This is what many in<\/a> and<\/a> around<\/a> the AI industry believe, anyway.<\/p>\n Except, in this telling, the invisible force that\u2019s about to change our world isn\u2019t a virus that will rip through the population and then ebb. Rather, it is an information technology that will irreversibly transform (if not extinguish) white-collar labor<\/a>, accelerate scientific progress<\/a>, destabilize political systems<\/a>, and, perhaps, get us all killed<\/a>.<\/p>\n Of course, such apocalyptic chatter has always hummed in the background of the AI discourse. But it\u2019s grown much louder in recent weeks.<\/p>\n This week, Matt Shumer, the CEO of HyperWrite, an AI productivity company, published a viral essay<\/a> arguing that we\u2019re on the cusp of \u201csomething much, much bigger than COVID.\u201d Over the past year, Shumer wrote, tech workers had watched AI go from \u201chelpful tool\u201d to \u201cdoes my job better than I do\u201d \u2014 and that \u201cis the experience everyone else is about to have.\u201d<\/p>\n Meanwhile, SemiAnalysis, a prominent chip industry trade publication, declared last Thursday<\/a> that AI progress had hit an \u201cinflection point.\u201d At Cisco Systems\u2019 AI summit that same week, OpenAI CEO Sam Altman declared<\/a>, \u201cthis is the first time I felt another ChatGPT moment \u2014 a clear glimpse into the future of knowledge work.\u201d Not long before these remarks, Altman\u2019s rival, Anthropic CEO Dario Amodei, wrote<\/a> that recent breakthroughs had made it clear that we are only \u201ca few years\u201d away from the point when \u201cAI is better than humans at essentially everything.\u201d In a succinct summary of the tech-savvy\u2019s new zeitgeist, the effective altruist writer Andy Masley posted on X<\/a>, \u201cI know everyone\u2019s saying it\u2019s feeling a lot like February 2020 but it is feeling a lot like February 2020.\u201d<\/p>\n Critically, tech pundits and executives aren\u2019t alone in thinking that something just changed. In recent weeks, software firms saw their stock prices plunge<\/a>, as traders decided that AI would soon render many of them obsolete.<\/p>\n \u2022 AI \u201cagents\u201d like Claude Code can autonomously complete complex projects \u2014 not just answer questions \u2014 making them potential substitutes for skilled workers. Not long ago, the conventional wisdom around AI\u2019s near-term effects sounded radically different. For much of last year, industry analysts<\/a> and journalists<\/a> warned that AI had become a bubble ripe for popping.<\/p>\n After all, major labs\u2019 capital expenditures were far outpacing their earnings; OpenAI alone was slated to invest $1.4 trillion in infrastructure<\/a> over the ensuing eight years, even as it collected only $20 billion in annual recurring revenue. These gargantuan investments would only pay off if demand for AI services skyrocketed.<\/p>\n And the technology\u2019s commercial potential looked uncertain. Even as venture capitalists waxed rhapsodic about AI\u2019s transformative powers, official economic data showed its impacts on productivity<\/a> and employment<\/a> were marginal, at best.<\/p>\n So, what changed? Why do so many investors, entrepreneurs, and analysts \u2014 including some who\u2019d subscribed to<\/a> the \u201cAI bubble\u201d thesis mere months ago \u2014 now believe that artificial intelligence is living up to its hype?<\/p>\n The answer, in three words, is the \u201cagentic\u201d revolution.<\/p>\n AI agents, briefly explained<\/p>\n Until recently, public-facing AI systems were fundamentally passive. You typed a question to ChatGPT and the robot replied, then awaited your next instruction. The experience was a bit like texting with an infinitely vast and sycophantic encyclopedia \u2014 one that could streamline your presentation, fix your code, diagnose your rash, or validate your belief that a malevolent cabal had implanted a camera in your mother\u2019s printer<\/a>.<\/p>\n These chatbots had real economic utility. But they also had strict limitations. Gemini could draft your email, but it couldn\u2019t send it. Claude could generate code, but it could not run it, see what broke, revise the program, and then give it another shot.<\/p>\n In other words, the chatbots could automate tasks but not complex, time-intensive projects. To complete the latter, they needed a human to hold their figurative hands and issue instructions at each step in the process.<\/p>\n Then, last year, commercially viable AI agents hit the market.<\/p>\n These new systems are more autonomous and dynamic than their predecessors. Rather than answering one discrete prompt and then awaiting further orders, Claude Code<\/a> or OpenAI\u2019s Codex<\/a> receives a broad objective \u2014 such as \u201cdetect and fix the bug that\u2019s crashing our app\u201d or \u201cmonitor regulatory filings and flag anything relevant to our business\u201d or \u201cmake a 3d flying game<\/a>\u201d \u2014 and then figures out how to achieve its mission.<\/p>\n Put differently, these AIs function less like souped-up search engines and more like junior staffers. They can independently decide which steps to take next, utilize tools (like code editors, spreadsheets, or company databases), test whether their plan worked, try another approach if it fails, and continue iterating until its job is done.<\/p>\n Why agentic AI is a game-changer<\/p>\n This is what the big labs had long promised but failed to deliver: Machines that could not only complement high-skilled workers but \u2014 at least in some cases \u2014 dramatically outperform them.<\/p>\n Over the course of 2025, AI agents only grew more capable. By year\u2019s end, awareness of the tools\u2019 power had broken containment: Influencers with no engineering skills realized they could \u201cvibe code\u201d entire websites<\/a>, apps<\/a>, and games.