Thoughts on the Market

As AI demand surges, our Asia Energy Analyst Mayank Maheshwari discusses the new multi-trillion-dollar investment cycle to secure the power, fuels, grids and storage that keep modern life running.
Read more insights from Morgan Stanley.

----- Transcript -----

Welcome to Thoughts on the Market. I’m Mayank Maheshwari, Morgan Stanley’s Asia Energy analyst.
Today: how AI’s rapid growth is forcing Asia into a massive energy buildout across power grids, fuels, storage and dependable energy and power generation.
It’s Tuesday, June 9th at 8am in Singapore.
Every time you ask AI to draft a note, summarize a file, plan a trip or generate an image, the response feels instant and easy. But behind it sits a very physical system: data centers, electricity, cooling, fuel, metals, power lines, storage tanks and ships.
There is no AI without energy. And in Asia, the power and energy needs could get much bigger. And right now, we are at a critical inflection point where energy, AI, and security converge into [a] once-in-a-generation investment cycle.
We see a super cycle with $5 trillion plus in new investments in energy over next five years, almost double of what we have seen in the past decade. And this has global implications as Asia consumes almost half of the world's energy needs – but produces only about a third of it at home. Energy markets may be global, but energy insecurity is local. It shows up in electricity prices, fuel shortages, factory delays, food supply pressure and household budgets.
By 2030, Asia’s energy use could rise by about 38 exajoules. That increase is roughly equal to all the energy the Middle East consumes today. Power demand alone could reach about 19 trillion units a year when expressed in kilowatt-hours. That is around four trillion more units of electricity usage than in 2025, driven by data centers, industry, and onshoring of businesses.
AI is now part of that demand story. By 2030, data centers could use roughly one-sixth of all new power units in Asia. That makes AI a major new load on the power system.
Meeting this demand requires a major investment cycle. Asia’s annual energy investment could rise to roughly US$1.1 trillion a year over the next five years. Much of that spending goes into the power system itself: generation, grids, storage and the equipment needed to connect everything.
Grids may be the biggest bottleneck. Think of [the] grid as the highway system for electricity. You can build more power plants, but if the roads clog up, the power does not reach homes, factories or data centers. Asia’s grid investment needs could reach close to about US$1 trillion by 2030. Transformer lead times have stretched to years in some cases, which shows how tight the equipment supply chain has become.
The hardest part is keeping the lights on every hour of the day. Baseload power means electricity that can run around the clock. Asia is adding a large amount of renewable power to its energy infrastructure. But that source depends on when the sun shines or the wind blows. That is why coal, gas and nuclear remain part of the conversation.
Storage also moves from useful to essential. Batteries help smooth out renewable power demand when supply rises and falls during the day. Global energy storage installations could rise from about 500 gigawatt hours in 2025 to around 3,000 gigawatt hours in 2030.
Powering AI also reaches beyond electricity. Data centers need power, but the system around them needs dependable fuels, grids, batteries, metals, refining, storage and shipping. Electricity has to be generated, moved, backed up and supplied through physical infrastructure. That is why this story pulls in copper and aluminum for grids, fuel refining for transport and petrochemical supply chains, and fertilizers because energy security also connects to food security.
The future may look digital, but it will be powered by something far more physical: the largest energy buildout Asia has seen in decades.
Thanks for listening. If you enjoy Thoughts on the Market, please leave us a review wherever you listen and share the podcast with a friend or colleague today.

The Head of our Europe and Asia Technology Team, Shawn Kim, explains how AI’s appetite for memory chips is boosting the cost of everything from data centers to smartphones, with consequences that may reach far beyond the tech industry.
Read more insights from Morgan Stanley.

