--- image: "/images/notes/no-sci-fi-upgrade-needed.jpg" title: "AI Doesn't Need a Sci-Fi Upgrade to Upend the Economy" date: 2025-06-19 url: "https://x.com/ccatalini/status/1935708804274119141" tags: ['ai-agi', 'stablecoins-payments', 'policy'] deck: "Even if progress stopped tomorrow, the disruption is already underway. Leaders need to understand which tasks are exposed — current models are enough." tweetCount: 34 likes: 158 reposts: 29 --- AI doesn’t need a sci-fi upgrade to upend the economy—current models, and the cheaper, more capable versions already in the pipeline, are set to disrupt nearly every corner of the labor market. There are major questions about how much more powerful AI tools might become—and how soon. Yet even if progress stopped tomorrow, the disruption is already underway. To navigate this, leaders need to understand which tasks and responsibilities are most likely to come under pressure and charting a course to move the enterprise up the intelligence value chain before time runs out. What Is Not at Risk of Automation? Academic researchers and practitioners have extensively debated which jobs and tasks are most vulnerable to automation. Some threats are obvious: self-driving vehicles may soon be in a position to displace millions of ride-sharing, bus, and truck drivers. Meanwhile, language translation, swaths of creative writing, design, and even everyday coding are being handed off to AI. AI research pioneers [@professor_ajay](https://x.com/professor_ajay), [@joshgans](https://x.com/joshgans), [@avicgoldfarb](https://x.com/avicgoldfarb) argued in 2018 that as AI advances, the last bastion of human advantage will be judgment—the ability to weigh options and make decisions under uncertainty. Yet that insight hands us an impossible homework assignment: pinning down exactly what qualifies as judgment at any given moment. Tasks that demand human judgment today could soon pass to AI as models tap richer data and greater compute. Nor can we assume people will always prefer a human therapist, counselor, or mediator, according to recent research. An AI counterpart can operate around the clock, at a fraction of the cost, and—aside from a handful of human superstars—may offer more consistent quality. So, how can we separate the tasks AI will automate next from those that will require new breakthroughs in AI technology to do so? To answer that, we must go back to first principles and revisit where it all began. Back in the mid-2000s, [@drfeifei](https://x.com/drfeifei) saw that the field of computer vision was dealing with a bottleneck: algorithms were pixel-starved, ingesting too little visual data to reach human performance. Her solution was refreshingly brute-force: she built ImageNet—a vast, meticulously labeled image trove assembled with help from Amazon Mechanical Turk. But her true stroke of genius came in 2010, when she bolted a global leaderboard onto the dataset—transforming image recognition into a gladiatorial contest for researchers. For two years, the annual leaderboard inched forward... Then, in 2012, Alex Krizhevsky, [@ilyasut](https://x.com/ilyasut), [@geoffreyhinton](https://x.com/geoffreyhinton) blew the competition away. Using two off-the-shelf NVIDIA cards, the trio was able to train a CNN in just a few days—proving that you could bend computer-vision history on a grad student budget. That moment ended the decades-long AI winter, put neural nets at the center of progress, and revealed the playbook the field still runs on. The framework that propelled the image recognition breakthrough is far more general than most realize. It can be invoked whenever we can a) define the task environment and assemble its data; b) specify a target reward, explicit or implicit (inferred from observing human behavior); and c) provide the computational power to let the system iterate. Stack those three ingredients and you get a general-purpose automation engine. Two data trends now accelerate the flywheel. First, models can mint limitless synthetic examples—for instance, generating virtual "driving miles" that cover every oddball scenario, rather that relying on data from real world drivers. And second, AI is increasingly fielded across a variety of devices and sensors—on phones, in cars, and elsewhere—as a low-cost surveyor, capturing and quantifying real-world signals that were once too expensive or impractical to measure. If you can shoehorn a phenomenon into numbers, AI will learn it and reproduce it back at scale—and the tech keeps slashing the cost of that conversion, so measurement gets cheaper, faster, and quietly woven into everything we touch. More things become countable, the circle resets, and the model comes back for seconds. That means that any job that can be measured can, in theory, be automated. AI now slashes the cost of precise measurement, making continuous, fine-grained sensing the default. Lightweight models run beside the sensors, trimming bandwidth and latency, while synthetic data fills gaps when the real world is slow or awkward to capture. We already live in the era of "artificial-metrics intelligence," where anything we can quantify is swiftly queued for automation. Humans are evolutionary generalists, selected to navigate half-drawn maps. We don’t merely survive unknown unknowns—we thrive on them, and that resilience is our defining edge. But the cornerstone of our advantage is our highly plastic, densely wired prefrontal cortex. This neural command center lets us spin endless "what-ifs," rehearse counterfactual futures, and pivot strategy the instant conditions shift. Short of a true singularity, even quantum machines will struggle to match our talent for open-ended, cross-domain counterfactual planning. As AI accelerates progress, it creates fresh unknown unknowns, so our maps keep being redrawn. Meanwhile, it routinizes the predictable. AI will also struggle in domains where measurement verges on the impossible. It will also lag where measurement is throttled by privacy, ethics, or regulation; where society requires transparent reasoning—at least until model interpretability catches up; and where people simply prefer a human touch. But one crucial carve-out in what can be measured may prove decisive: tasks that defy quantification because their outcome odds are fundamentally unknowable—the realm of Knightian uncertainty, where you can’t assign any probabilities. Scaling a startup, allocating capital or talent into uncertain ventures, containing a novel pathogen, drafting AI ethics, inventing a new artistic medium, igniting a fashion trend, or creating a new genre-bending blockbuster—all sit in zones where probabilities vanish. Some creative acts and discoveries amount to little more than clever recombinations of the familiar, but the truly ambitious hinge on our singular ability to envision genuinely new and complex counterfactual worlds. The list is fluid—tasks drop off the moment they become measurable, and new ones surface just as quickly. Each shift forces painful economic and social adjustments, squeezing more work into a superstar economy... ...that concentrates outsized rewards at the peaks of creativity, talent, and capital. Yet AI offers a paradoxical gift: by democratizing education and serving as everyone’s personal copilot, it hands more people than ever the tools to reach those peaks. For leaders, what lies beyond the spreadsheet? It’s everything that won’t fit in a cell: the skills that refuse to be tallied, the problems with no precedent, the intangibles—trust, taste, quality & experience—and the conviction to press ahead when every metric says "wait." Manage only what you can measure, and you surrender the most valuable ground to rivals who cultivate what can’t be counted. Amar Bose, proved the point: while others worshipped spec-sheet numbers, he zeroed in on how music sounded to people in real rooms—a quality no existing metric could catch—and in doing so, he rewrote the rules of the audio industry. The prescription is simple. Back wildcard bets with fuzzy ROI, reward teams that reframe problems and lean into the unknown, and rotate talent through roles that confront uncertainty across R&D, new markets, and complex customer, partner, and policy interactions. Carve out slack time and engineer cross-team collisions to spark serendipity and idea recombination. Treat those pockets of planned ambiguity not as liabilities, but as strategic assets. Only leaders who pay attention to what is measurable—and, more crucially, to what stubbornly isn’t—will be ready when the next shift arrives. Full piece on [@HarvardBiz](https://x.com/HarvardBiz): [hbr.org](https://hbr.org/2025/06/what-gets-measured-ai-will-automate)