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OpenAI on Thursday launched ChatGPT Work, a new AI agent embedded inside its flagship chatbot that aims to transform ChatGPT from a question-and-answer tool into an autonomous work platform capable of executing complex, multi-step tasks across users’ email, calendars, code repositories, and messaging apps.The product is powered by OpenAI’s latest flagship model, GPT-5.6, and is designed to go far beyond generating text. ChatGPT Work can gather context from connected apps, files, and workflows to produce finished documents, spreadsheets, presentations, reports, and websites. The agent takes a stated outcome, breaks it into smaller steps, and stays with complex projects for hours, completing them independently.The launch marks OpenAI’s clearest attempt yet to reposition ChatGPT as a workplace platform rather than a chatbot — and it arrives at a moment of extraordinary financial significance for the company. Last month, OpenAI confidentially submitted a draft S-1 registration statement to the SEC, initiating what could become one of the largest technology IPOs in history, with reported valuations clustering between $730 billion and $852 billion and annualized revenue that has blown past $25 billion.In a short demonstration and conversation with VentureBeat on Friday, Ty Geri, a product manager at OpenAI who helped build ChatGPT Work, said the product’s mission is to democratize the kind of agentic AI capabilities that OpenAI’s internal engineering tool, Codex, has already demonstrated. “What’s really exciting is we’ve seen how much Codex has been able to push the frontier of what we can get done with these AI tools, as opposed to just getting information or answers or guidance,” Geri said. “Our internal adoption of Codex is literally an exponential curve across every single product function and every single use case.”Why OpenAI built a persistent virtual machine that works from the beachThe core architectural bet behind ChatGPT Work is a persistent cloud-based virtual machine that runs on OpenAI’s servers, always available to the user regardless of which device they happen to be on. That marks a deliberate departure from competitors whose agents require a local machine to remain powered on and connected.”What’s really exciting about ChatGPT Work is that it’s a virtual machine in the cloud that’s always on for you, and this is available across all of our paid tiers,” Geri said. “All Plus users are getting this. I think that’s a very unique aspect of this.”The mobile-first aspect of the launch is something Geri described as “missing from the market.” He pointed to the ability to create a website on a phone and share it with collaborators as a particularly novel capability. “Sites are new in general to Codex. They launched in Codex about a week and a half ago, but now we’re launching also in web and mobile. You can create a site on your phone at the beach and share it with your friends,” he said.ChatGPT Work will roll out beginning with Pro, Enterprise, and Edu users, and will expand to Plus and Business users over the next few days. In the interview, Geri emphasized that the availability of the product to Plus subscribers — not just premium tiers — is central to OpenAI’s strategy. “It’s accessible to all paid plans, including Plus users, which in my opinion is a really big feat, and really part of that OpenAI mission, which is about bringing all this power to as many people,” he said.How MCP plugins connect ChatGPT Work to Slack, Gmail, and GitHubThe product relies on MCP-based plugins to connect to external services like Gmail, Google Calendar, Slack, and GitHub. When asked whether the plugin architecture is based on the Model Context Protocol standard, Geri confirmed: “These are all based on MCP.” He added that connecting multiple Gmail accounts — a frequent user request — “is definitely on the roadmap.”The experience is designed to be action-oriented from the first interaction. ChatGPT Work offers a personalized onboarding flow that surfaces different suggested use cases depending on the user’s role. Geri demonstrated how the system, detecting his role as a product manager, immediately suggested tasks like evaluating AI systems, building research artifacts, and managing his calendar. “You can start with a simple task like catch me up on Slack or Teams or read today’s calendar,” Geri said. He described a scenario where the system reviewed his calendar, identified scheduling conflicts, flagged meetings requiring preparation, and then — on his instruction — declined, accepted, or rescheduled events directly.Users can also customize the agent by teaching it their writing style, organizing outputs into projects, and — in a lighter touch — choosing a virtual pet that accompanies them in the interface. The interface also introduces a hosted website feature that allows users to build and share interactive sites directly through ChatGPT Work, turning what would typically be a static slide deck into a dynamic, collaborative artifact. “Now we suddenly have a collaborative interface that’s actually more exciting and more accessible than a slide deck, which has all these formatting restrictions,” Geri said.Scheduling 10 bug bashes at once: what agentic productivity looks like in practiceGeri’s own usage of ChatGPT Work illustrates the breadth of tasks the system can handle. In the run-up to the product’s launch, he needed to organize pre-release testing sessions — known internally as “bug