Countering China's weaponisation of its manufacturing dominance

China’s weaponisation of its manufacturing dominance, most famously through its control of rare earth magnets production, is generally accompanied by a narrative of resignation that its trading partners must live with this reality till they develop alternative supply chains. It is widely perceived that China has a definitive upper hand, and all other countries, including the US, must play catch-up. 

This begs a few questions. Isn’t China susceptible to imported products and services that are essential to its economy? Aren’t there rare earth equivalents that the US and Europe can restrict access to China, thereby bringing a bargaining equivalence between the two sides? What are those rare-earth equivalent dependencies for China today? What is the economic leverage that the West has over China that can be exercised in response to the rare earths restrictions? 

This post will examine this question in greater detail.

In a 2025 G-7 summit, the European Commission President Ursula von der Leyen aptly described China’s industrial policies as creating a pattern of “dominance, dependency, and blackmail”.

Having built up dominance and dependency, the blackmail is now intensifying. The hide your strength and bide your timephase is past. In the recent past, China has come up with several measures to leverage its dominance. 

China’s widely known weaponisation of its manufacturing dominance has been underpinned by a series of regulations that impose restrictions and penalties. Pre-empting efforts by Western multinationals to diversify away from China, in April this year, to “prevent security risks in industrial and supply chains”, the State Council issued Regulations on Industrial and Supply Chain Security to investigate and punish foreign firms that stop using Chinese suppliers in response to political pressure from their governments. This is a summary.

Under the new rules, regulators can question employees and examine corporate records during investigations. The regulations also allow the authorities to bar companies and individuals from leaving China if they are suspected of moving supply chains elsewhere under foreign pressure… The State Council, China’s cabinet, justified the measures as necessary to protect the country’s economic stability and national security… China’s global network of ports and port-management software gave Chinese officials detailed insight into multinationals’ supply chains, allowing them to detect when companies shift to suppliers elsewhere.

In February, it amended the state secrets law by broadening the scope of the type of information that would be considered a national security risk. It includes a new legal concept called “work secrets”, defined as information that is not an official state secret, but that “will cause certain adverse effects if leaked”. This broad sweep allows for interpretation as convenient for the government, and makes foreign companies and their employees further vulnerable. 

The restrictions are not confined to foreign companies. In early June, the State Council announced rules requiring national security screening for Chinese companies seeking to invest overseas. 

The rules also give the authorities new powers to scrutinize Chinese companies seeking opportunities abroad, subjecting them to national security reviews that place investments into one of three categories: encouraged, restricted or prohibited. Part of the motivation for this, lawyers say, is to keep money, talent and intellectual property in fields where China has a competitive edge from leaving the country… The measures restrict the movement of certain talent in sectors deemed sensitive, though Beijing has not defined which sectors qualify. They also give officials broader authority to review the movement of capital, including the power to force investors to sell shares or halt investments if national security concerns arise. The rules also lay the legal groundwork for regulators to bar foreign entities from investing or operating in China, including expelling them from the country, in retaliation for actions taken by their governments against Chinese investments.

In this context, it is surprising that even as China increases its bellicosity both in trade and in its foreign policy, especially the breadth and frequency of military actions in the Taiwan Straits, the response from the West has been remarkably muted. Doubtless, the dysfunctional nature of the Trump Presidency has been a major contributor. But the lack of proportionality of response from the US holds, even including the Biden Presidency. 

So what are the chokepoints and vulnerabilities that China faces from the West?

Ironically, China’s biggest vulnerability is in the very industry that it utterly dominates. China dominates the downstreamof electronics (assembly, packaging, volume manufacturing) and the upstream of raw materials (rare earths, gallium, refining), but it is deeply dependent on imports for the midstream - the tools and ultra-pure materials that actually make advanced chips. Underlining this reality, China imported $385 billion of integrated circuits in 2024, more than the $325 billion it spent on crude oil, making chips its biggest single import. 

China's vulnerabilities cluster at the highest-precision, most knowledge-intensive nodes, the "tools that make the tools." Photoresist is the cleanest analogue to rare earths: a narrow, chemically exotic input where Japan holds a near-monopoly and a cut-off would, in one analyst's framing, leave Chinese manufacturing with "no rice to cook with". China could meet only about 5% of its own demand for the KrF resists used in 110–180nm chips, and high-end localization was under 5% in 2022. In simple terms, semiconductor equipment is the broadest lever by value, and machine tools are the most pervasive across general manufacturing. More than 60% of the indigenous passenger jet C919’s components, including engines and flight controls, are imported.

The graphic below plots the levers (or chokepoints) that the West have over China in terms of how hard it bites and how long it would take China to replace them. The colour represents the usability of the lever, without unacceptable self-harm; the dashed arrows show the levers China is actively closing.

