Matt Pottinger, the former US deputy national security adviser during the first Trump administration, has warned that China could spark “a very serious crisis” in Taiwan without even resorting to a full-scale war – an escalation he said could occur in the next four years and should “keep President Trump and many more people awake at night”.

The comments came soon after China marked the 80th anniversary of World War II and the Second Sino-Japanese War with a military parade at Tiananmen Square, where President Xi Jinping addressed over 12,000 troops of the People’s Liberation Army in a decisive show of national security force.

Also present at the parade was Xi’s close ally, Russian president Vladimir Putin, who is escalating his war against Ukraine. This worries Taiwan’s defence ministry, whose deputy chief of the general staff for intelligence Hsieh Jih-sheng recently told an international security conference that the defeat of Ukraine could embolden China to seize Taiwan.

But Pottinger said there are many ways for China to make Taiwan’s life harder without initiating a hot war. For example, the Chinese government could issue a notice to the world’s major shipping companies – which are predominantly Asian and European – requesting that access to Taiwanese ports be granted only with its permission.

“That immediately will put the world’s shipping industry on the horns of a dilemma,” Pottinger, who is distinguished visiting fellow at the Hoover Institution, told the Fiduciary Investors Symposium at Stanford University.

“They will either have to salute and basically cede the idea that Taiwan has any sovereignty, even over its own trade, or they risk not being able to do business with China, which will collapse most of the world’s shipping companies.”

China’s focus on Taiwan reflects its strategic significance in the region as well as their complex cultural and historical ties. Claiming Taiwan would be a “strategic coup” as it would allow China to break free from the geographic constraints of its military power – the so-called “first island chain”, Pottinger said.

“Anytime China wants to send its ships, submarines and aircraft, they really have to go through a toll booth and be permitted to do that,” he said.

“So Chinese military doctrine says if we take Taiwan, we can effectively surround Japan, collapse the entire strategic defensive concept for Japan, and cut off Japan’s access to energy and even food.”

The fact that even a “bureaucratic circular” could set off a serious crisis in Taiwan should make the US and the world very concerned, he added.

Meanwhile, having worked with Trump in his first term, Pottinger said the president is “someone who does not actually prioritise national security”. Trump’s focus leans towards tightening immigration policy – which to him, is a form of economic foreign policy rather than a security policy – and expanding his control over other levers of power such as the Federal Reserve and the media.

Adding to potential myopia in the US’ security strategy is the fact that Trump gutted the National Security Council this year.

“As of a month ago, there were only about 36 policy staffers in the White House working on national security. To put that in context, that’s sort of around where it was at the dawn of colour television,” Pottinger said, adding that these functions have been effectively replaced by private sector advisers.

“[These are people like] David Sacks, who is a part-time AI and crypto czar; Jensen Huang, who is not at all a government official, but is the lead adviser – really replaced Elon Musk – to the president on technology and AI, including national security implications of those things,” he said.

“We’re in a weirder space, I would argue, than we were in the first administration where President Trump was surrounded by generals… and they would bring up [national security] things before it was a crisis.”

Looking ahead at the US-China relationship, Pottinger said Trump will be caught between the “hard realities” of trying to impose tough tariffs on China, and realising China is making speedy progress on its decoupling from the US.

“[Xi] may agree to buy some new shipments of soybeans or a few more Boeing jets, but he’s not going to shift his economic model, which is… explicitly making China independent of any inputs from the United States and other industrialised democracies.”

At the end of Trump’s first term, he was under pressure politically because of the havoc that the Covid pandemic wrought on the US economy – a “grievance” he has been nursing since 2020, Pottinger said.

“[If Trump meets with Xi later this year], he’s going to come away with a few soybean sales, and he’s going to start through that cycle of nursing grievances again.”

“Everything is permissible, but not everything is helpful.” 1 Corinthians 10:23, International Standard Version

In my last thought piece on misinformation (Data ‘slop’ and disinformation emerge as systemic risks for investors), I noted the likely ballooning in quantity of ‘AI slop’ (AI generated information) that we will have to deal with in our search for meaningful information as time passes.

This reminded me of the TS Eliot quote, “Where is the wisdom we have lost in knowledge? Where is the knowledge we have lost in information?”.