bashes” — across dozens of features and team members.”I just come to ChatGPT Work and say, ‘Set up a bug bash for all the distinct features in ChatGPT Work. Add all the people that worked on that feature,’ and it can check Slack, it can check GitHub, it can check Docs, and find a time that works for the four highest contributors to that feature,” Geri said. “It went and scheduled 10 bug bashes, all coordinated across all those different people. That would have taken me 30 minutes at least.”But Geri pushed back against the characterization that ChatGPT Work is limited to rote administrative work. He described using it for analytically complex tasks like identifying the biggest causes of user churn for specific product features and generating product solutions — work he said would previously have taken months. “Things that we would have spent three months doing, we can now spend a week doing — and do much more, and make a much better product,” Geri said. “Bugs that we would have found three or four weeks from now, we can now find within two days and fix for our users.”He also described handing off the tedium of product testing itself. “It used to be that even though like the most interesting part of my job is like what to test, I would actually end up having to spend most of my job doing the testing, which is like me taking a mouse and like clicking on the same thing over and over again, like five times,” Geri said. “Instead, now I can define what do we want to test, and ChatGPT Work or Codex can actually go test it for me, deliver me that bug report, and then we can work on fixing that bug.”What OpenAI says about data privacy when AI reads your Slack and emailWhen pressed on data privacy concerns — given that ChatGPT Work pulls sensitive information from workplace tools like Slack, Google Drive, and email — Geri said privacy “is incredibly important, and the most important part of this is it’s always in the user’s control.”He pointed to OpenAI’s existing enterprise security infrastructure, noting that “enterprise accounts have ZDR, and users can always opt out of letting their conversations help improve future models, which many users do.” The comment aligns with assurances OpenAI made when it first launched ChatGPT Enterprise in August 2023, when the company wrote in a blog post that it does “not train on your business data or conversations.”The privacy question carries additional weight now because of the sheer volume of sensitive workplace data ChatGPT Work is designed to access. Unlike a chatbot session where a user voluntarily pastes text into a prompt, ChatGPT Work actively reaches into connected systems — reading Slack messages, scanning calendar invitations, pulling GitHub commit histories — to assemble context for its tasks. That represents a fundamentally different data surface area than anything OpenAI has offered before, and one that enterprise security teams will scrutinize carefully before granting access.ChatGPT Work enters a three-way arms race with Anthropic and MicrosoftChatGPT Work lands squarely in the middle of what has become the defining competitive battlefield in enterprise AI: the race to build autonomous workplace agents that can go beyond generating text and actually execute tasks.The product arrives months after Anthropic took Claude Cowork out of preview and into general availability in April, bringing its AI agent to web and mobile platforms aimed at helping enterprise users monitor and manage long-running AI-driven tasks from anywhere. Meanwhile, Microsoft made Copilot Cowork generally available worldwide on June 16, built in partnership with Anthropic to move beyond chat and into execution. The three products — ChatGPT Work, Claude Cowork, and Microsoft Copilot Cowork — now compete directly for the attention of enterprise IT departments and individual knowledge workers alike.The convergence is striking. All three products share a remarkably similar vision: a persistent AI agent running in the cloud that can break complex tasks into steps, connect to workplace tools via plugins, and produce finished outputs rather than just conversational replies. All three work across desktop, web, and mobile.What distinguishes OpenAI’s approach is its raw consumer distribution advantage. ChatGPT has reached 900 million weekly active users, and OpenAI now has 50 million paying subscribers. More than 9 million paying business users rely on ChatGPT for work, and 92% of Fortune 500 companies now use ChatGPT. By making ChatGPT Work available to Plus subscribers at $20 a month — not just Enterprise or Pro customers — OpenAI is betting that broad accessibility will drive adoption faster than any competitor can match.OpenAI’s product manager says AI is a partner, not a replacement — with a caveatWhen asked about the potential impact on the labor market, Geri was careful with his framing. He declined to speak broadly about workforce disruption but offered his personal experience as a product manager whose day-to-day work has been substantially reshaped by the tool.”My job is not to schedule bug bashes and find out who contributed to a specific feature. That’s a task I do in my job, but that’s not my job,” Geri said. “My job is to make an amazing product.” He described ChatGPT Work as “a partner” and “an extension of me, certainly not a replacement,” adding: “Everybody feels far more productive than before, but is also almost working harder than before, because you get to work on all the things you want to work on as opposed to the drudgery