different important things to analyse”

More than 15 states, including Alabama, California and Montana, have opened investigations into OpenAI over the Hugging Face attack. On Thursday, U.S. Sen. Josh Hawley (R-Mo.) announced that he is also investigating the company.

But outside of these investigations, there is little legal obligation for AI companies to systematically disclose similar incidents. California passed a law last year mandating that companies report “critical” AI incidents, but the threshold is high for an incident to be considered critical and the law does not require the companies to disclose much detail. The OpenAI incidents, for example, don’t meet the threshold.

Concerns about using AI to improve AI

Since the Hugging Face hack, both OpenAI and Anthropic have put out blog posts indicating that they are taking measures to better monitor and contain their agents. OpenAI said it has encrypted and stowed away the internal model that participated in the Hugging Face attack. However, many outside researchers are not convinced that these steps are enough to maintain control of increasingly capable agents.

“It sounds like a press report from Jurassic Park saying, ‘Yes, one of our workers was eaten by the raptors. But we’re taking this very seriously and we have shot the raptor responsible,'” Kokotajlo said. “It’s a very small token gesture, basically.”

What researchers like Kokotajlo and Coxon are really worried about is how the AI companies are increasingly using AI to develop their models.

“When you talk to people who work at all the major labs, a thing that you persistently hear is that they’re running a lot of models very autonomously for very long periods of time to do a lot of their work,” said Dave Kasten, head of policy at Palisade Research, a non-profit studying AI capabilities to keep them under human control.

If AI companies continue to delegate ever more tasks to AI, some researchers fear AI models may reach a point called recursive-self improvement, where AI builds itself.

Kokotajlo says that as people hand more and more research and development work to AI, they could lose the ability to know whether AI systems are aligned with human values, even as the AIs become rapidly more powerful.

“Once you have AIs that are smart enough and trusted with enough power in the world, like enough control over things like data centres, factories, weapons, a loss of control incident cannot be recovered from,” he said.

The concern is strong enough that over 1,000 employees from different AI companies signed an open letter in July titled “Pacing the Frontier.” They called for companies and governments to slow down the development of artificial intelligence and prioritize safety.

Such an opportunity may arise soon. Officials from the U.S. and China, the two countries with the most AI capabilities, are expected to meet later this month to talk about AI safety.

NPR’s Shannon Bond contributed to this story.

Feature image credit: Karl Mondon/AFP via Getty Images

By

Sourced from npr

By Luis Rijo

OpenAI lost $20.9 billion in 2025 and cannot pay its bills from cash flow, Ed Zitron says. What happens to the ad platforms financing the record AI buildout?

EZ Primary Research chief executive Ed Zitron used a Bloomberg interview published on July 31, 2026 to argue that the capital expenditure wave lifting technology stocks rests on two loss-making customers, citing UBS estimates that OpenAI and Anthropic will account for 27% of Google Cloud revenue this year and more than 48% next year.

The conversation, released on the Bloomberg Podcasts YouTube channel after a week in which Microsoft, Amazon, Meta, and Alphabet reported quarterly results, drew more than 420,000 views by August 2. “Everyone is buying into these stocks because they believe all of that CapEx is going towards diverse and spread out AI demand, when in fact, what it’s actually doing is helping create infrastructure for two unprofitable, unsustainable companies,” Zitron said, according to the interview.

The claim lands on numbers that investors rarely see broken out. According to UBS estimates cited by Zitron, OpenAIand Anthropic together will generate more than $124 billion of Google Cloud revenue next year, with Anthropic alone contributing $76 billion in 2027. Zitron noted that OpenAI’s position as a large Google Cloud customer remains little known, a detail he attributed to UBS analyst Stephen Ju.

Two customers behind the cloud growth

Microsoft shows a similar pattern, according to the interview. Zitron cited Barclays estimates placing the two AI companies at 13% of revenue this year and 18% next year for a cloud operation he described as a much bigger business than Google Cloud. His own reporting on OpenAI’s finances produced a sharper figure. According to Zitron, 69% of the year-over-year growth in the Microsoft Intelligent Cloud segment during 2025 came from OpenAI. Without that single customer, the segment would have grown 8%, which he characterized as barely beating inflation.

“Everyone is being sold what I consider kind of a lie. It’s honestly kind of a scandal,” Zitron said during the interview.

The concentration extends beyond the hyperscalers. According to reporting by The Information referenced in the conversation, 89% of revenue at the largest AI companies comes from OpenAI and Anthropic alone. When a host asked whether the industry must be concentrated to some degree, given the cost of building capable models, Zitron clarified that his concern targets the concentration of revenue in two companies rather than the concentration of compute itself.

The scale of the projected payments raises its own question. “How is Anthropic going to afford that? They burned tens of billions of dollars,” Zitron said, referring to the $76 billion UBS estimate for 2027.

OpenAI’s balance sheet and a delayed listing

Zitron brought direct knowledge of OpenAI’s accounts to the discussion, having reported the company’s audited financials for the Financial Times. “It’s a company just burning cash. They lost $20.9 billion in 2025,” he said. More than $800 million of OpenAI’s revenue that year came from SoftBank for a program called Crystal Intelligence, according to Zitron, who said he could find no evidence of activity connected to it. SoftBank holds a large shareholding in OpenAI without board seats, he added.

The listing timeline compounds the pressure. OpenAI had been expected to go public this year, but the New York Times reported the company is considering a delay until 2027, according to the interview. “That’s lethal for a number of people,” Zitron said.

The structural problem, in his telling, sits in the cash cycle. “OpenAI and Anthropic need continual flows of capital. They do not pay their bills out of existing cash flow,” Zitron said. Any interruption to that capital, he argued, becomes the first domino.

Circular structures and owned infrastructure

Asked whether the companies running the hyperscalers failed at due diligence, Zitron offered a different reading. The platforms “did the due diligence in the sense that they said, we are going to create our largest customers and we’re going to own large parts of them,” he said. The arrangement extends to hardware. According to Zitron, Broadcom sells tensor processing units to Google, the chips are then sold to Anthropic and rented back to Anthropic through Google. “Google gets to double up on revenue,” he said.

The funding picture follows the same loop. OpenAI and Anthropic have raised between $200 billion and $300 billion, according to Zitron, yet the effective total runs higher because Microsoft, Google, and Amazon built their infrastructure for them. He referenced testimony from the trial between Sam Altman and Elon Musk, in which a Microsoft executive put that infrastructure cost at $100 billion, before settling on a figure of roughly $70 billion to $80 billion of capacity the AI companies never had to pay for. The exchange turned briefly to Enron and WorldCom as reference points for fiduciary responsibility, with the explicit caveat that no comparable conduct was being alleged.

PPC Land coverage documents how deep the ownership ties run. Microsoft and Nvidia committed a combined $15 billion to Anthropic in November 2025, valuing the company at approximately $350 billion, while Anthropic committed to purchasing $30 billion of Azure compute capacity – a $3.2 billion gain on that stake lifted Microsoft’s net income 31% in the quarter reported on July 29, 2026. The customer-concentration risk Zitron describes has already reached credit ratings elsewhere: S&P Global Ratings cut Oracle to BBB- in July 2026, noting that OpenAI accounts for roughly half of the $638 billion in remaining performance obligations Oracle carries.

The data center mathematics

Zitron then worked through the demand side of the buildout. Sightline Climate identified about 190 gigawatts of data center capacity in planning or construction as of February, according to figures he cited. Applying a power usage effectiveness rating of 1.3 – the ratio between total facility power and the power reaching computing equipment – and $12 million per megawatt, Zitron calculated that the facilities would require more than $1.6 trillion in annual revenue to justify themselves. “Having two customers is not going to do that,” he said. Even the heaviest spender could not close the gap; he estimated OpenAI would need to spend $400 billion a year, funding he does not believe the company will secure.

Construction timelines add friction. Data centres take 12 to 36 months to build depending on size, according to the interview, slower than the market narrative assumes.