----- Transcript -----

Shawn Kim: Welcome to Thoughts on the Market. I’m Shawn Kim, Head of Morgan Stanley’s Europe and Asia Technology Team.
Today, we’re talking about chipflation – when memory chips stop getting cheaper over time, and become more expensive and even harder to find.
It’s Monday, June 8th, at 3pm in London.
Memory chips are easy to ignore, until your laptop slows down, your phone costs more, or your cloud bill jumps.
Memory is the computer’s workspace. It holds whatever the machine needs at that moment, whether that is a web search, a video, a spreadsheet, or an AI model answering a question. DRAM is the fast memory inside servers, PCs and phones. NAND is what stores files in solid-state drives. And HBM, or high bandwidth memory, is the high-performance version sitting right next to the AI chip, helping them move huge amounts of data quickly.
That last one – HBM – is key because AI has become intensely memory hungry. Memory prices have risen more than six-fold over the last year, a sharp break from decades when the cost of DRAM generally kept falling.
The pressure is coming from AI infrastructure buildouts. We see servers accounting for 59 percent of DRAM demand by 2028, up from 37 percent in 2023. We also see enterprise solid-state drives reaching 65 percent of NAND demand, up from 18 percent. And simply put, data centers are taking a much bigger share of the memory pie.
AI memory use is climbing fast, and at every scale. A newer AI chip uses 7.2 times more HBM than earlier generations. A full system uses about 65 times more. Across an entire AI data center buildout, the jump gets even bigger. HBM has gone from roughly 10 terabytes in 2020 to about 18 petabytes in 2026, orders of magnitude more.
This demand is running into a supply chain that cannot respond quickly. New memory capacity takes years to build, qualify and ramp up. Supply relief is a process, not a switch. And that creates a two-tier market. Large AI and cloud buyers can sign long-term agreements, prepay and secure priority access. Traditional buyers, including PC makers, smartphone makers and industrial hardware companies, must compete for what remains.
This impacts everyday products. In 2027, we see PC memory demand potentially facing a 15 percent shortfall, equivalent to about 58 million PCs. Smartphones could face a 12 percent shortfall, equivalent to about 134 million units. Companies may have to raise prices, cut specifications, delay launches, and accept lower profits.
The dollar numbers are striking. We see the memory market growing from about $220 USD billion in 2025 to about $890 billion in 2026. Expectations for 2026 memory revenue rose 71 percent in just three months. That implies roughly $600 USD billion of incremental memory revenue in 2026, more than the annual market for smartphones, PCs, or servers, each taken on its own.
The broader economy may not see a significant direct inflation shock. We estimate the direct impact on headline CPI at about 0.1 percent in 2026. But pressure is showing up in producer prices, in corporate margins, cloud costs, capital spending plans and delayed technology upgrades.
AI has turned memory from the cheapest part of the digital economy into one of its most contested resources. These tiny chips most people never think of may now decide what gets built or delayed, and how much we all end up paying.
Thanks for listening. If you enjoy the show, please leave us a review wherever you listen and share Thoughts on the Market with a friend or colleague today.

Trade policy is once again in the news with the announcement of new tariffs. Our Head of Public Policy Research Ariana Salvatore digs into why tariffs may not be a disruptive factor for markets this time.
Read more insights from Morgan Stanley.

----- Transcript -----

Ariana Salvatore: Welcome to Thoughts on the Market. I'm Ariana Salvatore, Head of Public Policy Research for Morgan Stanley.
Today, I'll be talking about how investors should be digesting the latest tariff headlines and what they could mean for the broader economic and market outlook.
It's Friday, June 5th at 10am in New York.
Tariffs are back in focus as the U.S. administration has proposed new levies following Section 301 investigations into more than 60 of our trading partners. At the same time, USMCA negotiations appear to have begun in earnest, with recent headlines focused on autos, including the possibility of raising regional content requirements for vehicles and auto parts.
Now, at first glance, these developments sound like a meaningful escalation in trade policy. But we think these headlines are best understood as a continuation of the existing tariff regime rather than a new and more disruptive phase.
Let's start with Section 301. Listeners may recall that the administration replaced the IEEPA tariffs with Section 122 following the Supreme Court's decision back in February. However, that was done under a temporary authority that expires in the end of July. It's been our view that as we approach that deadline, the administration would seek to replace the existing regime under a new authority.
The conclusion of the Section 301 investigations is really a step in that direction; or said differently, a continuation of existing policy. We see the administration preserving the current tariff regime come July, but without a larger inflation or growth shock.
The second issue is the USMCA. Raising regional content rules may be part of the negotiation now, and those changes could create sector-level friction. Similarly, we think it's possible we see escalation ahead of the July deadline as all three countries work to improve the existing trade deal.
Now that being said, we're still constructive on the longer-term trade alignment between the U.S., Mexico, and Canada, and we see structural and procedural constraints that are going to limit the downside risk to something like a potential withdrawal from the agreement.
We still expect the USMCA carve-out to remain in place even for Section 301 goods on a range of trading partners. That's because we think the administration sees value in maintaining supply chain integration within North America across a number of sectors. In general, we actually think the recent pattern on tariffs has been toward less, not more, trade pressure at the margin.
Recent months have come with several carve-outs, exemptions, and delays on broad-based and sectoral tariffs. That suggests that the administration is still sensitive to the downstream cost impact of tariffs, and of course, affordability matters politically heading into the midterm elections in November.
That view also fits with our broader U.S. economics outlook. Our economists continue to see a relatively benign macro backdrop. Growth is expected to remain trend-like, with consumer spending slowing but not collapsing, and strong AI-led CapEx offsetting some of the drag from higher energy prices and policy uncertainty.
On inflation, tariffs remain part of the story, but much of the pass-through appears to be already in the data. That pairs with a more constructive outlook for equity markets as well, as our strategists there see a strong earnings story supported by things like positive operating leverage, AI adoption, improving pricing power, and a broadening out in earnings growth.
So, the key message for investors is this: tariff policy is still noisy, and it will remain a source of headline risk. But in our base case, the administration is moving toward a more durable version of the current tariff regime, not a materially more disruptive or restrictive one. Section 301 replaces Section 122, the USMCA carve-out stays in place, and selective exemptions continue where the affordability or supply chain costs are too high.
Thanks for listening. As a reminder, if you enjoy Thoughts on the Market, please take a moment to rate and review us wherever you listen, and share the podcast with a friend or colleague today.