around it.”But Geri was also careful not to minimize the sophistication of the work the agent can handle. “I also don’t want to say that it’s only doing mundane tasks because, like something like hill climbing retention curves on a given feature is not mundane. It’s actually really hard to do,” he said. The distinction matters. If ChatGPT Work were merely automating calendar invitations and expense reports, it would be a convenience tool. The fact that Geri describes it compressing three months of analytical product work into a single week suggests something with far greater implications for how teams are structured and staffed.An IPO-bound company needs ChatGPT Work to prove enterprise AI can generate revenueThe timing of ChatGPT Work’s launch is impossible to separate from OpenAI’s IPO trajectory. The company needs to demonstrate that it can convert its massive consumer user base into durable enterprise revenue — a narrative that becomes significantly more compelling with a product explicitly designed around professional workflows.OpenAI said it is generating $2 billion in revenue per month, growing four times faster than Alphabet and Meta did at comparable stages, with enterprise now making up more than 40% of revenue and on track to reach parity with consumer by the end of 2026. But OpenAI remains heavily loss-making, and the company does not expect to reach profitability until around 2030, with internal projections suggesting losses of $14 billion in 2026 alone.The competitive dynamics are unprecedented. Anthropic filed for its own IPO on June 1 at a $965 billion valuation, setting up simultaneous public listings from the two most prominent AI startups in history. Whether both can sustain their lofty valuations under the scrutiny of public market investors will depend in large part on whether products like ChatGPT Work and Claude Cowork deliver measurable productivity gains to paying enterprise customers.The launch also caps a product trajectory that began with ChatGPT Enterprise in August 2023, accelerated through the release of OpenAI’s Operator agent in January 2025, and continued through Operator’s deprecation and shutdown on August 31, 2025, when its capabilities were folded into the ChatGPT agent framework. ChatGPT Work is the consolidation of those efforts into a single, unified product — one that pairs GPT-5.6’s three model variants (Sol for power, Luna for speed, and Terra for balanced everyday use) with a persistent cloud environment and an expanding library of MCP plugins.The future of work may already be running in the cloudWhen asked whether ChatGPT Work signals a shift toward a new kind of operating system — one where users interact with their computers primarily through an AI agent rather than through traditional mouse-and-keyboard interfaces — Geri stopped short of making sweeping predictions. But he hinted at the direction OpenAI sees ahead.”Anybody who has worked with Codex or now ChatGPT Work will realize how exciting it is to interact with your environment and your computer via the agent,” he said. “Especially in the desktop app, where the model has access to your entire machine and can interact with websites on your behalf — it’s really able to be an extension of you and a real partner, and that certainly feels like the future.”At the end of the interview, Geri circled back to something personal. “I’ve never enjoyed work as much as I have in the last month using ChatGPT Work and Codex,” he said — a striking admission from a product manager who, until recently, spent a meaningful share of his days clicking through the same interface five times in a row just to see if it would break. OpenAI is now asking 900 million users to believe that feeling scales. For a company weeks away from one of the largest public offerings in history, the answer to that question is worth roughly $850 billion.

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We have largely come to accept a modern digital bargain: When we interact with AI chatbots, our prompts become fodder for their ongoing training.These models feast on a constant stream of user data to survive. Now, however, that massive accumulation of chat history is exactly what has landed OpenAI in hot water.The New York Times and more than a dozen other publishers asked a federal judge on Thursday to sanction OpenAI over how the company handled evidence in their copyright lawsuit.The original case asked whether OpenAI trained ChatGPT on stolen journalism. This motion asks something narrower and more damaging: whether OpenAI lied about what it could already prove.OpenAI accused of obstructionThe filing, submitted in the Southern District of New York, accuses OpenAI of a “deliberate and systemic effort to obstruct discovery,” according to a Bloomberg Law report.For two years, OpenAI told the court that searching ChatGPT training data and logs for copyrighted material was not technically feasible, a Reuters report noted. Plaintiffs say that was false.The claim rests on a February deposition of Vinnie Monaco, OpenAI’s privacy engineering lead, a TechCrunch report confirmed.Monaco reportedly testified that OpenAI had already searched its training corpus and built a database of roughly 78 million de-identified ChatGPT conversations before the Times filed suit in 2023. He also said it had developed an internal tool called Project Giraffe to detect reproduced text.That timeline undercuts OpenAI’s central excuse, since the tools existed before the lawsuit, not because of it.