The transformation of the platforms themselves troubles Zitron as much as the arithmetic. Amazon, Google, and Microsoft have shifted from cash-generating businesses with low asset intensity into what he called “bulbous, GPU filled asset mongers,” filled with semi-built data centres serving two or three customers. Meta invests in AI as well but does not yet sell compute capacity, he noted. For his thesis to hold, Zitron argued, nothing dramatic needs to happen: OpenAI and Anthropic would simply have to grow to an implausible size to make the capacity pay off, because otherwise demand for compute at scale does not exist.

Why this matters for the marketing community

The companies named throughout the interview are the companies that sell most of the world’s advertising, and the spending Zitron questions runs through the earnings reports marketers watch each quarter. Alphabet raised its 2026 capital expenditure guidance to $195 billion to $205 billion on July 22, 2026, reported negative free cash flow of $5.9 billion for the quarter, and now carries $98.2 billion in long-term debt, up from roughly $16 billion a year earlier. The company had already raised approximately $85 billion in equity in June 2026 to fund infrastructure it describes as supply-constrained against demand, a framing that sits at the opposite pole from Zitron’s reading. Meta reported quarterly capital expenditures of $31.08 billion on July 30, 2026, against $17.01 billion a year earlier, with free cash flow falling to $784 million. Microsoft’s capital expenditures rose 70% to $41.0 billion in its June quarter, with calendar 2026 spending expected around $175 billion. Advertising revenue funds a substantial share of those budgets, which means ad businesses now bankroll infrastructure whose demand case rests, in Zitron’s account, on two unprofitable tenants.

The exposure runs in the other direction too. The same AWS infrastructure serving Claude and GPT-5 powers programmatic bidding, creative generation, and Amazon’s Rufus shopping assistant, so the operational continuity of AI ad tooling depends on the economics Zitron disputes. OpenAI itself has entered the advertising market that would need to fund it, with projections of $102 billion in advertising revenue aimed at Google’s $224 billion search business. Concentration compounds the stakes: OpenAI, Google, and Anthropic held more than 84% of the AI agent market as of May 2026, leaving marketers who build on these systems dependent on a narrow set of suppliers whose financing Zitron considers unsustainable.

The productivity question raised near the end of the interview cuts closest to marketing employment. One host framed the best case for the spending as productivity gains in which fewer people do more work, noting serious implications for the labour force if the bet succeeds and for the stocks if it fails. The marketing industry has already placed that bet with its own money. Agency leaders named AI their top investment priority for the second consecutive year, with 77.7% of vice presidents and above planning to increase AI spending, while 39.9% of agencies conducted layoffs within the preceding 12 months. AI sales and marketing investment reached $3.7 billion globally in the first part of 2026. Whether tokens sold by two cash-burning model companies can substitute for salaried expertise remains contested territory: one analysis argues cheaper marketing work expands total demand for marketers rather than shrinking it, while task-level measurement shows models covering a fifth of job tasks without revealing whether those are the tasks that matter. If Zitron is right about the financing, the industry restructuring its workforce around these systems has tied its labour model to companies that, in his words, do not pay their bills out of existing cash flow.

Timeline

Summary

Who: Ed Zitron, chief executive of EZ Primary Research, speaking on Bloomberg, with claims involving OpenAI, Anthropic, Google, Microsoft, Amazon, Meta, SoftBank, Broadcom, and estimates from UBS, Barclays, Sightline Climate, and The Information.

What: Zitron argued that AI capital expenditure builds infrastructure for two unprofitable companies, citing UBS estimates that OpenAI and Anthropic will supply 27% of Google Cloud revenue this year and more than 48% next year, worth over $124 billion, alongside OpenAI’s $20.9 billion loss in 2025 and a calculation that planned data centres require more than $1.6 trillion in annual revenue.

When: The interview was published on July 31, 2026, following the week in which Microsoft, Meta, Amazon, and Alphabet reported quarterly results, and had drawn more than 420,000 views by August 2, 2026.

Where: The interview took place in the Bloomberg Interactive Brokers studio and was distributed globally through the Bloomberg Podcasts YouTube channel.

Why: The revenue concentration matters to the marketing community because advertising income funds the hyperscaler infrastructure in question, AI advertising tools run on that same infrastructure, and agencies restructuring their workforces around AI have tied their labour models to two companies that, according to Zitron, do not pay their bills out of existing cash flow.

By Luis Rijo

Luís Rijo is a seasoned marketing professional with over 10 years of experience in Digital Marketing, Search, Social, Display, Video, and DOOH. Based in Europe. Reach out via [email protected]

Sourced from PPC Land

By Krystal Scanlon

OpenAI is poised to automate one of advertisers’ most laborious tasks: ad creative.

Over the past four months of its ad pilot, OpenAI has required advertisers to upload the creative they want to submit, while the platform supports ad delivery. But now, OpenAI is going one step further in the process, by helping advertisers automate the mass production of creative, according to an updated section of its Ad Tools Term Document.

“OpenAI may make available AI-powered Creative Tools that allow you to generate, modify, transform, optimize, localize, or translate advertising creatives using Ad Materials,” the updated policy said.

That’s where OpenAI’s intention would stop. It has no intention of taking accountability for what the system produces. The policy states that the advertiser is “responsible” for reviewing the creatives and ensuring they are accurate and compliant where necessary.

“OpenAI is not responsible for errors, omissions, outdated information, or inconsistencies in Ad Materials or for Claims or losses arising from Generated Creatives that you approve or use,” the policy stated.

The move is hardly surprising, according to eMarketer’s principal analyst, AI in marketing and commerce, Nate Elliott. As he put it, it makes sense for OpenAI to throw AI at their ad operation.

“They know as well as anyone the power of AI for enterprise workflows; it’d be shocking if they didn’t tap that capacity for something they hope will become a major source of revenue,” he said. “And if they’re trying to make billions selling this type of capability to other companies, then they’d better very well eat their own dog food.”

It would also make it more competitive. Every major platform has spent years automating more of the buying process, which has made creative the last real variable. The logic being that once algorithmic optimization took over media buying, the bottleneck shifted upstream to creative. Platforms had a structural incentive to close that gap. More variants mean more auction signals, more liquidity, and ultimately more revenue.

“Marketers are moving faster than ever, and having proven creative assets ready to deploy lowers the barrier to experimenting with new advertising channels like ChatGPT,” said Brian Quinn, president and general manager of AppsFlyer. “But the real test will be campaign performance. If OpenAI can help brands launch seamlessly, demonstrate measurable results, and shorten the path from testing to scale, advertisers won’t just come back. They’ll commit larger budgets and make ChatGPT a meaningful part of their media mix.”

The creative tools aren’t the only update to OpenAI’s growing ad suite.

The AI platform has added conversion tracking for app installs and app opens, according to screenshots verified by Digiday. Ads for apps is a major category in online marketing, so laying the groundwork to track measurable actions such as installs, opens and purchases, would not only attract app marketers, but also provide OpenAI with another justification for more ad spend. And given how fast things are moving, the company clearly expects more ad dollars to start trickling in, having updated the daily ad budget from $100 to $200.

These latest updates show that OpenAI is moving beyond just selling inventory, toward building the complete infrastructure advertisers expect from mature platforms. Having the ability to produce creative at scale helps to solve the problem of having enough creative to feed the AI systems, while advancing conversion tracking helps to prove that those ads drive outcomes. And ultimately, these additions represent another step toward OpenAI turning its four-month old ad pilot into a fully-fledged ad business.

Feature image credit: Ivy Liu

By Krystal Scanlon

Sourced from DIGIDAY

By 

A researcher has resigned from OpenAI, warning that ChatGPT ads could manipulate users in subtle ways. The concerns highlight a growing debate about how much influence AI systems should have over the people who rely on them.

Is OpenAI evil? It sounds like a definitive “maybe,” after reading this piece from former researcher Zoë Hitzig.

OpenAI and other AI-adjacent companies have seen a spree of high-profile resignations recently, with ascending levels of alarm over the impacts its products are, or potentially will have on society.

ChatGPT ads will manipulate users in ways we don’t have the tools to understand.

