It was fitting that, just hours after the first public screenings of the Elizabeth Holmes documentary began last Thursday, Elon Musk put on a show of his own.

That night, in Texas, Musk unveiled the company’s new Roadster prototype, a car that would defy gravity. Tesla built what was roughly one-half of a roller-coaster loop, which the Roadster climbed before momentarily hanging upside down and then executing a controlled flip and gliding down to Earth. The demo worked. In another stunt, it did a barrel roll over a semi-truck. Each maneuver was powered by air thrusters that guided the car to a soft landing.

The demo was impressive. But watching the clips, I could feel my brain struggling to process what I was being shown. Was this real? The car did climb vertically, flip, and land. But Musk, who has built wildly successful companies, is also famously prone to outlandish claims that don’t always pan out. I couldn’t help but wonder what, if anything, Tesla’s demonstration or Musk had elided. “This will be an actual car that’s on the roads,” Musk said. He got the flying-car headlines he wanted, but will this car actually be released? There are already reasons to doubt it: The car displayed on Thursday won’t be in production until at least 2028, and it features 10 thrusters powered by 600 liters of compressed air. The flying version of the Roadster will be sold “invite-only.” Its price tag is likely to be astronomical. And so Musk isn’t exactly lying here. Rather, he’s making bold proclamations and asking people to take his pledge to deliver as an article of faith.

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Less than 48 hours later, I found myself sitting in a crowded theater, watching the new documentary You Can See Everything, about the disgraced and convicted founder of Theranos, Elizabeth Holmes, and her husband, Billy Evans. In one scene, Holmes and Evans are hurriedly trying to court investors and research talent to build out what they refer to as Theranos 2.0. This scene is arguably the only time in the film where the duo don’t appear to be filled with existential dread. During phone calls and Zoom meetings, they spit out jargon and look earnestly excited about what they can build if they “get the band back together.” At the end of the sequence, Holmes is asked about why she’s spending her last days with her family building a company, and she responds that this is what she’s meant to be doing here on Earth. It is one of the few moments where I confidently believe her. Whatever she earnestly believes about her own guilt, it seems clear that her identity is inextricably tied to her notion that she is, in Silicon Valley parlance, a builder: a high-agency, visionary type with a remarkable risk tolerance who makes things and isn’t afraid to fail, because she has a monomaniacal obsession with pushing the bounds of what’s possible.

The film, which had the biggest opening for a documentary in more than 20 years, is extremely well suited to a post-truth era. The co-directors, Nathan Fielder and Lance Oppenheim, bury the audience with unreliable narration such that, when you get to the end, you feel like a participant in the film. It also left me exhilarated, slightly repulsed, and feeling as if I was going insane. In that way, the documentary replicates what it’s like to navigate the informational hall of mirrors we all inhabit. But what I found most bracing about the film—more so than the supposed unknowability of anyone or anything—is how Holmes’s act and affect do not feel like an aberration in 2026. In fact, her behavior embodies the current ethos of Silicon Valley.

When she built Theranos, Holmes was a college dropout with boundless ambitions who styled herself after Steve Jobs. Jobs famously said things such as “People don’t know what they want until you show it to them,” and Holmes, like Jobs, projected the aura of a person ready to pull the future into the present by any means necessary. Jobs routinely delivered on these promises, and kept many projects secret until they were ready for the masses. Holmes did the opposite, offering a fantastical version of a product that did not exist, obscuring the actual capabilities and lying about the financials. She wasn’t alone; over time, the idea of the builder became more synonymous with a founder’s ideas, charisma, and salesmanship than with the products themselves. “I watched countless founders of hard tech companies present fraudulent products at conferences,” Nikita Bier, a tech entrepreneur who was recently Elon Musk’s head of product at X, wrote on the platform this weekend in response to the documentary. “They had fake studies with fudged numbers. Most of the adults in the room excused it as ‘faking it ‘til you make it.’”

