The one piece of data that could actually shed light on your job and AI

MIT Technology Review AI lagi ngeluarin cerita yang cukup penting: This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. Within Silicon Valley’s orbit, an AI-fueled jobs apocalypse is spoken about as a given. The mood is so grim that a societal impacts researcher at Anthropic, responding Wednesday to…. Buat AI, ini biasanya bukan cuma soal model atau demo baru, tapi soal arah product strategy. Kalau lo ngikutin ai updates, cerita kayak gini sering jadi tanda bahwa batas antara “eksperimen” dan “alat kerja harian” makin tipis.

Kalau kita lihat lebih jauh, This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here . Within Silicon Valley’s orbit, an AI-fueled jobs apocalypse is spoken about as a given. The mood is so grim that a societal impacts researcher at Anthropic, responding Wednesday to a call for more optimistic visions of AI’s future, said there might be a recession in the near term and a “breakdown of the early-career ladder.” Her less-measured colleague Dario Amodei, the company’s CEO, has called AI “a general labor substitute for humans” that could do all jobs in less than five years. And those ideas are not just coming from Anthropic, of course.  These conversations have unsurprisingly left many workers in a panic (and are probably contributing to support for efforts to entirely pause the construction of data centers, some of which gained steam last week). The panic isn’t being helped by lawmakers, none of whom have articulated a coherent plan for what comes next. Even economists who have cautioned that AI has not yet cut jobs and may not result in a cliff ahead are coming around to the idea that it could have a unique and unprecedented impact on how we work.  Alex Imas, based at the University of Chicago, is one of those economists. He shared two things with me when we spoke on Friday morning: a blunt assessment that our tools for predicting what this will look like are pretty abysmal, and a “call to arms” for economists to start collecting the one type of data that could make a plan to address AI in the workforce possible at all.  On our abysmal tools: consider the fact that any job is made up of individual tasks. One part of a real estate agent’s job, for example, is to ask clients what sort of property they want to buy. The US government chronicled thousands of these tasks in a massive catalogue first launched in 1998 and updated regularly since then. This was the data that researchers at OpenAI used in December to judge how “ exposed ” a job is to AI (they found a real estate agent to be 28% exposed, for example). Then in February, Anthropic used this data in its analysis of millions of Claude conversations to see which tasks people are actually using its AI to complete and where the two lists overlapped. But knowing the AI exposure of tasks leads to an illusory understanding of how much a given job is at risk, Imas says. “Exposure alone is a completely meaningless tool for predicting displacement,” he told me. Sure, it is illustrative in the gloomiest case—for a job in which literally every task could be done by AI with no human direction. If it costs less for an AI model to do all those tasks than what you’re paid—which is not a given, since reasoning models and agentic AI can rack up quite a bill —and it can do them well, the job likely disappears, Imas says. This is the oft-mentioned case of the elevator operator from decades ago; maybe today’s parallel is a customer service agent solely doing phone call triage.  But for the vast majority of jobs, the case is not so simple. And the specifics matter, too: Some jobs are likely to have dark days ahead, but knowing how and when this will play out is hard to answer when only looking at exposure. Take writing code, for example. Someone who builds premium dating apps, let’s say, might use AI coding tools to create in one day what used to take three days. That means the worker is more productive. The worker’s employer, spending the same amount of money, can now get more output. So then will the employer want more employees or fewer?  This is the question that Imas says should keep any policymaker up at night, because the answer will change depending on the industry. And we are operating in the dark.  In this coder’s case, these efficiencies make it possible for dating apps to lower prices. (A skeptic might expect companies to simply pocket the gains, but in a competitive market, they risk being undercut if they do.) These lower prices will always drive some increase in demand for the apps. But how much? If millions more people want it, the company might grow and ultimately hire more engineers to meet this demand. But if demand barely ticks up—maybe the people who don’t use premium dating apps still won’t want them even at a lower price—fewer coders are needed, and layoffs will happen. Repeat this hypothetical across every job with tasks that AI can do, and you have the most pressing economic question of our time: the specifics of price elasticity, or how much demand for something changes when its price changes. And this is the second part of what Imas emphasized last week: We don’t currently have this data across the economy. But we could .  We do have the numbers for grocery items like cereal and milk, Imas says, because the University of Chicago partners with supermarkets to get data from their price scanners. But we don’t have such figures for tutors or web developers or dietitians (all jobs found to have “exposure” to AI, by the way). Or at least not in a way that’s been widely compiled or made accessible to researchers; sometimes it’s scattered across private companies or consultancies.  “We need, like, a Manhattan Project to collect this,” Imas says. And we don’t need it just for jobs that could obviously be affected by AI now: “Fields that are not exposed now will become exposed in the future, so you just want to track these statistics across the entire economy.” Getting all this information would take time and money, but Imas makes the case that it’s worth it; it would give economists the first realistic look at how our AI-enabled future could unfold and give policymakers a shot at making a plan for it. ngasih petunjuk tentang apa yang lagi dicari pasar: speed, reliability, dan output yang bisa diukur. Di AI, yang menang bukan yang paling heboh ngomongin capability, tapi yang paling gampang dipakai tim buat nyelesaiin kerjaan nyata.

