The desk maintains that the AI boom will continue to reshape the global economic landscape in 2027, with significant implications for productivity, investment, and inflationary pressures. Per the full note , rising investments supported by robust demand place challenging constraints on future growth, suggesting we may be reaching a peak in the US's contribution from AI. Meanwhile, risks associated with financing structures are growing, as reliance shifts from cash flows to more complex debt arrangements, hinting at broader economic implications and potential volatility in the market.
What the desk is arguing
The desk posits that while the AI investment boom will persist into 2027, its contribution to continuous growth in the US economy is likely nearing its zenith. Per the full note , the sheer scale of investment in AI infrastructure—extending beyond traditional sectors into energy and support systems—demands careful monitoring as the economic landscape evolves.
Supporting evidence indicates that today’s technological giants, unlike during the dot-com era, are less leveraged and financially healthier, but face pressure from rising costs in labor and resources, reflecting current inflationary trends. The expected productivity gains from AI may take longer to materialize than initially hoped, as they become entangled with immediate inflationary pressures in energy and skilled labor costs.
Where it sits in our coverage
Our consensus target for the relevant pair reflects a stable outlook, pegged at 1.075, with a range between 1.04 and 1.12. Notably, firms such as: - jpmorgan: 1.10 (Mar26) - bofa: 1.04 (Mar26)
The desk's outlook is consistent with jpmorgan's bullish stance, situated at the upper end of the consensus range, while diverging from bofa, which anticipates lower valuations. This positioning suggests a belief that the AI narrative may play a crucial role in driving currency dynamics as investor sentiment adjusts to the macroeconomic implications.
How other firms see it
Across the board, firms aligned with the desk's bullish perspective on AI include jpmorgan and goldmansachs, both foreseeing potential price stability bolstered by ongoing AI investments. In contrast, firms like bofa maintain a more cautious outlook, highlighting risks stemming from over-leveraged investment structures.
The anticipated developments in USD prominence could influence currency pairs such as EUR/USD, especially as the ECB navigates its own policy adjustments surrounding inflation and investment in technology amidst the broader AI transition.
01The AI boom is expected to continue driving investment growth into 2027 despite reaching peak contributions to US growth.
02Inflationary pressures are emerging from increased demand for energy and skilled labor in the AI sector.
03Current financial structures are shifting towards debt and private credit, raising risks of market volatility.
04The most significant gains may accrue to those companies effectively integrating AI rather than merely those building infrastructure.
Market implications
Traders should watch for movements near the consensus target of 1.075, as fluctuations tied to AI-driven sentiment could impact positioning within the FX market. Continued focus on sectors benefiting from AI investments may also reshape investor strategies ahead of upcoming economic indicators.
Risks to this view
Should the anticipated productivity gains from AI investments fail to materialize in a timely manner, or if inflation continues to unsettle financial markets, this could prompt a reassessment of growth forecasts. Additionally, unexpected geopolitical developments could also redirect investment flows away from AI-centric sectors.
Articles AI in 2027: 20 questions for the global economy Published 08:10 AI Translate Share X LinkedIn E-mail Copy link Share X LinkedIn E-mail Copy link Download The AI boom is reshaping growth, productivity, trade, jobs and markets. Investment still has room to run in 2027, but financing is getting riskier and expectations are sky-high. We ask how long it can last, who stands to gain and what happens if the promised returns fail to arrive Carsten Brzeski , Bert Colijn , James Knightley , Deepali Bhargava , James Smith , Lynn Song , Jan Frederik Slijkerman and Amrita Naik Nimbalkar In a nutshell The AI boom still has room to run.
Investment is likely to continue rising in 2027, supported by strong demand and constrained supply. But its contribution to US growth is probably close to its peak as the sheer scale of investment makes further acceleration harder. The boom is entering a new phase.
The story is expanding beyond chips and data centres into power generation, grids and cooling. At the same time, financing is shifting gradually from hyperscalers' enormous cash flows towards debt, private credit and more complex structures, increasing the risks if expected returns fail to materialise. The economic effects are becoming broader and more complicated.
AI is supporting US growth and global technology supply chains, but may also be diverting investment from other parts of the economy. It is adding to inflationary pressures today through demand for energy, equipment and skilled labour, while the hoped-for productivity and disinflation benefits are likely to take longer to emerge. The winners will not necessarily be those building the most AI infrastructure.
Europe and Asia are benefiting from critical positions in the supply chain, while ultimately the largest gains may go to companies and economies that use AI most effectively. Our base case is a plateau, not a bust. Today's leading technology companies are highly profitable and much less leveraged than their dotcom predecessors.