Holmes’s high-profile failure and the collapse of Theranos in 2018 didn’t kill the builder archetype. If anything, the very structure of Silicon Valley became more deeply intertwined with the builder mindset. Fake it ’til you make it (provided it isn’t outright fraud) is a necessary outcome of an industry that’s funded by venture capitalists, who often make strategic bets on projects so early in development that the products are still hypothetical. And so VCs threw unfathomable money at promising but half-baked ideas in the hopes of fabulous payouts. Failures were overlooked, even celebrated as necessary or teachable moments—honorable proof of being a “man in the arena.” In 2022, after Sam Bankman-Fried’s crypto empire was revealed to be an elaborate Ponzi scheme, the writer John Ganz argued that, in an industry built on big bets, the line between frauds and visionaries becomes difficult to suss out until everything implodes. “This is the Fyre Festival era of capitalism: outlandish promises and then a ludicrous debacle.”

Much of that rush to invest has been driven by a culture progressively gripped by the fear of missing out on the next industry-shaking payout. Untold billions were poured into crypto, then NFTs, Web3, and the ill-fated metaverse. In crypto, rug-pulling scams and Ponzi schemes abounded; its boosters said things such as “Have fun staying poor.” Today we’re living through what many suggest could be an AI bubble, and the same mentality persists. Have fun staying poor has been replaced with the idea of a “permanent underclass” of people who failed to get in early and now stand to lose their economic viability, and potentially their human agency altogether. Like Theranos, crypto, and NFTs, the AI industry is defined by claims of its inevitability and its future power (to be certain, chatbots and coding agents are real and powerful in ways the other technologies were not). In this way, AI is a faith-based technology; one’s desire to work on it, bet the success of the global economy on it, love it, or fear its End Times powers is typically predicated on what one can imagine it might become.

The past eight years have warped the tech industry to the point that it can feel untethered from the constraints of reality. You can see it in headlines everywhere if you’re looking. On Saturday, an Anthropic co-founder, Jack Clark, suggested that the U.S. GDP could jump in the next four years from about $30 trillion to $44 trillion thanks to rapid AI-driven progress in many industries. This would be one of the largest periods of growth in U.S. history, a prediction based on nothing more than the most “extreme parameter setting” of an Anthropic economic model the company released this fall. You can see it in the IPO prospectuses for a company like SpaceX, which includes in its ambitions the goal to “extend the light of consciousness to the stars” and initiatives such as asteroid mining and space tourism. And earlier this month, Musk noted that “this will sound super crazy, but I see a path to SpaceX being worth orders of magnitude more than the current Earth economy.” You could call all these claims tech optimism or, as the famed venture capitalist Marc Andreessen wrote in his 2023 manifesto, necessary for the “glory of human ambition and achievement, the spearhead of progress, and the realization of our potential.” You could also call it speculative fiction.

About halfway through the film, Holmes heads to prison and the documentary shifts to focus on Billy Evans, the man with whom Holmes has two children. With Holmes in prison, Evans has become her unreliable narrator. He is, as others have noted, also a helpful lens to understand the transformation of Silicon Valley. Evans has seemingly embraced Musk and Andreessen’s brand of vice signaling. We see him shooting guns, extolling Charlie Kirk, and lashing out at people on X under the cloak of anonymity (posting as Holmes, using her X account). He makes grandiose claims about his new company but seems embittered and incapable of submitting to hard questions about it. The veneer of excitement that the couple project in the Theranos 2.0 montage is replaced by a paranoia and resentment that people, including possibly Fielder, might be trying to take him down.

