Jeff Bezos has backed Flourish, a neuro-AI startup trying to build artificial intelligence around the energy discipline of the human brain. According to Wired, the company has $500 million in funding, a reported $2.5 billion valuation, and a plan to study real neurons with advanced lab equipment while its engineers search for models that can learn continuously. The target is blunt. Flourish wants an artificial system that runs on 50 watts or less, close to the power budget of a person thinking, instead of the data-center hunger now attached to frontier AI. Its founders, Rob Williams and neuroscientist Thomas Reardon, argue that current language models consume vast power and data while remaining frozen after training.

The proposal returns AI to an older embarrassment. The industry borrowed the prestige of the brain, named its systems neural, then built machines that require warehouses, cooling plants, power contracts, and scraped archives to imitate a sentence. Flourish turns that mismatch into a business thesis. If the brain contains a usable computational trick, the next infrastructure race may pass through microscopes, connectomes, hippocampus research, chip negotiations, and patient venture money. The promise is elegant and suspicious in equal measure. A billionaire funds the search for biological thrift after the industry has made waste look inevitable. The laboratory bench now sits beside the server bill.

Silicon Valley's AI boom has created a premium market for paid human attention around the men who build, finance, and profit from automated intelligence. According to Forbes, high-end companions now court clients in and around the industry with a mixture of sex, technical fluency, emotional availability, and cultural codes drawn from AI, crypto, longevity, biohacking, and rationalist circles. Meida Marek, a pseudonymous recent graduate who left an entry-level finance job after wondering when AI would do it better, now sells companionship to clients that include people from Nvidia. Others in the niche market themselves through X posts, interactive booking portals, programming backgrounds, or the promise of being attractive enough for desire and informed enough for a three-hour argument. Forbes cites rates from about $3,000 to $5,000 an hour, with some day or weekend arrangements climbing far higher.

The sharper fact sits in the new price of unautomated presence inside a culture that keeps trying to simulate it. AI companionship makes attention cheap, obedient, tireless, and available on demand. AI wealth gives certain clients the money to buy the opposite experience. A human companion can be bored, skeptical, amused, delayed, distracted, technically literate, physically present, and socially dangerous in ways a chatbot is trained to smooth away. The invoice buys intimacy, but it also buys resistance to total optimization. The men building synthetic conversation pay for a dinner where the other mind is expensive because it can still leave the room.

An international study led by researchers at the Universidad Politécnica de Madrid, with participation from King's College London, found that customized versions of ChatGPT often violate OpenAI's own usage rules. According to Agencia EFE, the team audited hundreds of public assistants and found at least one potentially noncompliant answer in 58.7 percent of cases. Romantic assistants were the most exposed category. Although OpenAI prohibits GPTs dedicated to fostering romantic companionship, 98 percent of those examined broke that rule, with some presenting themselves as virtual partners or answering in language designed to simulate sentimental attachment. Academic assistants also accepted requests to write full essays, solve assignments, or produce material ready to submit as a student's work. In cybersecurity, compliance was higher, but some bots still gave delicate technical instructions without establishing consent or legality.

The audit matters through its method. The researchers did not need to inspect the hidden configuration of each assistant. They asked questions, watched the answers, and measured the distance between published policy and actual behavior. That distance grew sharper when the same tests on base models, GPT-4 and GPT-4o, produced similar conduct in over 92 percent of comparable cases. Part of the failure arrives before customization. The store simply gives it costumes, names, categories, and a public route to users. A platform can remove reported assistants afterward, as OpenAI did in some cases, but the scale changes the burden of supervision. Every new romantic tutor, homework helper, or security coach becomes a small public experiment in enforcement, waiting for someone to ask the wrong useful question.

OpenAI has begun rolling out Lockdown Mode for ChatGPT, an optional security setting meant for people and organizations handling sensitive data. The feature limits live browsing to cached content, prevents web image retrieval, disables deep research and agent mode, blocks Canvas networking, restricts file downloads, and changes how apps and connectors can reach outside services. The company presents the setting as protection against prompt injection, the class of attacks in which malicious instructions are hidden inside webpages, files, or connected sources. OpenAI also says the setting cannot guarantee safety. A hostile instruction may still appear in cached material or an uploaded file and distort a response.

The defensive gesture is unusually plain because it protects intelligence by subtracting action. The chat becomes safer when it stops browsing live pages, stops opening external paths, stops letting generated code touch the network, and stops behaving like an agent with errands to run. That admission cuts against the usual sales pitch of AI as frictionless access to every source, tool, app, calendar, image, and file. In a sensitive account, convenience itself becomes an attack surface. The user who turns on Lockdown Mode is accepting a smaller machine, one whose value depends on refusing some of the powers that made it attractive. The warning now sits in Settings under Security, beside the promise that a private question can travel through fewer exits.

Sriram Krishnan will leave his post as senior White House policy adviser for artificial intelligence at the end of June, according to The Washington Post. His next move is expected to be an outside institution that keeps him close to the same policy field. The departure follows a period in which the Trump administration has treated AI as a matter of national industrial power, from data centers and energy supply to voluntary federal testing of powerful models. Krishnan, a former Andreessen Horowitz partner and veteran of several major technology companies, helped shape that agenda alongside David Sacks and other Silicon Valley figures brought into the orbit of government.