The red bubbles (EUV, photoresist, EDA) are near-monopolies a single country can switch off. The amber ones (equipment, foundry, CNC tools, bearings, jet engines, instruments) are oligopolies that only work as leverage if allies coordinate, which is exactly why the US has spent two years building the Netherlands-Japan-Germany-Taiwan coalition rather than acting alone. 

The West’s strongest cards (AI chips, EDA software, jet engines) are precisely the ones with a shelf life, because Beijing is pouring state money into domestic substitutes. The genuine bargaining equivalence lives in the top-right quadrant - frontier technology that bites hard and takes a decade-plus to replace (EUV lithography, leading-edge foundry). 

It is to be noted that the arrows point left because it points to China travelling in the direction of self-sufficiency. It highlights that while real and binding today, there is a declining shelf-life or option value with these chokepoints.

However, having said this, any escalation risks retaliation. In 2025, the US suspended engine and EDA exports, China tightened rare earths, and within weeks, both sides walked it back into a one-year truce because each could hurt the other badly. It also points to the value of a bargaining strategy where the West gradually introduce restrictions on multiple such products where China depends on imports. The restrictions should be phased in carefully and subtly by plugging the procedural and process links that China exploits to its advantage. 

In this backdrop, Ely Ratner and Nick Danby have a very good essay in Foreign Affairs that outlines the broad contours of a plan to identify and squeeze China’s vulnerabilities and do unto it what it is clinically doing by weaponising its strengths. The most obvious one is, as discussed above, to tighten the restrictions on access to the semiconductor chip supply chain.

China continues to leverage chip-smuggling networks, overseas data centers, and model distillation, a technique that exploits access to frontier AI models to replicate their capabilities. New policy measures should target the channels China uses to acquire restricted chips and supporting architecture, including shell companies and unlisted subsidiaries, as well as cloud-based access to U.S. computing power and servicing arrangements that keep older semiconductor manufacturing equipment operational. Equally urgent is synchronizing U.S. export restrictions with those of the Netherlands and Japan, whose companies—ASML and Tokyo Electron—control critical chokepoints in the advanced semiconductor supply chain. Although both governments began strengthening their own policies in 2023, their controls on equipment sales, servicing, and subcomponent exports to Chinese fabrication plants and toolmakers fall short of U.S. restrictions. Washington should press The Hague and Tokyo to close these gaps. If diplomacy fails, it should consider invoking the Foreign Direct Product Rule, which extends the extraterritorial reach of U.S. export controls to restrict products made with U.S. software or technology.

The authors also write that China’s huge export volume and $1.2 trillion trade surplus can be as much a liability as it is a strength, especially at a time when the economy is weakening and struggling for anchors of growth.

It should push back against China’s export surge by bringing advanced economies facing deindustrialization together with developing countries whose own manufacturing aspirations are being displaced. This coalition could then coordinate trade measures to protect their industries, including steel, shipbuilding, batteries, and drones. Alongside tariffs, the United States could pursue high-standard trade agreements that institutionalize requirements for subsidies, state-owned enterprises, and forced technology transfers that China cannot meet. Like-minded partners could also create an anticircumvention regime by strengthening rules of origin, sharing customs data, and imposing penalties on goods routed through third countries to avoid trade restrictions. They could further impose outbound investment screening to prevent companies or individuals in the United States and allied countries from financing Chinese capabilities that the controls seek to limit.

Notwithstanding its large reserves, for a country which imports three-fourths of its crude oil with 90% delivered through vulnerable sea routes, China is extremely vulnerable to energy security. 

Below the threshold of a full blockade, the U.S. Treasury Department, through the Office of Foreign Assets Control, can use maritime sanctions to dissuade shipping companies, insurers, brokers, and banks from supporting prohibited shipments. Pressure on insurance, port access, and flag registration would raise costs and create uncertainty for China-bound tankers without requiring direct military action. The Pentagon should nevertheless demonstrate its ability to disrupt or interdict China’s seaborne energy imports by exercising U.S. naval control over key chokepoints along energy trade routes.

Commodity imports are another chokepoint

China imports roughly 80 percent of its iron ore, a foundation of its steel industry, predominantly from Australia. And most of its copper and lithium inputs, which are critical to battery and defense manufacturing, come from Australia, Chile, the Democratic Republic of the Congo, and Peru. As with oil, these dependencies offer additional pressure points that can be leveraged to strengthen deterrence and compound China’s challenges across multiple sectors simultaneously. If Australia were prepared to restrict exports of iron and lithium ore, and the United States and its partners had a plan to tighten access to copper and cobalt, they would send a message to China that its industrial base could be easily disrupted and its defense production capacity degraded if circumstances warranted.