It all made me wonder how Silicon Valley might respond to Theranos’s collapse if it were happening today. Reactionary types in the industry might rally around Holmes, insisting that she was too early or needed more time. They might attack the press more forcefully for trying to bring her down (Holmes and Theranos did try to retaliate against whistleblowers and the reporter who broke the story). Trust in traditional media is even lower now than it was back then, and Silicon Valley has a robust ecosystem of very popular, industry-friendly podcasts and creators who could champion Holmes’s narrative. Evans, for instance, appeared last week on the popular All-In podcast. X is now owned by Musk. The truth takes longer to come out; in some cases, it might feel almost irrelevant. The universal outrage that Theranos sparked in 2015, when news first emerged that something was deeply wrong, sounds quaint now.

“When people ask how she got so far, it’s largely explained by the period she did it in,” Bier wrote in his X post about Holmes. “And she got caught when the world suddenly woke up.” I agree with the first part of Bier’s statement and take issue with the second. The brand of delusion that defined Holmes and the twisted epistemological game that You Can See Everything showcases is still deeply embedded in the industry. None of this is to suggest that everyone in Silicon Valley is engaged in rampant, Theranos-like provable fraud. But today’s entrepreneurs are emboldened to make even more outlandish claims about the future, and it’s more difficult than ever to parse what is real, what is knowingly fake, and what is being offered in good or bad faith.

In response to Bier’s post, Jason Calacanis, an investor and a co-host of All-In, noted that there was “much truth” to Bier’s explanation before he offered a rebuttal befitting a true builder—AI will soon solve the Theranos problem: “In the next ten years, a blood testing product will emerge that will make folks go, ‘See, @ElizabethHolmes was just 20 years too early!’”

The builder is never wrong. If anything, they are just early. The future is coming. Just have faith.

*Illustration sources: Justin Sullivan / Getty; Liesa Johannssen-Koppitz / Bloomberg / Getty; Tayfun Coskun / Anadolu / Getty; USA Today / Reuters.

Close your eyes, and imagine a frictionless day.

You wake up to a notification that a grocery-store employee will deliver all of the ingredients for the three recipes you saved last night to your “Weeknight Dinner” folder. While you brew your coffee, a program browses online retailers to compare toilet-paper prices, and another solicits competitive bids for Sheetrock work you need done in the garage. During your commute, a bot moves your upcoming dentist appointment to avoid a scheduling conflict. On your lunch break, you get an alert that computers acting on your behalf noticed some unclaimed funds and class-action payouts in a state database and claimed them. Also, the machine has negotiated a cheaper internet plan. By the time you get home, the groceries are on your doorstep, and you’ve gotten back $150.

This is the future that OpenAI, Meta, xAI, Google, and the rest are selling with a spate of always-on AI assistants that work for you. “Imagine every person on earth with a second mind beyond their own,” Meta’s chief AI officer, Alexandr Wang, wrote of the company’s new AI helper, Muse. A few weeks after Muse’s launch, OpenAI launched its competing product, Dots, which Sam Altman claimed would let you “get more of your time and attention back.”

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These AI agents promise to bulldoze the inefficiencies of daily life. Think about all of the opportunities you’re currently leaving on the table: You don’t change cell-service providers for a flash sale because of the paperwork involved. You haven’t fiddled with your 401(k) investments in years. You have kids to pick up, a date to dress for, sleep to catch up on. But your AI agents are different—they’re relentless, tireless, and shameless. They work in the background, without supervision, eagerly helping with all of these tasks and a bunch you didn’t even ask for. They use every tactic and loophole at their disposal to get you the best price and the highest-quality service because they’re able to pay attention to everything at once.

The AI companies see a massive opportunity for hyper-personalized agents to become a new intermediary layer for the internet and the physical world. This future is already unfolding: People are giving Muse access to their phone number, email, credit cards, and bank accounts to make dinner reservations, contact customer service, and book vacations. Mark Zuckerberg says that he is using Muse to coach his martial-arts form and help him bake with his daughter (he isn’t very familiar with baking, being “normally into smoking meats”).

Silicon Valley’s vision of boundless efficiency is shortsighted, or at least incomplete. Near the end of his blog post about Muse, Wang writes that “we’ve never seen humanity with every single person’s agency fully switched on.” He paints a utopian vision of laptop-class optimization. But such frictionless commerce, if it ever does come to pass, could bring about something else entirely: chaos.

