The game of musical chairs for AI executives continues. Barret Zoph, a co-founder of the AI startup Thinking Machines who left the company earlier this year to rejoin OpenAI, has found yet another job.
Zoph spent two years at OpenAI and left in October 2024 to co-found Thinking Machines with Mira Murati, who had left the AI lab the month prior. In January, Zoph and another Thinking Machines co-founder, Luke Metz, quite dramatically departed from the startup to return to OpenAI. It was later revealed that Zoph had been fired.
His return didn’t quite stick. Zoph spent only five months at OpenAI, where he was tasked with heading AI enterprise sales. He left the company in June. And now we know where he landed.
Zoph has taken a position as vice president of research at Google (which happens to be another company where he previously worked). “We look forward to Barret returning to Google and bringing his RL and post-training expertise to Gemini,” a Google spokesperson told the Wall Street Journal.
TechCrunch reached out to OpenAI and Google for more information.
It’s not always easy to divine why tech executives seem to be spending less time in their roles. The turnover rate in the AI industry is high, and it’s been especially high at OpenAI — a company that, despite readying itself for an IPO and being one of the most powerful presences in the tech world, has lost a lot of critical staff over the last eight months. The turnover of high-level executives — from the departure of its COO to the recent loss of one of its top data center execs — has left onlookers scratching their heads.
This story has been updated to include the fact that Zoph was fired from Thinking Machines.
Over a hundred tech companies — including OpenAI, Anthropic, Google, and Microsoft — have signed an open letter urging both the private and public sectors to work together to defend themselves from AI-related cyber threats.
The letter — which was also signed by prominent cyber firms like CrowdStrike, Okta, and Fortinet, as well as prominent financial institutions and internet infrastructure firms — calls for the adoption of new forms of cyber defense, while also encouraging governments at the “local, national, and international levels” to collaborate on security.
“In the coming months, AI-enabled cyber attacks will become far more widespread and sophisticated as models around the world become increasingly capable,” the letter states. “The companies and public services our communities depend on — from hospitals to water treatment plants to the infrastructure that powers the internet — are at risk.”
The problem AI poses to traditional cybersecurity defenses has been thrust into the spotlight lately by a string of bizarre incidents in which AI agents have attacked companies.
The Hugging Face incident — in which one of OpenAI’s agents autonomously broke out of its sandboxed environment and attacked the tech company — has been followed by a trail of other reported break-ins involving agents developed by other AI companies, including Anthropic and Meta. These incidents have bolstered the argument that the field of cybersecurity has been fundamentally altered and that bold new commercial solutions are necessary to mitigate them.
The letter further suggests the mobilization of a “collective response,” one in which “new partnerships” are formed “to raise security standards and find new solutions to emerging cyber threats.”
Several of the AI companies that have signed the letter are still actively developing ever more advanced AI models, highlighting their conflicted position. At the same time, they are also offering programs to use frontier AI models for defensive purposes, including OpenAI’s Daybreak program, Anthropic’s Mythos, and Microsoft’s new cyber platform Perception.
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Frontier AI models have converged within 5% of each other (Stanford 2026 AI Index). Gartner predicts 40%+ of agentic AI projects will be canceled by end of 2027. Oxylabs SVP Gediminas Rickevičius argues the differentiator has shifted from model selection to data infrastructure: web indexes built for agents (structured content, not blue links) and real-time access layers for dynamic information. McKinsey finds 88% of organizations use AI but only 6% are high performers.
In 2026, the way we talk about AI is beginning to change. Two years ago, every boardroom argument circled the same question: which model do we bet on? Today, that question barely registers. Frontier systems have converged so tightly that, according to Stanford’s 2026 AI Index, leading models gained roughly 30 percentage points in a single year on key benchmarks and now cluster within a hair’s breadth of each other on most tasks. The model is no longer the variable; something else is.
That something is data, specifically, what the model sees, when it sees it, and how well it is structured. What makes the difference now is more fundamental: the quality, freshness, and structural depth of the information a model receives. Organizations are running into two walls at once. One is hit by the AI agents, they continue to produce confident errors. The other is long-term and more foundational, finding fresh information requires a new generation of search infrastructure. Both walls lead back to the same foundation: data.
Agentic AI – systems that can plan, search for data, use tools, and execute multi-step tasks with limited human oversight – is being deployed across competitive monitoring, pricing intelligence, market research, procurement, and lead qualification. Sadly, agents often fail at most of these.