The New York Times and other publishers asked a federal judge to sanction OpenAI for hiding evidence in their copyright lawsuit.boonchai wedmakawand / Getty Images

OpenAI court case: disputed evidence and deleted logsPlaintiffs originally sought a sample of 120 million chat logs. OpenAI negotiated that down to 20 million, then submitted a version in December so heavily redacted that the court called it unusable, according to TechCrunch.The newspapers also allege that OpenAI deleted billions of ChatGPT conversations after a court preservation order took effect.More OpenAI:OpenAI’s $1 trillion ambition could delay its IPOOpenAI just built a chip to cut Nvidia out of one jobOpenAI makes IPO decision amid Anthropic, SpaceX fervorThe remedy plaintiffs want is where the real leverage lies. They are asking the judge to bar OpenAI from using its own reduced log sample as evidence and to simply rule as fact that the logs would show OpenAI reproduced their journalism, Reuters noted.Ian Crosby, the Times’ lead attorney at Susman Godfrey, said OpenAI concealed what it knew for more than two years, and that resolving discovery this way would settle the case’s most contested technical question without a trial.OpenAI’s defense and the financial stakesOpenAI rejects that framing entirely. Spokesperson Drew Pusateri said in a statement that The Times is using privacy claims to compensate for a weakening case, and that the company will keep defending user privacy and fair use.Notably, the sanctions motion does not target Microsoft, OpenAI’s co-defendant and largest financial backer, according to The Times’ own report on its filing.The dollar figures already on the table elsewhere in AI litigation show what is at stake. Anthropic agreed to pay authors $1.5 billion to settle a separate training-data case, the largest AI copyright settlement to date, according to The Associated Press.Related: Your wallet is being put in danger by OpenAIThat amount is a small fraction of Anthropic’s $965 billion valuation, but it set a price for what a court finding against an AI company can cost.The Times itself has spent more than $28 million fighting AI companies in court, including $4.2 million in this year’s first quarter alone, a Variety report confirmed.NYT shares traded near $73 this week with little visible reaction to the filing. Discovery motions rarely move markets the way settlements or verdicts do, even when the allegations are this severe.The bigger stakes as OpenAI eyes a public listingWhat the sanctions fight really tests is how AI companies answer for their conduct during litigation, separate from what they did while building their models.OpenAI is preparing for a public listing that bankers have discussed at valuations approaching $1 trillion, and its eventual prospectus will need to disclose litigation risk like this to new shareholders.A finding of discovery misconduct would hand publishers, and every publisher watching this case, leverage that a fair use defense alone cannot buy back.Related: Venice AI raised $65M to exploit OpenAI’s blind spot

Enterprise companies are running AI agents ahead of the controls needed to manage them — and they deployed that way knowingly. That is the central finding from VentureBeat Research’s June survey of 573 technical leaders at companies with 100 or more employees, fielded across five parallel surveys of the agentic stack. Enterprises are now retrofitting to catch up with their own standards, and they are budgeting for it: Roughly six in 10 enterprises plan to switch or add vendors in each of five control layers within the next 12 months, and roughly a third — depending on the layer — plan to move within the quarter, the research finds.There are five main layers where enterprises are building: identity for agents (which agent is allowed to do what, under whose credentials); evaluation of agent output (whether the work is any good); cost telemetry (what each agent costs to run); the context layer (the business data and definitions agents draw on to answer); and the orchestration control plane (the software that coordinates multi-step agent work).Enterprises are already paying the price for deploying agents ahead of adequate control functions. Fifty-four percent of companies had an agent security incident or near-miss caught before harm in the past 12 months. Twenty-seven percent exercise only reactive control of agent spend — they learn what an agent costs when the invoice arrives, with no per-agent budget or ceiling in place.Here are the five findings that anchor the set — one finding per layer of the tech stack — and what the data suggests doing first in each.Expensive hardware is idle: 86% of GPU operators report utilization of 50% or lessEighty-six percent of enterprises that run their own GPUs report utilization of 50% or less. Wall Street has spent the quarter debating whether the AI buildout is overbuilt. This is buy-side measurement, from the enterprises doing the buying, and the research says the most expensive hardware in buildings of these enterprises runs at no more than half its capacity.The measurement gap compounds it: A minority 44% rigorously track what their AI compute actually costs and returns. Everyone