AI and the Deflationary Future: How Tech Might Reshape the Economy Over the Next Decade

Introduction

After a period of post-pandemic inflation, an opposite force is on the horizon: the deflationary pressure of artificial intelligence. Imagine a 2035 economy where AI is embedded in factories, offices, hospitals, and schools – pumping out goods and services at lower costs than ever. Will prices actually fall, and what would that mean for workers and policymakers? This article dives into how AI could drive deflationary trends (persistent downward pressure on prices) in the global economy, focusing on the United States over the next ten years. We’ll explore how AI-fueled productivity gains, automation, and supply-chain efficiencies might increase output per dollar, displace and transform jobs, cut costs across sectors, and ultimately affect consumer prices and the Federal Reserve’s dual mandate. Along the way, we’ll compare this AI era to past technology revolutions (from the Industrial Revolution to the Internet boom) and address counterarguments – from surging energy use to rising inequality. The goal is an analytical yet engaging look at whether AI could usher in “good deflation” (falling prices with strong growth) and how to harness its benefits while managing the risks.

Productivity Boom Due to AI: More Output per Worker

The most direct way AI can exert deflationary pressure is by boosting productivity – the output per worker (or per dollar of input). Higher productivity means more goods and services can be produced with the same or fewer resources, which tends to drive down unit costs and, in competitive markets, prices. Recent research is indeed optimistic about an AI-driven productivity boom. For example, Goldman Sachs economists estimate that generative AI could lift U.S. labor productivity growth by ~1.5 percentage points per year over a 10-year period following widespread adoption (Rebalancing AI-Daron Acemoglu Simon Johnson). That is a massive bump – by comparison, U.S. productivity growth averaged only about 1.1% annually from 2000–2019 (AI and Productivity Growth: Evidence from Previous Technologies). In global terms, that productivity surge translates to adding $7 trillion to world GDP (about a 7% increase) over the decade (Generative AI could raise global GDP by 7% | Goldman Sachs). McKinsey likewise projects that generative AI alone (tools like ChatGPT) could boost annual productivity growth by 0.1 to 0.6 percentage points through 2040, with faster gains if adoption is rapid (Economic potential of generative AI | McKinsey). And when combined with other automation technologies, total productivity growth could get a 0.5 to 3.4 percentage point annual uplift (Economic potential of generative AI | McKinsey) – essentially an economic step-change.

These projections are grounded in early evidence that AI can dramatically increase output in certain tasks. Federal Reserve Governor Lisa Cook noted recent studies where an AI assistant for customer support agents raised their productivity by 14%, as measured by more customer inquiries resolved per hour (Speech by Governor Cook on artificial intelligence and the labor force - Federal Reserve Board). In software development, AI coding tools (like GitHub’s Copilot) have cut the time to complete programming tasks by 50% in controlled trials (Speech by Governor Cook on artificial intelligence and the labor force - Federal Reserve Board). In other words, an hour of work with AI assistance might produce as much as two hours of work previously – a doubling of productivity for those tasks. If such “small” examples are replicated across millions of workflows – from drafting marketing copy to analyzing data – the economy-wide gains could be enormous. As Cook emphasized, growth in output per person ultimately ties to growth in real incomes and living standards (Speech by Governor Cook on artificial intelligence and the labor force - Federal Reserve Board).

Crucially, higher productivity is inherently deflationary: When each worker (or each dollar of capital) produces more, the cost per unit of output falls. Businesses can then offer products at lower prices (or at least restrain price increases) without hurting profitability, because their costs have decreased. We’ve seen this dynamic in past technology revolutions. During the late 1800s, the Second Industrial Revolution introduced mechanization (Bessemer steel, steam shipping, railroads), yielding “dramatic increases in productivity” and a sustained decline in prices (~2% deflation per year from 1870–1890) (The Great Deflation - Wikipedia) (The Great Deflation - Wikipedia). Notably, that period of “The Great Deflation” did not spell economic doom – on the contrary, real incomes rose and the middle class emerged as goods became cheaper (The Great Deflation - Wikipedia). A more recent example: in the late 1990s and early 2000s, the IT and internet boom helped double U.S. total factor productivity growth (to ~1% annually in 1991–2004, vs 0.5% prior), enabling strong GDP growth with low inflation (AI and Productivity Growth: Evidence from Previous Technologies). Fed Chair Alan Greenspan famously observed that technology was allowing faster growth without price pressures, and he held off on raising rates, ushering in a rare combo of low unemployment and low inflation. AI could mark a similar “productivity boom” era. As IMF Managing Director Kristalina Georgieva puts it, we are “on the brink of a technological revolution that could jumpstart productivity, boost global growth and raise incomes around the world” (AI Will Transform the Global Economy. Let’s Make Sure It Benefits Humanity.) – all factors that, if managed well, point to plentiful supply and downward pressure on prices.

That said, realizing these gains will depend on how quickly AI diffuses through the economy and how well workers can adapt. New general-purpose technologies often have lag times: electricity and computers took decades to spread and require organizational changes before productivity surged. For AI, the initial excitement (ChatGPT reaching 100 million users in two months) suggests fast uptake, but integrating AI into everyday business processes at scale will take investment and training. McKinsey cautions that fully capturing generative AI’s potential will require supporting workers through job transitions and skill-building (Economic potential of generative AI | McKinsey). In other words, productivity won’t magically jump everywhere overnight – there’s a transition period where firms experiment and workers learn to leverage AI. Still, even a 0.5% annual productivity bump would be significant over 10 years. By 2035, we could see tangible impacts: higher output and efficiency holding down costs in many industries. As we’ll explore next, however, these efficiency gains come with major shifts in the labor market, which in turn influence wages and prices.

Labor Displacement and Wage Dynamics: The Human Side of AI

If AI enables one person to do the work of two or ten, what happens to all the other workers? Labor displacement is the flip side of productivity. AI will automate certain tasks entirely, reducing the need for human labor in those functions – which can be deflationary by lowering labor costs, but also raises tough questions about jobs and wages. A much-circulated analysis by Goldman Sachs found that roughly two-thirds of U.S. occupations are exposed in some degree to AI automation, and that generative AI could automate 25%–50% of the workload in those exposed jobs (Generative AI could raise global GDP by 7% | Goldman Sachs). Extrapolated globally, they estimate the equivalent of 300 million full-time jobs could be impacted by automation to some extent (Generative AI could raise global GDP by 7% | Goldman Sachs). These figures don’t mean 300 million people immediately unemployed, but they indicate a vast scope of disruption. Roles heavy on routine data processing, customer service, and even skilled work like drafting legal documents are vulnerable to partial or full automation by advanced AI. Indeed, the IMF finds about 60% of jobs in advanced economies could be affected (either complemented or replaced) by AI – a higher share than in developing countries (AI Will Transform the Global Economy. Let’s Make Sure It Benefits Humanity.).