Gartner predicts that over 40% of agentic AI projects will be canceled by the end of 2027, citing escalating costs, unclear business value, and inadequate risk controls. A 2025 MIT NANDA study put it even more starkly: roughly 95% of generative AI pilots have failed to deliver measurable returns, with brittle workflows and a lack of contextual learning cited as core reasons.
The instinct, when an agent produces a confident error, is to blame the model – a flaw in reasoning, a hallucination. In reality, the failure can usually be traced to something much simpler: the agent was working with outdated or incomplete information. Sound logic – stale facts, to put it plainly. An agent assessing a competitor’s pricing strategy is limited by whether it can access current and geographically accurate pricing, not by whether it can reason about it. A fleet of such agents across business-critical functions is a structural liability no amount of prompt engineering can fix.
There is a persistent tendency in enterprise AI budgeting to treat data acquisition as overhead, something that lives below the line and, once handed to IT, shouldn’t surface again in strategy conversations. This is no longer tenable. The industry has even coined a term for the discipline of fixing it: context engineering, which, in late 2025, Anthropic defined as the practice of curating the optimal set of information available to a model at inference time.
For organizations whose AI depends on web data, product listings, financial disclosures, news, job postings, regulatory updates, competitor activity, the raw information is out there. The difficulty is in accessing it consistently. Dynamic content rendering, inconsistent data formats, and other common website features become obstacles when you need to extract public data quickly and at scale. Organizations that underinvest in solving them let convenience determine what their AI knows about the world.
To feed an agent fresh information, you need a web index: the organized database search engines consult instead of the live internet. It functions like a library catalog, allowing systems to locate information across billions of pages in milliseconds without reading every “book” from scratch.
The process is continuous: crawlers discover pages, parsers strip away noise like ads and code, and the system stores clean content with quality signals for ranking. While traditional indexes provide links for humans to click, agents require structured data they can use immediately. Standard indexes force agents to fetch, clean, and summarize pages individually, adding delay and token costs. Agent-specific indexes skip this by storing ready-to-use content with verifiable sources, turning search into a reliable foundation for action rather than a list of blue links.
Where it gets interesting for anyone building with AI is that traditional search indexes were designed for people: a title, a link, a short snippet, enough to decide which blue link to click. An agent does not want a list of links. It wants the actual information, already pulled out and structured for a language model.
When an index only hands back links, the agent has to fetch each page, strip out the useful parts, and summarize them itself, which adds delay, drives up token costs, and creates more places to break. Indexes built for agents skip that scramble by storing content the model can use straight away, with source and date attached so every fact can be traced back. That last part matters, because showing where a claim came from is one of the more practical defenses against an agent confidently making something up. So, when someone says agents need better data, it partly means that agents need indexes built for them.
A good index gets you a long way, but even the best one is a snapshot. It reflects the web as of the last crawl, and for a lot of what agents do, that lag is the difference between a right answer and a useless one.
Model commoditization used to be a fringe prediction, but it is not anymore. McKinsey’s 2025 State of AI survey found that 88% of organizations now use AI in at least one function, yet only 6% qualify as high performers extracting meaningful enterprise-wide value. Adoption is no longer the constraint, whereas execution is. And a big part of execution is what the model is fed when it goes to work, which comes down to the infrastructure around it rather than the model itself.
Such infrastructure serves two core purposes: an index to store structured web content for AI models, and live access to reach real-time web data. Standard indexes miss immediate updates like dynamic pricing, regulatory changes, or new product launches, requiring agents to navigate obstacles such as dynamic content and varied formats. Consequently, competitive edge depends less on model selection and more on the breadth and immediacy of the data an AI can access.
Models matter, and building agents is genuinely demanding work. But as models become more capable and more similar, the question of where to direct the next dollar deserves more honest scrutiny than it usually gets. Deloitte’s 2026 State of AI in the Enterprise report says it plainly: companies moving from pilot to production are those investing in the underlying foundations, not just the visible AI layer.
More capable models on their own will not rescue an agentic deployment starved of fresh information. The organizations that capture durable value will be the ones that pair good models with two things working together: indexes built for agents, and live access to the current web for everything those indexes cannot have caught yet. An agent is only as good as the information it can reach at the moment it acts. Closing the distance between what agents can reason about and what they can actually see is what comes next for AI, and building the data infrastructure underneath is how we get there. That shift is already underway, and agentic search is about to change because of it.