Finally, the US dollar’s dominance is the nuclear option available.

Were Washington to restrict China’s dollar access—moving from sanctions on banks supporting PLA activities to broad limits on dollar transactions in advanced technology and military manufacturing—it could impose severe costs on Beijing, disrupting Chinese financial markets and potentially triggering wider economic instability… Washington must prepare for this scenario by first communicating unambiguously that only severely destabilizing acts would trigger consequences of this magnitude: for example, large-scale cyberattacks on critical U.S. infrastructure, Chinese export restrictions that seriously imperil the U.S. economy, or an armed attack against U.S. allies and partners.

The Cold War and trade tensions between the West and China are here to stay for the foreseeable future and are most likely to be ratcheted up over time. It is therefore important that others can mobilise sufficient bargaining levers with China. All the aforesaid are likely to be very effective in restricting the Chinese economy if deployed in a coordinated manner. This would require the mobilisation of a global alliance, something the US-led West did with great effectiveness during the Cold War with the Soviet Union. 

Now, with the hostility and dysfunctionality of the Trump administration, any such cohesive and credible global alliance looks very unlikely. In its absence, whatever restrictions are imposed by the US and EU independently are merely band-aid solutions, and likely to get circumvented in various ways against an antagonist who is disciplined, plays the long game, and does painstaking groundwork to accumulate its strengths and overcome restrictions. Trump 2.0 is, therefore, perhaps the best thing that an embattled Chinese government could have been gifted by its opponents. 

Examining the gravitational pull of index investing

The blockbuster IPO of SpaceX has drawn attention to the role of the index investing market segment, which originated fifty years ago. The scrutiny will only intensify in the days ahead as both Anthropic and OpenAI are listed. 

Despite only about 4 per cent of SpaceX’s shares being listed, a high level of demand was built in, also because some index investors will soon be required to add the company to their portfolios. Nasdaq amended its rules (as also CRSP and FTSE Russell) to allow SpaceX to enter the Nasdaq-100 index via fast-track, thereby providing it free liquidity through a captive market. Morningstar follows 6,006 US-registered mutual funds and 5,100 ETFs, covered by 3,203 separate benchmark indices against which $41.1tn of assets are managed. 

The current entry requirements for indices are very liberal for large companies.

However, what distinguishes SpaceX is its very low float (just 4.25% compared to above 90% for the other big companies) and the fact that it does not yet make any profit. Toby Nangle and Co at FT Alphaville tabulated that active fund managers (who want to ignore Musk) and passive portfolio managers (who have no choice) must purchase $14.2bn of SpaceX stock in the first three weeks of trading to avoid going short. 

We think that managers who have absolutely no interest in going either long or short Elon will need to collectively buy $8.5bn of SpaceX stock on the 19th June, a further $1bn on 26th June and a final $4.7bn on July 3. And so we’re talking a cumulative, de facto mandated $14.2bn of mutual fund and ETF orders by July 4 to avoid having to take a view on Elon. That number would’ve been around $11bn higher if the S&P 500 index committee had leaned a different way. But it’s $13.2bn bigger than the $1bn it would’ve been if index committees had sat tight on their existing fast-track methodologies. And this is before we count institutional assets like pension funds, insurers and foundations, as well as every single foreign owner whose benchmarking habits we currently lack information.

So this is a big deal. Clearly, the market has been brazenly manipulated by changing the rules to create the stage for SpaceX (and the other two mega IPOs). 

I took the help of Claude to generate a few graphics to understand the scale of index investing, its dynamics and distortions, and possible reforms. 

The conventional measures dramatically understate index investing because they count only labelled index mutual funds and ETFs. However, using the indexation definition, passive ownership of global equity mutual funds and ETFs is 50% and 60% for US equities. In fact, a stunning three-fourths of the equity market exposure of the big three US asset managers is through ETFs.

SpaceX’s shares could have an index exposure of 43% in a year when it joins the S&P 500, and that too on a volume which would be a fraction of that for the Mag7 firms. It is estimated that S&P 500 funds would need to absorb 19% of SpaceX's public float upon inclusion, with the Russell 1000 and Nasdaq-100 funds absorbing another 24%. A tiny supply of $22-27 bn would meet a mandatory demand of 43%. 

The research on the impact of index inclusion on stock price offers striking findings. Gabaix and Koijen find investing $1 in the stock market increases the market’s aggregate value by about $5. Since marginal holders of equity (index funds, pension funds, insurance companies) operate under mandates that fix their equity allocations within narrow bands, when aggregate demand shifts, few participants can absorb the change, and prices must move substantially to clear the market. 