From a pure economic standpoint, reducing labor input for the same output is a cost savings that puts downward pressure on prices. A factory that automates assembly line tasks can produce widgets more cheaply (labor expense falls) and thus charge less per widget. A software company that uses AI to handle customer support with half as many call center reps can lower its operating costs. All else equal, these are deflationary forces. Wage dynamics play a big role here. If AI displacement leads to higher unemployment or workers shifting to lower-paying jobs, aggregate wage growth may slow. In the extreme, a surplus of labor would bid down wages – reducing households’ incomes and spending power (a demand-side drag on inflation). A key concern among economists is that AI could further erode labor’s share of national income, meaning more of the economic pie goes to owners of capital/technology. Researchers at the Philadelphia Fed warn that AI poses a “once-in-a-lifetime decline in labor’s share of national income” if it mainly substitutes for human work (Generative AI: A Turning Point for Labor's Share?). Since wages are the source of consumer spending for the majority, a shrinking labor share can contribute to weaker demand and a deflationary environment (too little money chasing abundant goods).

However, this dark scenario is not preordained. Historically, technology replaces some jobs but also creates new ones and raises productivity in ways that eventually boost wages. For example, over 85% of U.S. employment growth in the last 80 years came from the emergence of new occupations that didn’t exist in 1940 (Generative AI could raise global GDP by 7% | Goldman Sachs) – think of everything from software developers to digital marketers, spawned by past innovations. AI could similarly give rise to entirely new industries and roles (AI ethics specialists, data curators, robot maintenance, etc.). Fed officials like San Francisco Fed President Mary Daly emphasize the need for reskilling: “Jobs are being created, as well as jobs being replaced. If we can get people to upskill or reskill to take the jobs that are being created, we’ll have a successful and growing economy”, she says (S.F. Federal Reserve Bank President on AI, the Labor Market | TIME). In other words, human capital investment can help labor share in the AI-driven gains. There is even some early evidence that AI tools help junior or less-skilled workers relatively more, potentially narrowing skill gaps. One study found that within certain professions (law, software engineering, writing), less-experienced workers saw larger productivity boosts from AI assistance than their expert counterparts (AI’s impact on income inequality in the US). MIT’s David Autor has hailed these results as a sign that AI could lift middle-class wages and reduce inequality, if the technology acts as a leveling tool that makes average workers nearly as efficient as star performers (AI’s impact on income inequality in the US). That would be a welcome reversal of the past 40 years, during which technology often widened wage gaps.

For the next decade, we should expect a bit of both: elimination of some jobs, transformation of many, and creation of new ones. AI will likely drive down wages or employment in some routine occupations (e.g. administrative support, basic coding, call centers) due to automation of their tasks. At the same time, it will raise demand for AI-skilled talent (prompt engineers, ML developers) and complement other jobs, making those workers more productive (and potentially higher-paid). The net effect on wages and inflation is complex. If the economy manages to reabsorb displaced workers into new productive roles (the optimistic scenario), overall employment can stay high and wages could eventually rise with productivity. In that case, higher incomes support demand even as prices fall – a “good deflation” outcome. If instead displacement outpaces the creation of new opportunities, we risk a deflationary spiral: unemployed or underemployed workers, weak wage growth, and insufficient demand, even as AI pumps out ample supply. Policymakers are acutely aware of this balance. Georgieva notes that “roughly half” of jobs exposed to AI in advanced economies could see lower labor demand and wages, and in the “most extreme cases” disappear, while the other half are enhanced by AI (AI Will Transform the Global Economy. Let’s Make Sure It Benefits Humanity.). This puts “greater risks from AI – but also more opportunities” on developed countries, she says (AI Will Transform the Global Economy. Let’s Make Sure It Benefits Humanity.). To seize the opportunities (higher productivity, new jobs) without simply impoverishing workers, deliberate strategies will be needed. We’ll revisit policies like education, universal basic income, and labor market reforms in the synthesis section.

For now, the key point is that AI’s deflationary push comes partly via the labor market: by reducing the labor required per unit of output, AI can contain costs and prices. But this process must be managed to avoid simply undercutting workers’ livelihoods. If it goes well, we get cheaper prices and rising real wages (as happened in the late 19th century, when nominal wages held steady while prices fell, boosting purchasing power (The Great Deflation - Wikipedia)). If it goes poorly, we get a deflationary stagnation with high inequality. The stakes are high, and they play out across every sector of the economy – which we turn to next.

Automation Across Sectors: Cost Reduction in Manufacturing, Services, and More

AI is not a single monolithic technology; it’s a suite of capabilities (machine vision, natural language processing, robotics control, predictive analytics) being applied across virtually all industries. Its deflationary impact will likely first be felt in sectors where automation and data-driven efficiency can most easily reduce costs: manufacturing, logistics, and certain service industries. Let’s examine a few key sectors to see how AI-driven automation is cutting costs and what that means for prices.

Manufacturing & Logistics: “Lights-Out” Factories and Leaner Supply Chains

In manufacturing, AI and advanced robotics promise to do for the 2020s what assembly lines did in the 1920s – dramatically raise output per worker while lowering errors and downtime. Many factories are already deploying AI for predictive maintenance (anticipating machine failures), quality control (machine vision systems spotting defects), and even fully automated production lines. The result is a more efficient use of resources and lower production costs. For example, General Motors implemented AI-driven production planning and managed to reduce material waste by 30% across its plants ( AI Benefits in Manufacturing: Technology Transforming Industry - New Horizons - Blog | New Horizons ). Using only the materials actually needed saves money and ultimately can lower the cost per vehicle produced. BMW has introduced AI-powered robots for tasks like welding and painting in its assembly process, yielding a 20% increase in production efficiency without sacrificing precision ( AI Benefits in Manufacturing: Technology Transforming Industry - New Horizons - Blog | New Horizons ). Siemens uses AI analytics to optimize factory settings in real time, netting about a 15% improvement in throughput in its operations ( AI Benefits in Manufacturing: Technology Transforming Industry - New Horizons - Blog | New Horizons ). These gains illustrate how AI can squeeze out inefficiencies that used to be accepted as inevitable. Every 1% gain in manufacturing efficiency – whether through less waste, less downtime, or faster assembly – is effectively a 1% reduction in cost per unit, which in competitive markets translates into lower prices or at least slower price increases for manufactured goods.

The concept of “lights-out” manufacturing, where factories run 24/7 with minimal human intervention, is becoming more realistic with AI. While fully autonomous factories are still rare, many plants are moving toward automation of repetitive tasks. One interesting effect of this is reshoring: if labor costs are no longer a major factor because robots do most of the work, production can be located closer to the end consumer (e.g. in the U.S. rather than in a low-wage country) without making the product expensive. This trend could shorten supply chains and reduce transportation and inventory costs. AI is also revolutionizing logistics and supply chain management itself. Early adopters of AI in supply-chain planning have seen logistics costs drop by about 15% and inventory levels improve by 35% due to better demand forecasting and routing optimization (The Role of AI in Developing Resilient Supply Chains | GJIA). When companies know exactly what is needed where, they can avoid overproduction and excess stock (which ties up capital and often leads to markdowns). They can also avoid the opposite problem – shortages and bottlenecks that drive up prices. The COVID-era supply shocks showed how disruptions can spike prices (e.g. scarce goods becoming expensive). AI, by improving resilience and foresight in supply chains, could mitigate such shocks. McKinsey surveys find the highest reported cost savings from AI are in supply chain management (The Role of AI in Developing Resilient Supply Chains | GJIA), which implies significant deflationary potential in the cost of moving and storing goods.