The country is continuing to reel from multiple infectious disease outbreaks in America, even as childhood vaccination rates continue to fall. Measles, cyclosporiasis, whooping cough: you don’t actually need me to go on. With RFK Jr. using his position as Secretary of HHS to further muddy the waters on vaccines and their safety, some states are going even further than the federal government. Even as states like South Carolina have fielded proposals to end religious exemptions for public school inoculations, states like Florida have attempted to go the opposite direction and simply end all school vaccine requirements by law. While that attempt has so far failed, there are state laws that mandate schools accept religious exemptions for vaccines.
And not just public schools. This mandate is written to affect private religious schools as well and the irony here is thick. For example, Florida AG James Uthmeier wrote a letter to the Florida Conference of Catholic Bishops warning them that they might not qualify for state scholarships and vouchers any longer. Why? Because Florida Catholic schools don’t accept religious exemptions for vaccine requirements.
As you can read for yourself in Uthmeier’s post, his letter did far more than issue that warning. It also went into great detail in explaining to a group of Catholic Bishops why they don’t have any legitimate religious reason to refuse those exemptions. Uthmeier did correctly identify that there is irony in all of this, but his targeting computer is off. The actual irony is in a government official, an Attorney General no less, telling religious elders what their own religion says, all while blatantly violating the First Amendment.
Here’s the First Amendment’s opening text, in case you need it.
Congress shall make no law respecting an establishment of religion, or prohibiting the free exercise thereof; or abridging the freedom of speech, or of the press; or the right of the people peaceably to assemble, and to petition the Government for a redress of grievances.
If an AG can tell a religion what it means to express its faith, then there is no point to the opening of the First Amendment. They can’t. This is obvious.
But let it never be said that Florida Bishops aren’t a patient lot, I suppose. The FCCB took the time to respond to Uthmeier to demonstrate precisely their religious justification for refusing vaccination exemptions.
Florida’s Catholic schools do not accept religious exemptions for vaccines. In a letter to Attorney General James Uthmeier, made public last week, the Florida Conference of Catholic Bishops said Florida bishops don’t have to justify their position, but are doing so “out of respect for you and your office.”
The letter goes on to say that the position against religious exemptions for vaccines is rooted in the Catechism of the Catholic Church, which states that, “Life and physical health are precious gifts entrusted to us by God. We must take reasonable care of them, taking into account the needs of others and the common good.”
A better justification for vaccinations in general this writer could not come up with. And the debunking of Uthmeier’s misguided attempt to tell Catholic leaders what they should believe is fairly easy, as well. The AG’s letter makes much of the idea that today’s vaccines include the cells of presumably newly aborted fetuses and, since the Catholic Church is very much anti-abortion, that means that they should I guess be anti-vaccine as well. Which, if you’re following along, isn’t an argument that Catholic schools should accept religious exemptions for students, but rather an argument that no Catholic anywhere should get vaccinated at all.
Except he’s wrong. Vaccines produced today, and even going back decades and decades, don’t contain any cells from newly aborted fetuses.
Vaccines do not contain fetal cells. As the American Academy of Pediatrics explains, some vaccines involve growing viruses in human cell cultures originally developed from two aborted fetuses in the 1960s: “These cell lines are still going, so no new aborted fetuses are ever needed. Purification processes filter the vaccine during production, and no fetal tissue remains.”
The Catholic Church says it’s morally permissible to receive these vaccines and that people who choose to vaccinate their children can do so with a clear conscience because, as the Florida bishops note, “the parent is not cooperating with the original immoral act.”
Additionally, in 2021, Pope Francis told people to get the COVID-19 vaccine, calling it an “act of love,” even though fetal cell lines were used to develop both the Moderna and Pfizer shots.
Does AG Uthmeier really intend to argue with the Pope, the vicar of Christ to Catholics, about what is permissible under Catholic dogma and what is not? This from the same party that absolutely adores playing religious victim when it suits their own political desires, or when they want to encroach on American secular society.
If the Uthmeier had any shame, he’d immediately apologize for disrespecting Florida Catholics, Catholic leadership, and for wildly overreaching in his attempt to carry out his state government’s stupid attempt to make everyone sicker. But he won’t. At best he’ll slink away and say no more on the topic at all.