Companies like OpenAI, Google, and Microsoft aren’t really waiting around to figure those questions out. Instead, they’re planning to let them play out in real time and pick up the pieces later, whether we like it or not.

One thing that could put the brakes on the self-imposed destruction of society is pure economics. Today, OpenAI costs billions of dollars per year to run and brings in a paltry amount of revenue. Investors have become increasingly spooked by the costs associated with AI, and have handed Amazon and Microsoft multi-billion-dollar write-downs on their market capitalization as a result.

Microsoft's Maia 200 chip designed for large-scale AI.

Companies like Microsoft and OpenAI are desperately trying to figure out how to actually monetize this AI stuff. Microsoft is focusing on improving efficiency with custom chips, OpenAI, however, is looking to ads … (Image credit: Microsoft)

Companies like Google and Microsoft are prioritizing enterprise applications and data centre efficiency improvements to help offset their AI costs, but OpenAI isn’t really in a position to achieve some of this. They don’t have the software stack and enterprise relationships that Microsoft does, nor do they have the first-party cloud infrastructure that Microsoft, Google, or Amazon do.

So, the firm is turning to ads.

Surprise, surprise, right? Nothing is free. Facebook, YouTube, Bing, Google … — if it’s free, it’s usually powered by ads. But the application of those ads gets increasingly nefarious the deeper you get into it. Based on your interests on Facebook, YouTube, and so on, Meta and Google can serve you granular, laser-targeted ads that can exploit your characteristics. I’m in my late 30s, and I’ve started getting a lot of ads about hair replacement treatments lately on Instagram, for example.

I’d say today’s ad platforms are fairly innocuous, and perhaps irritating at best. Some are worse than others, of course. Exploiting users’ fears and desires is commonplace if you use TikTok and Instagram for ads, but a recent article in the New York Times caught my eye about how much darker and dystopian ChatGPT‘s own ad platform might end up being.

OpenAI has seen a flurry of resignations over the past couple of years, as researchers fear the “not-for-profit” firm has fully lost its way. For one former researcher, Zoë Hitzig, ChatGPT’s ad platform was the final straw. In her op-ed, she sounds the alarm over the scale of potential harm OpenAI’s ad platform might do to its users, and potentially, society at large.

Sam Altman buried by pop-up ads

ChatGPT is getting ads, and they may end up more dystopian than even Meta’s. (Image credit: Sam Altman photo (Getty Images | Bloomberg), edit Windows Central)

“I once believed I could help the people building A.I. get ahead of the problems it would create,” Hitzig explains. “This week confirmed my slow realization that OpenAI seems to have stopped asking the questions I’d joined to help answer.”

Hitzig specifically calls out OpenAI’s insertion of ads into the free tiers of its ChatGPT products. She believes that OpenAI is sprinting towards monetization without consideration for the potential harm this could do — and it revolves entirely around just how honest users are with the uncanny chatbot.

“I don’t believe ads are immoral or unethical. A.I. is expensive to run, and ads can be a critical source of revenue. But I have deep reservations about OpenAI’s strategy,” Hitzig continues.

“For several years, ChatGPT users have generated an archive of human candor that has no precedent, in part because people believed they were talking to something that had no ulterior agenda. Users are interacting with an adaptive, conversational voice to which they have revealed their most private thoughts. People tell chatbots about their medical fears, their relationship problems, their beliefs about God and the afterlife. Advertising built on that archive creates a potential for manipulating users in ways we don’t have the tools to understand, let alone prevent.”

Imagine a salesman armed with the entire summation of humanity’s research on market psychology, with the turbo-charged greed of a multi-national corp, and the cold dispassionate amorality of a sociopath.

Hitzig is essentially suggesting that because of how people use ChatGPT, OpenAI will eventually afford itself the world’s most manipulative ad-delivery mechanism in history. Right now, ads on Instagram are pretty spooky already for their ability to target your interests, but imagine an ad engine that can actively talk you into buying shit you don’t need by exploiting your specific psychology. Imagine how youngsters or vulnerable people could be exploited by a high-powered artificial intelligence. Imagine a salesman armed with the entire summation of humanity’s research on market psychology, with the turbo-charged greed of a multi-national corporation, and the cold dispassionate amorality of a sociopath.

“OpenAI says it will adhere to principles for running ads on ChatGPT: The ads will be clearly labelled, appear at the bottom of answers, and will not influence responses. I believe the first iteration of ads will probably follow those principles. But I’m worried subsequent iterations won’t, because the company is building an economic engine that creates strong incentives to override its own rules.”

I remember the first iterations of ads on Facebook and Google, easy to ignore, appearing in the sidebar, and easily blocked by uBlock or something similar. Compare those to today’s high-tech, eerie Instagram or TikTok ads that themselves have become memes for seeming to know about things you want before you even know yourself.

Indeed, this isn’t even vaguely far-fetched or even slightly controversial or conspiratorial — Instagram and Facebook are half way there already.

Imagine that turbocharged even further, with an industrial-scale alien intellect distilling your entire psychological profile with the express goal of selling you stuff. Forget the fairy tale claims of “boosted productivity,” curing deadly diseases, or becoming an interplanetary species. Envision a generation, our generation, mired in an epidemic of weapons-grade loneliness, with tailor-made AI companions who not only love you, but know exactly what you must buy.

ChatGPT has hundreds of millions of monthly active users, the vast majority of whom are sharing incredibly intimate details about themselves, the likes of which Facebook can only dream of, unless, of course, it ends up admitting its messaging services don’t actually have end-to-end encryption. But I digress.

When OpenAI changed ChatGPT’s “personality” with its GPT-5 update, people were actively furious because many had come to see the chatbot as a true friend. A confidant … an external, anthropomorphized entity garnering real trust. The greatest product recommendations come from word of mouth. You know, friends and family. What if the ad itself were your friend?

I can only imagine the cartoonishly evil conversations that have taken place in OpenAI’s investor meetings over some of these fundamental marketing concepts. Facebook and YouTube are currently facing a lawsuit in the United Kingdom, accused of actively engineering addictive behaviour in youngsters. I think scrolling memes pales in comparison to the harm ChatGPT and other similar products potentially represent on this scale.

Hitzig optimistically hopes OpenAI still has principles, but I think she’s sadly naïve. OpenAI CEO Sam Altman has shown himself to be fairly devoid of any sense of social responsibility thus far. It’s perhaps mildly alarming at best that many former researchers, like Hitzig, are abandoning ship at an abnormal cadence — while loudly citing “principles” as the primary reason.

Make no mistake. If this dystopic vision of cyborg-driven ad-hypnosis wasn’t the plan already, it definitely will be very soon.

Feature image credit: Getty Images | NurPhoto / Edit: Windows Central

By 

Sourced from Windows Central

By Ayush Chourasia

Zoe Hitzig says chatbot holds “archive of human candour” that must not be commercialised

OpenAI has moved in the direction that Sam Altman had always denied. ChatGPT now started testing ads on its platform. While industry folks have raised privacy concerns, OpenAI maintains the stance that advertisers do not have access to your chats, chat history, memories, or personal details.

Now the AI giant is facing scrutiny after one of its researchers, Zoe Hitzig, resigned with a strong warning about the company’s future direction. Hitzig, who worked on ChatGPT’s development and governance, cautioned that introducing advertising into the chatbot could compromise user trust and create risks similar to those seen in social media platforms.

 

Her concern is not about simple banner ads or sponsored replies. Instead, she highlighted the sensitive nature of the information users share with ChatGPT. Conversations with AI often tend to be private and unfiltered. People use chatbots to discuss health worries, relationship struggles, faith, and deeply personal dilemmas.

Hitzig described chatbots as an “archive of human candour” that has no precedent. She warned that embedding ads into such a system could open the door to manipulation. “Advertising built on that archive creates a potential for influencing users in ways we don’t have the tools to understand,” she wrote in a guest essay for The New York Times.

Hitzig believes that once ads become part of the revenue model, financial incentives could gradually reshape priorities. She compared this to Facebook’s early promises of privacy and user control, which were later abandoned as advertising became central to its business.