In practical terms, by 2035 consumers might notice that products like electronics, appliances, or even cars aren’t steadily rising in price each year as they used to – perhaps even the opposite. Consider that the prices of many basic commodities and mass-produced goods fell continuously during the late 1800s as industrial productivity soared (The Great Deflation - Wikipedia). We may see a similar trend: more “stuff” for less money. To be sure, manufacturers could choose to keep prices the same and simply enjoy higher profit margins from cost savings. But competition tends to force efficiencies to be shared with consumers over time. If one automaker can produce an EV $5,000 cheaper thanks to AI automation, its rivals will have to match that or undercut it. Thus, manufacturing and logistics automation create a strong supply-side deflationary pressure: cheaper production and distribution of goods on a massive scale.

Services and Retail: Automating White-Collar Work and Customer Interaction

AI’s impact goes beyond factories – it is increasingly automating services, including many white-collar and customer-facing tasks. This could lead to cost reductions (and price declines) in sectors that historically have been labor-intensive and prone to price inflation, such as retail, customer support, and financial and professional services.

Take retail and customer service. We’ve all encountered AI chatbots and virtual assistants when contacting customer support. These AI agents are rapidly improving in their ability to handle routine inquiries, troubleshoot problems, and even upsell products – tasks that used to require a human rep on the phone or in a chat. A well-implemented AI support system can handle a large volume of customer issues at near-zero marginal cost (once the system is built, answering one more query is practically free). This drastically lowers the cost of customer service. In one study, a customer support center that gave its agents an AI copilot (a tool that suggested responses and solutions) saw those agents’ productivity jump such that each agent handled 14% more calls per hour (Speech by Governor Cook on artificial intelligence and the labor force - Federal Reserve Board). In effect, the company could serve more customers with fewer staff hours – or serve the same number with 14% less labor. That efficiency can translate into lower costs for consumers (for instance, tech support fees or wait times could decrease). In retail settings, AI is enabling automated checkout (reducing cashier labor), personalized marketing (more effective sales with less ad spend waste), and optimized pricing. E-commerce giants like Amazon use AI algorithms to dynamically price products and manage inventory, squeezing out excess costs. This has contributed to what economists dub the “Amazon effect” – the downward pressure on retail prices due to online competition and efficiency. Even brick-and-mortar retailers use AI for demand forecasting and supply management, which means fewer clearance sales due to overstock (saving them money that can be passed on as lower regular prices). Overall, the retail sector is seeing slimmer margins and intense competition, partly thanks to AI optimization, which benefits consumers in the form of slower price growth for many goods. Indeed, before the recent inflation spike, prices for consumer goods (especially those easily comparable online) barely rose for years. That trend is likely to resume as AI-driven efficiency intensifies competition.

Now consider financial services and other “knowledge” industries. These are areas that historically have high salaries and have contributed to inflation in service costs (think of hefty banking fees or legal bills). AI is poised to streamline many of their operations. Banks are using AI to automate loan processing, detect fraud faster, and handle compliance – tasks that consumed countless human hours. For consumers, these savings could materialize as lower fees, better interest rates, or new free services (if one bank doesn’t pass on the savings, a fintech competitor might). In wealth management, robo-advisors (AI algorithms) now provide portfolio advice at a fraction of the cost of human advisors. The cost of investment advice has accordingly fallen, and even big firms have had to cut fees. In the legal realm, AI research tools can sift through case law in seconds, potentially reducing the billable hours a client pays for basic legal research. Some law firms might lower fees or take on more clients with the same staff. Telemedicine and AI diagnostics in healthcare could reduce the need for expensive specialist consults for every case (e.g., an AI reading an MRI might flag normal results, reducing how often a radiologist’s time is needed). While many of these service AI applications are in early stages, the common theme is doing more with less human labor or specialized expertise. That implies a reduction in the cost of delivering the service. Over a decade, as these tools mature, we could see service sector disinflation: slower growth in prices for healthcare, education, finance, etc., which have historically risen faster than goods.

To put a concrete number on one high-cost sector: healthcare. A 2024 study by Nikhil Sahni, David Cutler and colleagues estimates that wider AI adoption could save 5–10% of U.S. healthcare spending annually – about $200 to $360 billion per year (in 2019 dollars) (The Potential Impact of Artificial Intelligence on Health Care Spending | NBER). These savings come from use cases attainable in the next five years, like automating administrative tasks, improving care coordination, and aiding diagnosis, without sacrificing quality or access (The Potential Impact of Artificial Intelligence on Health Care Spending | NBER). If realized, that could significantly slow down medical price inflation (which is critical as healthcare has been a big contributor to CPI growth historically). Similarly in education, experts suggest AI could help “bring down the cost of college” by automating student services and advising. For instance, an AI system could analyze students’ records and recommend personalized course pathways, freeing up human advisors’ time (How AI Could Help Bring Down the Cost of College - Wharton AI & Analytics Initiative). Universities spend a lot on administrative overhead; if AI can trim those costs (one Wharton AI report suggests up to 30% reduction in administrative costs in higher ed is possible), tuition might not need to rise as much each year (AI On Campus: What It Means For Your College Investment - Forbes) (AI On Campus: What It Means For Your College Investment - Forbes). Or alternatively, colleges could serve more students without expanding payrolls, effectively lowering cost per student. Even if tuition prices don’t literally drop, they may increase more slowly – a form of disinflation.

In short, across services and retail, AI is chipping away at labor and operational costs that used to seem fixed. Whether it’s a bank processing your mortgage in 1 day instead of 1 week, or a retailer running a warehouse with 100 robots instead of 300 workers, the efficiency gains are deflationary in nature. Consumers may experience this as smaller annual price hikes for services, more generous discounts, or entirely new low-cost offerings (e.g. free basic banking, cheap online courses, AI-driven medical triage at minimal cost). There’s a virtuous cycle potential: as AI reduces business costs, competitive pressure and new tech-based entrants force incumbents to lower prices or fees, which then spreads the benefits to the wider public.

AI in Supply Chains: Speed, Precision, and Stockpile Reduction

Since supply chains are so critical to product costs and prices, it’s worth underscoring AI’s role there as a separate point. The last few years taught everyone that supply-chain disruptions (ports jammed up, sudden shortages of chips or PPE) can send prices skyrocketing. AI can help create more efficient and resilient supply chains, which would dampen such inflationary shocks in the future. By processing vast real-time data – weather, shipping schedules, geopolitical news, factory IoT sensor data – AI systems can predict potential disruptions and reroute supplies proactively. They can also optimize inventory levels so that companies don’t hold excess stock (which is costly) but also don’t run out of stock (which lost sales or panic buying can drive prices up).