But if we’re really lucky, there will be a conflict that pits the Florida government against the Catholic Church with the latter crying foul over an encroachment on its own religious liberty. Were I of a praying sort, that is what I would pray for.
Filed Under: fccb, florida, james uthmeier, religious exemption, vaccines
There’s plenty of substances that can theoretically be extruded from a nozzle, but only a regrettably small subset of them can actually be used for 3D printing. One limiting factor is the liquid range: too high a melting point and it’s hard to reliably extrude, too low and it tends to ooze and flow once extruded. Embedded 3D printing offers a way around this: it submerges the entire print in a shear-thinning support gel which keeps liquids in place until they solidify. [Riley] of Riley’s Lab recently built such a 3D printer and used it to print in silicone and epoxy.
In place of the extruder, [Riley] mounted a mostly 3D printed syringe pump, which allowed him to squirt out almost any liquid. For a test, he printed a tardigrade model out of cream cheese. This was a good test material for several reasons: it’s cheap, easy to extrude, and holds it shape well after extrusion. Silicone and epoxy, however, won’t hold their shape, which is where the support bath comes it. This was a mixture of mineral and vegetable oil, with some fumed silica added to make it thick yet shear-thinning. This lets it contain the extruded liquid, yet flow as the extrusion needle slices through it.
For the first test, [Riley] printed a Benchy out of Sylgard 184. The outcome looked good in the bath, bar some stringing, but it seemed to have limited adhesion between layers, and disintegrated upon removal from the bath. A second test with a two-part epoxy worked much better; it also had some stringing, but it held together while the bath was washed away in isopropyl alcohol, and even seemed decently shock-resistant afterwards.
It’s great to see a hacker working on this technology; we’ve previously covered a commercial take on it, as well as some of the research that led to it.
Canada is recruiting 64 researchers to universities across the country (source paywalled; alternative source), including 48 from U.S. institutions such as Harvard, Yale, and MIT. The hires are backed by more than $364 million in government funding and are part of a broader effort to attract researchers in fields such as AI, climate science, and medicine. The New York Times reports: While scientists often shy away from political discourse, some are saying the Trump administration’s assault on science is behind their departure. “I used to live in the country that I thought was the most enthusiastic about the prospects for science improving the human condition, of any country in the world,” said Phillip Zamore, the chair of RNA Therapeutics Institute at the University of Massachusetts. “And I woke up one day and that wasn’t true anymore.” He has been recruited to McGill University in Montreal, which has also hired five other researchers, and will join the medical faculty. “If scientists don’t stand up for truth, no one will,” Dr. Zamore said.
The Canada-bound brain drain from U.S. institutions began last year as the Trump administration put forward policies that targeted foreign students, academic freedom and funding for equity-related programs. Kevin Hall, a nutrition scientist who left the U.S. National Institutes of Health last year, accusing federal officials of censoring his research on ultraprocessed foods, has been hired at the University of Ottawa. “While certain countries are cutting research and turning their back on academic freedom, we’re doubling down on science,” Melanie Joly, Canada’s industry minister, told reporters at the announcement, in Vancouver, of the new university hiring. She billed it as the world’s “largest talent attraction” project. The European Union has made a similar push. “Years from now, we will look back at today’s announcement, and we will be able to seize the lasting impact of our choices,” Ms. Joly said.
Unpredictable decisions about funding prompted Seth Guikema, a professor in civil and environmental engineering, who has specialized in natural hazards modeling at the University of Michigan, to look elsewhere. His work focuses on how climate hazards inequitably affect communities, and that work has become harder to fund, he said. “Every country sets its priorities in terms of what is going to get funded, and I think Canada has done a very good job of supporting research in areas that really matter to society,” said Dr. Guikema, who will start at Western University in London, Ontario in January.
Read more of this story at Slashdot.
Light a small spirit lamp of ethanol and heat finds one end of a sealed brass cylinder. Air inside expands, a piston moves, a flywheel gathers speed, and a cheap electric motor wired as a dynamo starts feeding current into a wooden box. An analog voltmeter on the front climbs. A 2.42-inch monochrome screen flickers to life. Sixteen hex keys wait for input. For as long as the flame holds, YouTuber PickentCode has a working computer.