Haddad-Huebner-Loualiche point to a Mathew Effect in indiex investing. They find that a $3.6trn market cap company is only about five times as liquid as a $100bn company, despite being 36 times the size. The largest stocks are disproportionately impacted by each dollar flowing into passive funds, causing the largest stocks to outperform and stock market concentration to rise. Passive investing has reduced market efficiency by over one-third.

Index investing brings the benefits of access to a low fee, diversified, and tax efficient asset pool to the retail investors. In the words of the legendary investor Jack Bogle, index investing is the equivalent of not looking for the needle but instead buying the haystack. 

However, it distorts price discovery, creates inelastic demand, amplifies concentration in stocks and asset managers, and erodes governance. All of these distortions are greater for the mega-cap stocks. 

Further, index-investing comes from asymmetric flow elasticity. While passive funds are mechanically symmetric (the 1:5 multiplication), the rest of the system (margin lenders, derivatives dealers, momentum funds, retail behavioural agents) is not. When flows reverse, the multiplier still applies, but additional positive feedback channels switch on, creating an overshoot below fundamental value before mean-reversion can bring prices back. The resultant instability can tip markets into prolonged and deeper downturns. This is an illustrative example

Suppose 2 years post-IPO, growth disappointments and rate-policy reversal trigger a ~20% net outflow from Nasdaq-100 trackers ($280 bn × 20% ≈ $56 bn). Applying the firm-level multiplier (~2 for stock-specific flows, higher for concentrated names): mechanical impact on SpaceX share price could be ~30–45%, on top of any fundamental revaluation. The same flow proportionally affects all Mag 7. Index investing does not just amplify upside — it removes the price-discoverers who normally cushion downside.

Below is a listing of some channels that amplify the downside. 

All things taken together, index investing must reconcile the conflicting requirements of democratising investing (through passive funds) and limiting market instability. The answer lies in the empirical fact - the more capital becomes index-tracking, the less price discovery the market performs, and the more the system is exposed to flow-driven valuation and procyclical unwinding. Clearly what is optimal for individual investor is not optimal for market function. In the circumstances, this can be an illustrative framework to reconcile the conflicting requirements. 

The problem with index investing is that of internalisation of its negative externalities. Passive investors capture the upside of cap-weighted indexation (low fees, diversification, momentum) without bearing the price-discovery cost, whereas active investors bear the cost of analysis but cannot compete on fees. 

This free-riding can be resolved only if passive investors internalise the marginal cost they impose on the system without eliminating the substantial benefits they deliver to retail investors. Like with carbon emissions, this can be done through fees, governance obligations, or structural caps. 

To dive a bit deeper, there are perhaps four levers for reforming index investing - index methodology (rules of index construction), voting and governance obligations, fund-level liquidity thresholds, and limiting structural concentration in the passive investment ecosystem. A combination of all of them is a formidable regulatory toolkit. 

Each lever will have its set of reforms. It may be required to prioritise a limited set reforms that have the highest-impact-per-cost. The simplest and highest value reform could be mandatory free float minimums (say 10-15%), a twelve-month public trading history before index inclusion, and a 4-5% cap on any single stock in an index. Second, bring index providers under SEC regulation as “investment advisers” subject to fiduciary duty and disclosure requirements. 

Third, for passive funds with more than 1% holding, return governance to the ETF or index fund holders by allowing them to vote their proportionate share directly via the fund, thereby reducing the influence of the Big Three asset managers. Fourth, cap the passive fund ownership of US banks at 10% (the regulatory "controlling interest" threshold) and impose mandatory liquidity stress tests for funds with more than $5 bn AUM in cases of extreme outflows and market dislocation. 

Fifth, the Department of Labour should tighten defined-contribution retirement-plan portfolio diversity rules, thereby providing a structural counterweight to cap-weighted default portfolios. Such plans must offer either equal-weight or capped-weight default or cap cap-weighted defaults at e.g. 80% of the plan portfolio. This would reduce forced concentration in 401(k) default portfolios. Finally, there could be regulatory roadmap for orderly winding-down of overweight positions during the life-cycle or retirement transition. A coordinated guidance, including some rule-based gradual decumulation, reduces fire-sale risk. 

The proposals above seek to reconcile the diversification and fee benefits of indexation while also internalising its costs. Once index providers face fiduciary duty, once Big Three votes are partially passed through, once retirement defaults include non-cap-weighted options, once liquidity is stress-tested, and there is some transition guidance, the same products can continue to serve retail savers while delivering far less of the distortion that has been documented.

Weekend reading links

1. Ruchir Sharma goes behind the record corporate profits share of GDP in the US (11% of GDP, up from 7% in the 1990s) and finds two problems. One, the share is boosted by the lower corporate tax rates which translate into higher fiscal deficits.