We already cited a stat that AI adopters cut logistics costs by 15% and inventory by 35% (The Role of AI in Developing Resilient Supply Chains | GJIA). To appreciate what that means: leaner inventories free up cash and reduce warehousing needs (lower costs), and fewer stockouts mean less “rush” ordering or price gouging when demand unexpectedly spikes. For example, if a retailer’s AI notices a surge in demand for a certain item, it can instantly signal factories to scale up production or find alternate suppliers before the item sells out – preventing the scenario where scarce supply would have allowed a big price hike. AI route optimization can also cut transportation fuel costs and time (a win for cost and potentially for lower consumer prices). Over a decade, as more firms integrate these AI supply-chain systems, we might see a world where shortages are rarer and gluts are rarer – a more balanced flow of goods. That translates to more stable prices. In economic terms, AI is reducing frictions and information lags in the supply chain, making supply more responsive to demand in real time. The closer we get to that ideal, the less likely we are to see the kind of sudden inflation that comes from mismatched supply and demand.

Government and industry leaders recognize this connection. It’s no coincidence that the Biden Administration has simultaneously pushed initiatives on supply chain resilience and on AI development (The Role of AI in Developing Resilient Supply Chains | GJIA) (The Role of AI in Developing Resilient Supply Chains | GJIA) – they see AI as a tool for supply chains. If the U.S. can harness AI to, say, monitor the supply chain of critical pharmaceuticals and predict shortages months in advance, it can ramp up alternative production and avoid the price spikes that come with scarcity. All told, AI-driven efficiency in supply chains will act as an “invisible hand” keeping costs down behind the scenes. Consumers won’t see it directly, but they’ll benefit from a world with fewer nasty price surprises (be it for gasoline, groceries, or holiday toys).

Effects on Consumer Prices and Inflation Metrics

Bringing the discussion up a level, how might all these productivity and cost reductions show up in consumer prices and inflation indices? The U.S. Federal Reserve and other central banks are certainly pondering this. If AI truly is deflationary, we would expect to see lower readings in measures like CPI (Consumer Price Index) and PPI (Producer Price Index) over time, at least relative to a world without these AI effects.

One likely outcome is continued price declines or slow inflation in tech-heavy goods and services. Even before the recent AI breakthroughs, many consumer electronics saw price deflation – e.g., the quality-adjusted price of computers, TVs, and software has fallen for decades as technology improved. AI could extend that trend to new categories. We could see, for example, the price of data-driven services (streaming, cloud storage, digital personal assistants) remain very low or even free, subsidized by AI-driven ad efficiencies. Physical goods like cars or appliances might see zero inflation or outright price drops if manufacturing automation cuts costs drastically. To draw a parallel: during the 1870–1890 deflationary period, “the prices of most basic commodities and mass-produced goods fell almost continuously” even as quality and variety increased (The Great Deflation - Wikipedia). Already today, something like a smartphone or a flatscreen TV gets better each year and often cheaper relative to performance – AI will broaden that pattern to more product types.

Prominent tech voices are explicitly predicting an AI-driven deflation. Sam Altman, CEO of OpenAI, recently highlighted that AI’s deflationary impact is “one of the impacts of this technology that is most underappreciated and misunderstood” (Sam Altman Spoke at a Private Conference. Here's What Was Discussed. - Business Insider). At a private conference, Altman argued that as AI massively boosts efficiency across sectors, it will exert downward pressure on prices – a view echoed by Morgan Stanley analysts who noted that greater global productivity “would help offset inflation.” (Sam Altman Spoke at a Private Conference. Here's What Was Discussed. - Business Insider). Essentially, as AI makes production cheaper, it counteracts many inflationary forces (like wage increases or input shortages), resulting in a net disinflationary push. We may even witness outright deflation in certain years or sectors, if productivity gains outpace demand growth. For instance, if by 2030 autonomous vehicles and AI logistics make transportation of goods 50% cheaper, that could significantly lower food and consumer goods prices (transport cost is a component of final prices). If at the same time AI has not yet generated commensurate new demand, the imbalance could cause price indices to tick down.

To be clear, a few years of mild deflation (say, -1% annual CPI) caused by abundant supply and tech efficiency is not the same as the harmful deflation of a recession (which is driven by collapsing demand). Many economists would consider the former a benign or even welcome scenario – more bang for each buck. The late 19th century “good deflation” is a case in point: living standards rose as prices fell (The Great Deflation - Wikipedia). However, central bankers today are wary of sustained deflation because our debt-driven economies and policy frameworks are geared to slight positive inflation. The Fed’s 2% inflation target is partly to ensure a buffer above zero. If AI consistently pushes inflation below target, the Fed might face a dilemma (more on that in the next section).

One might ask: haven’t we seen huge tech advances in the 2010s without deflation? True – factors like globalization and e-commerce kept goods prices low, but services inflation and housing kept overall inflation positive (albeit low). The argument here is that AI could be a much more pervasive general-purpose technology, hitting even services and driving a broader disinflation. There’s also a time factor: initially, the cost of implementing AI could be inflationary in pockets – e.g., surge in demand (and prices) for AI-specialized chips, higher energy usage, investments in new systems (which might be passed on to customers early on). In fact, some analysts note that in the short run, heavy AI investment might raise certain prices (say, cloud computing costs) before the efficiency gains kick in across the board (AI Power Consumption: Rapidly Becoming Mission-Critical - Forbes) (How AI can boost productivity and jump start growth). But those are likely transient, and once AI deployment reaches critical mass, the deflationary effects dominate. The cost to use AI models is already plummeting – as Morgan Stanley observed, “the cost to access and use generative AI models has been collapsing” thanks to new techniques and scale (Sam Altman Spoke at a Private Conference. Here's What Was Discussed. - Business Insider). This means AI tools that were extremely expensive a year ago (think of a large language model query) are rapidly getting cheaper, enabling wider adoption and greater impact on costs elsewhere.

We should also consider consumer behavior changes. AI assistants might help consumers find the cheapest products, automatically negotiate bills, or optimize their energy usage to off-peak times – all of which effectively reduce the prices consumers pay. For example, an AI energy manager in your home might cut your electricity bill by scheduling appliances when rates are low, creating personal deflation in your cost of living. At scale, if consumers en masse become better at cost-saving via AI, companies may have to lower prices or offer better deals to compete for savvy AI-empowered buyers.

Overall, the expectation is that inflation rates will be lower on average in an AI-permeated economy, barring other shocks. We might see the Fed struggling to reach 2% inflation, instead of struggling to contain it. It raises the question: how will central banks respond if AI keeps inflation muted and unemployment low (the ideal), or if it keeps inflation muted but unemployment high (less ideal)? That’s our next focus.

