The Deflationary Impact of AI, AGI and Super-Intelligence on the Future of the Global Economy
August 7, 2025
Authors
Thomas H. Sorensen, Founder and CEO of Oneday Lab
Walther V. Bech Sorensen, Principal at Oneday Lab
Inflationary Headwinds: “Tariffs Turbulence” and Climate-Driven Food Shocks
The deflationary power of Artificial Intelligence (AI), Artificial General Intelligence (AGI) and (eventually) Super-Intelligence may be stronger than most people realize. Before analysing those forces, we need a clear view of the inflationary pressures now pushing prices the other way. Two stand out today:
A man-made, near-term wave of protectionism (“tariffs turbulence”) that raises input costs and squeezes supply chains.
A structural, climate-driven squeeze on global food production that threatens to embed higher prices in the world’s most inelastic consumer category – food.
Tariffs as a Short-Term Inflation Shock
A sequence of tariff hikes announced since early 2025 has lifted the effective U.S. import-tariff rate to levels “not seen in a century,” according to the IMF’s April 2025 World Economic Outlook. The Fund’s July update warns these measures behave like a negative supply shock, passing through to U.S. consumer prices in H2 2025 even while inflation elsewhere drifts lower.
Micro-data bear this out. According to a Reuters corporate-earnings analysis (5 Aug 2025), industrial and consumer-goods manufacturers report raw-material and component costs running 6 – 7 % above a year earlier, squarely blaming new border levies. Firms such as Caterpillar and Molson Coors explicitly cite tariffs as the main driver of cost inflation.
Importantly, this bout of price pressure is reversible. If forthcoming tariff rounds are scrapped – or earlier rounds rolled back –IMF simulations show U.S. headline inflation converging toward the 2 % target within 6 – 9 months. Tariffs turbulence is therefore a policy-contingent risk: powerful in the short run yet discretionary in nature.
Climate Change as a Long-Term Inflation Engine
Tariffs can, in principle, disappear overnight. The climate crisis cannot – making its inflationary footprint deeper and longer-lived. Global cocoa prices nearly tripled during 2024 as extreme heat, shifting rainfall patterns and swollen-shoot disease slashed West-African yields. According to a Reuters investigation Ghana harvested its smallest crop in 20 years, while Côte d’Ivoire suffered its weakest output in eight. El Niño piled extra stress on plantations, pushing futures above US $8,000 / t early in the year, which is about eleven times the contemporaneous price of crude oil by weight. Confectionery makers from Hershey to Britannia have warned of further consumer-price hikes as earlier hedges roll off.
The squeeze is not limited to “luxury” crops. A March 2025 paper in Field Crops Research projects potato yields in parts of East Africa to fall 6 – 27 % by mid-century, even under moderate warming. Consequently, Chinese agronomists are already testing heat-tolerant varieties in simulated end-of-century conditions to avert what they call a “future starch shock.” Staple-food inflation hits low-income households hardest and is far stickier than price swings in manufactured goods because calories are non-discretionary.
Climate-driven food inflation is far more serious than tariff-driven inflation
There are significant differences between tariff-driven inflation and climate-driven food inflation, and understanding these distinctions is essential to assessing the risks each poses.
Tariff-driven inflation stems from policy decisions. It is a product of political choice and can be reversed relatively easily by removing or suspending those tariffs. Its impact is felt quickly, often within months, and tends to concentrate within specific sectors – particularly traded goods like industrial components, machinery, and consumer products. Because of this narrow scope and the fact that tariffs can be rolled back, it is considered highly reversible. The main policy tools available to address this form of inflation include trade negotiations and tariff adjustments.
By contrast, climate-driven food inflation originates from the physical realities of a changing environment. It is not dictated by policy but by global ecological shifts, such as rising temperatures, droughts, floods, and changing weather patterns that affect agricultural productivity. This form of inflation builds up gradually over time, but it is cumulative and persistent. Unlike tariffs, the effects of climate change cannot be undone quickly. Addressing them requires long- term investments in climate mitigation strategies and research into resilient agricultural technologies.
Climate-induced inflation also tends to be more widespread because food is a universal necessity, affecting all populations, particularly the most vulnerable. It embeds long-term upward pressure on food prices and presents a much more difficult challenge for central banks, which must balance inflation control with economic growth.