This implies a hierarchy, where volume and value are inversely correlated. Think of a shallow-sided pyramid where there is masses of data at the bottom, which must be filtered into rarer information, which must be heavily filtered into knowledge, which in turn yields only a few nuggets of wisdom.

My argument in this piece will be that intelligence is not our goal – whether artificial or not. Our goal should be the much more valuable wisdom. Now, if we were to start hearing about ‘artificial wisdom’ then maybe we would be on the brink of something interesting…

In the Institute we have, occasionally, promoted the idea that ‘wisdom’ equals ‘what I should do on Monday’. If the implication is that we have spent the weekend in thought and reflection, and the emphasis is placed on the ‘should’, then maybe all is OK.

But in the absence of reflection, and if the emphasis is on the ‘do’, then we have likely introduced a bias to action which could run directly contra to wisdom. This piece will argue that a large part of wisdom lies in ‘not doing’, even though we could – or are tempted to.

The next idea I want to introduce is the Jevons paradox (see The efficiency trap). The paradox observes that increasing efficiency should lead to us using less materials, but we actually end up using more (because the price drop makes other uses newly viable).

Jevons was commenting on the efficiency of steam engines, which should trigger a fall in the use of coal. But cheaper coal led to more steam engines for more uses, and more coal was burned in total. Economic history from that time to the present is littered with similar examples.

Contrast this ‘reality’ with a story, possibly apocryphal, that I heard recently about an indigenous people group. They innovated a new fishing hook which made their fishing more efficient. Through the lens of our reality we would expect more fish to be caught, and to be put to more uses (feeding livestock, or selling).

Instead, they spent less time fishing. They enjoyed the same amount of fish, exerted no new pressure on fish stocks (sustainability!), and had more time to invest in social capital.

The fundamental difference between these two possible paths is best captured by the word ‘constraint’.

I will label the first path, of catching more fish and selling the excess, the ‘increasing financial capital’ path. It is relatively unconstrained. The same amount of time is spent fishing, the same amount of time can be invested in social capital, and the group will have more financial capital.

There is a big implicit assumption, however, that the new, higher rate of extraction is sustainable. If it isn’t… well that is a problem for the future. If the financial capital is stewarded wisely, it can possibly be converted back into fish later.

The second path I will label as ‘increasing social capital’.

There are a couple of possible implicit assumptions here (when viewed through a Western lens).

First, that the current rate of extraction is sustainable while a higher one might not be. And, second, that social capital is more valuable than financial capital. There is also a fairly heavy constraint – either self-restraint, or community-imposed – to catch fewer fish than they could.

So, which course of action is wiser – increasing financial capital or increasing social capital?

I am not claiming there is an easy or obvious answer to this question, as the Western and indigenous lenses may conflict. It will depend on the objective function, the time horizon and the beliefs.

However, one of them looks unarguably riskier (the financial capital path carries the risk of over-fishing). And, if we extend our time horizon to include multiple future generations, then the wiser course of action increasingly looks like the social capital path (there is less chance of existential risk).

To me, therefore, intelligence is a device for expanding the opportunity set, while wisdom is a device for shrinking it – “everything is permissible, but not everything is helpful”.

Now comes the hard part: how do we sift the dirt pile into those things we could, but shouldn’t, do – and those things that will be helpful over the long term (the jewels)?

Again, I cannot provide an easy or obvious answer. If I may, I refer back to a previous piece I wrote, titled What’s love got to do with it?.

In it I talked about head knowledge and heart knowledge, and argued that heart knowledge had access to all the same inputs as head knowledge but, rather than run them through a cost-benefit analysis, it ran them through a ‘love algorithm’.

I think this is the closest I can currently get to wisdom. If the proposed course of action expands the boundary of love – for others, for non-humans, for life not-yet-born – then it is likely to be wise.

If it shrinks the boundary of love, for example by inflicting cost on others, on non-humans or on lives not-yet-born, then it is probably intelligence rather than wisdom.

Tim Hodgson is co-founder and head of research of the Thinking Ahead Institute at WTW, an innovation network of asset owners and asset managers committed to mobilising capital for a sustainable future.

Stephen Kotkin, global geopolitical expert and Stanford academic, has warned that there is an “increasing governability challenge in high-income democracies” where government departments face declining capacity to perform core functions due to complex regulatory systems and bureaucratic tasks. 