else is only estimating. And the enterprise shopping process continues regardless: 45% of these enterprises say the emerging compute option they are most likely to evaluate in the next 12 months is an AI-specialized cloud (CoreWeave, Lambda, Crusoe, Nebius). However, under 2% of these enterprises report using one of these neoclouds today. Moreover, roughly one in three companies appears to be considering a hedge against Nvidia: Asked which emerging compute option they are most likely to evaluate in the next 12 months, 32% of enterprises named non-Nvidia accelerators (AWS Trainium, Google TPUs, AMD), while 28% named next-generation Nvidia GPUs. The data suggests that enterprises should measure the utilization and per-workload cost of the GPUs they already own before committing budget to new compute — whether that’s an AI-specialized cloud contract, new accelerators, or more GPUs. Most deployed “agents” do single-prompt work: 71% say a quarter or fewer complete multi-step tasks on their ownSeventy-one percent of enterprises say a quarter or fewer of their deployed “agents” can complete multi-step work on their own; the rest are single-prompt chatbots. Only 10% say true agents are the majority of what they run. To be sure, the respondents reported that they are in a position to know these things: 81% said they recommend or decide AI purchases at their companies.That finding — that most agents are actually just chatbots in trenchcoats — lands amid adoption claims across the industry running well ahead of what enterprises are actually running. Gartner predicted 40% of enterprise applications will be integrated with task-specific AI agents by the end of 2026, up from less than 5% in 2025. It also warned that the most common misconception is referring to these AI assistants as agents, a misunderstanding known as “agentwashing.”Meanwhile, Zapier’s enterprise survey said 72% reported deploying or testing autonomous agents; and Writer’s 2026 survey has 97% of executives saying their company deployed AI agents in the past year. Those surveys asked whether companies have deployed something called an AI agent, and companies said yes. Our survey asked the people running those deployments a harder question: Of the agents you have in production, how many can complete a multi-step task without a person driving each step? The gap matters for two practical reasons. First, the inflated adoption figures are the benchmark boards and vendors use to pressure technical leaders into moving faster — and this data says the real bar is far lower than the headlines suggest. Second, the label determines the bill: A single-prompt chatbot with a human reading every answer needs none of the identity, evaluation, and cost controls this report covers, while a true multi-step agent needs all of them. 66% let agents push to production on automated evals alone — or are engineering toward it. 5% fully trust those evalsTwo-thirds of enterprises fall into one of two camps: 34% already allow an AI agent to push a code or system change to production based on automated evaluation results alone, with no human reviewing it, and another 33% are actively engineering their pipelines to allow that within the next 12 months. Only five percent fully trust the automated evaluations that would make that decision.The distrust is earned. Half of enterprises shipped an agent that passed internal evaluations and then caused a customer-facing failure in the past year; a quarter watched it happen more than once. Asked to name the biggest weakness in their current evaluations, more enterprises chose “poor alignment with real-world outcomes” than any other answer — 29% of respondents.And most of the checking happens before an agent ships, then stops. Once agents are live with real users, only 23% of enterprises run real-time quality checks on the answers those agents produce. Another 51% monitor system health only — uptime, request traces, and gateway logs — which tells them the agent is running, and nothing about whether its answers are right. The first move: Before removing human review from any workflow, test your evaluations against production outcomes rather than internal benchmarks, and instrument answer quality, not just uptime. This finding is explored in more depth in VentureBeat’s related coverage of the evaluation gap, which found that larger enterprises are moving faster toward zero-human deployment while also failing more often — and outlines a regression-testing framework built on production outcomes rather than internal benchmarks. 