In summary, tariff-driven inflation is a short-term, man-made shock that can be switched off through policy decisions. Climate-driven food inflation is a long-term structural challenge rooted in physical reality, requiring decades of adaptation. The former is discretionary; the latter is enduring.
Tariffs turbulence therefore represents a transitory, man-made headwind – one monetary and trade authorities can, at least in theory, switch off. Climate-linked food shocks, by contrast, embed a persistent upward drift in the price level, challenging central banks’ ability to keep expectations anchored without suppressing growth.
Why This Matters for the AI-Deflation Narrative
AI-driven automation, algorithmic optimization and eventually AGI-powered research breakthroughs promise powerful deflationary forces – lower marginal production costs, faster productivity growth, even new materials, or crop varieties. Yet those forces will emerge against a backdrop of episodic (tariff) and structural (climate) inflation pressures. Understanding that tension is crucial before we examine how AI, AGI and Super-Intelligence might tip the balance.
The Age of Abundance: From Digital Intelligence to Physical Productivity
A growing circle of Silicon-Valley insiders argues that AI’s compounding breakthroughs will push humanity beyond material scarcity.
According to Sam Altman (OpenAI) the falling costs for intelligence and energy will make the world much more abundant and much better every year enabling people to hand routine work to machines and pursue the highest tier of Maslow’s hierarchy – self-realization in art, sport, literature or any leisure they value most. Eric Schmidt (former Google chairman) makes a similar case, contending that AI can offset shrinking labor forces and “significantly boost productivity” even in ageing societies.
AI Meets Humanoid Robotics: Taking Abundance into the Physical World
Digital models alone cannot deliver food, assemble homes, or repair infrastructure. Achieving abundance therefore hinges on embodied intelligence – robots able to perceive messy real-world environments, learn on the fly and manipulate objects with human-like dexterity.
2025 is already dubbed “the year of the humanoid robot factory worker,” as Tesla, Figure AI and Boston Dynamics begin pilot deployments on automotive and electronics lines. According to WIRED’s Spring 2025 survey, early adopters cite 24/7 uptime and rapid re-tasking as decisive advantages over fixed industrial arms.
Market researchers expect mass-production of general-purpose humanoids to begin this year, with hardware prices dipping below US $80,000 per unit as actuator and sensor volumes scale. Once robots become a subscription – “robot-as-a-service” contracts already exist – labor availability ceases to bind many physical tasks, exerting deflationary pressure on everything from construction to elder care.
Autonomous Vehicles as the First Scalable Embodiment
Road traffic is a semi-structured environment that sensors and HD-maps already model with high fidelity, and transportation is economically huge: U.S. households devoted 17 % of total spending to it in 2023, more than food at home or healthcare. Strong points are:
Miles at scale. Vehicles in the Autonomous Vehicle Industry Association have logged 145 million fully autonomous miles on U.S. public roads – more than double a year earlier.
Safety delta. SAE-Level-4 robotaxis in Phoenix and San Francisco show collision rates significantly below human baselines.
Cost structure. In long-haul trucking, fuel and driver wages account for >60 % of per-mile costs; autonomous rigs slash the labor component and optimize speed for fuel efficiency
Expected Macroeconomic Impact
Autonomous vehicles, especially in the form of robotaxis and self-driving trucks, are expected to generate profound macroeconomic effects, both in the near term and over the long run.
In the near term (2025 to 2030):
Households will experience lower transportation costs per mile, along with a reduced need for private car ownership. This will increase discretionary income as a smaller portion of household budgets is spent on transport. Meanwhile, the labor market will undergo a disruptive shift. Roughly 3 million driving-related jobs in the United States alone are projected to be displaced due to automation. However, this loss will be partially offset by the creation of new, higher- paying roles in fleet operations, mapping, remote vehicle supervision, and maintenance.
Autonomous trucks operating 24/7 will also enhance productivity and efficiency in logistics. By significantly reducing delivery lead times – up to 40% – businesses will require less working capital to be tied up in inventories, enabling more streamlined operations and greater agility in supply chains.
In the longer run (post-2030):
The shift in household budgets away from car ownership and toward on-demand mobility services will cause transportation’s weight in the Consumer Price Index (CPI) to decline, contributing to a sustained disinflationary trend across the economy.