This has enabled populist politicians – who “don’t want to fix the government” but “want to remove the institutional constraints on executive power” – to thrive, said Kotkin, a senior fellow at the Hoover Institution. 

“They don’t want governance to function. They want to disable those governance structures so that they have what they consider free reign,” he told the Top1000funds.com Fiduciary Investors Symposium.   

“This is a deep and fundamental problem that we don’t have a solution for.” 

Looking at historical data in the US and European Union countries, while the population and size of government have only increased marginally since 1979, the regulations and responsibilities government has to carry out have experienced an “exponential” jump, Kotkin said.  

“Each regulation has a kind of logic, but they just accumulate. Something happens in the world, whatever it might be, and people say ‘Do something. Don’t sit on your hands’. So they pass a rule, a regulation or maybe even a law through the parliament,” he said.  

“In a complex system, nothing interacts with the other things the way you expect. One regulation might be for environmental restrictions, to defend the environment, but then it becomes the principal instrument for not building any more housing. 

“It’s the tasks of government that we’ve put on them that they are not designed to do and can’t cope with. So when they mess up, the populists come along and say, ‘government is failing’.” 

US president Donald Trump’s Department of Government Efficiency (DOGE), previously headed by Elon Musk, is the most notable attempt recently made for the purpose of improving government performance. As of October, it claimed to have saved $206 billion in government spending on its website.  

However, Kotkin is of the view that reducing the number of federal employees is not ultimately about cost-cutting, but “political purging”.   

“The federal bureaucracy is predominantly leftist, and if you are a Republican, elected-by-the-people president, you face this again and again and again that the bureaucracy sabotages your programs,” he said. 

“It happened to Bush. It happened to Reagan. It happened to the first Bush. It happened to Nixon. It’s a long-standing problem.” 

DOGE is a failure because it failed to understand that the public sector does not function like a private company, but a big, interwoven system where few people understand its inner workings, he said.  

“My argument is not getting rid of the officials, it’s getting rid of the tasks that the officials are being imposed with. Because there’s too much that we expect them to do, that they cannot do, that they don’t have the bandwidth for if they’re not designed to do those things, and those things are now perversely used.” 

There is a difference between a policy’s intention and its outcome, and not every government official has grasped the idea. Kotkin noted that things like subsidies can look like a clever fix but often backfire. Subsidising ethanol, for example, could be considered support for a cleaner fuel alternative, yet its production depends on oil-powered farming of corns which props up the industry and its lobbyists.  

He suggested that politicians refocus on the real challenge when enacting new policies, which is not to come up with ideas, but to get the system to enact them.  

“And not the imaginary system at a [policymaking] seminar where you go for sherry when you’re done, but the actual system that you’re facing. 

“I agree that we could think about what those [government reforms] are, but I just want to know how I’m going to implement them with the system that I have, with the politicians that I have, with the incentive structures that I have and with the voters’ preferences that might change over time.” 

Norges Bank Investment Management is a lean organisation despite managing a $2.2 trillion portfolio. Across the fund’s four global offices, there are only 700 staff, or $3 billion per person, which is why it has made pursuing AI-driven efficiency a core organisation initiative – and a non-negotiable requirement for its employees. 

The momentum for change came from the top. At the Fiduciary Investors Symposium, Thomas Larsen, lead portfolio manager of external strategies at NBIM, recalled CEO Nicolai Tangen once told staff in a town hall that “this [AI] ship is sailing, get on board or find a new place to work”. 

“There’s not an awful lot of space for building out redundancies and building out big teams. We don’t want to be more people. We want to stay as nimble as we can,” Larsen told the symposium at Stanford University.  

“We are limited in number of people, this will allow you to 10x your output. This will allow us to do more targeted work and focus more on the things that you are good at.” 

NBIM has seven portfolio managers who can each spend up to a quarter each year on the road visiting external managers – a total of 110 people managing more than $100 billion. How AI can make these visits less overwhelming is by conducting preliminary analysis on manager reports and other performance data.  

The fund’s AI agent, Claude by Anthropic, is plugged into its database, which has daily trading data for every manager it has had since 1998. 

“If someone sends me a snapshot of the portfolio a couple of periods backwards, I just drop that in [to Claude]. Then I can see what all my other portfolios in the same market did at the same time,” Larsen said.  