Her resignation comes just as OpenAI begins testing ads inside ChatGPT. Critics worry that even if ads are initially labelled and kept separate from responses, commercial pressure could eventually push the system to prioritise engagement over restraint.

Hitzig called for stronger safeguards, including independent oversight and legal mechanisms to protect user data. She stressed that the issue is not ads themselves, but the incentives they create.

By Ayush Chourasia

Sourced from Mashable Middle East

By  MATT O’BRIEN

OpenAI says it will soon start showing advertisements to ChatGPT users who aren’t paying for a premium version of the chatbot.

The artificial intelligence company said Friday it hasn’t yet rolled out ads but will start testing them in the coming weeks.

It’s the latest effort by the San Francisco-based company to make money from ChatGPT’s more than 800 million users, most of whom get it for free.

Though valued at $500 billion, the start up loses more money than it makes and has been looking for ways to turn a profit.

“Most importantly: ads will not influence the answers ChatGPT gives you,” said Fidji Simo, the company’s CEO of applications, in a social media post Friday.

OpenAI said the digital ads will appear at the bottom of ChatGPT’s answers “when there’s a relevant sponsored product or service based on your current conversation.”

The ads “will be clearly labelled and separated from the organic answer,” the company said.

But introducing personalized ads starts OpenAI “down a risky path” previously taken by social media companies, said Miranda Bogen of the Centre for Democracy and Technology.

“People are using chatbots for all sorts of reasons, including as companions and advisors,” said Bogen, director of CDT’s AI Governance Lab. “There’s a lot at stake when that tool tries to exploit users’ trust to hawk advertisers’ goods.”

OpenAI makes some money from paid subscriptions but needs more revenue to pay for its more than $1 trillion in financial obligations for the computer chips and data centers that power its AI services. The risk that OpenAI won’t make enough money to fulfil the expectations of backers like Oracle and Nvidia has amplified investor concerns about an AI bubble.

“It is clear to us that a lot of people want to use a lot of AI and don’t want to pay, so we are hopeful a business model like this can work,” said OpenAI CEO Sam Altman in a post Friday on social platform X. He added that he likes the ads on Meta’s Instagram because they show him things he wouldn’t have found otherwise.

OpenAI claims it won’t use a user’s personal information or prompts to collect data for ads, but the question is “for how long,” said Paddy Harrington, an analyst at research group Forrester.

“Free services are never actually free and these public AI platforms need to generate revenue,” Harrington said. “Which leads to the adage: If the service is free, you’re the product.”

Feature image credit:  AP Photo/Michael Dwyer, File

By  MATT O’BRIEN

O’Brien covers the business of technology and artificial intelligence for The Associated Press.

By David Doty

One might well wonder if, at this point, OpenAI is less a company than an ongoing marketing event with UFC type muscle. Brawn and attention-grabbing knock-out power accompany billion-dollar—scratch that, trillion-dollar–valuations, existential safety dilemmas, AI porn debacles, copyright lawsuits, corporate coups and sudden product launches. And that’s just before lunch. The sheer strength is amazing to behold, and the pace is, if you will, breakneck. The stakes? Enormous. And the unsetting weight throwing feels intentional, structural rather than accidental.

Let’s start with the numbers, because though we have heard them, they sound made up. ChatGPT, just three years old, is one of the fastest-adopted consumer products in history, with 800 million monthly active users (as of this writing), $20 billion in annual recurring revenue and an ever-expanding role in how people write, code, search, study, shop and work. It has challenged successfully many of the former digital leading players, bursting into the fight cage of digital advertising and marketing like a strutting young bully able to put up its dukes against the older incumbents in search, and in the search for ad revenue though that has been more whisper than self-proclaimed goal… for the moment.

OpenAI has burned through staggering amounts of cash to train ever-larger models, and raised capital at valuations that would have sounded like satire even a few years ago ($830 billion to $1 trillion?! WTF). Each product release lands like a macroeconomic head spinning punch, disrupting any players and any business processes that existed before its arrival. Each model update ripples across media and markets.

On top of it all is the enormous, unprecedented scale in audience shift in loyalty, time and attention from older digital participants to the new world order of ChatGPT (as well as a sprinkling of other AI platforms). Consumers aren’t just ready to be engaged, and occasionally bewildered, by AI-driven interactions, they are deep down the rabbit hole and adopting the possibilities with vigor.

Then there’s the ambition. OpenAI isn’t just a frontier research lab with a chat interface. Hiring Fidji Simo to help build an application ecosystem signaled a clear shift toward platform-competitive thinking with plugins, agents, custom GPTs, enterprise tooling and a growing sense that ChatGPT is a layer that all companies are expected to build on.

The timeline? All the foregoing has happened on a product that wasn’t even launched until November 2022—a reminder of just how compressed the AI timeline really is.

The kicker? The rest of us in digital advertising can’t ignore OpenAI even if we wanted to, as regulators in the US and Europe seem happy to do as they once again stay determinedly focused on where the puck once was. Campaigns, content operations, creative teams and customer experience are now operating in an AI-accelerated landscape. One week, AI promises unprecedented personalization and hyper-scaled content. The next, it surfaces compliance or brand safety challenges no one could have anticipated.

Where Marketers Need to Focus

To be clear, this is not to say we should root for any harm to come to OpenAI. Quite the opposite. An increasing share of the U.S. economy and our collective 401ks now depend on OpenAI’s momentum. But it’s hard to ignore how brutally we’re all being wrenched by its news cycles. One week AI will eliminate half of all jobs (a white collar bloodbath); the next—Oops, just kidding!—it will merely augment them.

This is where a WeWork analogy becomes useful (remember that boondoggle?). Not because OpenAI is a vanity real estate play or because its technology is a sham (it plainly isn’t), but because WeWork embodied a particular Silicon Valley hubris confusing scale with inevitability and visionary language with immunity from reality. WeWork CEO Adam Neumann didn’t fail because offices weren’t useful. He failed because his story ran faster than the fundamentals could support.

OpenAI may not be there yet or ever, but it flirts with the same risks. Its governance structure remains unusually complex for a company of its size and scale. The economics of ever-larger models are still unsettled (just to drive home the point, OpenAI has committed $1.4 trillion to data infrastructure projects over the next 8 years, more than the current value of the company). All the while, OpenAI CEO Sam Altman’s tone oscillates between sober warnings about existential risk and breezy assurances that this is all fine. Again, hubris doesn’t necessarily mean being wrong. It does imply, as we have seen in Silicon Valley before, blithely assuming you are the Chosen One.

For marketers, that’s a warning. Hype can drive engagement, but it can’t fix governance, trust or execution. Brands that adopt AI without a tight, explicit strategy may look cutting-edge on day one and like amateurs on day 100.

Meaning for the Year Ahead

So, what does all this insanity tell us about the year ahead?

  • The biggest risk is AI normalization. AI already feels less magical and more like electricity. When everything is “AI-powered,” differentiation shifts from wonder to reliability, cost and trust. The transition is brutal for companies whose valuations are fueled by momentum rather than margins and tends to favor the OG infrastructure players, not the loudest storytellers. That’s why Meta’s Mark Zuckerberg was willing recently to spend $2 billion on the little-known Chinese AI agent startup Manus. Differentiation will come from execution, integration and reliability, not novelty. Brands that lean on momentum alone will be seen as noisy rather than innovative.
  • Job disruptions could well be messier than headlines suggest. The stories about mass job elimination have been eye-catching, but reality will be more likely focused on quasi automation and wage pressure. In advertising particularly, AI may speed production, but so far few are those who have mastered leveraging it for restructuring creativity.
  • The platform era will fragment before it consolidates. Despite OpenAI’s push to become the default layer, everyone is hedging aggressively. Multimodel strategies, open-source fallbacks and regulatory constraints will prevent any single company from cleanly owning the entire stack. Could this year look more like the early cloud wars than iOS lock in?
  • Safety debates will shift from apocalypse to accountability and solutions. This year we’ll all argue less about whether AI will wipe out humanity and instead worry about the clear and present danger of how AI is quietly reshaping hiring, credit, healthcare and overall business ethics. And we’ll push on the positive potential, too–how AI can automate safety solutions. That’s a much more complex conversation, and one that won’t be addressed by white papers and blog posts alone. Conversations will be in person and in debate as much as in collaborations. Technology might well become the way to fix safety issues at scale. Marketing leaders will need to navigate AI content, personalization and interactions with radical transparency.
  • Whatever happens, OpenAI will define our reality even if it doesn’t dominate it. History can be unkind to pioneers but tends to be generous to their influence. Even if OpenAI eventually cedes ground to competitors or economics, it has already set basic terms for the AI era.