The era of the agentic web started less than 24 months ago. Anthropic’s Claude Code, introduced in early 2025, allowed agents to write software and use other programs on your laptop. OpenClaw, launched later that same year, could navigate the web on your behalf. In the spring of 2026, Instinct, an invite-only app that let users text requests to a bot over iMessage or WhatsApp, began capturing the imagination of Silicon Valley insiders. It could book museum tickets, purchase gifts, and find in-network doctors.  

But it wasn’t until the launch of Meta’s Muse in early September that personalized agents hit the mainstream. Muse has been downloaded more than 5 million times in the past month and is especially popular among early adopters—entrepreneurs, investors, software developers. These users are now reporting back on their experiences and offering previews of our potential agentic future, which sounds both strange and jagged.

When we reached out to Rajan Lukose, a start-up founder in the Bay Area, to discuss his use of AI assistants, he had his Muse set up a calendar invite. In the late 1990s and 2000s, Lukose worked for corporate research-and-development teams in Silicon Valley and did early research on web agents. Now, he told us, “they’re actually, finally becoming sort of real.” Lukose has been using Meta’s program for “annoying things that I would never do”: tracking travel sites over time to book the cheapest flights, submitting receipts for DoorDash credits, sorting out problems with a magazine subscription. “That’s something that’s a real pain in the ass that just gets done,” he said.

Lukose said that the primary upside of using an agent is clawing back time and money. A few years ago, for instance, Lukose installed an automated tool that went through his Gmail, looked at various transactions, and filed claims under price-purchase guarantees. “That was like $600 that I just would not have gotten myself,” he said. The agentic era promises to deliver those kinds of savings to everyone.

Decades ago, the internet was supposed to create perfect markets, with users able to compare the cost and quality of products and services from anywhere in the world. But it failed: People continued to make lots of decisions based on habit. That, in turn, is good for business. “A lot of things online are premised on limited human attention,” Andrey Fradkin, an economist at Boston University, told us. Digital advertisements, recommendation algorithms, brand reputation, and celebrity endorsements steered consumers toward making suboptimal purchases.

AI agents are now here to finally fulfill the promise of perfect commerce. “The agent knows your preferences; they can go to every single platform, find the best products, the best deal for you, and then help you make that purchase,” Fradkin said. The agent won’t be fooled by sponsored posts or give up on shopping when they get distracted or hungry. “The current attention-based economy—if you gain the eyeball, you have a better chance to sell certain goods—that may change,” Ken Huang, who studies agentic AI at the University of San Francisco and runs an AI consultancy, told us. The bots are impervious to human-targeted nudges. Their attention is infinite.

In the short term, however, this automated quest for efficiency is creating some unsettling dynamics. Over the past month, X, Reddit, and other forums have been overrun with examples of agents haggling customer-service representatives and contractors. Take the case of the Muse user Luke Whiting, who sent his agent to check on a prescription at Walgreens. Whiting, who works for an open-source-database company in San Francisco, told us that the pharmacist at the other end of the call refused to speak to an AI system. His Muse suggested that the best way forward would be for Whiting to abandon using the agent altogether: “Honest take: other stores might say the same thing,” it said. Having the Muse call other pharmacies “could just burn time. The fastest path is probably you calling yourself.”

On a Subreddit for call-center employees, a recent thread is titled, in all caps, “I AM NOT AI.” “I have been asked 3 times this week ‘are you human?’” one person replied. Other users who identify themselves as call-center workers discuss their frustrations. One says that when customers question whether they’re talking to AI, they like to respond, “I’m a real boy,” like Pinocchio. Another, fed up with rude callers, vented, “Customers deserve ai.”  