But behind this progressive technological push, Larsen acknowledged that staff development is top of mind for team leaders. The fund is striving to find the balance between improving efficiency while leaving room for junior staff to make mistakes and learn.  

“With these new tools, a lot of what I’m asking AI to do now is exactly the tasks that my PMs asked me to do when I joined as an analyst 12 years ago,” he said. 

“I can ask those questions at 2am in the morning or from an airport in Singapore – I’m not beholden to what time my analyst is awake anymore, but it also means that I am at risk of destroying my own pipeline of talent. 

“We’re thinking a lot about how do we still curate a talent pool and a pipeline of people who are going to be the next guys making the decisions, when they don’t actually have the luxury of making the mistakes that everyone else makes in the first couple of years.” 

Another progressive use case of AI in NBIM’s investment process is a scoring system for internal and external portfolio managers that can pick up subtle behavioural biases when they are executing a trade.  

For example, portfolio managers who are good at timing trades would receive the suggestion from the AI system to “swing bigger” while placing trades, and those who have a tendency to destroy value when trading would receive the opposite advice, Larsen said.  

“[With external managers] we can see do they make money when they are putting contrarian bets, if they trade before or after earnings, if they trade into something with momentum, if they trade out of something that is downward revisions,” he said. 

“We took it out to some of the managers, and 10 out of 10 came back and said ‘can we have two hours with the team that built this? Because we wanted to understand more’.” 

Above all the experiments, Larsen said it is critical that the board and stakeholders are brought into the process early through advocacy and education about the technology.  

“Show them how it works, show them what it can do,” he said. 

“Make sure that you have compliance on board, you have all the people who can say no, that they are comfortable, and can also speak to it [the technology] in an intelligent way, because otherwise you are going to run into roadblocks.” 

Funds are operating in an extraordinary social and economic environment, with Scott Chan, chief investment officer of CalSTRS, saying he has never witnessed so many “large shifts stacked on top of the other” in his investment career. Amid the change, investors are increasingly shifting to a scenario and regime-based approach to asset allocation.  

At the Top1000funds.com Fiduciary Investors Symposium, Chan – who took over the top investment job at the $374 billion CalSTRS a little over a year ago – said the fund had moved to a more defensive position ahead of Liberation Day in April, expecting a higher probability of a recession in the US than what was priced in.  

“That meant increasing the more diversifying elements of our portfolio – fixed income and hedge funds – and raised cash. We felt pretty good as the market was going down. 

“I tell the team that this was actually a highlight of the year, because one of the things that’s unique to CalSTRS is we have about 44 per cent of our assets allocated to private markets and alternatives. So liquidity management is becoming very key to this environment. 

“To ballpark, we probably have to have something like $35 billion in liquidity if we go into a correction… we actually were in position in front of what we thought could have been a correction with enough liquidity.” 

However, Chan acknowledged the approach has its risks: the defensive position cost around one basis point of performance during the subsequent market recovery and the team needed to “trade up quickly”, he said. “But I’ll take that, because I think we were ready again in front of what we thought could be something way different, like a crisis might have ensued, or a recession.” 

Sentiment shifts 

Anna Langs, managing director of asset allocation, risk management and innovative solutions at San Francisco Employees’ Retirement System, said the fund has similarly been strengthening its liquidity management. Around half of the fund’s assets are allocated to private markets, but it also has close to a 10 per cent allocation to hedge funds, which Langs said provided both return and liquidity during COVID when the two traditionally liquid asset classes – equity and fixed income – failed to support the liquidity needs.  

Matilde Segarra, president and US chief executive of the $722 billion APG Asset Management, said the Dutch fund is also focusing more on regime analysis to inform more dynamic and nimble asset allocation.  

One big question the fund is grappling with now is how it approaches geographical diversification. Investors are rethinking their US exposure due to nervousness around debt and deficit, but Segarra pointed out that the problem is not unique to the world’s largest economy: “there are a lot of indebted governments around the world”, she said.  

What she is really worried about is whether investors’ sentiment towards a certain country is increasingly dominated by short-term events.  

“I am very worried about sentiment at the moment. What I mean by that is if I see the conversations that are going on in the company – again, I work for a firm that has offices across the world – I really see that the Europeans look at the United States right now with angst and scepticism,” she said. 