Central Banks and AI: Rethinking the Fed’s Dual Mandate

The U.S. Federal Reserve’s dual mandate is maximum employment and stable prices (2% inflation). AI’s rise potentially pulls these goals in different directions – or changes what those goals even mean. How are central banks viewing AI’s role in inflation and employment, and what adjustments might they need to make?

So far, Fed officials have acknowledged AI mainly as a potential boost to productivity (which, in theory, is great news for growth and non-inflationary expansion). In a September 2024 speech, Fed Governor Lisa Cook remarked that economists “do not know what the ultimate magnitude or intensity” of AI’s effects will be yet, but noted early studies suggesting potentially large productivity gains (Speech by Governor Cook on artificial intelligence and the labor force - Federal Reserve Board) (Speech by Governor Cook on artificial intelligence and the labor force - Federal Reserve Board). Importantly, she and others at the Fed see AI as akin to past major innovations – transformational but uncertain in timing (Speech by Governor Cook on artificial intelligence and the labor force - Federal Reserve Board). The Fed’s baseline approach is likely to monitor whether AI is raising the economy’s productive capacity. If productivity is accelerating, the Fed can allow the economy to run hotter (lower unemployment) without triggering inflation – essentially shifting the trade-off between employment and inflation. This was the lesson of the late 1990s: Greenspan recognized a productivity uptick and held off on preemptive rate hikes, resulting in unemployment falling to 4% with stable inflation. Current Fed Chair Jerome Powell could face a similar “Greenspan moment” if, say, by 2026 inflation is trending down to 1% even as GDP growth is solid – a sign that supply-side improvements (perhaps AI-driven) are at work. The Fed might then tolerate a super-tight labor market to get wages up and inflation back to target, rather than worrying about overheating.

In fact, a “Goldilocks” scenario is conceivable: AI eases inflationary pressures while sustaining growth, making the Fed’s job of bringing inflation to target easier in the near term (The impact of artificial intelligence on output and inflation). The Bank for International Settlements (BIS) ran model scenarios and found that greater use of AI could ease inflation in the near term, helping central banks rein in post-pandemic price surges, while in the longer run the boost to demand from higher incomes might push inflation up slightly (but by then central banks can manage it through normal tightening) (The impact of artificial intelligence on output and inflation). In other words, AI might buy central bankers some breathing room now, and any inflationary effects come later alongside stronger growth – a manageable trade-off. Fed officials like Mary Daly have even speculated that AI could ultimately increase labor market dynamism, making workers more productive and potentially filling labor shortages, thus reducing wage-push inflation. She believes if workers are upskilled for new AI-augmented roles, the economy can grow “successfully” without spiking inflation (S.F. Federal Reserve Bank President on AI, the Labor Market | TIME).

However, the Fed is also keenly aware of the employment side of the mandate. If AI leads to higher structural unemployment (people whose jobs are automated faster than they can find new ones), the Fed might face a painful dilemma: prices stable or falling (which normally calls for loosening monetary policy), but lots of displaced workers (which also calls for loosening to boost demand). The Fed can cut rates to near zero, but if deflationary pressure is strong, monetary policy might be pushing on a string – this is what happened in Japan, which had deflation and stagnant wages for years despite zero interest rates. The Fed may not want the U.S. to fall into that trap. One could imagine discussions about raising the inflation target above 2% if technology consistently holds inflation below target; a bit more inflation could give more cushion against deflation. This is speculative, but economists have floated the idea of a higher target in a world of chronically low inflation (a concept called “secular stagnation” when demand is persistently weak relative to supply). AI could accentuate that by flooding supply of goods/services and suppressing costs.

From a dual mandate perspective, there is also the question: if AI boosts productivity growth, does it alter what “maximum employment” looks like? It might mean the natural rate of unemployment (the lowest jobless rate consistent with stable inflation) could drop, because productivity gains offset wage pressures. Alternatively, if AI causes job churn, maybe the natural rate rises temporarily (due to mismatches in skills). The Fed will have to feel this out with incoming data. We might see, for example, inflation falling below 2% while unemployment is still, say, 4% – something rare historically. Should the Fed then aim to stimulate more to hit the price target (thus driving unemployment even lower)? Possibly, yes. That could lead to a novel situation where the Fed is essentially trying to create some inflation (through low interest rates or asset purchases) at a time when technology is pushing the other way.

Fed communications have started to reference AI in the context of long-term structural factors. The Fed’s February 2025 Monetary Policy Report noted that many foreign central banks cited “declining inflationary pressures” and the potential impact of technology in their recent rate decisions (Monetary Policy Report – February 2025 - Federal Reserve Board). The implication is that central bankers see a shift in the inflation paradigm that might, in part, be tech-driven. Some Fed watchers even dub an expected easing of inflation as entering a “Tech Disinflation Era.” To navigate this, central banks might need new tools or coordination with fiscal policy. If deflationary pressures get too strong, monetary policy alone (already limited by the zero lower bound on interest rates) might need help from fiscal stimulus – perhaps direct government spending or redistribution (like UBI) to support demand. This blurs the lines of the Fed’s strict mandate, but the Fed can advocate for fiscal solutions if needed, as it subtly did when inflation was too low in the 2010s.

One concrete aspect the Fed is studying is how AI could affect the natural rate of interest (r*). If productivity and potential growth rise, r* (the equilibrium real interest rate) should rise too, meaning the Fed might eventually normalize rates at a higher level than the ultra-low of the 2010s. But if AI also increases savings (e.g., higher inequality could mean more income flows to high-saving wealthy or to corporate profits) or reduces investment needs (if AI makes everything more efficient with less capital), r* might actually fall or stay low. The Fed’s decisions on where to set rates in the long run could hinge on these opposing forces. It’s a bit wonky, but essentially AI might require the Fed to recalibrate its models of the economy’s speed limit and the neutral interest rate.

Finally, it’s worth noting the Fed’s mandate is domestic, but AI is global. If the U.S. embraces AI and has very low inflation while other countries are slower and have higher inflation, the dollar’s value, trade flows, and capital movement could be affected. There could be pressure on the Fed to adjust policy not just based on U.S. inflation, but on preventing destabilizing capital inflows (if U.S. yields end up higher in an AI-boosted growth scenario, for instance). Internationally, central banks (Fed, ECB, etc.) are sharing research on AI’s macro impact. Christine Lagarde of the ECB has mentioned both the promise of AI and the need to monitor its effects on productivity and inflation expectations. Central banks might incorporate AI scenario analysis in their forecasting – e.g., a scenario where unemployment rises 1% from tech layoffs but productivity adds 2% to output, what happens to inflation and what policy response is needed?