69% run credential sharing somewhere in the agent fleet — and those companies get hit far more oftenSixty-nine percent of companies allow agent credential sharing somewhere in their agent fleet during runtime – meaning multiple agents operating under one API key or service account. Those companies were far more likely to get hit: Organizations with credential sharing anywhere in the fleet experienced a security incident or near-miss at a 63.5% rate (47 of 74), against 40.9% (9 of 22) where every agent has its own scoped identity. The takeaway for enterprises is this: Give every agent its own scoped identity, starting with the agents that touch production systems.57% traced a confident, wrong agent answer to their own missing or inconsistent business contextFifty-seven percent of enterprises traced at least one confident, wrong agent answer in the past six months to missing or inconsistent business context: wrong metrics, stale definitions, absent documents. Most of them watched it happen more than once.Most enterprise companies are fixing this, even though they’ve moved forward with agent deployment already: 25% already run a governed semantic layer, or one governed definition of the business that every AI reads from, in production. However, 34% are still building one, and 41% haven’t started. The takeaway: Govern the definitions your agents answer from, metrics and entities first, before scaling the agents that depend on them.The quarter where agent technology “portability” became a priorityOne more shift is worth reporting with its limits stated plainly. In our spring orchestration survey wave, the top concern about provider-controlled orchestration was security and permissioning limits (32%). By June, vendor lock-in led at roughly a third, with security limits at 28%. Those are two snapshots one quarter apart, and here’s one possible explanation for why portability became a top issue for enterprises. Our June survey went into market after a June 12 U.S. Commerce Department export order took Anthropic’s Claude Fable 5 offline for enterprises for roughly three weeks. Meanwhile, Chinese company Z.ai released GLM-5.2’s open weights under an MIT license on June 16 at roughly one-sixth of GPT-5.5’s price; and Tencent’s Hy3 arrived July 6 under Apache 2.0; and OpenAI previewed GPT-5.6 on June 26 to a small group of government-vetted partners, opening it broadly on July 9 after the government’s review cleared. The open-weight releases in particular promise enterprises more control over their agents, and while we haven’t established a causal link here, the timing is worth noting.The posture data matches the mood: 51% now expect their primary control plane for enterprise agents to be hybrid — provider-native plus external orchestration — by the end of 2026, up from 34% in the spring survey wave. Enterprises reporting that they rely purely on provider-managed agent services fell from 12% to 7%.Five layers, no incumbents, 12 monthsThe synthesis across all five surveys reveals a huge “buying” window. In each of the five control layers, 57% to 64% of enterprises plan to switch or add vendors within 12 months — 64% in infrastructure and in evaluations, 59% in agent security, 57% in retrieval and context — and 26% to 38%, depending on the layer, plan to move within a quarter. No layer has an established incumbent: The most common evaluation tooling is the model provider’s built-in evals, tied with no dedicated tooling at all (17% each); 82% of respondents name provider-native or hyperscaler controls as their primary agent security layer; and provider-native retrieval leads the context technology layer (RAG, etc) as well. Most enterprises are defaulting today to the built-in tools that ship with the big AI platforms they already use: Anthropic, OpenAI, Google, Microsoft, and AWS. That holds true across every one of these agentic technology layers: enterprises are looking to their primary cloud and model providers to supply the guardrails, evaluations, and retrieval solutions already bundled into those providers’ offerings.Those defaults are winning on convenience, and they’re also what the coming spending decisions will test. The survey didn’t ask which direction that money moves — toward the platforms’ built-in tools or toward the specialists challenging them — which is exactly why every contract in these five layers is worth watching over the next four quarters.The Q3 survey wave will measure whether the enterprises made good on these budget plans: whether their agents gained scoped identities, whether evaluations got tested against production outcomes, whether GPU utilization rose, and whether the semantic layers under construction shipped.VentureBeat will release the full Q2 reports across all five VB Pulse trackers at VB Transform, July 14–15 at Hotel Nia in Menlo Park, where we convene enterprise technical leaders building autonomous agents in production. Disclosure: VentureBeat produces both this research and VB Transform

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The future of U.S. roadways will feature a lot more automated vehicles, so the country must work out the kinks now before the AV population starts really increasing. One of the biggest issues already poking its head through is the multiple instances of automated vehicles driving through or otherwise ignoring emergency response situations where the police or first responders are involved. The tragic mass shooting in Austin, Texas, earlier this year is a prime example of this.Police and first responders raced to and from the scene where two people were killed and 14 were injured, but one emergency vehicle was filmed being severely delayed by an obstruction in the road that refused to move: a malfunctioning Waymo. Eventually, a police officer opened the vehicle and moved it, but by the time he did, the ambulance’s human driver had already navigated around the malfunctioning Waymo.