” I think AI if anything – believe it or not – I think it’s underhyped. I think it’s going to be even bigger than most people realise.” – Professor Erik Brynjolfsson – Stanford economist, Director of the Stanford Digital Economy Lab
The striking claim that artificial intelligence is still underhyped speaks to a structural mismatch between the technology’s rapidly compounding capabilities and the pace at which economies, organisations and institutions are absorbing and exploiting those capabilities.1 The underlying issue is not whether AI can perform impressive benchmarks in coding, language and reasoning; that is already evident. The tension lies in the lag between what systems can do and how quickly business models, workflows, skills and policy frameworks are being redesigned to convert those capabilities into broad-based economic value.
From capability curve to economic J-curve
Over the past few years, frontier AI models have improved at an extraordinary rate on tasks such as software development, document analysis, mathematical reasoning and multi-step planning.1,7 Measured purely as a capability curve, the trajectory looks exponential. Yet the impact on aggregate productivity statistics, labour markets and GDP remains relatively modest.1,7 Brynjolfsson describes this as a technology adoption J-curve: an early phase in which investment, experimentation and organisational disruption rise sharply, while observable economic gains appear muted.1 Only after firms re-architect processes, data flows and decision rights around the new technology does the curve inflect and value creation become visible at scale.
The factual context of his remark is a bet with another economist, Robert Gordon, who is sceptical that AI will materially accelerate productivity.1 Official projections for US productivity growth into the late 2020s remain anchored around pre-AI assumptions.1,13 Brynjolfsson argues that these forecasts are systematically too low, because they implicitly treat current institutional arrangements as fixed rather than adjustable. In his view, once organisations complete the redesign necessary to integrate AI deeply into production and services, productivity growth will exceed the Bureau of Labor Statistics baseline by 2030, with knock-on effects for fiscal sustainability, healthcare and living standards.1,13
AI as a general-purpose technology
Another layer of meaning in the statement is the classification of AI as a general-purpose technology comparable to electricity or the internal combustion engine.1,2 General-purpose technologies display three properties: they improve rapidly over time; they become pervasive across sectors; and they enable complementary innovations in products, processes and organisational forms. Historically, their full economic impact has been delayed by the need for complementary investments. Electric motors, for instance, were installed into factories designed for steam, producing few early gains; only when factories were rebuilt around decentralised power did productivity accelerate.1
Brynjolfsson’s argument is that AI now sits in a similar transitional zone.1,7 Providing employees with chatbots or code assistants is equivalent to installing motors in old steam-era layouts. The larger effects will only appear when firms deliberately redesign end-to-end workflows, redefine roles, restructure data infrastructure and launch new AI-native products and services. Because digital systems can be reconfigured far faster than physical factories, he expects the AI transition to play out over roughly 3 to 5 years rather than the 30-year lag observed with electricity.1 That compressed timeline is one reason he judges AI to be underhyped: most forecasts still treat the transformation as distant, when in his view the economy is already turning the corner of the J-curve.
The labour market tension behind the optimism
The claim that AI is underhyped sits alongside clear evidence of labour market disruption, especially for younger workers in highly exposed occupations.1,12 Research from the Stanford Digital Economy Lab and ADP shows employment among workers aged roughly 22 to 25 falling sharply in jobs where generative AI can perform core tasks, such as junior coding, call-centre work, paralegal support and some routine marketing functions.1,12 The same datasets show far milder effects for older workers and less-exposed occupations, and even positive outcomes where AI is used to augment rather than fully automate tasks.1,11
This duality creates a strategic tension. On one hand, AI boosts the productivity of workers and can improve customer satisfaction, retention and job quality when used as an assistive tool.11,14 On the other hand, the disappearance of entry-level roles threatens traditional talent pipelines, making organisational structures more diamond-shaped: fewer juniors, continued demand for mid-level and senior staff, and an emerging shortage of people with accumulated experience.1 Brynjolfsson’s optimism about AI’s aggregate impact therefore coexists with a warning that firms and societies must explicitly redesign education, apprenticeships and early-career development rather than relying on routine work to train future leaders.1,2
Demand elasticity and why automation can expand jobs
Another factual foundation for the underhyped claim is a subtle point about demand elasticity.1 Automation does not automatically reduce employment; the outcome depends on how buyers respond to lower costs. Using the standard downward-sloping demand curve, Brynjolfsson distinguishes between relatively inelastic markets, where falling prices lead to only modest increases in quantity and thus lower total spending, and highly elastic markets, where price reductions trigger large increases in quantity and potentially higher overall expenditure.1
In sectors such as air travel, the introduction of jet engines cut per-trip costs but created such a surge in demand that total spending and employment in airlines grew rather than shrank.1 He expects roughly half the economy to behave in this expansionary manner as AI reduces costs and increases quality.1,13 Radiology offers a contemporary example: early headlines predicted that image-recognition systems would eliminate radiologists, yet many countries now face radiologist shortages as cheaper and faster imaging has increased demand for scans.1,2 In this framing, AI is underhyped not because it will avoid disruption, but because commentators fixate on job destruction and miss the employment created by lower prices, new services and expanded markets.