But expectations are exceptionally high. The crucial question for 2027 is whether AI revenues and productivity gains begin to justify the extraordinary investment. If they do not, overcapacity, tighter financing and falling valuations could turn a plateau into a much harder landing.
How big, how long? Railroads, electrification, dotcom fibre: how does today's AI boom compare with the great investment manias of the past? Bigger than the dotcom boom, more important for growth and, so far, more safely financed.
Investment in US data centre structures, computers and software reached 5.6% of GDP in the second quarter of 2026, up from 3.8% in 2022, or 5.2% once imported hardware is netted out. That already puts the current boom above the dotcom peak in 2000, when business investment in computers, communications equipment and software reached about 4.5% of GDP, although still below Britain’s railway mania of the 1840s, when railway investment alone reached roughly 6-7% of GDP. So while this is not the biggest investment boom in history, it is already bigger than the last great technology boom.
And it is having a much bigger economic impact: IT investment accounted for 16-18% of US GDP growth in 1998-2000; AI-related investment now accounts for 37%. The biggest difference though is how this boom is being financed. Railways and telecom networks were built on debt and ended in defaults, largely through unrated railroad bonds in the 19th century and high-yield telecom debt after 2000.
This time, investment has so far been funded mainly from the cash flows of some of the world’s most profitable companies, with debt and equity only now being added at the margin. The assets are different, too. Rails and fibre lasted for decades.
Data centre shells, grids and power plants will also have long lives, but GPUs may have an economic life of just three to six years. That much shorter shelf life is central to the debate over whether today’s investment will generate sufficient returns. Still, the payoff could come faster.
Factories took decades to redesign their floors around the electric motor. AI coding tools are paying off within months, and adoption is already disrupting software, accounting, online retail and advertising. The common thread is that transformative technology does not necessarily mean good returns for those who finance the transformation.
The Nasdaq lost almost 80% between 2000 and 2002, yet the fibre laid during the boom went on to underpin the streaming economy. Investors build the road; others get to drive on it. How much more will be invested in AI in 2027, and is the boom still accelerating?
Still accelerating in dollars, but probably peaking in its contribution to growth. Momentum remains strong. Taiwan's leading semiconductor companies report order books that are full on a rolling 18-month basis, with any new capacity snapped up as soon as it comes online.
Industry forecasts point to tight supply-demand conditions until 2028. Barring a wave of order cancellations or defaults, that points to another year of heavy AI investment in 2027, with hyperscaler capex expected to exceed US$1 trillion, increasingly geared towards AI infrastructure. So is the boom peaking?
In dollar terms, no. The constraint today is not appetite but supply: chips, power, permits and skilled electricians. When the bottleneck is the factory gate rather than the order book, spending is more likely to be stretched out than cut back.
But economists should be careful what they wish for. GDP growth responds to the change in investment, not its level. Investment that rises 50% a year adds a lot to growth; investment that rises 10% from a much bigger base adds far less; and investment that merely holds steady at a record high adds nothing at all.
As the base gets bigger, the arithmetic gets tougher. That is why we expect AI investment to keep rising in 2027 while its contribution to US growth should peak. What would signal a genuine top?
Shortening lead times for chips and equipment, cancelled or postponed data centre projects, capex guidance that stops being revised up, and a widening gap between investment and AI revenues. For now, the bigger risk is not that the money runs out in 2027, but that it keeps flowing so fast that the questions about returns grow much louder in 2028. Chips and data centres are only the first layer.
How big is the second wave of investment in grids, power generation and cooling? Bigger, slower and longer-lived than the first wave – and potentially more important for the macro outlook. As a rule of thumb, US$1bn buys roughly 25MW of fully equipped AI compute capacity.
Not all of the c.US$1 trillion of hyperscaler capex expected in 2027 will go to AI compute; some will fund conventional cloud infrastructure. But even if only half does, that implies more than 12GW of additional power demand – roughly the output of a dozen large nuclear reactors, needed around the clock. That is where the second wave comes in: transmission lines, substations and transformers, gas turbines and renewables, battery storage and industrial cooling.
In the US, grid connection queues and transformer lead times have already become binding constraints, and some developers are building their own generation on site rather than waiting for the grid. This second wave differs from the first in three ways. First, the assets last for decades rather than years.
A GPU may be obsolete by 2030; a substation built today will still be humming in 2060. That makes grid investment less vulnerable to a sudden AI downturn and more like the railway build-out of the 19th century. Second, it is slower.
Permitting, planning and construction take years, which spreads the investment impulse over time and could stretch out the cycle. Third, it is more widely shared: grid and power equipment draws on suppliers in Europe and Asia as well as US construction and engineering firms, so the benefits travel further. There is a catch.