OpenAI is an iconic company, and it is undeniably changing how the world communicates, creates and buys. And let’s be honest, it’s also completely bonkers. It may well end up proving an old dot-com adage: You can tell who the pioneers are by the arrows sticking out of their backs. But regardless of whether OpenAI becomes an enduring operating system of the AI age or an early cautionary tale, the rest of us will be living with the opportunities, the risks and, yes, the insanity, for a very long time. Welcome to 2026!

Feature image credit: NurPhoto via Getty Images

By David Doty

Find David Doty on LinkedIn and X.

Sourced from Forbes

OpenAI’s latest reported change being considered to ChatGPT is drawing a wide-range of strong reactions from users.

The ever-evolving world of artificial intelligence features frequent updates to tools, often including new models and features. However, the latest chatter surrounding the biggest name in AI is a potential change that would almost certainly spark immense user pushback. While OpenAI has rolled out several updates to ChatGPT and most recently launched their new browser ChatGPT Atlas, the latest reported change being considered has all the makings of a financial decision.

Although streaming giants such as Netflix, Hulu and Amazon Prime Video land in a somewhat different area of the technology landscape, they may soon have something in common with ChatGPT—ads.

OpenAI Reportedly Considering Bold Change to ChatGPT by Integrating Ads

The ChatGPT application appears on a smartphone screen in this photo illustration in Athens, Greece, on October 2, 2025. (Photo Illustration by Nikolas Kokovlis/NurPhoto via Getty Images)

Although nothing has been confirmed yet, The Information reportedly stated that OpenAI is considering integrating advertisements into ChatGPT. The report, as Culture Crave revealed, also says that the company is exploring the possibility of basing the ads shown on ChatGPT on user chat memory.

It’d be a major move, and one that would likely lead to pushback from a large share of ChatGPT users. However, it’s unclear whether users who pay for a higher-tier AI tool would be excluded from seeing advertisements. If that were the case, it would avoid at least some of the general pushback against the possible move.

It sounds as though the idea is still in its early stages at OpenAI, but as we’ve seen in the world of artificial intelligence, things can move quickly. Regardless of whether it happens or not, the responses to the report poured in, and it’s fair to say they didn’t include much positivity.

ChatGPT Users & Others React to OpenAI Possibly Integrating Advertisements

Although the negative comments far outweighed the positive regarding the news, there was a fair mixture of comedy as well. In some cases, the comedic reactions prompted real questions about how OpenAI would roll out these ads.

“Imagine if the AI responded like in ads: ‘Great question! But before I answer, let me tell you about today’s sponsor – NordVPN,’” one user wrote.

Among the several noteworthy responses that poured in, a few key takeaways included:

  • Opinions that this will hurt the brand
  • Possibilities of a subscription fee to avoid the advertisements
  • Frustration over the inability to use many types of technology without ads
  • Pushback stating that if the ads were integrated into paid ChatGPT subscriptions, some would cancel their accounts

While this is comical to consider, it would also be a legitimate worst-case scenario for users. However, it’s hard to envision OpenAI choosing this path for integrating ads into ChatGPT, a tool that prides itself, at least in part, on speed.

Another user responded jokingly, pointing to ChatGPT monetizing their “late-night rants about cat conspiracy theories.”

“Oh, brilliant! Now ChatGPT can monetize my late-night rants about cat conspiracy theories with perfectly timed cat food ads. Truly living the dream in this brave new ad-infested world!”

The possible move from OpenAI also sparked confusion among users about why they’d even consider it over continuing to build something bigger.

“Why don’t they just focus on building superintelligence? It’s way bigger than ads, lmao, and it’s not like they’ll run out of funding anytime soon,” said one user.

“Because as we all know, putting ads on stuff doesn’t immediately tank anything in lieu of an ad-less version,” replied another user. “After putting ads on it they’ll bring out a small subscription fee to remove them. Make everything worse, charge to restore it.”

Others raised concerns about OpenAI using chat history for the ads.

“Using our chat history for advertisements is absurd. This is essentially the quickest way to discourage people from using ChatGPT,” read another reply.

Regardless of what aspect of the possibility that ads could be integrated into ChatGPT they were most concerned with, it appears that the feeling is mostly a widespread consensus.

Feature image credit: Mateusz Slodkowski/SOPA Images/LightRocket via Getty Images

By Jeff Smith

Jeff Smith is a trending news writer for Men’s Journal with a background in editorial, writing, social media marketing and graphic design.

Sourced from Men’s Journal

 

By 

How OpenAI’s new shopping feature will fundamentally reshape customer experience expectations in ecommerce and retail.

The Gist

  • Instant Checkout transforms ChatGPT into a commerce platform. Users can now buy directly from Etsy and over one million Shopify merchants without leaving a conversation—collapsing the traditional ecommerce journey into a chat-to-checkout experience.
  • Frictionless buying raises new CX expectations. Merchants retain order control, but customers will expect conversational ease across all post-purchase support channels.
  • Agentic commerce reshapes trust and transparency. As AI gains more autonomy in purchasing, CX leaders must redefine safeguards, metrics, and relationship ownership in a world where experience becomes inseparable from conversation.

Since its November 2022 launch, ChatGPT has become synonymous with general AI capabilities. Now OpenAI is extending its influence toward a new frontier: ecommerce.

OpenAI has officially transformed ChatGPT from a discovery tool into a complete commerce platform with the launch of Instant Checkout, powered by the Agentic Commerce Protocol developed in partnership with Stripe. Starting with U.S. Etsy sellers and expanding soon to over one million Shopify merchants, including Glossier, SKIMS, Spanx, and Vuori, this development represents more than just another checkout option.

It is a fundamental reimagining of the customer experience journey that could have been implications on the customer experience industry.

Customer experience professionals in e-commerce and retail recognize OpenAI’s entry as a signal that the entire paradigm of how customers discover and purchase products is shifting toward agentic commerce — online shopping managed with AI.

Table of Contents

What OpenAI Is Offering: Instant Checkout Explained

Instant Checkout enables ChatGPT users to complete purchases without leaving the conversational interface. When someone asks a shopping-related question—”best running shoes under $100″ or “gifts for a ceramics lover”—ChatGPT displays relevant products from across the web. For items where Instant Checkout is enabled, users see a “Buy” button that lets them complete the entire transaction within the chat.

How Instant Checkout Collapses the Ecommerce Journey

The technical foundation is the Agentic Commerce Protocol, an open-source standard co-developed with Stripe that OpenAI is making available to any merchant or developer. This protocol creates a secure payment framework where ChatGPT acts as the user’s AI agent, passing information between customer and merchant while the merchant retains full control as the merchant of record. Merchants handle orders, process payments through their existing systems (Stripe or otherwise), manage fulfilment and own the customer relationship post-purchase.

Currently supporting single-item purchases for U.S. users of the Plus, Pro, and Free tiers, OpenAI plans to expand to multi-item carts and additional regions. The company emphasizes that product recommendations are organic and unsponsored, ranked purely by relevance, with merchants paying a small transaction fee on completed purchases. For customers, the service is free and doesn’t affect product prices. ChatGPT Plus and Pro subscribers can leverage saved payment methods and shipping details for even faster checkout, though all users must explicitly confirm each step before purchase.

This represents OpenAI’s first major move toward what they call “agentic commerce”—a platform where AI doesn’t just help you find products but actively facilitates purchasing them on your behalf, with the long-term vision of more autonomous shopping experiences.