In 1816, Robert Stirling patented a heat engine as a safer alternative to steam boilers, which had a bad history of blowing up. Two centuries later, the identical concept has been simplified and now sits on a desk. Pickencode took approximately a month to get the build started after designing the layout in Blender using a computer. The case is composed of wood and finished with a rich dark Victorian oil. A handmade blue-laser engraver was used to make a portrait of Stirling as well as a sketch of his original engine for the side panels. This allowed the image to be etched into the wood as a succession of tiny burnt dots, a brilliant approach that allows a photograph to be printed directly onto the wood. The housings and keycaps were created using a 3D printer, and old shower tubing painted gold hides the wiring while also adding a decorative touch to the frame with its ‘pipes’.
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The power output is insanely low, at 150 milliwatts, and is only useful if every watt is treated as if it were gold dust. To make the best use of this limited amount of power, the ESP32-C3 has been directed to turn down its radio and keep the screen brightness as low as possible. While the computer is rendering pixels or executing opcodes, the clock speed reduces to 80 MHz, then to 10 MHz when idle. Several tiny capacitors were added to smooth out the dynamo’s stuttering output. The panel has two toggle switches that control both the main circuit and the meter.
The software had to be designed with these limitations in mind. CHIP-8, a virtual machine from the 1970s, was an ideal match because it is built for a box with a sixteen-key keypad and a 64-by-32 monochrome display that scaled up nicely to a 128-by-64 OLED screen. The interpreter itself contains sixteen 8-bit registers, four kilobytes of RAM, a call stack, two timers, and a staggering thirty-four two-byte opcodes. Preloaded ROMs include Tetris, Snake, and Pong, and if you’re feeling daring, there’s also a built-in editor that allows you to type raw opcodes into the keypad and run them right away. One homebrew test draws random pixels and clears the screen after ten of them
This small computer’s “fuel” is merely ethanol in a spirit lamp. Heat one end of the cylinder while keeping the other cool to create rotation from a displacer that transfers hot and cold air around. A power piston then converts that into motion, which is stored in a flywheel and continues to function as long as there is a temperature difference. When the lamp is turned off, the computer shuts down. The main purpose of this exercise is that you can get some practical work out of 150 milliwatts, enough to play a game or even enter a few lines of machine code while watching the voltmeter tick away, all thanks to a flame from a spirit lamp performing the function of a wall wart. Stirling never saw a microcontroller, but would most certainly recognize his engine.
[Source]
YouTube announced on Thursday that eligible creators in the U.S. can now tag Amazon products in their content and receive a cut of sales. Creators can link Amazon products in their shorts, long-form videos, and livestreams.
The update turns product recommendations into a more direct revenue stream for creators, and for Amazon, the move puts its massive online marketplace inside one of the most popular video platforms.
Although YouTube already runs a Shopping affiliate program with participating retailers, the addition of Amazon’s vast catalog essentially allows creators to recommend a variety of products through a native integration. By bringing Amazon into the Shopping Affiliate Program, creators no longer need to paste Associates links in the video’s description, then hope viewers copy the link when they make a purchase.
Instead, YouTube says Amazon will provide it with a curated catalog of highly requested and trending products that creators can tag in their videos. If a creator can’t find a specific product that they want to tag, they can request to add it by reaching out to YouTube Support.
The feature also includes auto-tagging support. If enabled, YouTube’s systems can automatically review a creator’s recent uploads to identify and tag eligible Amazon products.
YouTube notes that creators won’t see breakdowns for specific products or individual videos in their analytics, but that they’ll see their overall daily earnings in YouTube Studio. If a viewer ends up returning an item, that commission will be deducted from the creator’s balance.
While only eligible U.S. creators can currently tag Amazon products, those tags can be seen globally. YouTube says it may automatically match a tagged product with a trusted local merchant offer to allow creators to earn commissions on eligible international purchases. If a local merchant isn’t available, the tag will route viewers to the Amazon U.S. website, where commission will be earned if a purchase is finalized on the U.S. site.
To be eligible, creators must be enrolled in YouTube’s Partner and Shopping Affiliate programs, have an active Amazon Influencer or Associates account, and link it to their YouTube channel.
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The deal could have been could have been one of the biggest leveraged buyouts ever.
Stripe and buyout firm Advent are no longer pursuing their acquisition of PayPal as was reported earlier this month, people familiar with the matter told Bloomberg. If it had gone through, the deal could have been could have been one of the biggest leveraged buyouts ever at well over $50 billion, according to the report.