Somebody has to pay for the grid, and in regulated markets that somebody is often the household electricity customer. Rising power bills are already turning data centres into a political issue in parts of the US, and they feed straight into the inflation debate. Chips are the sprint.
Power is the marathon – and the marathon may end up mattering more. Is this an American party, or does Europe get an invitation? Who supplies the picks and shovels?
So far it is very much an American party – but Europe supplies the catering, and may turn out to be the smarter guest. The investment gap is stark. US imports of computer equipment and semiconductors, a rough proxy for AI investment, have risen by about 258% since 2019, compared with 38% in the UK.
Europe's own build-out is held back by high energy prices, scarce grid capacity – think of the data centre moratoria in Dublin and Amsterdam – and fragmented capital markets. The EU's AI gigafactories initiative is an attempt to catch up. But Europe sells crucial picks and shovels.
ASML expects €44-45bn of sales in 2026 and says customers keep raising their equipment-demand forecasts because of the scale and duration of AI investment. Infineon expects its AI-related revenues to rise from around €1.5bn in FY2026 to €2.5bn in FY2027. Add electrical and grid equipment, cables, power generation and cooling, and Europe's industrial base is well plugged into the American build-out.
Asia has been one of the biggest beneficiaries of the AI boom. The gains extend well beyond Korea and China, which have benefited from surging demand for memory chips and advanced technology hardware. Across ASEAN, economies have also attracted significant FDI into semiconductors, electronics manufacturing, and supporting supply chains as firms expand capacity and diversify production networks.
More importantly, the broad economic gains from AI come from using it, not building it – and here Europe is surprisingly good. Uptake is strong, and European SMEs use AI almost as intensively as large corporates, because the upfront investment is low. Nor does AI look like a winner-takes-all technology: switching costs are low and network effects limited.
If model providers end up competing on price, the spoils go to the users – and Europe does not need to build nearly as many data centres to compete. Late to the construction site, Europe could still be early to move in. There is a silver lining, too.
Europe is far less exposed if the boom turns to bust. Its AI capex is smaller and the wealth channel much weaker: equities make up 13% of UK households' financial assets, versus 44% in the US. Missing the party also means skipping the hangover.
Which sectors stand to win most, and which risk being left behind? The winners sell the infrastructure or use AI to do more; the losers are those whose business model is the very task AI now performs. The clearest winners are the suppliers of the build-out: semiconductors and chip equipment, utilities and grid operators, electrical equipment, construction, cooling and data centre real estate.
They are already seeing it in their order books. Software is mixed. AI is a powerful selling point, but it also threatens the per-seat pricing model: if one employee with an AI agent does the work of five, the customer needs fewer licences.
Most at risk are legacy IT services and business process outsourcing, call centres, parts of professional services, and media and advertising models that depend on search traffic. AI-based services may also disrupt online retail, including online travel agencies, as AI assistants increasingly do the browsing and the booking. But don't write off every exposed sector.
The Jevons paradox – cheaper supply raising demand – could favour some of them. Legal services is a good example: if AI makes contract review or legal advice much cheaper, demand may rise faster than the cost per task falls. Growth may also come from unexpected places.
In healthcare, AI-driven drug discovery and diagnostics could open up new treatments, creating demand rather than merely cutting costs. Ultimately, productivity gains depend more on the tasks employees perform than on the sector they sit in. Workers handling large amounts of data, text or graphics are likely to see the largest gains.
The chart below, from our 2023 work, shows how the potential benefits of generative AI across tasks relate to the degree of digitalisation across sectors: the more digital a sector already is, the faster AI can travel through it. In short, the winners will be those that build AI or use it to work smarter. The losers will be those whose core activities can increasingly be automated.
Growth, prices and trade How much of US growth in 2027 will come from AI capex, and what would growth look like without it? Today, more than a third. In 2027, probably less – which is exactly why the rest of the economy matters.
At first glance, AI looks like the engine of the entire US economy. Data centre construction, equipment investment and software spending have grown at a pace that makes their headline contribution to GDP growth look spectacular. But much of that money leaves the country straight away: from Taiwanese chips to servers assembled abroad, the hardware is largely imported.
Netting out those imports, we estimate that AI-related investment accounts for 37% of US real GDP growth. Substantial, but a long way from the 'AI is the only game in town' narrative. The more uncomfortable finding lies outside tech.
Rather than lifting the overall growth rate, the AI boom seems to have cannibalised growth elsewhere. Strip out AI, and the rest of US private non-residential investment had fallen year-on-year for six consecutive quarters by mid-2026. Before 2021, tech and non-tech investment moved broadly in step; since then, they have moved in opposite directions.