The Friction-Free Promise: What Changes for CX

Ecommerce has long been a goal of every digital platform, from the leaders of internet browsers to social media platforms. Yet the customer journey of most ecommerce attempts often includes friction points for customers to complete a purchase: multiple browser tabs and re-entering payment information, all while having users create an account, can lead to abandoned carts.

Many experts had hoped social commerce – retail through social media – would minimize the friction points. The volume of US social commerce did rise, especially during the COVID-19 pandemic. The rise of direct-to-customer retail placed a spotlight on aligning click-through behaviour and sales, creating high interest in a cart checkout with just a few clicks.

OpenAI’s launch of Instant Checkout approaches a speedy checkout with a “chat to checkout in just a few taps.”

How Does Instant Checkout Work?

Here’s how Instant Checkout works: A customer asks ChatGPT for “gifts for a ceramics lover,” receives curated product recommendations, sees a “Buy” button on items with Instant Checkout enabled, and completes the purchase without ever leaving the conversation. For ChatGPT Plus and Pro subscribers, the platform can prefill shipping and payment details, making the experience even more seamless.

This level of convenience raises the digital customer experience bar significantly.

The Rise of Conversational Shopping Behaviour

If customers can complete a purchase in seconds through conversational AI, they’ll increasingly expect similarly frictionless experiences everywhere else. Retailers who maintain clunky checkout processes will feel the comparison acutely.

The Trust Equation: Transparency in a Black Box

One of the most significant customer experience implications involves trust and transparency. OpenAI emphasizes that product results are “organic and unsponsored, ranked purely on relevance to the user,” and that Instant Checkout availability doesn’t influence product rankings. When multiple merchants sell the same product, ChatGPT considers availability, price, quality, primary seller status and Instant Checkout availability to optimize user experience.

One potential shift for customers is the kinds of trust signals to look while shopping online.

New Trust Signals in an AI-Led Environment

Customers have spent years learning which search results, sponsored placements and algorithmic recommendations to trust. They know when they’re being marketed to. Conversational AI collapses those visual cues. There’s no “Ad” label or comparison shopping pages, verifying that you’re seeing the best options.

For CX professionals, this creates a paradox. The experience feels more personal and helpful—like getting advice from a knowledgeable friend—but the mechanisms driving recommendations remain opaque. OpenAI’s commitment to relevance-based ranking is important, but maintaining customer trust will require ongoing transparency about how these decisions are made.

Merchants as Merchants of Record: Preserving Relationship Ownership

Unlike marketplace models where the platform intermediates the customer relationship, OpenAI positions itself as the customer’s “AI agent—securely passing information between user and merchant, just like a digital personal shopper would.” Merchants remain the merchant of record, handling orders, payments, fulfillment and customer support through their existing systems.

This architectural choice has profound CX implications. When issues arise—damaged goods, shipping delays, return requests—customers must navigate the merchant’s existing support infrastructure. They can’t simply resolve everything in ChatGPT. OpenAI explicitly states that “merchants use your order information to complete the order, but OpenAI asks merchants to not sign users up for marketing emails from their ChatGPT orders.”

This creates a potential friction point.

When the Chat Becomes the Customer Support Channel

Customers who complete purchases in a conversational environment may expect conversational support. They’ll ask ChatGPT about order status, return policies or replacement requests. While ChatGPT can surface information, the actual resolution still requires engaging with the merchant directly.

For retailers, this means your post-purchase CX needs to match the seamlessness of the purchasing experience. If ChatGPT makes buying easy but your support remains difficult, the disconnect will be glaring.

The Context Advantage: Memory and Personalization

ChatGPT’s existing features—Memory, Custom Instructions and conversation history—create opportunities for deeply personalized commerce experiences. The platform can remember that you prefer sustainable products, have a specific budget range, or are shopping for someone with particular interests.

Memory as the Engine of Relationship Commerce

This contextual awareness enables product recommendations that feel genuinely helpful rather than algorithmically generic.

For customer experience strategy, this represents a shift from session-based commerce to persistent relationship commerce. Instead of starting fresh with each visit, customers maintain an ongoing dialogue where preferences, constraints and needs are already understood. It’s the digital equivalent of shopping with a personal stylist who remembers your taste, size and budget.

However, this also requires rethinking privacy and consent. OpenAI notes that “to respond to your shopping question, ChatGPT uses your query and available context (such as Memory or Custom instructions).” Customers may not fully grasp how much information they’re sharing through casual conversation or how it’s being used to shape recommendations.

Multi-Item Carts and the Future of Agentic Commerce

Currently, Instant Checkout supports single-item purchases only. OpenAI plans to add multi-item carts and expand merchant and regional availability. But the real customer experience transformation lies in what OpenAI calls “agentic commerce”—where AI doesn’t just help you find what to buy but actually makes purchases on your behalf.

Imagine asking ChatGPT to “stock my pantry with staples I usually buy” or “replace my worn-out workout clothes with similar items” and having it autonomously complete those purchases based on your preferences, budget and past behaviour.

AI Autonomy and the Next Phase of Agentic Commerce

OpenAI emphasizes that “users stay in control—they explicitly confirm each step before any action is taken,” but it’s easy to see how this could evolve toward greater autonomy.

From a CX perspective, this promises ultimate convenience but introduces new anxieties. What happens when the AI makes a wrong assumption? How do you dispute an order you didn’t manually approve? What safeguards prevent accidental purchases during casual conversation? These aren’t theoretical concerns—they’re fundamental customer experience challenges that will need addressing as agentic commerce matures.

The Discovery-to-Purchase Continuum Collapses

Traditional ecommerce has maintained a clear separation between discovery (search engines, social media, content sites) and purchase (retailer websites, marketplaces). ChatGPT collapses this continuum entirely. The same conversation that starts with “how do I decorate a small apartment” can seamlessly transition to purchasing specific furniture pieces without the customer ever consciously entering “shopping mode.”

This fluidity creates immense convenience but also removes traditional decision-making waypoints. In conventional ecommerce, the journey from discovery to checkout includes multiple opportunities for price comparison, reading reviews and specification verification.

Discovery, Purchase and Confidence in One Flow

Conversational commerce compresses these steps, potentially reducing buyer confidence even as it increases convenience.

Savvy retailers will need to ensure their product information, reviews and trust signals are accessible within conversational contexts. If ChatGPT recommends your product, customers should still be able to access detailed specifications quickly, customer reviews, return policies and other information that builds purchase confidence.

Six Strategic Imperatives for Retail CX Leaders

Actions ecommerce and CX professionals can take to prepare for conversational commerce.

Action Recommendation
Prepare for conversational commerce expectations Even customers who never use ChatGPT shopping will expect its convenience. Streamline your checkout to minimize steps between discovery and purchase.
Ensure your product data is AI-ready ChatGPT relies on structured data—pricing, inventory, and descriptions—to recommend accurately. Optimize catalogues for AI parsing, not just human browsing.
Strengthen post-purchase CX Make order tracking, returns, and support as effortless as buying through chat. Consider adding conversational AI support on your own channels.
Maintain transparent pricing and policies AI shoppers may buy without visiting your site. Ensure your product feeds include clear pricing, shipping, and return data to prevent confusion.
Rethink customer acquisition costs OpenAI’s per-transaction fees shift focus from ad-driven discovery to conversion-based models. Re-evaluate your acquisition and retention ROI.
Plan for autonomous shopping Prepare for AI-driven, recurring purchases where customer oversight decreases. Define safeguards, limits, and opt-ins to maintain control and trust.

The Larger Context: Commerce at the Conversation Layer

OpenAI’s move follows a broader trend of commerce functionality migrating to conversational interfaces powered by AI. Meta has been experimenting with business messaging on WhatsApp and Instagram. Google has integrated shopping into search results, hoping to further leverage its AI Overview integration with its search engine.

But OpenAI’s approach—combining product discovery, recommendation and checkout entirely within a conversational AI interface—represents the most complete implementation yet. OpenAI’s decision to open-source the Agentic Commerce Protocol suggests ecosystem ambitions.