Companies, meanwhile, are starting to tap AI systems to squeeze more out of their customers. According to Fradkin, many hospitals don’t charge as much as they could, simply because medical-billing codes are countless and labyrinthine. But using AI to document and code patient visits reliably allows hospitals to charge for more, and more expensive, services. That likely contributed to patient costs increasing by $650 million across Blue Cross Blue Shield from 2023 to 2025. And that’s only the beginning; health payments are projected to rise another 9 percent next year, largely because of AI billing. McDonald’s is reportedly using AI to analyze millions of consumer transactions and adjust prices at each location, for each menu item, based on local demand. (McDonald’s has said that its pricing portal is “a tool, not a mandate” to help franchisees.) It’s not hard to imagine vendors using AI to develop personalized, on-demand inventory management and dynamic pricing to maximize profits for streaming subscriptions, sports tickets, drug prescriptions, consumer electronics, insurance policies, and loans.

This optimization assumes that the agents work as intended. So far, there have been plenty of stumbles: In a paper published earlier this year, a group of AI researchers found that agents, given assistant-style tasks in a virtual environment, leaked Social Security numbers and were easily hijacked by other people. Agents powered by Chinese models refused to complete tasks that involved politically sensitive topics. Two weeks ago, a tech executive’s AI agent randomly posted his financial information in a workplace chatroom for employees to see.

Things could get weirder still: Tech companies market their agents as perfectly rational, when, in actuality, they can be extremely erratic and unpredictable. Sophia Kazinnik, who studies AI agents at Stanford, found some models to be overly generous and others to be exceedingly greedy. Across the board, they were far less patient than humans. When two personal AI agents are haggling over prices in the wild, the outcomes might not be maximally profitable. And there’s no assurance that all future agents will be created equal: More powerful models may have an upper hand in negotiations.

Even if the agents are able to effectively shop and negotiate for us, important rules governing the privacy rights, liability, and economic allegiance associated with these tools will have to be codified. Does your Muse have a fiduciary responsibility to you, or a legal responsibility to Meta? If Muse pays for a $2,000 espresso machine instead of a $200 one, can you be refunded? If Muse leaks your account and routing numbers, is Chase required to refund any fraudulent payments?

At some point, everyone’s agents could reach parity. “Just like today, every business has a website and social-media accounts and email addresses and all these things, every business is going to have agents too,” Zuckerberg said in an interview this week.

In this future, communication and commerce function almost entirely via computer proxies. Your agent calls a fancy restaurant for a dinner reservation, and a digital maître d’ picks up. Your agent and your insurer’s agent and your hospital’s agent all negotiate the price for your recent appointment. Prices fall, buyers and sellers are better matched, and businesses are incentivized to deliver higher-quality, customized services and products. This is “what economists dream about,” Fradkin said.

But friction can serve a purpose for businesses. Banks make great sums when customers leave money in low-interest checking or savings accounts, and studies show that sizable portions of retailer subscription revenue are driven purely by “inertia,” another way of saying that people forget to cancel. (Friction can help consumers too: Inattention and incomplete information at times lead landlords, pharmacies, and other businesses to undercharge.) Our entire economy and basically every company are structured around the notion that humans have mental and physical limitations. Without them, existing market structures could collapse.

“There’s this strange protection provided by the inefficiencies in our economy,” Rory Van Loo, a lawyer and a professor at the Wharton School, told us recently. In 2019, well before the rise of large language models, Van Loo envisioned a phenomenon he dubbed “digital market perfection.” Imagine everyone has a digital agent that is always looking for a lower price or a newer, better service. In this world, Van Loo argues, agents could, all at once, direct millions of consumers toward a new product or service—a process he calls “hyperswitching.” That would in theory be good news for consumers, but it would be sheer hell for companies. The nightmare scenario that Van Loo and others have conjured is a style of agentic bank run, where similarly attentive agents all pull money from a bank at once to get their clients better interest rates elsewhere.