Lately the US deficit has risen to more than 6 per cent of GDP and a deficit that high reflects a large transfer of income to households and corporations. Under a well-established accounting formula, the Kalecki-Levy Equation, corporate profits are in part a mirror image of the government’s deficit. Based on this framework, deficits were the single largest contributor to the increase in earnings as a share of GDP since the late 1990s. And in this decade, deficits have accounted for more than half of corporate profits, twice the level of the dotcom era. Strip away government support, and US profits look less extraordinary.

Second, the share falls sharply when we include the universe of private companies.

Since the dotcom bust in 2000, the number of publicly listed businesses has fallen by half, with many new companies remaining private for longer, funded by private equity and venture capital. This is the new home of excess and weak earnings. As a result, the profit growth of the average business listed in standard indices provides a misleading picture of the overall economy. Profit growth has been less impressive once the private companies are included in the data. Further, private firms planning to go public are much larger and less profitable today than in the 1990s. The biggest names in the IPO pipeline, including SpaceX, Anthropic and OpenAI, have little to no profits.

2. Britain's much-delayed HS2 railway project's cost has increased by another £20bn to £102.7bn (a range of estimates ( £87.7bn to £102.7bn in 2025 prices) and will be completed only by the 2040s, with the first trains not expected till 2036. 

3. Why has oil not hit $150 or $200 despite about 12-15 mbpd being taken out?

One of the biggest surprises for the oil market has been China, the world’s largest importer. It slashed inbound shipments by almost 40 per cent in May compared to last year’s average, according to Vortexa Ltd. The reduction is enough to offset anywhere between a third and a fifth of the barrels lost to the war, depending on the estimates used. At the same time, the US has emerged as the world’s most important swing supplier since launching strikes on Iran in late February. American crude and fuel exports in May were more than 2 million barrels a day higher than the average for all of last year. Other emergency measures have also eased the strain. Governments around the globe coordinated a historic release of strategic reserves, while Gulf producers rerouted shipments through alternative export routes. Some tankers continued moving cargoes via the strait despite the risks, using increasingly opaque methods to avoid military threats... Saudi Arabia’s East-West pipeline shipped millions of barrels a day to the Red Sea, while the United Arab Emirates has been piping barrels to the port of Fujairah outside the gulf.

And this is important. 

Another factor keeping a lid on prices has been Trump’s relentless jawboning, making it hard for even the most bullish traders to hold long positions for prolonged periods of time. Open interest in Brent crude futures is the lowest since August as elevated market volatility forces traders to roll back risk exposure. Steep price drops on the prospect of peace have pushed many oil bulls to the sidelines, leaving them to hold small positions for very limited periods of time, several traders said. The lack of risk-taking has helped keep a lid on financial flows, while supply levers have averted the worst hit to the market. The question now, is whether that can last without a peace deal.

4. The evidence to date points to widespread AI use not translating into anything proportionate in terms of business value creation. Sample this from the software industry pointed out by John Burn-Murdoch. 

5. EU energy subsidies during the Ukraine invasion and spike in gas prices. 

Between late 2021 and mid 2023, EU countries spent about €540bn on subsidising energy prices to protect consumers from price shocks in the aftermath of Russia’s full-scale invasion of Ukraine and to cushion energy-intensive industries from soaring costs. Of that total, €158bn was accounted for by Germany, whose industry heavily depended on Russian gas supplies. In the wake of the Ukraine energy shock, Brussels also relaxed state aid rules for the rest of the decade to allow governments to subsidise clean technologies and industrial decarbonisation.

6. Emerging AI-related roles in the software industry.

Agent engineers who build and fine-tune agents; architects who determine how humans and agents divide tasks; AI governance specialists who keep agents compliant and accountable; AI transformation advisors who help enterprises navigate workflow reimagination; solution designers who translate business problems into agentic solution architectures; and AI assurance partners who help clients govern their own AI deployments.

7. Rana Faroohar makes the point that we may be at the cusp of a new investment super-cycle driven by the combination of AI, clean energy, defence, and manufacturing.

In a recent issue of his TPW Advisory Monthly report, investor Jay Pelosky did just that, collating data on AI, clean energy and defence spending around the world from sources including Gartner, BloombergNEF (on energy), the Stockholm International Peace Research Institute and the International Institute for Strategic Studies (on defence) and others. So far, $6.9tn was spent globally in 2025 in the three areas, and the number will probably reach $10tn by the end of this year and $16tn by 2030. 

What’s more, says Pelosky, these three areas reinforce one another, amplifying potential investment. AI requires more energy. The move towards tech sovereignty in the US, China and even Europe (in a nascent way) adds to the need for investment in AI and energy, while the move towards a more 19th-century “spheres of influence” geopolitics calls for greater defence spending globally. Add to this the desire of policymakers in all three regions to increase resilience in critical sectors affected by concentration or globally dispersed supply chains: products such as advanced semiconductors, active pharmaceuticals and lithium batteries, for example.