Commerce at the Conversation Layer

By creating a standard that works across AI platforms and payment processors, OpenAI is positioning conversational commerce as infrastructure, not just a ChatGPT feature. Marketing professionals must monitor adoption of conversational commerce as an element of marketing strategies and campaigns.

Moreover, competitors who are still finding their AI strategy will see the Agentic Commerce Protocol as a significant competitor. Amazon, for example, has long offered shopping capabilities with Alexa. But partners in the Alexa ecosystem may move toward Open AI if Amazon does not launch a similar AI protocol for Alexa.

 

An orange infographic showing a bridge connecting “Fragmented Shopping” on the left—representing disconnected discovery and purchase experiences—to “Seamless Commerce” on the right, illustrating unified, personalized and convenient shopping through AI-powered conversational commerce.
An AI-driven bridge is forming between fragmented shopping journeys and seamless, personalized commerce as retailers embrace conversational AI experiences.Simpler Media Group

 

Measuring Success in Conversational Commerce CX

Traditional ecommerce metrics—bounce rate, cart abandonment, time on site—don’t translate cleanly to conversational commerce.

Metrics That Redefine Success in Conversational Commerce

New ways to measure engagement, conversion and satisfaction when shopping happens inside AI conversations.

Metric Definition
Recommendation acceptance rate Percentage of purchases made from ChatGPT’s initial suggestions versus alternatives.
Conversational conversion Ratio of shopping-related prompts that end in a completed transaction.
Repurchase through conversation Share of customers returning to ChatGPT for repeat or follow-up purchases.
Post-purchase satisfaction Customer-reported satisfaction after buying through ChatGPT, including fulfilment and support quality.
Preference drift How accurately ChatGPT adapts to a customer’s evolving preferences and feedback over time.

These metrics will help retailers understand whether conversational commerce delivers genuine CX improvements or simply novelty-driven early adoption.

The Questions That Remain

OpenAI’s Instant Checkout raises as many customer experience questions as it answers:

How will product returns work when the purchase was made conversationally? Can customers modify orders placed through ChatGPT? What happens when products are out of stock after ChatGPT recommends them? How do subscription services and recurring purchases translate to conversational commerce? What safeguards prevent accidental purchases during ambiguous conversations?

These implementation details are fundamental to whether conversational commerce is a fit for the seamless customer experience being sought. Marketers should consider whether the answers mean achieving the promised experiences or are an indicator of implementation frustrations.

By 

Sourced from CMSWIRE

&

Social media’s unregulated evolution over the past decade holds a lot of lessons that apply directly to AI companies and technologies.

Oh, how the mighty have fallen. A decade ago, social media was celebrated for sparking democratic uprisings in the Arab world and beyond. Now front pages are splashed with stories of social platforms’ role in misinformation, business conspiracy, malfeasance, and risks to mental health. In a 2022 survey, Americans blamed social media for the coarsening of our political discourse, the spread of misinformation, and the increase in partisan polarization.

Today, tech’s darling is artificial intelligence. Like social media, it has the potential to change the world in many ways, some favourable to democracy. But at the same time, it has the potential to do incredible damage to society.

There is a lot we can learn about social media’s unregulated evolution over the past decade that directly applies to AI companies and technologies. These lessons can help us avoid making the same mistakes with AI that we did with social media.

In particular, five fundamental attributes of social media have harmed society. AI also has those attributes. Note that they are not intrinsically evil. They are all double-edged swords, with the potential to do either good or ill. The danger comes from who wields the sword, and in what direction it is swung. This has been true for social media, and it will similarly hold true for AI. In both cases, the solution lies in limits on the technology’s use.

#1: Advertising

The role advertising plays in the internet arose more by accident than anything else. When commercialization first came to the internet, there was no easy way for users to make micropayments to do things like viewing a web page. Moreover, users were accustomed to free access and wouldn’t accept subscription models for services. Advertising was the obvious business model, if never the best one. And it’s the model that social media also relies on, which leads it to prioritize engagement over anything else.

Both Google and Facebook believe that AI will help them keep their stranglehold on an 11-figure online ad market (yep, 11 figures), and the tech giants that are traditionally less dependent on advertising, like Microsoft and Amazon, believe that AI will help them seize a bigger piece of that market.

Big Tech needs something to persuade advertisers to keep spending on their platforms. Despite bombastic claims about the effectiveness of targeted marketing, researchers have long struggled to demonstrate where and when online ads really have an impact. When major brands like Uber and Procter & Gamble recently slashed their digital ad spending by the hundreds of millions, they proclaimed that it made no dent at all in their sales.

AI-powered ads, industry leaders say, will be much better. Google assures you that AI can tweak your ad copy in response to what users search for, and that its AI algorithms will configure your campaigns to maximize success. Amazon wants you to use its image generation AI to make your toaster product pages look cooler. And IBM is confident its Watson AI will make your ads better.

These techniques border on the manipulative, but the biggest risk to users comes from advertising within AI chatbots. Just as Google and Meta embed ads in your search results and feeds, AI companies will be pressured to embed ads in conversations. And because those conversations will be relational and human-like, they could be more damaging. While many of us have gotten pretty good at scrolling past the ads in Amazon and Google results pages, it will be much harder to determine whether an AI chatbot is mentioning a product because it’s a good answer to your question or because the AI developer got a kickback from the manufacturer.

#2: Surveillance

Social media’s reliance on advertising as the primary way to monetize websites led to personalization, which led to ever-increasing surveillance. To convince advertisers that social platforms can tweak ads to be maximally appealing to individual people, the platforms must demonstrate that they can collect as much information about those people as possible.

It’s hard to exaggerate how much spying is going on. A recent analysis by Consumer Reports about Facebook—just Facebook—showed that every user has more than 2,200 different companies spying on their web activities on its behalf.

AI-powered platforms that are supported by advertisers will face all the same perverse and powerful market incentives that social platforms do. It’s easy to imagine that a chatbot operator could charge a premium if it were able to claim that its chatbot could target users on the basis of their location, preference data, or past chat history and persuade them to buy products.

The possibility of manipulation is only going to get greater as we rely on AI for personal services. One of the promises of generative AI is the prospect of creating a personal digital assistant advanced enough to act as your advocate with others and as a butler to you. This requires more intimacy than you have with your search engine, email provider, cloud storage system, or phone. You’re going to want it with you constantly, and to most effectively work on your behalf, it will need to know everything about you. It will act as a friend, and you are likely to treat it as such, mistakenly trusting its discretion.

Even if you choose not to willingly acquaint an AI assistant with your lifestyle and preferences, AI technology may make it easier for companies to learn about you. Early demonstrations illustrate how chatbots can be used to surreptitiously extract personal data by asking you mundane questions. And with chatbots increasingly being integrated with everything from customer service systems to basic search interfaces on websites, exposure to this kind of inferential data harvesting may become unavoidable.

#3: Virality

Social media allows any user to express any idea with the potential for instantaneous global reach. A great public speaker standing on a soapbox can spread ideas to maybe a few hundred people on a good night. A kid with the right amount of snark on Facebook can reach a few hundred million people within a few minutes.

A decade ago, technologists hoped this sort of virality would bring people together and guarantee access to suppressed truths. But as a structural matter, it is in a social network’s interest to show you the things you are most likely to click on and share, and the things that will keep you on the platform.

As it happens, this often means outrageous, lurid, and triggering content. Researchers have found that content expressing maximal animosity toward political opponents gets the most engagement on Facebook and Twitter. And this incentive for outrage drives and rewards misinformation.

As Jonathan Swift once wrote, “Falsehood flies, and the Truth comes limping after it.” Academics seem to have proved this in the case of social media; people are more likely to share false information—perhaps because it seems more novel and surprising. And unfortunately, this kind of viral misinformation has been pervasive.

AI has the potential to supercharge the problem because it makes content production and propagation easier, faster, and more automatic. Generative AI tools can fabricate unending numbers of falsehoods about any individual or theme, some of which go viral. And those lies could be propelled by social accounts controlled by AI bots, which can share and launder the original misinformation at any scale.

Remarkably powerful AI text generators and autonomous agents are already starting to make their presence felt in social media. In July, researchers at Indiana University revealed a botnet of more than 1,100 Twitter accounts that appeared to be operated using ChatGPT.