Our Global Commodities Strategist Martijn Rats discusses why the restart of oil flows through the Strait of Hormuz may be slower and tighter than the market expects.
Read more insights from Morgan Stanley.

----- Transcript -----

Welcome to Thoughts on the Market. I’m Martijn Rats, Morgan Stanley’s Global Commodities Strategist. Today – how fast can Middle East production return?
It is Thursday, June the 4th, at 3pm in London.
Every time you pull into a gas station, those prices are staring back at you. What you see at the pump is just the front end of a global system we’ve been watching for months: tankers, storage, insurance, and shipping lanes, all still constrained by the Strait of Hormuz. But while prices at the pump are still high, Brent has actually fallen back to around about $92 a barrel.
In inflation-adjusted terms, today’s Brent price is actually right at the 50th percentile of the last 20 years – suggesting that the market is assuming a clean, near-term recovery in supply. Yet the disruption continues to be extraordinary. Roughly 11 million barrels per day of Gulf crude remains offline, close to half the region’s pre-conflict output.
We think the market may be too optimistic. Our working assumption is now that meaningful export recovery through the strait begins only in the second half of July. Even then, normal does not return with the flip of a switch.
First, ships need to be willing to sail. Owners and insurers need confidence that the waterway is safe. If mines remain in traditional shipping lanes, the strait can be technically open but still operate at reduced capacity. Clearing that risk can take weeks, and potentially several months.
Second, the tanker fleet is in the wrong place. When ships cannot work in the Gulf, they move elsewhere. Bringing enough empty tankers back to lift crude takes time.
Third, storage is a limiting factor. Oilfields cannot restart if export tanks are full. For producers that rely heavily on seaborne exports, empty tankers are therefore essential.
Last, oilfields themselves need restarting. Before the closure, around 36,000 wells were active across six Gulf producers. Roughly 10,000 of those are currently offline. After a shut-in of nearly five months, about 4,000 to 5,000 wells could face restart constraints. Reservoir pressure can decline, equipment can fail after sitting idle, and flowlines need cleaning and safety checks.
All told, around 75 percent of lost supply can probably come back within four months after flows through the Strait of Hormuz resume. But the final 25 percent may take well into 2027.
So why have prices not moved more? The market began this shock with buffers. Inventories were elevated, oil-on-water was high, and emergency relief releases helped. The U.S. increased seaborne net exports of crude oil and refined products from roughly 5 million barrels a day to 9 million barrels a day. At the same time, China’s seaborne net oil imports fell from around 13 million barrels a day a year ago to just over 7.5 million a day over the last 30 days.
But these cushions are thinning. Strategic reserve releases are scheduled to drop from about 2.5 million barrels per day in April through June to about 0.7 million in July and August. U.S. gasoline and diesel inventories are already well below five-year seasonal lows. China is already on track for five consecutive months of unusually low crude buying for April through August delivery. But that starts to raise the probability that Chinese buyers return for September barrels. Buying for September typically starts mid to late June.
Now, oil is trading like the disruption is nearly over. But at the same time, the physical system is telling a slower story. Prices may look calm on the screen, but the bottleneck is in tankers, storage tanks, wells, and crews.
Our Brent forecasts remain $110 per barrel for the second quarter and about $100 a barrel for the third quarter. We recently raised our estimates for the fourth quarter to $95 and the first quarter of 2027 to $85 a barrel, and expect a return to $80 eventually thereafter.
Thanks for listening. If you enjoy the show, please leave us a review wherever you listen and share Thoughts on the Market with a friend or colleague today.