One explanation: shareholders reward companies that embrace AI, so capital budgets are being redirected towards tech at the expense of everything else. So the boom isn't so much about creating additional investment as redirecting investment from elsewhere in the economy. What would US growth look like without AI?
Not simply 37% lower. Part of the capital, engineers and electricity absorbed by AI would have found other uses. A world without the AI boom would most likely show weaker growth, but not by the full amount of AI's contribution – and with a more balanced investment mix.
For 2027, our plateau base case implies a smaller AI contribution to growth. That would give the non-tech economy room to breathe – but only if it is ready to take over. For now, AI is a capex story, not a consumer story.
And capex stories have a habit of ending with a hangover. The productivity question: when, if ever, does AI show up in the statistics? In the US and China, it already does – partly.
The big gains are still to come, and may arrive abruptly. It depends on where you look. In the US, labour productivity has picked up markedly since the start of the AI boom.
Part of that comes from high-skilled services, exactly where you would expect AI to show up first. But part of it reflects the enormous data centre investments, which are capital-heavy and need very little labour. Total factor productivity – the best available measure of gains from technological progress – has improved decently in the US, but without an exceptional acceleration.
While AI-related sectors are providing a significant boost to several Asian economies, this largely reflects rapid investment and higher capital utilisation rather than a sustained improvement in underlying productivity. However, in China, we’ve seen labour productivity growth pick up in the last two years, breaking what had been an extended downtrend as Chinese companies race to incorporate AI in workflows. In the eurozone, which lacks comparable AI investment, labour productivity growth remains lacklustre, with very few exceptions, like for example, in the Netherlands.
The technology holds real promise for broad-based productivity gains. But reaping them requires wider adoption and investment in infrastructure and skills, both within companies and across the economy. That suggests a J-curve: heavy upfront intangible investment – time spent learning new skills and reorganising workflows – could initially mute measured productivity before the investment pays off, possibly quite abruptly.
Electrification is the classic precedent: the electric motor arrived in the 1880s, but factory productivity only took off in the 1920s, once factories had been rebuilt around it. And even then, a shift of the workforce towards sectors where productivity gains are harder to achieve – think healthcare and care work – would dampen the macroeconomic effect. Measurement doesn't help either: when AI makes a free digital service better, GDP statistics barely notice.
Still, after decades of slowing productivity growth, AI is likely to be a game changer. The question is not so much whether it will show up in the statistics, but when. In 1987, Robert Solow quipped that you could see the computer age everywhere but in the productivity statistics.
It took about a decade to prove him wrong. Is AI inflationary or deflationary? Electricity, memory chips and construction costs point one way; long-run efficiency gains point the other.
Inflationary today, disinflationary tomorrow – but don't hold your breath for the outright deflation Silicon Valley promises. It depends on your time horizon. For now, the effect runs in the inflationary direction.
AI's enormous energy demand is adding to the current energy shock, memory chip prices have surged, and the scramble for electricians, transformers and construction capacity is pushing up costs. Meanwhile, the investment boom is pouring fuel on the fire of a US economy already running with above-target inflation. In the longer run, once the technology lifts productivity, it should lower unit labour costs and exert disinflationary pressure across the economy.
Like most technological breakthroughs, AI will probably end up being disinflationary. But the effect will be concentrated in services and much smaller for goods with physical inputs. Algorithms can't make steel, copper or concrete cheaper.
There is also a less comfortable twist. AI allows companies to price dynamically, adjusting prices to what each customer is willing to pay and thereby capturing more of the consumer surplus. That could make some prices stickier rather than lower.
Together with the physical constraints, this makes the outright deflation Sam Altman envisages rather unlikely. For AI itself, the picture is different. As we argued in 2024 , we expect a downward price trend for AI services, as with other software categories, and at the sector level we continue to hold that view.
Competition between model providers should keep the pressure on. For central banks, this means the AI disinflation dividend is a story for the second half of the decade, not for 2027. Until then, the boom adds to demand faster than it adds to supply – the textbook recipe for inflation, not deflation.
How is AI reshaping global trade and investment flows, from Taiwanese chips to Dutch lithography? AI is redrawing global trade around a single supply chain – and rewarding the countries that own its choke points. AI is increasingly sorting the global economy into winners and laggards.
The IMF has raised its outlook for economies benefiting from the AI investment boom, particularly those embedded in the global technology supply chain, while countries outside it see much less of a boost. Taiwan is riding booming semiconductor demand, South Korea is benefiting from demand for high-bandwidth memory, and the effects travel upstream to the suppliers of advanced chipmaking equipment, including the Netherlands. This illustrates how AI is reshaping both trade and investment flows.
Spending on data centres and computing capacity in th