They argue that the next decade will still be dominated by foundational layers that make AI applications possible.\u00a0<\/p>\n
“A lot of the more boring productivity components of AI are already getting monetised, and that\u2019s the clearest opportunity over the next five years \u2014 because they have real scale and applications across every sector,\u201d Wei says.<\/p>\n
And as Walsh added, the power dynamics of this cycle will be defined by whoever controls the infrastructure layer:\u00a0<\/p>\n
\u201cIt’s a gold rush. You own companies that are building shovels. That’s the way you’d do it.\u201d<\/p>\n
What follows is a breakdown of where in the AI value chain economic value is being created, where speculation still dominates, and how long-term investors can position for the infrastructure powering the next decade of AI.<\/p>\n
<\/p>\n
Dr David Walsh and Dr Wang Chun Wei of RQI Investors<\/p>\n
The three-layer stack that will define AI economics<\/p>\n
RQI breaks the AI ecosystem into three distinct layers:<\/p>\n
Semiconductor design and manufacturing (Nvidia, AMD, Broadcom, TSMC)
\nCloud infrastructure and hyperscalers (Amazon, Microsoft, Google)
\nSoftware and applications<\/p>\n
According to Wei, today\u2019s economic value is overwhelmingly accruing to the bottom two.<\/p>\n
\u201cWe kind of feel that most of the value add is going to be at the bottom two layers. We’re not seeing things to be as attractive at the top layer. Primarily, it’s quite competitive and also at the moment slightly commoditised.\u201d<\/p>\n
While the application layer will eventually mature, RQI believes the pace will be slower than many investors expect \u2014 and far more competitive.<\/p>\n
Where monetisation is already happening<\/p>\n
Amid excitement around robotics, autonomous agents and generative AI, RQI sees the clearest near-term monetisation in enterprise productivity. These are the tools that automate the \u201cboring\u201d but time-consuming white-collar tasks that exist inside every business.<\/p>\n
Wei emphasised, \u201cAI tools to help programmers better code\u2026 AI productivity tools to do better document reading\u2026 These things are already getting monetised\u2026 and it has applications across all sectors.\u201d<\/p>\n
This category has three advantages:<\/p>\n
It delivers immediate efficiency gains
\nIt scales across industries
\nIt requires no behavioural change from the end user<\/p>\n
By contrast, categories like robotics and agent-based systems remain interesting \u2014 but still \u201cnot very mature or likely to have real applications in the next five years.\u201d<\/p>\n
Risks: valuation, capital intensity and the \u2018hunger games\u2019<\/p>\n
Walsh argues that the biggest risks to the AI thesis are not conceptual but practical. They revolve around over-investment, energy constraints, and stretched valuations.<\/p>\n
On capital intensity, he warns, \u201cYou might find that some kind of race to the bottom and some kind of hunger games is going on where you’ve got all these guys continuing to spend: eventually the winner takes all.\u201d <\/p>\n
He also flags the extreme multiples now embedded in the megacap AI trade: \u201cThese things are trading on 30 to 40 times forward earnings and earnings growth is small\u2026 that’s clearly valuation risk.\u201d <\/p>\n
His guidance for investors is blunt: \u201cHand waving about disruption or about CapEx is probably too general a question to be answered properly.\u201d Investors need to drill deeper than narratives if they want to survive this cycle.<\/p>\n
Walsh continues, \u201cUnderstand in some detail\u2026 what those risks actually represent\u2026 If you don’t understand it, you shouldn’t [take the exposure].\u201d<\/p>\n
Why stock-picking won\u2019t work in a 10-year AI horizon<\/p>\n
When asked to identify the companies most likely to outperform through 2035, both Walsh and Wei pushed back. The speed of technological change makes long-range stock picking extremely unreliable.<\/p>\n
Wei noted, \u201cThe companies that might succeed in 2035 probably don’t even exist right now.\u201d<\/p>\n
RQI\u2019s own portfolios rebalance weekly, and their signals focus on measuring risks rather than forecasting technological winners.<\/p>\n
They also caution against the assumption that the best AI applications will dominate the value chain. Vertical integration can work \u2014 but only after control of the foundational compute and infrastructure layers is established.<\/p>\n
\u201cNo company can sustainably own the application layer without first securing the compute and infrastructure layer beneath it.\u201d<\/p>\n
What this means for investors: positioning signals in the AI decade<\/p>\n
While RQI avoids long-dated stock calls, their framework offers clear signals about where the strongest structural tailwinds lie:<\/p>\n
Most attractive (high conviction structural tailwinds)<\/p>\n
Semiconductors and advanced chip design \u2014 the shovel-sellers of the AI boom
\nHyperscalers and cloud infrastructure \u2014 control access to compute and pricing power<\/p>\n
Attractive now (clean near-term monetisation)<\/p>\n
Enterprise productivity software \u2014 coding assistants, workflow automation, document AI<\/p>\n
Speculative (high uncertainty, long runway)<\/p>\n
AI agents and robotics \u2014 exciting, but lacking scale or maturity<\/p>\n
Challenging (competitive + commoditised)<\/p>\n
Application-layer AI businesses without infrastructure advantages<\/p>\n
The unifying message: returns will come from understanding which risks you choose to own \u2014 not from betting on long-dated predictions.<\/p>\n
The most important insight: portfolio construction beats thematic conviction<\/p>\n
While AI will dominate the market structure for the next decade, RQI argues that thematic correctness is secondary to portfolio construction.<\/p>\n
Wei is clear: \u201cHaving good portfolio construction and risk modelling is probably even more important than just picking the thematic AI.\u201d<\/p>\n
Walsh reinforced the same idea: diversified exposures, disciplined risk management, and clarity around what you actually own will matter far more than chasing the headline AI winners.<\/p>\n
The implication is simple: the AI opportunity is real, but durable returns will flow to those who understand the plumbing, not just the promise.<\/p>\n
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