Redefining work around defining and evaluating
A central mechanism behind Brynjolfsson’s optimism is his view of future work as structured into three stages: defining the problem, executing the work and evaluating the output.1 AI agents are becoming remarkably capable in the middle stage once a problem is well specified: writing software, drafting documents, analysing data and completing procedural tasks.1,7 Yet they remain limited in selecting economically meaningful problems, understanding organisational context and exercising judgement about the adequacy and implications of their outputs.
He expects most knowledge workers to manage fleets of specialised AI agents, operating more like chief executives of small digital workforces than individual contributors.1 Competence will shift towards the ability to frame questions, set objectives and constraints, interpret ambiguous results and iterate when literal answers miss the true need. This is where he sees human value persisting and potentially increasing: in agency, initiative, domain knowledge, interpersonal skills and situational judgement.2,5 If work is redesigned accordingly, AI amplifies human capability rather than simply substituting it, which in his view justifies a more optimistic forecast than typical narratives of wholesale job loss.
Strategic underdeployment inside organisations
Despite these possibilities, Brynjolfsson observes that many corporate AI initiatives remain superficial, misdirected or disconnected from core value creation.1 Hackathons produce playful applications such as automated lunch menus, while high-stakes processes in revenue generation, risk management, supply chains or customer service remain largely unchanged.1 This behaviour reflects a familiar pattern from earlier technologies: executives experiment at the periphery, but delay difficult redesign of central workflows, incentives and governance.
His argument that AI is underhyped therefore contains a critique of business strategy. The bottleneck is not access to models, but managerial imagination and organisational willingness to tackle the messy work of restructuring. In his policy testimony and interviews, he consistently urges leaders to focus AI efforts on economically material use cases, decompose roles into tasks that can be automated or augmented, and build agent-management skills across the workforce rather than treating AI as a bolt-on tool.2,6,7 He believes that firms which move fastest on this agenda will capture outsize gains in productivity and profitability, reinforcing his view that conventional forecasts underestimate AI’s upside.
Distributional risks and concentrated power
The underhyped assessment does not imply that outcomes will be uniformly positive. Brynjolfsson repeatedly warns that AI could intensify inequality and concentrate economic and political power in a small cluster of companies or states.4,5 If ownership of data, models and digital infrastructure remains narrow, the benefits of higher productivity may accrue disproportionately to capital, senior talent and platform monopolies. Earlier waves of information technology already contributed to skill-biased technical change, widening gaps between workers with and without advanced education.3
He argues that repeating the policy failures of globalisation would be a grave mistake.1,6 Trade raised aggregate output but left many displaced workers unsupported, generating political backlash. With AI, the stakes are higher: the same tools could enable extraordinary prosperity or unprecedented surveillance, autonomous weapons and engineered biological threats.1,4 Brynjolfsson’s underhyped claim is therefore conditional. AI can be far more transformative than mainstream commentary allows, but whether that transformation yields shared prosperity or extreme concentration depends on deliberate choices around taxation, education, entrepreneurship, competition policy and safety regulation.4,6
Beyond GDP: measuring the invisible value
One reason he believes mainstream assessments understate AI’s impact is the inadequacy of conventional economic metrics. Standard GDP records market transactions and assigns zero weight to free digital goods such as search, email, Wikipedia and widely accessible AI tools.1,6 Brynjolfsson’s research on alternative welfare measures, particularly GDP-B, tries to quantify consumer surplus by asking how much people would need to be paid to forego a given service.6
Early results indicate that free digital services generate trillions of dollars in value that never appear in GDP figures.6 Similar surveys applied to language models suggest rapid growth in perceived value as adoption and usefulness increase.1 In other words, even if measured GDP and productivity rise only gradually, underlying welfare may be climbing much faster. This measurement problem reinforces his belief that AI’s true economic significance is underhyped: official statistics and headline narratives are simply not capturing where value is created, especially when tools are free or bundled into existing products.
