In our view, markets are being driven by two key factors that are to a degree interwoven: the clear strength of the global economies which are driving diversification towards non-tech/AI trades; and significant levels of uncertainty and confusion around the AI trade. Regardless of what market commentators claim to know about AI and the investment cycle, it is purely conjecture and the only certainty is that there is no certainty. Markets hate uncertainty and so volatility is picking up, and investors are being buffeted by strong investment winds. The reaction has been to move into safer havens.
Looking at the rotation in tech – software versus hardware and beyond
The following charts highlight the most visible element of the rotation and pain trade within technology that has been accelerating this year – hardware versus software, using the Goldman Sachs (GS) baskets for AI Hardware and Expensive Software for the last 12 months and a factor performance table for the year to date.
Software has been under pressure for almost a year in relative terms and the end of 2025 and start of 2026 has seen this accelerate. The reason for the moves has been attributed to the belief that AI will eat software, or that much traditional software will become totally redundant as it is replaced by AI products spun up at a moment’s notice to create proprietary systems to handle everything from HR to CRM.
We believe that fundamentally this is impossible to envisage. Corporate software systems are complex, not only in the software itself but also the protocols surrounding that software. Enterprises require enterprise level service agreements, and they rely on brands and customer support.
Source: Bloomberg, 12 months to 6 February 2026.
Source: Jefferies, 2 February 2026. YTD = Year to date.
There is no doubt that AI will have a profound impact on the creation of code but the notion that traditional software will be replaced is fanciful. Jensen Huang of Nvidia made this very point at the Cisco Ai summit on 3 February. He raised the important question of whether we want to use tools or reinvent them. His answer was: "The notion that AI is somehow going to replace software companies is the most illogical thing in the world, and time will prove itself."
Bill McDermott, CEO of ServiceNow, reinforced this view in a recent earnings call, saying: "The speculation that AI will eat software companies is out there. Let's clear it up with the facts. Enterprise AI will be the largest driver of return on the multi-trilliondollar super-cycle of investment in AI infrastructure. The real payoff comes when trillions of tokens move beyond pilots to be embedded directly into the workflows where business decisions are made."
The following chart shows current positioning in the markets. We believe the consensus underweights bucket contains names that will in time show themselves to have been very cheap at around 4 times revenues. The CEO of Salesforce went on a buying spree in 2016 when software was in the doldrums and valuations of 4 times revenue were on offer, often seen as a line in the sand for software valuation.
The Software Sentiment Matrix:
(Last updated 27.01.26)
Source: Schilsky’s Software Sentiment Matrix, JP Morgan, as of February 2026.
To further drive home the momentum trade into AI winners and losers, the market last week started to heavily punish companies that use data sets to drive subscription models. The catalyst for this latest move was the launch of Claude and the legal plug ins that were released allowing for access and management of legal datasets.
It doesn’t matter how clever AI is, it can only work with the data it is given or it can access. So, the defining barrier to entry, as we have always said, is the data itself. If AI can access the data, the competitive threat is real; but if the data is proprietary, it is not. For example, S&P Global has a huge amount of proprietary data around its indices – AI can’t access this without paying for it and licensing it like any other paying customer. This is the framework for deciding who wins and who loses: who owns the data?
What will reverse this extreme
We are constantly asked when this trend will reverse. We use a proprietary T Score system to help us decide when to own names we like, how much to own and how to risk manage them. These indicators are still set firmly negative on software and positive on hardware. We will not add to software until our indicators change, thus avoiding the ‘catching a falling knife’ syndrome. However, we have a shopping list.
Some of the hardest hit software names like Workday, Adobe and Salesforce are trading around the 4x revenue level that we consider to be fundamentally cheap. At the same time, hardware names have been stratospheric and valuations are looking stretched. What we need is a catalyst. We think it is possible the latest round of mega scaler capex plans might deliver that catalyst, Capital spending plans have been pushed up across the board, with Google being the latest company to raise 2026 numbers from $120 billion to an expected $175-185 billion.
The first order response is likely to be that this is great for the hardware companies. Counter intuitively, we believe the opposite. The faster the capex numbers go up, the quicker we reach peak capex levels. Markets thrive on growth surprises: there is a huge difference between 100% growth split across two years versus 100% growth in year one and zero growth in year two. The second order derivative matters and so this might be a sign that capex names are close to the top for now. The counter side to this trade will be that software is close to the bottom. Time will tell!
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Mark Hawtin
Mark Hawtin is head of the Global Equities team. Mark joined Liontrust in 2024 from GAM, where he was an Investment Director running global long-only and long/short funds investing in the disruptive growth & technology sectors. Before joining GAM in 2008 he was a partner and portfolio manager with Marshall Wace Asset Management for eight years, managing one of Europe’s largest technology, media and telecoms hedge funds.