Labor markets will evolve further, as the automation of driving accelerates a broader societal transition toward service-oriented and creative occupations. This shift may allow more people to dedicate a larger share of their lifetimes to leisure, learning, and personal development – central to the broader "Age of Abundance" vision promoted by AI futurists.
At the same time, supply chains will become increasingly efficient. When autonomous vehicles are paired with humanoid robots that can load and unload goods, logistics processes may become fully automated – or “lights out” – even for small and medium enterprises. This would allow for just-in-time production models to be viable at much smaller scales than before, making highly responsive, decentralized manufacturing feasible.
In sum, autonomous vehicles are poised to reduce household expenses, disrupt and reshape labor markets, and unlock significant productivity gains across supply chains – all of which reinforce the broader deflationary impact that intelligent automation exerts on the global economy.
Bridging to Abundance
If today’s inflationary shocks arise from tariffs and climate-stressed food supplies, tomorrow’s disinflationary counter-force will come from fleets of intelligent machines that shrink the labor, time and energy embedded in every good and service. Autonomous vehicles show how quickly an AI capability can leap from lab demo to billion-dollar cost savings; humanoid robots promise to replicate that curve across the entire physical economy. As Jensen Huang (NVIDIA) recently noted, the next big exponential growth in AI will be physical AI.
Now, let's have a look of how widespread robot adoption (physical AI) could reshape labor markets, income distribution and the possible need for universal basic income (UBI).
When humanoid robots begin to permeate factories, warehouses, construction sites and even homes, the very structure of work will shift in waves. In the first few years, repetitive manual roles in logistics, retail warehousing and basic manufacturing will be automated fastest, while new posts in robot-fleet operations, maintenance and AI-safety oversight absorb part of the displaced labour. Over the next decade, robots will expand into construction, hospitality and health-care support. Millions of people worldwide could find their current jobs redundant, yet a parallel boom in “cobot” occupations – where humans supervise or choreograph teams of machines – will emerge. By the time robots have mastered most physically intensive tasks, the bulk of human work is likely to coalesce around supervision, creative design, interpersonal care and other domains where empathy or imagination remain paramount.
Very high productivity gains accompany this transformation. As humanoids cut unit production costs across almost every good and service, a massive deflationary pressure builds. Prices fall faster than nominal wages can adjust, which means real purchasing power rises even for workers whose pay cheques stagnate. At the macro level, plunging prices will push nominal interest rates toward the floor and keep real rates low, making capital extraordinarily cheap. Cheap capital fuels even more investment in automation, reinforcing a virtuous circle of expansion without price inflation. In short, the economy enjoys a deflationary boom: real output races ahead while the general price level drifts gently downward.
Yet deflation and prosperity do not automatically translate into fair distribution. Unless ownership structures broaden, capital owners may capture a disproportionate slice of the new robot-driven surplus, sharpening wealth inequality even in an era of abundance. Here universal basic income (UBI) offers a pragmatic stabilizer rather than a mere safety net. If funded from an “automation dividend” – for example, a small levy on robot leases or returns on publicly held AI-equity stakes – UBI can recycle a share of automated profits back into every household. Pegging the benefit to a cost-of-living basket ensures its real value stays constant even as prices decline; coupling it with low marginal tax rates on earned income preserves the incentive to pursue fulfilling work.
With such mechanisms in place, governments can steer the economy toward a long-run equilibrium marked by low interest rates, vigorous growth and minimal inflation. Public borrowing becomes cheaper, giving states room to finance re-skilling programmes, digital infrastructure and further research. Private investors, meanwhile, direct plentiful, inexpensive capital into ever more sophisticated forms of automation, deepening the productivity reservoir. Living standards climb for virtually everyone, provided education systems and ownership models evolve as rapidly as the technology itself.
Conclusively, widespread use of humanoid robots could lead to very high productivity gains resulting in a massive deflationary pressure leading to very low interest rates, leading to a long-term economic boom without inflation.
Handled wisely – with forward-looking labor policies and a well-designed UBI – the Age of Abundance can translate that boom into broad-based prosperity rather than deepening inequality, completing the deflationary arc of AI, AGI and Super-Intelligence from silicon to society.