8. Rupee trajectory over the last two years

The rupee depreciating by nearly 6 per cent against the dollar in the first five months of 2026 alone, exceeding the combined full-year declines recorded in 2025 (5 per cent) and 2024 (2.8 per cent). The rupee touched a record intraday low of 96.57 per dollar on May 19 and has lost roughly 14 per cent of its value against the greenback over the past two years. Against the euro, the erosion has been equally stark, with the Indian currency declining by more than 7 per cent over the past 12 months and nearly 16 per cent in the past two years, he added.

The problem with a depreciating rupee is that foreign investors now must earn an extra 14% over the last two years to offset the depreciation.

9. India's FTAs may be creating a perverse incentive for even domestic manufacturers due to the inversion of duty structures.

Many finished goods now enter India at low or zero duty from partners such as ASEAN, Japan, South Korea, the UAE and Australia. As a result, Indian manufacturers often pay high duties on imported inputs, especially those sourced from non-FTA countries, while competing against finished products imported duty-free under FTAs. For example, steel and aluminium attract MFN duties of 7.5-10 per cent, but machinery, industrial equipment and engineering products made from these materials can enter India duty-free under several FTAs. Indian manufacturers, therefore, face higher input costs when competing with tariff-free imported machinery produced with globally priced inputs.

Similar distortions exist in chemicals, plastics, rubber and textiles. Duties on inputs such as caustic soda, soda ash, polypropylene, PVC and SBR raise production costs. At the same time, many finished products in these sectors can be imported at low or zero duty... the growing incentive for firms to manufacture outside India rather than within it. When raw materials and components attract duties in India, but finished products can be imported duty-free from FTA partners, companies may find it more profitable to locate production abroad and export back to the Indian market. In such cases, FTAs effectively encourage offshore manufacturing at the expense of domestic value addition. ASEAN countries are increasingly becoming manufacturing hubs for supplying the Indian market.

10. The Gulf War has resulted in a surge in Chinese exports of solar panels, especially to South East Asian and African countries. This came at a time when many Chinese firms laden with excess capacity, low margins, and large indebtedness were facing an existential crisis. 

11. The revival of manufacturing in the US hits the wall of labour scarcity.

Perhaps the largest problem for would-be reshorers is a lack of labour. Despite widespread nostalgia for manufacturing jobs, new US factories often struggle to find reliable workers. When Japanese group Panasonic started producing electric vehicle batteries near Reno, Nevada in 2017, the company suffered more than 100 per cent annual turnover in its early years. Not only were employees reluctant to take jobs in an access-controlled, sterile environment, but every November, the group would lose workers to seasonal logistics jobs at nearby Amazon facilities that paid a similar wage... Despite political enthusiasm for reviving manufacturing, jobs posted on LinkedIn receive fewer applications than other sectors it competes with, such as technology.

12. More on the SpaceX bubble.

Goldman Sachs, the investment bank leading the IPO, projects that SpaceX’s AI revenues need to increase 100-fold by 2030, reaching $322bn from $3.2bn today. But its AI lab remains far behind Anthropic, Google and OpenAI at the frontier, and shows little sign of catching up. Morgan Stanley also estimates that overall revenue needs to increase 180-fold to $3.4tn by 2040, up from $18.7bn last year. Earnings must flip from a $4.9bn loss in 2025 to $2.7tn.

SpaceX ends the first day of trading at 20% premium on its $135 listing price, which raised $75 bn and left it with a valuation of $2.1 trillion. The listing prospectus claimed at $28.5 trillion addressable market.

SpaceX plans to use the IPO proceeds for a range of ambitious projects, from its skyscraper-sized reusable Starship rocket, founding a 1mn-strong colony on Mars, starting a lunar economy to building a network of orbital AI data centres capable of delivering vast amounts of computing capacity... A portion must also go towards repaying a $20bn bridge loan the group took out in March to back its merger with Musk’s lossmaking AI start-up, xAI, and social media platform X.

This is staggering. 

It also hands Musk a vast financial windfall, with his 42 per cent stake in SpaceX valued at more than $800bn. Combined with his $280bn holding in electric vehicle maker Tesla, his wealth has now surpassed $1tn, equivalent to about a third of the market value of the UK’s FTSE 100 index.

Richard Waters explains the rationale driving the astronomical valuation.