“As our protocols are designed and we’ve trained first responders to do, a police officer disengaged the vehicle, and our roadside assistance team retrieved it,” Waymo said in a statement to 11Alive at the time.That wasn’t the first time Waymo vehicles have failed to navigate emergency situations properly.In February, a driverless Waymo vehicle with a passenger in the back drove into the middle of an active police scene before stopping in Atlanta, Georgia.A local television news station that had been covering Waymo’s failure to stop at school buses with their stop signs deployed just happened to be on the scene, filming the police standoff with an armed suspect, who had fired at law enforcement, grazing one in the head.Now the NHTSA is saying that not only Waymo, but other robotaxi operators like Tesla and other automated vehicle makers need to tighten up their protocols. NHTSA issues call to action over AVs interfering in emergency situationsThis week, in a letter dated July 8, the National Highway Traffic Safety Administration issued a call to action for all AV makers to fix the glitch causing their vehicles to ignore emergency situations.While admitting that it believes in the “immense potential” of AV technology to “reduce human error and improve safety” on the nation’s roads. Still, the regulatory bodies tasked with safeguarding the country’s streets have “documented multiple instances in which AVs drove directly into active emergency scenes, blocked the paths of ambulances and firefighters, or failed to recognize and respond to basic safety conditions like flashing lights, flares, smoke, fire, and traffic cones.”The letter called the AVs’ inability to navigate those situations a “functional insufficiency” that the agency expects to see progress on soon. The NHTSA says it will schedule meetings with AV system developers “by month’s end” to hear about how they plan to fix this problem. “Let me be clear: the inability to detect and appropriately respond to such situations represents a functional insufficiency,” NHTSA Administrator Jonathan Morrison said. “Emergency scenes are not rare or extreme ‘edge cases.’ As such, NHTSA is today issuing a call to action for AV developers and operators to immediately focus their resources on fixing this issue. Waymo did not immediately respond to a request for comment.

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U.S. Senators question Tesla FSD safety dataLast month, Reuters reported that Tesla was exaggerating its safety claims for FSD and that it is using a team of “data labelers” to help the AI that powers FSD be better.This revelation suggests that Elon Musk’s declaration that FSD is already up to 10 times safer than human drivers and ready for more widespread adoption is hollow.So Senators Edward Markey (D-MA) and Richard Blumenthal (D-CT) sent a letter to the National Highway Traffic Administration saying the Reuters report exposes “dangerous gaps” in its autonomous vehicle data collection.“Tesla has repeatedly told investors, consumers, and the public that FSD is far safer than human driving, but the data analysis justifying those claims is weak and misleading. These representations are not merely marketing claims; they may shape how drivers use Tesla’s FSD, how the public understands the risks of the technology, and how regulators evaluate potential safety defects,” the letter stated.According to the letter, Tesla’s data to come up with the “10 times safer” is flawed for several reasons, including:Comparing unlike crash outcomes that made Tesla look better.Comparing newer Tesla vehicles to the entire U.S. vehicle fleet.Counting FSD involved crashes only if it is active at the time of crash or within five seconds. The NHTSA uses a 30-second time threshold for all ADAS systems.Relying on incomplete automated telemetry.Tesla is cooking the books, according to the Senators and the NHTSA has not been able to get the real data it needs, which makes the whole situation more dangerous for drivers.“The push to allow more autonomous vehicles on public roads depends heavily on the claim that these driving systems are safer than human drivers,” the letter stated.“To the extent that Tesla or other vehicle manufacturers are misleading the public about their safety data, however, consumers may choose to purchase or ride in an AV based on the unproven expectation that they are safer than non-autonomous vehicles. This type of information asymmetry is a classic market failure, which will likely result in more AVs on the road — and potentially more traffic injuries and fatalities if those systems are not in fact as safe as they claimed.”Currently, the NHTSA does not require vehicle manufacturers to submit data on the number of vehicles they operate, the distances they travel, and other data that could help contextualize crash rates.They say that is the type of data that “would help prove or disprove Tesla’s safety claims.”So the Senators are asking the agency to “significantly expand autonomous vehicle data reporting requirements.”Related: Waymo outages disrupt traffic in this major city yet again

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