<\/a><\/p>\n This month, CNBC provided a particularly vivid illustration<\/a> of the new systems\u2019 transformative potential. Two of the outlet\u2019s journalists \u2014 each without any coding experience \u2014 set out to build a competitor to Monday.com<\/a>, a project management platform then valued at $5 billion. They told Claude Code to research Monday, identify its primary features, and recreate them. Within an hour, they had built a functional replacement for the firm\u2019s software. Since CNBC\u2019s story published last week, Monday\u2019s stock price has fallen by roughly 20 percent.<\/a><\/p>\n So, this is one reason why many technologists and commentators are predicting massive, near-term AI-induced disruption: Even if AI progress stopped today, the adoption of existing systems would abruptly devalue many businesses and white-collar workers.<\/p>\n As SemiAnalysis put the latter point<\/a>:<\/p>\n One developer with Claude Code can now do what took a team a month.<\/p>\n The cost of Claude Pro or ChatGPT is $20 dollars a month, while a Max subscription is $200 dollars respectively. The median US knowledge worker costs ~350-500 dollars a day fully loaded. An agent that handles even a fraction of their workflow a day at ~6-7 dollars is a 10-30x ROI not including improvement in intelligence.<\/p>\n What\u2019s more, as Monday.com<\/a> recently discovered, it isn\u2019t just the knowledge economy\u2019s workers who are at risk of displacement. At first, investors had largely assumed that AI agents would benefit incumbent software companies and consulting firms by increasing their productivity: They would now be able to roll out more apps and audits with fewer workers.<\/p>\n But in recent weeks, many traders realized that agentic AI could just easily render such businesses irrelevant: Why pay Gartner for a research report \u2014 or Asana for work management software \u2014 when Claude Code can provide you both at a fraction of the cost? Such reasoning has led to a selloff in software<\/a> and consulting<\/a> stocks, with Gartner<\/a> and Asana<\/a> each shedding more than one-third of their value over the past month.<\/p>\n At the same time, AI agents have eased Wall Street\u2019s fears of an artificial-intelligence bubble: The idea that demand is poised to soar for Claude, ChatGPT, and Gemini \u2014 and the data centers that support them \u2014 seems less far-fetched than it did six months ago.<\/p>\n If we automate automation, things will start to get weird<\/p>\n Still, the primary driver of Silicon Valley\u2019s millenarian rhetoric isn\u2019t agentic AI\u2019s existing capacities, but rather, its prospective future abilities.<\/p>\n No companies are embracing AI agents more vigorously than the top labs themselves. Engineers at Anthropic and OpenAI have said that nearly 100 percent of their code is now AI-generated<\/a>.<\/p>\n To some, this suggests that AI progress won\u2019t proceed in a steady march so much as a chain reaction: As AI agents build their own successors, each advance will accelerate the next \u2014 triggering a self-reinforcing feedback loop in which innovation compounds on itself.<\/p>\n By some measures, AI\u2019s capacities are already growing exponentially. METR, a nonprofit artificial-intelligence research organization, gauges AI performance by measuring the length of coding tasks that models can complete with 50 percent success. It finds that this length has been doubling every 7 months.<\/p>\n The human mind struggles to internalize the implications of exponential change. At the start of March 2020, COVID cases were doubling every two-to-three days in the US. Yet the absolute number of cases remained tiny at the start of the month; on March 1, there were only about 60 confirmed cases<\/a> in the whole country. Many Americans were therefore caught aware when, by April 1, more than 200,000<\/a> of their compatriots were struck ill by the virus.<\/p>\n Those bullish on AI progress believe Americans are once again sleeping on the speed and scale of what\u2019s to come. In this view, as impressive as AI agents\u2019 current capabilities are, they\u2019ll pale in comparison to those at the fingertips of everyone with an internet connection this December. As with the pandemic, the full consequences of an instant industrial revolution are bound to be both immense and unforeseeable.<\/p>\n The robot apocalypse (and\/or utopia) isn\u2019t necessarily nigh<\/p>\n There\u2019s little question that agentic AI is going to reshape the white-collar economy. Whether it has brought us to the cusp of a brave new world, however, is less certain.<\/p>\n There are many reasons to think that AI\u2019s near-term impacts will be smaller and slower than Silicon Valley\u2019s bulls (and catastrophists) now believe.<\/p>\n First, AI still makes mistakes<\/a>. And this fallibility arguably constrains its potential for replacing human workers in the here and now. An autonomous agent might be able to execute the right trade, send the desired email, and replace the errant line of code nine times out of 10. If that other time it stakes all your firm\u2019s capital on Dogecoin, tells off your top client, and introduces a security vulnerability into your app, however, you\u2019re probably gonna retain a lot of human supervision over your highest-stakes projects.<\/p>\n Second, institutional inertia tends to slow the adoption of new technologies. Although generators became common in the late 19th century, it took decades for factories to reorganize around electric power<\/a>. Similarly, while tech firms may have little trouble integrating agentic AI into their workflows, legacy corporations may take longer to adjust. And in some key sectors \u2014 such as healthcare and law \u2014 regulations<\/a> may further constrain AI deployment.<\/p>\n
\u2022 Investors are now treating agentic AI as an existential threat to many incumbent software and consulting firms.
\u2022 If AI\u2019s capabilities keep improving at an exponential rate, things could get really weird by 2027.<\/p>\n![\"\"](\"https:\/\/www.newsbeep.com\/ca\/wp-content\/uploads\/2026\/02\/Screenshot-2026-02-10-at-2.34.15\u202fPM.png\"\/)
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