“And American colleagues that feel constantly attacked at the moment by the questions that they get from their European counterparts, are constantly pointing out that Europe isn’t growing enough and that Europe has an innovation problem. 

“I see almost that the polarisation that you see in society and in political systems across the world is also happening in my organisation on a small scale.  

“We’re supposed to be long-term investors, and I see that the conversations at even the highest level of the company, are conversations driven by sentiment and with a lot of short-term emotions dominating the dialogue…  at some point that is going to affect the quality of decision making.” 

Amid geopolitical and economic uncertainty, CalSTRS’ Chans said investors should always be ready to pivot their strategy as a “bulletproof” investment now could unravel very quickly should capital or ownership control policies get enacted in different countries. To do that, the fund is applying a total portfolio approach in its investment process so that it can “map the total risk exposures” better.  

“With the geopolitical risks and the issues around governments becoming more involved, as well as all the other risks we describe, we could be in for a lot more volatility.  

“So we have to have a team in a design where we can get more diversified or dynamic, but also be able to trade up quickly too, which I think is something that, if I looked over the past couple decades, wasn’t as necessary. 

“We have been exposed to growth investments, and that’s worked rather well. I don’t think you want to be out of growth… but I’m similarly worried about how quickly that might disrupt other industries. We’re looking through our portfolio and asking ourselves the question of what do we need to sell here? Alongside the question of what we’re buying?” 

Mark Horowitz, a leading computer scientist and electrical engineer at Stanford University, has declared that Moore’s Law is “basically over”, which will have significant ramifications for artificial intelligence investors who are counting on more computing power to feed into more complex models.  

It is a view shared by Nvidia CEO Jensen Huang, who pronounced the law “dead” in 2022 while justifying a hike in chip prices.  

The law refers to the observation that the number of transistors on an integrated circuit doubles every two years while the costs decrease. That’s an economic factor that has driven the semiconductor industry, said Horowitz, who is chair of the department of electrical engineering and professor of computer science at Stanford University. 

“If I have a product and I’m selling it in high volume, when I move to the next generation technology, it’ll become cheaper to produce, therefore I will make more money, or it will prevent my competition from underselling me. We can then create either the same parts we have now cheaper, or we can build even better parts at the same price point,” he told the Top1000funds.com Fiduciary Investors Symposium.  

“We still are scaling technology. We’re still building more advanced processing nodes. We’re cramming more transistors per square millimetre, but unfortunately, the transistors are not getting cheaper.” 

Machine learning scaling rules state that bigger models tend to mean better performance, which is why the dismantling of Moore’s Law and sluggish computing power growth could be a roadblock for AI development, Horowitz said. 

“This is a major disruption, because our expectation is that we can do more computing and we’re pushing more stuff to the cloud,” he said. 

“Now Moore’s law is actually flat, so all that [AI model] complexity is going to have to be done through sort of algorithms or applications… if we think about the economics, where is money going to be made?” 

Horowitz is of the view that most companies are losing money on their AI projects. “All the hyperscalers in the world [like Google, Meta, Amazon and Alibaba] are spending enormous amount of money trying to protect some of the business they have because they’re worried about losing it,” he said.  

He suggested the profitability of an AI application hinges on two things: the service cost and the liability cost. The latter would be increasingly pronounced as the world moves towards agentic AI applications as service providers will need to start taking responsibility for decisions and suggestions of their AI agents.  

Taking these costs into account, Horowitz said it is highly likely that profitable AI models or applications in the future will not be large, but small. 

“Both of those, to me, indicate that what you’re going to try to do is reduce the scope of the model to something in a particular area, so you can make it cheaper to serve and less likely to make a bad thing,” he explained. 

“If that’s true, then the people who are going to start making money are not the people who are using the really big models. It’s the people who have used the big models to create smaller models or more domain specific models.” 

These views are not pessimistic predictions for the AI space, Horowitz said, adding that he is optimistic about models which can find useful and specific applications.  

“I do think there’s going to be some big craters, because there’s been billions of dollars invested in a lot of companies, and I’m not sure they’re going to be the people who survive,” he said. 

“I have no doubt that machine learning is going to change the world. The world’s going to be very different. Who’s going to be the player there, I think, is still to be decided.”