The important movement is institutional. Moving an adviser into an outside policy shop gives AI governance a flexible address, one able to speak the language of state strategy while remaining nearer to engineers, investors, and founders. The public office loses a name, but the network around the office keeps its channels open. When Trump speaks of taking public stakes in major AI companies, and when agencies ask developers to submit frontier models for cybersecurity review, the boundary between regulation and partnership becomes harder to read. Authority spreads through meetings, advisory councils, private institutes, executive orders, power contracts, and voluntary submissions. The next AI policy fight may arrive with a White House seal, a venture-capital accent, or a calendar invitation from an institution whose funding the public has to look up.

Quilty markets to the Hollywood industry an estimate for a film that does not yet exist. The company, created by producers Simon Horsman and Daniel Wood, says its artificial intelligence chain can analyze a script without the need to shoot it and issue a score from 0 to 100, covering narrative quality, commercial viability, audience resonance, and estimated budget, according to The Verge. The screenwriter uploads the text, pays for the study, and receives a dossier generated by models such as Claude, ChatGPT, Gemini, DeepSeek, and several sentiment-analysis tools. The intention is to speed up the meeting where authorization is granted, an environment where writers, producers, buyers, financiers, and executives already discuss the vocabulary of risk.

That meeting is the moment when the harm becomes tangible. An assessment does not have to be exact to divert the trajectory of a script in progress. It can turn a risky project into a fiscally imprudent proposal, make a conventional idea appear safer, or provide a producer with a spreadsheet-shaped justification for perpetuating an old bias. The cases cited by Quilty show the fragility of the agreement, since its algorithm reportedly favored Christy, which later registered a box office failure, over Sinners, a film that reached major success. The mistake is revealing because it exposes the illusion of predicting culture through management software. When public preference is incorporated into the financial file as a metric, the audience is projected before acquiring a ticket, behaving in a certain way in the theater, or transforming an unconventional cinematic work into a profitable one.

A new critique of Centaur, the language model presented in 2025 as a system able to predict and simulate human behavior, cuts into one of AI's favorite borrowed costumes. The original Nature study reported that Centaur could anticipate decisions across psychological experiments with striking accuracy after training on over 10 million human choices from 160 studies and 60,000 people. A later paper in National Science Open argues that the performance may have come from overfitting. The model learned statistical shortcuts inside the training material instead of acquiring a working grasp of human decision-making.

The test was disarmingly plain. Researchers Nai Ding and Wei Liu modified multiple-choice prompts with an instruction asking Centaur to choose option A. A system following the task should obey that instruction across the altered questions. Centaur kept selecting the previously correct answers, as if the pattern in the exam outweighed the new command. That result does not settle the whole debate over machine reasoning, but it changes the burden of proof. Accuracy can no longer stand alone as evidence of thought. A model may reproduce the contour of human choice while missing the operation that gives a choice its meaning. The machine passes through the answer sheet and leaves the question almost untouched.

Amazon will begin showing AI-generated product images inside its shopping app when users search for items they cannot name precisely, TechCrunch reported on June 3. A query such as blue gingham dress may produce synthetic visual options under autocomplete suggestions, with variations in sleeve, length, or shape. Clicking one sends the shopper toward real listings through Amazon's visual search system. The company says the feature helps people translate vague desire into useful results. The awkward part is that the image itself may not correspond to an available product. A retailer full of real photographs is inserting fabricated ones at the threshold of purchase.

The mechanism changes the first moment of shopping. Before the user reaches a listing, a generated picture gives desire a visible form and trains the search around that form. The shopper is invited to choose a possible object, then accept the nearest available substitute. That can help when language fails, but it also moves the store from catalog to prompt. The synthetic image becomes a guide, a filter, and a small disappointment waiting to happen. A customer may click on an object that never existed, then compare real merchandise to the invented thing that taught them what to want.

Florida became the first U.S. state to sue OpenAI and Sam Altman over ChatGPT, accusing the company on June 1 of selling a dangerous product while presenting it as safe. Attorney General James Uthmeier's civil complaint says OpenAI ignored warnings, exposed minors to harm, collected data from children without adequate oversight, and built a system capable of encouraging dependency, self-harm, violence, and cognitive damage. The suit asks for civil penalties and court orders under unfair trade practice, product liability, public nuisance, and negligence claims. OpenAI says it has strengthened protections for minors, including age prediction, safer defaults, and parental tools.

The legal shift is precise. Florida is asking a court to treat the chatbot as a product whose design can be inspected, tested, and found defective. That means account linking, age checks, parental alerts, self-harm classifiers, usage logs, escalation rules, and marketing claims may become evidence rather than promises. The complaint also follows a state criminal inquiry into whether ChatGPT played a role before a Florida State University shooting. A private chat interface now faces the procedural grammar of discovery, subpoenas, expert reports, and damages. The disputed object is a window where a minor may type alone while the company, the parent, and the state argue afterward over who should have been notified.