Chief Fixed Income Strategist Vishy Tirupattur takes a look at how credit markets are adapting to fund the new phase of AI capex.
Read more insights from Morgan Stanley.

----- Transcript -----

Welcome to Thoughts on the Market. I am Vishy Tirupattur, Morgan Stanley’s Chief Fixed Income Strategist.
Today – The critical question behind the AI-driven capex cycle that is front and center for markets year to date. How is credit market financing this ecosystem evolving?
It’s Wednesday June 3rd at 2 pm in New York.
When we first discussed the role of credit markets in financing the AI and data center build-out around the middle of last year, the direction of travel was clear. Realizing the transformative potential of AI requires unprecedented levels of capex. What has really surprised us since is the scale and speed of that spending, both of which have exceeded our expectations by a wide margin.
The upward revision to capex expectations has been dramatic. A year ago, we projected the combined capex of the five large hyperscalers at roughly $450 billion in both 2026 and 2027. After the first quarter earnings reports, Morgan Stanley’s internet equity analysts, led by Brian Nowak, now expect hyperscaler capex of roughly $800 billion in 2026 and $1.2 trillion in 2027. One data point really captures the surge in the underlying demand for compute. According to OpenRouter, the global weekly token usage, which is a key proxy for compute, has risen by roughly 350 percent since early January, increasing from about 6 trillion tokens to 28 trillion tokens.
Credit channels for financing this capex have not only been broader and deeper than we anticipated, spanning public and private markets, but have seen remarkable in the structural innovation that is blurring the lines between public and private markets. Over $200bn of public AI-related issuance across the different credit channels has happened just in the first five months of this year. We had previously assumed unsecured issuance would be limited by the scale of the largest non-financial issuers, confined to investment grade credit only, and largely USD denominated. Instead, some hyperscaler issuance has now far exceeded even the largest telecom names; funding has expanded well beyond USD into EUR, GBP, CHF, JPY and CAD markets. The issuer base has also broadened to include data center REITs and neoclouds, particularly in the high-yield market.
The scope of financing has also widened beyond the data center shells themselves. GPU financing, which we assumed would be funded entirely through equity capital, has begun to migrate into credit markets. Funding is now coming through broadly syndicated loans and asset based financing, with ABS structures not far behind.
Structural innovation illustrates how rapidly the credit ecosystem is adapting to the complexities of demands of AI-driven capex. Financings that combine elements of project finance, tranching, and residual value guarantees, along with high-yield issuance backed by hyperscaler guaranteed leases – these are innovations that we have never seen before. These structures have expanded the investor base, reduced the funding frictions, and further blurred traditional boundaries – between both corporate and project finance, and public and private credit markets.
At the same time, physical, operational, and political constraints are beginning to shape the pace and the composition of the AI infrastructure build-out – and, by extension, the demand for financing. Grid access, power generation equipment, skilled labor, and permitting delays are emerging as significant constraints. These are compounded by political and regulatory frictions at the local, national, and international level. As power availability becomes a gating factor, the AI build-out is likely to pull energy infrastructure financing more tightly into the orbit of AI infrastructure financing.
The clear takeaway is this. The capex requirements underpinning AI infrastructure are expanding exponentially, and with them the role of credit markets in financing this build-out. Along the way, there will be winners and losers, periods of adjustment, and a range of physical, financial, and political constraints that shape outcomes on the margin.
But the broader trajectory is certain. The scale, duration, and strategic importance of AI infrastructure investment mean that financing of this will remain a defining theme for credit markets and credit investors for years to come.
Thanks for listening. If you enjoy the podcast, please leave us a review wherever you listen and share Thoughts on the Market with a friend or colleague today.