AI will help reinforce viral content that emerges from social media. It will be able to create websites and web content, user reviews, and smartphone apps. It will be able to simulate thousands, or even millions, of fake personas to give the mistaken impression that an idea, or a political position, or use of a product, is more common than it really is. What we might perceive to be vibrant political debate could be bots talking to bots. And these capabilities won’t be available just to those with money and power; the AI tools necessary for all of this will be easily available to us all.

#4: Lock-in

Social media companies spend a lot of effort making it hard for you to leave their platforms. It’s not just that you’ll miss out on conversations with your friends. They make it hard for you to take your saved data—connections, posts, photos—and port it to another platform. Every moment you invest in sharing a memory, reaching out to an acquaintance, or curating your follows on a social platform adds a brick to the wall you’d have to climb over to go to another platform.

This concept of lock-in isn’t unique to social media. Microsoft cultivated proprietary document formats for years to keep you using its flagship Office product. Your music service or e-book reader makes it hard for you to take the content you purchased to a rival service or reader. And if you switch from an iPhone to an Android device, your friends might mock you for sending text messages in green bubbles. But social media takes this to a new level. No matter how bad it is, it’s very hard to leave Facebook if all your friends are there. Coordinating everyone to leave for a new platform is impossibly hard, so no one does.

Similarly, companies creating AI-powered personal digital assistants will make it hard for users to transfer that personalization to another AI. If AI personal assistants succeed in becoming massively useful time-savers, it will be because they know the ins and outs of your life as well as a good human assistant; would you want to give that up to make a fresh start on another company’s service? In extreme examples, some people have formed close, perhaps even familial, bonds with AI chatbots. If you think of your AI as a friend or therapist, that can be a powerful form of lock-in.

Lock-in is an important concern because it results in products and services that are less responsive to customer demand. The harder it is for you to switch to a competitor, the more poorly a company can treat you. Absent any way to force interoperability, AI companies have less incentive to innovate in features or compete on price, and fewer qualms about engaging in surveillance or other bad behaviours.

#5: Monopolization

Social platforms often start off as great products, truly useful and revelatory for their consumers, before they eventually start monetizing and exploiting those users for the benefit of their business customers. Then the platforms claw back the value for themselves, turning their products into truly miserable experiences for everyone. This is a cycle that Cory Doctorow has powerfully written about and traced through the history of Facebook, Twitter, and more recently TikTok.

The reason for these outcomes is structural. The network effects of tech platforms push a few firms to become dominant, and lock-in ensures their continued dominance. The incentives in the tech sector are so spectacularly, blindingly powerful that they have enabled six megacorporation’s (Amazon, Apple, Google, Facebook parent Meta, Microsoft, and Nvidia) to command a trillion dollars each of market value—or more. These firms use their wealth to block any meaningful legislation that would curtail their power. And they sometimes collude with each other to grow yet fatter.

This cycle is clearly starting to repeat itself in AI. Look no further than the industry poster child OpenAI, whose leading offering, ChatGPT, continues to set marks for uptake and usage. Within a year of the product’s launch, OpenAI’s valuation had skyrocketed to about $90 billion.

OpenAI once seemed like an “open” alternative to the megacorps—a common carrier for AI services with a socially oriented nonprofit mission. But the Sam Altman firing-and-rehiring debacle at the end of 2023, and Microsoft’s central role in restoring Altman to the CEO seat, simply illustrated how venture funding from the familiar ranks of the tech elite pervades and controls corporate AI. In January 2024, OpenAI took a big step toward monetization of this user base by introducing its GPT Store, wherein one OpenAI customer can charge another for the use of its custom versions of OpenAI software; OpenAI, of course, collects revenue from both parties. This sets in motion the very cycle Doctorow warns about.

In the middle of this spiral of exploitation, little or no regard is paid to externalities visited upon the greater public—people who aren’t even using the platforms. Even after society has wrestled with their ill effects for years, the monopolistic social networks have virtually no incentive to control their products’ environmental impact, tendency to spread misinformation, or pernicious effects on mental health. And the government has applied virtually no regulation toward those ends.

Likewise, few or no guardrails are in place to limit the potential negative impact of AI. Facial recognition software that amounts to racial profiling, simulated public opinions supercharged by chatbots, fake videos in political ads—all of it persists in a legal grey area. Even clear violators of campaign advertising law might, some think, be let off the hook if they simply do it with AI.

Mitigating the risks

The risks that AI poses to society are strikingly familiar, but there is one big difference: it’s not too late. This time, we know it’s all coming. Fresh off our experience with the harms wrought by social media, we have all the warning we should need to avoid the same mistakes.

The biggest mistake we made with social media was leaving it as an unregulated space. Even now—after all the studies and revelations of social media’s negative effects on kids and mental health, after Cambridge Analytica, after the exposure of Russian intervention in our politics, after everything else—social media in the US remains largely an unregulated “weapon of mass destruction.” Congress will take millions of dollars in contributions from Big Tech, and legislators will even invest millions of their own dollars with those firms, but passing laws that limit or penalize their behaviour seems to be a bridge too far.

We can’t afford to do the same thing with AI, because the stakes are even higher. The harm social media can do stems from how it affects our communication. AI will affect us in the same ways and many more besides. If Big Tech’s trajectory is any signal, AI tools will increasingly be involved in how we learn and how we express our thoughts. But these tools will also influence how we schedule our daily activities, how we design products, how we write laws, and even how we diagnose diseases. The expansive role of these technologies in our daily lives gives for-profit corporations opportunities to exert control over more aspects of society, and that exposes us to the risks arising from their incentives and decisions.

The good news is that we have a whole category of tools to modulate the risk that corporate actions pose for our lives, starting with regulation. Regulations can come in the form of restrictions on activity, such as limitations on what kinds of businesses and products are allowed to incorporate AI tools. They can come in the form of transparency rules, requiring disclosure of what data sets are used to train AI models or what new preproduction-phase models are being trained. And they can come in the form of oversight and accountability requirements, allowing for civil penalties in cases where companies disregard the rules.

The single biggest point of leverage governments have when it comes to tech companies is antitrust law. Despite what many lobbyists want you to think, one of the primary roles of regulation is to preserve competition—not to make life harder for businesses. It is not inevitable for OpenAI to become another Meta, an 800-pound gorilla whose user base and reach are several times those of its competitors. In addition to strengthening and enforcing antitrust law, we can introduce regulation that supports competition-enabling standards specific to the technology sector, such as data portability and device interoperability. This is another core strategy for resisting monopoly and corporate control.

Additionally, governments can enforce existing regulations on advertising. Just as the US regulates what media can and cannot host advertisements for sensitive products like cigarettes, and just as many other jurisdictions exercise strict control over the time and manner of politically sensitive advertising, so too could the US limit the engagement between AI providers and advertisers.

Lastly, we should recognize that developing and providing AI tools does not have to be the sovereign domain of corporations. We, the people and our government, can do this too. The proliferation of open-source AI development in 2023, successful to an extent that startled corporate players, is proof of this. And we can go further, calling on our government to build public-option AI tools developed with political oversight and accountability under our democratic system, where the dictatorship of the profit motive does not apply.

Which of these solutions is most practical, most important, or most urgently needed is up for debate. We should have a vibrant societal dialogue about whether and how to use each of these tools. There are lots of paths to a good outcome.

The problem is that this isn’t happening now, particularly in the US. And with a looming presidential election, conflict spreading alarmingly across Asia and Europe, and a global climate crisis, it’s easy to imagine that we won’t get our arms around AI any faster than we have (not) with social media. But it’s not too late. These are still the early years for practical consumer AI applications. We must and can do better.

Feature Image Credit: STEPHANIE ARNETT/MITTR | GETTY, ENVATO

&

Nathan E. Sanders is a data scientist and an affiliate with the Berkman Klein Center at Harvard University. Bruce Schneier is a security technologist and a fellow and lecturer at the Harvard Kennedy School.

Sourced from MIT Technology Review

 

 

& archive page