It is hard to find changes in SpaceX’s business prospects that account for a fourfold valuation jump within the space of a year. Rather, it is testament to Musk’s unrivalled grip on the public imagination, transmuted into Wall Street gold. When the history of SpaceX’s record-breaking IPO is written, it will go down as a textbook case of Musk’s ability to conjure a compelling vision of the tech future for both Silicon Valley and Wall Street, ably backed by an army of promoters in the financial world. (With fees estimated at about $500mn, it’s probably not surprising that there were few warnings from the Wall Street establishment that the shares might be overvalued.)... 

Musk has been busy in recent months spinning up new visions capable of embellishing his company’s prospects... What starts out sounding like science fiction fantasy doesn’t take long to seem not only plausible but even downright likely. Wall Street has been more than willing to translate Musk’s tech dreams into financial projections capable of supporting the sky-high valuation, implausible as those numbers may seem. Goldman Sachs, the lead bank on the IPO, predicted that revenue would soar to $474bn by 2030, most of it from AI...

The IPO involved only about 4 per cent of SpaceX’s shares, but a high level of demand was built in, partly because some index investors will soon be required to add the company to their portfolios. Even without the index funds, SpaceX’s sheer size — at about 2.5 per cent of US stock market capitalisation — has made it too big for many investment managers to avoid.

13. South Korean capitalism is spreading wealth around

Samsung Electronics... agreed last month for employees to share the chipmaker’s blockbuster profits from an AI-led boom... SK Hynix... handed employees a similar profit-sharing deal last year... Samsung is also going to give Won500mn loans at low rates to employees... Samsung and SK Hynix together control much of the market for the advanced memory chips used in AI servers. Employees at both companies are in line for average annual bonus payouts of Won600mn, which compares with a national average salary of about Won50mn... district of Hwaseong... expected to gain corporate income tax receipts of Won1tn to Won1.3tn from Samsung alone this year, an extraordinary sum for a city authority whose annual budget is about Won3.5tn.

14. Germany amps up spending on its ailing railways sector, where on-time arrivals of long-distance trains have fallen from 84% two decades back to 60%. 

By the end of 2026, Deutsche Bahn will have rebuilt 900 kilometres of train lines since 2024, close to a quarter of its 2036 target. That is equivalent to half of the roughly 1,900 kilometres of new lines built after 1945. Flix, which started as a coach operator but also runs long-distance trains, has earmarked €2.4bn for up to 65 new high-speed trains that it plans to roll out from 2028. Italian high-speed train operator Italo also has ambitions for Germany, promising up to €3.6bn of investment in new trains if it gets multiyear access to the network.

 

Some thoughts on the likely impact of AI in countries like India

This post will argue that the long-term impact of AI innovations on the typical household’s daily life in a developing country like India will be far smaller than the discourse suggests. Much the same could be said for basic human development and public services delivery in general. 

While there will be some overlap, I make the distinction between the macroeconomic impact in terms of automation and resultant job losses, and the material impact on the lives of people. Also, while much has been written about the former, this post will focus on the latter. 

As a note of caution, as is the case with any such debates, evidence and data to substantiate claims are hard to come by. So there is a lot of judgment in the argument below. And I’ll only be too happy if the judgment is proved wrong. 

The argument rests on four observations.

First, the bulk of the economy and daily life in these countries lies in domains where AI can barely reduce costs. The two main production modes, agriculture and manufacturing, are unlikely to be significantly affected (see the third point). The main products that we buy - homes, vehicles, jewellery, and consumer durables - and the everyday services we buy - haircuts, repairs, domestic help, transport, retail, eating out, etc. - are dominated by physical inputs of materials, energy, land, labor, and transport. 

AI can reduce logistics and coordination overhead costs, which form a marginal share of the total production cost of these goods and services. The share of any of these prices that AI can potentially influence is likely a fraction. Of India’s roughly 470–565 million workforce, around 85% is informal, ~45% is in agriculture, and the formal IT/BPM/GCC sector employs only about 5.8 million people. The set of workers directly exposed to AI displacement is small - plausibly 5–8% over a fifteen-year horizon - and the consumption basket of the median Indian is composed almost entirely of goods and services whose prices AI is unlikely to meaningfully alter.

Second, the obvious rebuttal, that AI’s impact will come not through cost reduction but through dramatically expanded access to healthcare, education, finance, and government services for India’s 800-million-plus internet users, runs against some hard evidence. The last twenty years of EdTech, Medtech, SkillTech, and AgTech across India and comparable low-income contexts amount to a rich natural experiment. The result is essentially zero examples of even significant, much less transformational, district-scale impact in any of these domains. 