In summary, the Fed is cautiously optimistic that AI can be a positive supply shock that makes its price stability job easier, at least initially. But it’s also aware of the employment mandate – ensuring the gains from AI don’t come at the cost of millions of workers left behind. The dual mandate implications might eventually spur policy innovation: we could see more emphasis on full employment (if inflation is consistently below target anyway), or even a rethink of the inflation target. For now, the Fed is mostly in study-and-see mode, occasionally cheering the potential productivity boost. “Maximum employment and stable prices” could look different in an AI-abundant economy, and the central bank of the future might need to coordinate with other policies to guide us through a deflationary boom.

Synthesis: Deflationary Mechanisms, Winners and Losers, and Policy Implications

Having surveyed the terrain – productivity, labor, sectoral impacts, price indices, and central bank views – let’s synthesize the findings. How exactly might AI bring about deflationary pressures? What data trends should we expect? Who stands to gain or lose in this deflationary-tech scenario? And what policies could ensure AI-driven deflation is a boon (more prosperity) and not a bane (stagnation or inequality)?

Mechanisms by which AI exerts deflationary pressure: In summary, AI mainly affects the supply side of the economy in ways that lower costs:

• 1. Productivity surge increases output: More goods and services for the same input cost means lower cost per unit (Rebalancing AI-Daron Acemoglu Simon Johnson). In competitive markets, increased supply relative to demand drives prices down or at least slows price increases. This is the classic “more supply, less inflation” story. With AI, we’re talking about a potential once-in-a-generation productivity jump – multiple analysts peg it at 1.5 percentage points addition to annual productivity growth (Rebalancing AI-Daron Acemoglu Simon Johnson). Such a jump essentially expands the economy’s capacity and puts downward pressure on prices unless demand grows just as fast. Historically, demand usually lags initially, causing a period of low inflation or deflation until incomes catch up.
  
  

• 2. Automation reduces labor and other input costs: AI automates tasks, directly cutting wage bills for companies (fewer workers needed for the same output) and often saving on materials and energy through efficiency ( AI Benefits in Manufacturing: Technology Transforming Industry - New Horizons - Blog | New Horizons ) ( AI Benefits in Manufacturing: Technology Transforming Industry - New Horizons - Blog | New Horizons ). Lower production costs enable lower prices. For example, if a factory uses 30% less raw material per product thanks to AI optimization, that’s a huge cost saving that can translate into cheaper prices for consumers ( AI Benefits in Manufacturing: Technology Transforming Industry - New Horizons - Blog | New Horizons ). Similarly, if a bank uses AI to handle customer service, it might handle more accounts without hiring more staff – potentially allowing it to offer no-fee accounts or lower loan rates to attract customers, effectively a price reduction for banking services.
  
  

• 3. Improved supply chain coordination cuts waste and prevents shortages: AI makes supply chains leaner and more responsive (The Role of AI in Developing Resilient Supply Chains | GJIA). This reduces the “friction” costs (like having to store excess inventory or expedite shipments). It also avoids the price spikes from mismatches – e.g. if AI prevents a shortage of a component by rerouting supply, the price for that component stays stable instead of spiking. The logistics cost reduction of ~15% reported by early AI adopters (The Role of AI in Developing Resilient Supply Chains | GJIA) is very significant – transportation and logistics are embedded in the price of virtually everything.
  
  

• 4. Competitive diffusion of technology: As AI tools become ubiquitous (and cheaper to deploy), even firms that might prefer to keep higher prices will be forced to pass on some savings. Why? Because new entrants or competitors will use AI to undercut them. Think of how new low-cost retailers forced older ones to match prices in the past. In the future, an “AI-native” company with very low overhead could disrupt an industry, compelling incumbents to lower prices. This competitive dynamic ensures that productivity gains reach consumers. Higher global efficiency and productivity would help offset inflation (Sam Altman Spoke at a Private Conference. Here's What Was Discussed. - Business Insider) – i.e., if everyone’s becoming more efficient, no one can get away with charging the old high prices for long, or they lose market share.
  
  

• 5. Changing consumer behavior and expectations: If people come to expect that tech makes things cheaper over time, it can become a self-fulfilling phenomenon (like how we expect each generation of electronics to be cheaper/better, which pressures manufacturers accordingly). AI could anchor low inflation expectations among the public, which tends to actually result in lower realized inflation (workers might accept smaller raises if they expect low inflation, companies might be cautious in raising prices if consumers expect price stability). Central bankers watch inflation expectations as a key indicator; widespread belief that AI = cheaper goods could put a lid on the inflation psychology.
  
  

What about data trends? If AI’s deflationary push materializes, we’d expect to see:

• Productivity statistics showing an acceleration. For instance, BLS labor productivity growth might climb above 2% consistently (versus ~1% recently) by late this decade. We might see multi-factor productivity in sectors like manufacturing and professional services grow notably faster. Already, there’s evidence of an uptick in total factor productivity growth to ~1% in the 1990s due to IT (AI and Productivity Growth: Evidence from Previous Technologies); AI could drive a similar or greater uptick in the 2020s. This would be the fundamental driver enabling deflationary pressure.
  
  

• Price indices (CPI/PPI) by sector revealing very low or negative inflation in AI-intensive areas. For example, core goods CPI (which includes appliances, electronics, vehicles, furniture, etc.) could consistently register negative annual rates if production costs drop. Services CPI could trend down toward zero for things like finance and education if efficiencies take hold. We might see something like producer price index (PPI) declines in manufacturing inputs or wholesale goods, reflecting cheaper production. Even the GDP deflator (broad price measure) might be subdued. This would mirror historical episodes of tech-driven deflation: e.g., many countries in late 1800s saw CPI declines alongside booming output (III Historical Experiences of Deflation and Policy Lessons in).
  
  

• Wage and unit labor cost trends: Possibly a divergence where productivity outpaces wage growth for a while (widening the “productivity–pay gap”), which is deflationary. If labor’s share falls, unit labor costs drop, which puts downward pressure on prices. We’ve already seen a dramatic divergence since 1980 where productivity soared ~3.5x but median wages only ~2x (The Productivity–Pay Gap | Economic Policy Institute). AI could either exacerbate that (if wages don’t rise with productivity – deflationary from cost side, but also suppressing demand) or, in a positive scenario, finally allow wages to catch up without inflation (because output is so much higher). Monitoring that gap will be telling.
  
  

• Corporate profit margins initially rising then possibly falling: At first, firms that adopt AI may enjoy higher margins (lower costs, prices not yet cut). But over time, competition should force margins down as prices are lowered to gain volume. This transfer of surplus from producers to consumers is deflationary (consumers pay less). We might thus see an initial jump in profit share of GDP, followed by a decline as the gains are competed away. If instead margins stay permanently high for some reason (e.g., monopoly control of AI tech), then the deflationary benefits might not fully reach consumers – that’s a risk (addressable by competition policy).
  