Why the next decade could be the best and the worst
Ultimately, the statement that AI is underhyped is less a prediction of automatic utopia than a call to recognise the scale of the stakes. Brynjolfsson argues that the coming decade could plausibly be the most prosperous period in human history, with breakthroughs in medicine, education, science and material abundance driven by AI-accelerated discovery and innovation.1,10 At the same time, he accepts that the same systems could enable catastrophic misuse, from bioengineered pandemics to pervasive manipulation and autonomous warfare.1,4
In his view, the decisive variable is human agency.1,4,6 AI is a powerful tool for amplifying intention: it expands the reach and speed of whatever goals individuals, firms and governments choose to pursue. If societies channel that amplification towards entrepreneurship, problem-solving and shared prosperity, the technology’s upside will be far larger than many current forecasts assume. If they neglect safety, inclusion and distribution, the downside could be equally dramatic. The remark that AI is underhyped is therefore both a descriptive claim about the technology’s potential and a normative challenge: to upgrade institutions, strategies and values quickly enough that the eventual scale of impact is used well rather than squandered or weaponised.
References
1. “Stanford’s Top AI Economist: The Next 10 Years Will Be the Best AND the Worst in History” – Silicon Valley Girl – Jul 21, 2026 – https://www.youtube.com/watch?v=72duHF7iZiU
2. What AI Is Really Doing to Jobs Right Now – Stanford Economist … – 2026-06-25 – https://www.youtube.com/watch?v=HpgjB4ZA7E0
3. New Interviews: Erik Brynjolfsson Speaks Out on Jobs and AI – 2017-09-05 – https://ide.mit.edu/insights/new-interviews-erik-brynjolfsson-speaks-out-on-jobs-and-ai/
4. The Jobs Equation-Erik Brynjolfsson – https://www.theatlantic.com/sponsored/google-2023/the-jobs-equation-erik-brynjolfsson-qa/3872/
5. Economist: The AI Risk Almost Nobody Is Talking About | Erik Brynjolfsson – 2026-06-14 – https://www.youtube.com/watch?v=TW0oaz_CF3E
6. Why Erik Brynjolfsson is a ‘mindful optimist’ about AI – Time Magazine – 2026-04-24 – https://time.com/partner-content/charter/why-erik-brynjolfsson-is-a-mindful-optimist-about-ai/
7. Erik Brynjolfsson is Director of the MIT Initiative on the Digital Economy, … – https://science.house.gov/_cache/files/2/8/284703aa-382d-42e7-845c-28f6c83452dc/9EA89F4A7AE55E71B6389C68AAA984D3E6533B08D035103854C404BE052E0252.2019-09-24-testimony-brynjolfsson.pdf
8. Erik Brynjolfsson on how AI is rewriting the rules of … – 2025-03-26 – https://www.hbs.edu/managing-the-future-of-work/podcast/erik-brynjolfsson-on-how-ai-is-rewriting-the-rules-of-the-economy
9. Erik Brynjolfsson: Will Technology Replace Human Jobs? – 2022-05-25 – https://www.youtube.com/watch?v=JCwIwCK8jpI
10. Addressing AI’s Impact on Employment: New Research – LinkedIn – 2026-02-09 – https://www.linkedin.com/posts/erikbrynjolfsson_canaries-interest-rates-and-timing-more-activity-7426769858417147904-QlbU
11. FP! Week in Review, Briefly #38 – 2026-06-27 – https://www.aei.org/articles/fp-week-in-review-briefly-38/
12. NBER WORKING PAPER SERIES – https://www.nber.org/system/files/working_papers/w31161/w31161.pdf
13. The Stanford economist who called the AI entry-level jobs … – 2026-06-27 – https://fortune.com/2026/06/27/what-is-ai-impact-entry-level-jobs-stanford-adp-canaries-brynjolfsson-richardson/
14. THE AI AWAKENING – https://www.insightinvestment.com/globalassets/documents/events/summit-2024/the-ai-awakening
15. Generative AI at Work* | The Quarterly Journal of Economics – 2025-04-08 – https://academic.oup.com/qje/article/140/2/889/7990658