Despite massive amounts and efforts on EdTech, aggregate learning outcomes, as measured in the likes of ASER scores, have hardly moved. In fact, the share of Class V children in rural India who can read a Class II text has actually declinedover much of the EdTech era. Like Diksha for school education, eSanjeevani logs vast consultation volumes with no measurable population health effect. It is hard to find meaningful signatures of MedTech in primary or secondary healthcare. In skill development, a succession of schemes has trained tens of millions with dismal placement outcomes, including using technology extensively. Digital Green and dozens of AgTech pilots have generated good papers, but no district has measurably transformed agricultural productivity because of them. Outside India, the picture is similar - Kenya's M-Health pilots, Brazil's rural EdTech, Indonesian AgTech apps. The hit rate on "scaled, measurable, transformational" is essentially zero.

The same could be said about most areas of public services delivery - primary health care and school education; municipal government services like tax assessment, building permissions, utility service connections; and the services of regulatory agencies. While there are pilots and small slivers of some success, aggregate impacts across all these realms attributable to digital technologies, notwithstanding numerous and repeated initiatives, have been minimal. 

Third, the reason this pattern matters for AI is that it points to perhaps a wrong diagnosis being made about why previous efforts, including using technology, failed. The standard story that “the tech wasn’t good enough yet” implies AI will finally break through because it is qualitatively better. However, I’m inclined to argue that the diagnosis is wrong. 

The binding constraint in these domains has never been information quality or delivery. The child in the village school does not fail to read only because she lacks access to a good pedagogical sequence; she fails also because she has accumulated large antecedent learning lags, the teacher is over-burdened, indifferent, or absent, the system has no consequence for non-learning, she is hungry and has chores in the evening, her parents cannot reinforce at home, and so on. A perfect AI tutor in Hindi and Math does not change those facts.

The villager seeking treatment doesn't suffer only because no one can diagnose her condition; she suffers also because the doctor is indifferent or isn't there, the PHC has deficient diagnostics or medicines, transport to the CHC costs a day's wage, and the prescribed drug regimen is incompatible with her work and food situation. It is not only the lack of plumbing knowledge that holds back the aspiring plumber. Instead, he also lacks an apprenticeship network, a credential the contractor trusts, and tools. The farmer is also constrained by the inertia to change long-standing practices, water, fertiliser subsidies, and the price he gets from the mill, not only by ignorance of best agronomic practice or market information.

In all four cases, the recipient is operating in a bound system where information is, at best, the fifth-binding constraint. Solving the fifth-binding constraint produces no visible improvement because the first four still hold. Even with this constraint, the ability of AI to make significant improvements at scale in these difficult contexts is questionable. More than two decades of the internet and digital technologies have made little or no impact on actual outcomes, except for a few oft-repeated pilots. 

This is exactly what the vast majority of development economics literature has been telling us for two decades, and it’s why “information-delivery” technologies have a flat impact curve regardless of which generation of tech is doing the delivery. AI is, fundamentally, a much better information-delivery technology. By the logic above, it should be expected to have roughly the same impact profile - better demos and pilots, but similar real outcomes - unless something about AI breaks the pattern.

Having said this, it is also logical to argue that AI can address and relax all these constraints just enough to enable outcomes that, while not the best, are far better than those achieved now. While appealing and comforting, I am not inclined to agree. 

Fourth, the genuine exceptions exist but are narrower than the transformational claims that mainstream discourse suggests. AI is plausibly different in three specific places: supply-side augmentation that flows through existing institutions (Qure.ai’s tuberculosis screening integrated into state programs is the cleanest example); voice and vernacular interfaces that break the literacy ceiling text-based apps could never cross; and AI built on top of India Stack to alter citizen-state interactions. These are real but bounded effects, not transformations.

So what’s the final assessment?

AI radiates a wide beam of capability (advice, diagnosis, tutoring, prediction); the beam hits a wall of thick structural constraints, each labelled with the human reality it represents and the domain it blocks (say, education, health, livelihood, farming). Only a thin sliver of information makes it past the wall to reach the villager with the phone below. AI delivers information, but not transformation. 

The mainstream discourse on AI is built on the worldview that assigns outsized importance to knowledge-based services over the production of goods. This is a real blind spot that obscures the reality of the vast majority of non-AI-influenced interfaces and interactions in the daily lives of most Indians. 

In conclusion, I’ll stick out my neck and argue that AI’s footprint on median Indian life will look much more like the mobile phone’s did - ubiquitous, individually useful, but hardly transformational on people’s daily lives. The mobile phone did not move India’s Human Development Index; it moved convenience and communication. AI is on track to do something similar: meaningful at the margin, but layered on top of structural conditions it cannot itself relax. 

The global discourse, calibrated on knowledge-work economies where AI strikes the dominant production input, badly overstates the implications for a country where physical and institutional constraints set the floor. 

Unfortunately, like with the internet and digital technologies, this is likely to be a costly distraction for development. Instead of working to get the plumbing right, it is likely to displace resources and efforts towards getting AI solutions to address these problems. I have blogged here, here, and here on this.