  

Next, winners and losers in markets under AI-driven deflation:

Winners: Consumers are clear winners if prices of many goods and services fall or rise more slowly than incomes. Their real purchasing power increases – effectively a raise in standard of living. Imagine your grocery bill, utility bill, and healthcare expenses all grow much more slowly than your salary because AI has made those industries efficient; your disposable income for other things would be higher. Another set of winners: companies that effectively use AI to improve productivity faster than their competitors. They can either enjoy larger market share (if they cut prices) or higher profits (at existing prices). Nimble firms, startups with AI-first models, and big tech companies providing AI services (cloud AI, enterprise software) stand to gain. For instance, NVIDIA, the leading AI chip maker, saw its market cap explode in 2024 due to the AI boom (Nvidia's market value gets $2 trillion boost in 2024 on AI rally) (Nvidia dominates the AI chip market, but there's rising competition) – capital owners of AI tech are reaping rewards. Highly skilled workers or those who complement AI (e.g., AI researchers, data scientists, prompt engineers, or simply professionals who adeptly use AI to amplify their productivity) should also win. They become more productive and likely more valuable, possibly commanding higher pay (though even if their pay doesn’t rise immediately, their ability to handle more work could give them an edge). Society as a whole is a winner if AI-driven deflation comes with high growth – it could mean a new era of abundance where necessities and even luxuries become cheaper (akin to how the Industrial Revolution eventually made things like cotton cloth affordable to the masses). Historically, periods of “good deflation” saw broad gains – for example, in the 1870s-1880s, real wages rose and savings grew as living costs fell (The Great Deflation - Wikipedia).

Losers: On the flip side, workers in roles heavily displaced by AI could lose out, at least temporarily. If their skills don’t transfer easily and they face unemployment or pay cuts, that’s a personal loss and, potentially, a macroeconomic issue (less consumer spending). There’s also a risk of increased inequality – if the gains accrue mostly to AI owners and top talent, while average workers see stagnant wages, then even if prices fall, their relative position might worsen. Another group of losers might be firms or sectors that do not or cannot adopt AI quickly. They will face higher relative costs and likely get undercut on price. For example, a small manufacturer that doesn’t automate could be driven out by competitors who did and can sell cheaper. Similarly, some emerging market economies that rely on cheap labor manufacturing could lose as rich countries automate production and reshore it. If Bangladesh’s textile factories are outcompeted by largely automated “micro-factories” in the U.S., Bangladesh could suffer job losses and income loss – a form of global re-distribution of manufacturing. In macro terms, countries slow to adopt AI might see declines in their exchange rates or living standards relative to AI-heavy countries (some have warned of a widening gap between AI “haves” and “have-nots” internationally (AI Will Transform the Global Economy. Let’s Make Sure It Benefits Humanity.)).

Traditional energy and commodity sectors might also face complex effects. AI is an intensive user of electricity (data centers), which could increase energy demand and prices in that sector, so energy producers might actually gain in the short run (higher demand for power, thus possibly higher prices). But AI also optimizes energy use elsewhere, potentially reducing waste and smoothing demand peaks. Over time, if AI helps accelerate green energy adoption and efficiency, fossil fuel demand might drop, hurting that industry. Data and computing infrastructure providers (those who invest in fiber optics, cloud storage, etc.) could initially win from AI demand, but in the long run AI may optimize data usage to be more efficient, which could temper the boom.

In a deflationary environment, debtors can be losers (since money owed becomes harder to pay back if prices and wages are falling or stagnant). If AI causes actual deflation, someone with a fixed-rate mortgage or student loan might find their wages not rising as they expected, effectively making the loan costlier in real terms. Conversely, savers benefit (their money gains purchasing power). This is one reason central banks don’t want deflation – it redistributes from borrowers to lenders. Given how much debt economies carry, sustained deflation could be destabilizing.

Therefore, even if consumers benefit on one side, policymakers must monitor financial stability on the other. It’s a nuanced picture: broadly, efficient firms, consumers, and complementary workers win; displaced workers, lagging firms, and possibly some economies lose unless adjustments are made.

Finally, policy implications to manage this AI-driven deflationary era:

1. Education and Workforce Training: To avoid a large pool of “losers” in the labor market, policies must facilitate reskilling/upskilling. This means investing in education (STEM and beyond) and vocational training tailored to an AI-rich economy. It could involve public-private partnerships to train workers in using AI tools in their jobs. If productivity gains are to translate into broadly higher incomes (and not just lower prices), workers need to move into more productive roles with AI rather than being sidelined. Governor Cook highlighted that workers will need support in learning new skills and even changing occupations due to AI (Economic potential of generative AI | McKinsey). Government funding for continual learning programs, subsidies for companies that retrain (instead of lay off) employees, and perhaps a stronger emphasis on STEM in early education are all on the table. Essentially, to complement AI rather than be substituted, the workforce must adapt – and that requires deliberate policy.
   
   

2. Social safety nets and redistribution: Even with training, there will be churn. Ideas like Universal Basic Income (UBI) have gained traction as a response to AI-induced job loss. Sam Altman himself is a proponent of UBI, suggesting that as AI creates great wealth, a portion could be redistributed to ensure everyone benefits and no one is left destitute (Will Universal Basic Income Save Us from AI? - OpenAI’s Sam Altman believes many jobs will soon vanish but UBI will be the solution. Other visions of the future are less rosy : r/Futurology). While UBI at a national scale is politically challenging, we might see movement toward more robust unemployment insurance, wage insurance (compensating some fraction of lost wages when workers have to take lower-paying jobs), or even targeted basic income trials. Tax policy might also shift – for instance, higher taxes on capital or tech monopolies and using that revenue to fund social programs or wage supplements. The goal of these measures would be to maintain aggregate demand (so that deflation results from productive efficiency, not demand collapse) and to ensure public support for the AI transition (people won’t support tech that only undermines their livelihood). Historically, when technology displaced workers (like mechanization in agriculture), societies coped through migration, new job creation, and in some cases social policies (e.g., Social Security and New Deal policies in the 1930s softened the blow of rapid productivity in farming and industry). For AI, novel policies like UBI or guaranteed government jobs might enter mainstream debate if unemployment rises.
   
   

3. Competition and Antitrust Policy: To realize deflationary benefits, AI tech must be competitive and widely accessible, not controlled by a few firms who could become rent-seeking monopolies. If only a couple of big companies hold all the key AI models and charge high prices for using them, they might keep prices for AI-affected goods higher than necessary (capturing the productivity gains as profit). Policymakers might need to enforce antitrust laws or at least promote open-source AI and a diverse tech ecosystem. For example, ensuring interoperability and preventing incumbents from locking out new entrants will encourage competition that passes on cost savings. The fact that “tokens” and AI models are becoming commodities, as Jensen Huang describes (every company becoming an “AI factory” producing tokens) (Jensen Huang Thinks the Company of the Future Will Be an 'AI Factory' - Business Insider) (Jensen Huang Thinks the Company of the Future Will Be an 'AI Factory' - Business Insider), suggests a world where many players use AI. But the infrastructure (cloud computing, foundational models) is currently dominated by a handful (OpenAI/Microsoft, Google, Amazon, etc.). Regulators might look at that concentration. The EU’s AI Act, for instance, is trying to set guardrails so that the AI market remains fair and safe (The Role of AI in Developing Resilient Supply Chains | GJIA). Pro-competition policies could include funding for open AI research (so more entities develop capabilities) or even treating key AI algorithms as public goods in some cases.
   