Maybe this sounds good or exciting to you—a kind of consumer’s revenge—but in practice, stable commercial markets would begin to look and function like the stock market. “If, all of a sudden, people could purchase whatever they wanted at the lowest price with the perfect ability to see every possible option, there’d be a lot of volatility in the world. It would seem highly efficient from an economic standpoint, but it could have devastating effects,” Van Loo said. Picture 1 million personal agents all switching their consumers to a new brand of paper towels on Amazon because a markdown made them three cents cheaper. The same Sweetgreen salad is $18 one week and $38 the next. Your electric bill is $150 one month and $450 the month after, and you’re uncertain with each payment whether your local utility is about to go bankrupt.

“Stocks are bought and sold fast; markets change and plummet—such that we have a stop button for it, because it can go off the rails,” Van Loo noted. “But we don’t have those kinds of protections in most other markets.” There’s potential for collateral damage everywhere. If millions of people switching to higher-yield accounts causes a bank run, that’s a disaster for any person or institutional investor with money locked up at that bank. If half of Verizon’s customers switch to AT&T in one week to take advantage of a flash sale, the business could collapse overnight, affecting the other half of its customers. Sometimes, this rapid switching could signal healthy competition. Other times, it could cause organizations to implode.

A world where AI proxies do the planning and talking could also mean a future where humans are constantly surprised by things they once were able to anticipate: a birthday present for your spouse ordered in a preposterous size that you must try to explain; a plumber showing up out of the blue.

Or perhaps none of this will happen. It’s possible that an assistant-dominated economy turns out to be far more mundane, defined by tacked-on fees to keep the agents in check. Instead of agents allowing you to negotiate and bring down health-care, insurance, and electrical bills, the companies responsible for all of these services charge your agents every time they request to pull your data. Banks, to bolster their revenues in the event of a bank run, institute new and higher fees on checking accounts. Amazon, unable to profit from dynamic pricing and ads in the face of perfectly rational agents, raises prices for consumers and takes a bigger cut from manufacturers. “Generally, if one fee goes away, you get another,” Fradkin said. In other words, everything could be faster, easier, and more efficient for the consumer—but potentially more expensive.

Van Loo said that the bigger AI companies will eventually find ways to strike deals with platforms via these protocols, negotiating access, commissions, and partnerships in ways that benefit the companies, not necessarily the consumers. This could result in a sort of preferential treatment like the deals already in place between Meta and Walmart, Sephora, and other retailers, which will effectively cause Muse to prioritize their products. Taking another approach, Amazon has blocked Muse and encourages visitors to browse with its own agent, “Alexa for Shopping,” meaning the company is basically negotiating on both sides of the deal. Meanwhile, this week, JPMorgan announced that it would not allow Instinct, Muse, or other agents into customer accounts. Ultimately, this might all lead to “the same old heavily obstructed, inefficient world we have now,” Van Loo said. “But it will look far more efficient, because it will save some people time and feel much more convenient.” Very powerful companies will design new structures for markets and the internet, determining what can be sold, by whom, and for how much—as they always have.

For all the time and money the agentic future could save us, the technology entails another sort of cost: an additional layer of abstraction and disengagement. We’ve largely outsourced our decision making to a small number of platforms: Amazon guides our shopping; Google Maps steers us toward certain restaurants and primary-care providers; Instagram dictates our news consumption. We are told that these recommendations and ads are, based on troves of personal data, already optimal. The companies pushing agents assure us that the decision making will now be even more rational and the deals even better. But checking these claims for ourselves will be harder, because the logic behind those recommendations will be buried in large-language-model chains of reasoning. Eventually, the skeptical instinct to check agents’ work could atrophy altogether. Over time, humans will play a smaller and smaller role in their own life.

In the age of the algorithm, it’s easy to throw out cliché terms such as late-stage capitalism in an attempt to describe the extractive, inhumane dynamics of the economy. But what is an agentic future if not the furthest extension of our current market logic? A future of capitalism so perfect, so efficient, that it doesn’t need us at all.