In July this year when PayPal was trading at historic lows and valued at $40 billion, Stripe and Advent reportedly made an offer worth around $53 billion. Their suitor was said to have rejected it, so the Stripe group was reportedly preparing a higher bid. PayPal was worth as much as $320 billion at its highest market value during the height of the COVID pandemic.
Stripe and its partner likely saw PayPal’s recent struggles as an opportunity to buy it at a significant discount. However, the pioneering payment company’s latest quarter beat most estimates which (along with the takeover rumors), helped boost its stock price by 40 percent. That may in turn have raised the selling price, making a leveraged takeover less feasible.
PayPal’s new CEO Enrique Lores, appointed in March, has been working to turn the company around by dividing it into three units focused on checkout, Venmo and payment/crypto. A Stripe-PayPal merger could have reduced the former’s reliance on Visa and MasterCard, while allowing Stripe to incorporate PayPal’s Venmo checkout system and its crypto features. Stripe and Advent could still opt to make a bid for PayPal in the future, Bloomberg said.
Wired reports that OpenAI is testing a new “Persistent mode” for Codex that would let the agent keep working until explicitly “put to sleep,” proactively creating follow-up tasks for itself across sessions and using prior interactions and knowledge of the user to decide what to do next. Code alluding to the feature was spotted in the product’s code base, though it has not been rolled out or officially announced yet. From the report: Persistent mode appears in Codex’s “reasoning effort” menu, in which users can select the level of computing power, tokens, and time they want to allow for an AI model to “think” before answering a prompt. It seems to be one of OpenAI’s most computationally intensive settings. When users have selected Persistent mode, OpenAI’s code base reads that Codex will “continue working until put to sleep.” That’s a stark contrast to currently available modes, which will stop working on a task after a few minutes or hours, even if it’s not complete.
In another file in the code base, OpenAI describes a feature within Persistent mode called “proactivity.” This appears to be a type of system prompt for agents in Persistent mode, which are told that their work is not done when they finish answering a user’s request. Instead, the agent is instructed to proactively create follow-up tasks for itself. The agent is capable of working on those tasks across sessions and using past user interactions and “knowledge of the user” to decide what to work on. It also has a tool to message the user without being asked but is told to send these sparingly.
The instructions also set limits for the agent, according to the file. The agent is told that Persistent mode does not expand what it is allowed to do and that altering anything outside the user’s own system requires the user’s approval first — seemingly intended to limit how dangerous a persistent AI agent could be. The file sits in the shared core of Codex rather than in the code specific to the terminal, seeming to suggest the proactivity feature is intended for more than the command line tool.
Read more of this story at Slashdot.
AI is helping many of us call time on taking professional advice without further research, concerning new reports have found.
Research by Use.AI has claimed professional advice from doctors and teachers is now researched further, with nearly two-thirds (64%) of people are now more confident challenging what they have been told.
In addition, an “expertise recession” has been identified, a scenario in which expertise is less sought after in a world where AI can assimilate all information and present a contextual and nuanced response.
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The survey of 15,428 adults has also revealed other professionals that people are more likely to challenge. Managers, journalists, and economists are included in the list.
Use.AI’s findings also highlighted a key split in age groups, as while 70% of 18 to 34-year-olds are “more comfortable” asking questions that challenge professional advice after checking an AI chatbot response, that figure shrinks to 49% among over-45s.
Also, while 64% are “more confident challenging advice” from professionals, 53% of people are using AI to check answers and information from experts ahead of deciding if they can agree with it. Furthermore, nearly half (46%) of respondents stated that professional credentials don’t hold the same reputation as they once did, thanks to AI making it much easier to check an expert’s response.
This reveals an interesting change in how expert information is received. No longer is it something accepted at face value. Credentials might indicate a professional’s experience and knowledge, but don’t necessarily mean that everything they say should be accepted without further confirmation – typically by AI.
In summarizing the report, Use.AI has referred to something it calls the “expertise recession.” This is, it says, evidence of a “decline in automatic deference” to professionals with years of education, expertise, and experience.
“AI has democratized confidence faster than it has democratised knowledge,” said Ihor Herasymov, Co-Founder & CEO of Use.AI. “Someone can use AI to arrive at better questions without becoming qualified to judge the answers.”
Use.AI tell us that the “gap between confidence and competence” will continue to grow in importance as more people use AI for information searches and fact checks.
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