   

4. Monetary Policy Adjustments: The Fed and peers might consider strategies for a low-inflation era. This could mean accepting that inflation runs below 2% at times due to AI, as long as growth and employment are good (essentially tolerating “benign deflation”). Some economists might push to raise the inflation target to, say, 3% so that hitting 1% (if AI pushes down) isn’t as problematic relative to target. The Fed could also revisit tools like quantitative easing or even direct financing if conventional cuts aren’t enough to stimulate demand in a deflationary environment. In a scenario of persistent mild deflation, the Fed might need to employ unorthodox measures (like Japan did – asset purchases, yield curve control) to prevent expectations of falling prices from setting in. Coordination with fiscal policy could become important: for instance, a standing fiscal facility that automatically sends money to people (helicopter money) if inflation is too low and unemployment too high, effectively bridging monetary and fiscal actions. These are somewhat radical ideas, but the point is central banks would not be helpless – they have learned from two decades of low inflation in many places and have an expanded toolkit. The dual mandate might even implicitly shift to put more weight on employment if inflation is consistently under control.
   
   

5. Fiscal Policy and Public Investment: Governments may need to step up investment in areas that AI won’t automatically fix. For example, while AI can reduce costs of many things, housing costs depend on zoning and construction – perhaps governments invest in affordable housing to ensure overall cost of living drops. Public infrastructure investment (like smart grids, transportation) could complement AI efficiencies and also provide employment. Additionally, if AI-driven inequality rises, more progressive taxation (e.g., higher capital gains or wealth taxes) could redistribute some of the gains and fund social programs. Such redistribution can also support aggregate demand – money in the hands of those more likely to spend it.
   
   

6. Energy and Internet Infrastructure: Since AI’s deployment hinges on robust energy and internet infrastructure, policy should ensure those scale up affordably, otherwise bottlenecks there could create inflation (e.g., electricity shortages driving up power prices). Wells Fargo projects AI-related power usage to rise more than 6-fold by 2026 and another 12-fold by 2030 (AI Power Consumption: Rapidly Becoming Mission-Critical - Forbes). Meeting that with sustainable energy (and upgrading grids) will require policy support (incentives for renewable energy, perhaps direct government build-outs). If done right, energy supply keeps pace, keeping energy prices stable even as AI demand soars. If not, we could see energy inflation eating into the deflationary benefits elsewhere. Likewise, ensuring widespread high-speed internet (possibly treating it as public infrastructure in rural areas) will help diffuse AI’s benefits geographically and across all communities.
   
   

7. International Collaboration and Aid: To address the uneven global effects, advanced economies might support developing ones with AI technology and investment. This could prevent a scenario where AI widens the global inequality gap by all growth accruing to a few tech-leading nations. It’s both an economic and geopolitical consideration – shared prosperity tends to be more stable. For instance, training programs or providing AI tools for agriculture in developing countries could boost their productivity (lowering food prices globally, another deflationary push, while raising their incomes). The World Bank, IMF, and others are starting to look at AI’s impact on emerging markets (AI Will Transform the Global Economy. Let’s Make Sure It Benefits Humanity.) (AI Will Transform the Global Economy. Let’s Make Sure It Benefits Humanity.); policies could include technology transfer programs or global standards to ensure even small nations can access AI for their benefit.
   
   

In essence, policy needs to guide AI towards “good deflation” and mitigate the social downsides. Ideally, AI makes everything cheaper and people still have good jobs and income to enjoy those cheaper goods – that’s a world of increasing abundance and broadly shared prosperity. Achieving that means actively managing the transition: investing in people, keeping competition fair, updating social contracts (perhaps through UBI or similar), and being ready to stabilize the economy if deflation gets out of hand.

To conclude, artificial intelligence holds the potential to be a truly deflationary force in the global economy over the next decade, especially in the U.S. which is at the forefront of adoption. Productivity is poised to leap, costs in many industries are likely to fall, and consumers could see benefits from cheaper goods and services. Central banks might find inflation easier to tame – perhaps too easy – and need to adjust to a low-inflation paradigm. The historical echoes from the Industrial Revolution and the IT revolution suggest that if handled wisely, such periods of rapid technological change can deliver higher living standards (and yes, sometimes falling prices) without descending into economic malaise. The next ten years will test our ability to integrate AI in a way that lifts all boats. The deflationary pressure from AI is not something to fear in itself – it’s a sign of progress and efficiency. The key is ensuring that the fruits of that efficiency are widely distributed: that displaced workers find new opportunities, that consumers’ greater purchasing power is realized, and that the economy’s institutional frameworks evolve (the Fed, labor laws, education, etc.) to support a new era. If we succeed, we could witness a scenario akin to the late 19th-century “Great Deflation” but without the hardship – a sustained period where innovation drives prices down and wealth up, creating a foundation for broad prosperity. As one Morgan Stanley analysis succinctly noted, higher productivity from AI “would help offset inflation” (Sam Altman Spoke at a Private Conference. Here's What Was Discussed. - Business Insider). In human terms, that means the technology could allow us to have our cake and eat it too – robust growth with stable or even falling costs of living. Achieving that nirvana will require proactive policy and perhaps some bold experiments (UBI? new education models? international tech accords?), but the prize is worth it: an AI-powered economy that works for everyone, making everyday life more affordable and more abundant.

Conclusion

AI’s deflationary impetus is not just a technocratic notion – it’s something that could tangibly improve daily life by making things more affordable, so long as we navigate the transition well. The next decade will likely see AI pervade the economy, boosting productivity in the U.S. to heights not seen in a generation and exerting downward pressure on prices from factory floors to doctors’ offices. This should be cause for optimism: after battling high inflation in recent years, we may be entering a period where technology does some of the Fed’s work for it, “bringing inflation back to target” and perhaps below (The impact of artificial intelligence on output and inflation). But it’s also a call to action to ensure that falling prices result from abundance, not lack of opportunity. With smart policies to support workers, promote competition, and manage macroeconomic stability, AI-driven deflationary pressures can be harnessed for good – delivering an era of cheap goods, accessible services, and rising quality of life. The coming challenge for policymakers, businesses, and society is to adapt to this new reality where, as OpenAI’s Sam Altman reminds us, “AI could be deflationary” (Sam Altman Spoke at a Private Conference. Here's What Was Discussed. - Business Insider) – and that can be a very good thing, if we make the right moves.

Deflation need not equal depression; with AI, it could well signify an economy racing ahead, not spiraling downward. History has seen good deflation before, and with the lessons learned and tools at our disposal, we have a chance to repeat that feat in the 21st century – this time supercharged by artificial intelligence.

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