Research tambahan ngasih konteks yang lebih tajam: Research lookup returned no usable results.. Ini bikin pembacaan awal jadi lebih grounded, bukan cuma bergantung ke judul atau ringkasan feed. Kalau ada detail yang saling nambah, gue pakai itu buat bikin cerita ini lebih utuh dan lebih berguna buat lo.

Di level produk dan operasional, cerita kayak gini biasanya nunjukin satu hal: perusahaan yang lebih cepat belajar bakal punya advantage. Kalau workflow makin otomatis, tim yang masih manual kebanyakan bakal kalah gesit. Kalau distribusi makin ketat, brand yang punya channel kuat bakal lebih unggul. Jadi meskipun judulnya kelihatan khusus, implikasinya sering masuk ke area yang jauh lebih dekat ke keputusan bisnis sehari-hari daripada yang orang kira.

Ada juga layer kompetisi yang sering kelewat. Begitu satu pemain besar bergerak, pemain kecil biasanya punya dua pilihan: ikut naik level atau makin susah relevan. Itu sebabnya gue suka lihat berita bukan sebagai peristiwa tunggal, tapi sebagai bagian dari pola. Siapa yang bergerak duluan? Siapa yang nunggu? Siapa yang bisa mengeksekusi lebih rapi? Dari situ biasanya kebaca apakah sebuah tren masih hype atau udah mulai jadi infrastruktur.

Buat pembaca yang peduli ke hasil praktis, pertanyaan yang paling berguna bukan “apakah ini keren?” tapi “apa yang harus gue ubah setelah baca ini?”. Kalau lo founder, bisa jadi jawabannya ada di positioning, pricing, atau channel distribusi. Kalau lo trader, mungkin yang perlu dipantau adalah sentimen, momentum, dan apakah pasar udah overreact. Kalau lo cuma pengin update cepat, minimal lo jadi ngerti kenapa topik ini muncul dan kenapa orang lain mulai ngomongin sekarang.

Gue juga sengaja ngasih ruang buat konteks yang sedikit lebih tenang, karena berita yang rame sering bikin orang lompat ke kesimpulan terlalu cepat. Tidak semua headline berarti revolusi. Kadang ada yang cuma noise, kadang ada yang benar-benar awal perubahan. Bedanya ada di konsistensi tindak lanjutnya. Kalau dalam beberapa siklus berikutnya topik ini terus muncul, besar kemungkinan kita lagi lihat pergeseran yang serius, bukan sekadar buzz harian.

Jadi kalau lo minta versi pendeknya: The one piece of data that could actually shed light on your job and AI penting bukan karena judulnya doang, tapi karena dia nunjukin arah pergerakan yang bisa berdampak ke cara orang bikin produk, baca pasar, dan nyusun strategi. Buat gue, itu inti yang paling worth it untuk dibawa pulang. Sisanya bisa lo simpan sebagai detail, tapi arah besarnya udah cukup jelas: pergeseran ini layak dipantau, bukan di-skip.