The software sector's reckoning has arrived, and it hasn't been pretty. Over the past three months, some of the most respected global growth managers have suffered drawdowns of 15% or more as the so-called "SaaSpocalypse" has torn through high-multiple enterprise software names. Munro Partners, by contrast, has emerged largely unscathed, and the way they've done it carries lessons for anyone allocating to growth strategies.

Speaking with InvestSense, Munro portfolio manager Qiao Ma explained that the fund's resilience comes down to two things: genuine diversification across uncorrelated structural themes, and a disciplined stop-loss process that forced hard conversations months before the worst of the damage arrived. Names in digital enterprise triggered Munro's 20% review threshold as early as mid-2025. The team debated, re-examined, and ultimately exited. Many of those stocks have since fallen a further 40-50%.

But the more interesting question isn't what Munro avoided, it's why the software sector was so vulnerable in the first place. The uncomfortable truth is that many of these companies were bugs waiting for a windshield. Trading at 40-45x earnings, priced for perpetual 15-20% growth, they needed everything to go right for a very long time. When AI began commoditising the very thing they sell — the ability to build and deploy software — the durability of that growth was suddenly in question, and the valuation cushion simply wasn't there.

This is a risk factor that growth investors may have become complacent about. Long-duration growth stories are seductive. They offer a compelling narrative, impressive TAMs, and the promise of compounding returns. But a lot can go wrong betwixt and between, especially when expectations (valuation multiples) are high and something changes. If your starting multiple is 45x and your earnings leverage is minimal, you need an awful lot of good news just to stand still. When the narrative shifts, the re-rating can be savage.

What makes Munro's positioning instructive is where they've rotated to. Qiao describes AI as deflationary for the digital economy but inflationary for the physical one. Her analogy is vivid: software used to be a pedicab, manual, labour-intensive, expensive. AI is the steam train. It makes the ride cheaper, but the train itself requires vastly more infrastructure. The supply chain bottlenecks are spreading from chips to power to switchgear, circuit breakers, cooling systems, and beyond. Qiao makes the point that change can bring opportunity, even if it challenges the space you are investing in, and that Munro is finding companies with accelerating earnings, orders in hand through 2028, and in some cases zero analyst coverage. It will be interesting to look back in a couple of years but maybe this is a point in time where active management can start to add value, coming out of a period where the big have just got bigger but maybe the tide is turning.

There is, of course, a broader anxiety hovering over all of this: if AI is this deflationary, what does it mean for jobs? It's a question preoccupying employers, graduates and parents alike. Qiao's take is optimistic and worth passing on. She sees AI as potentially an equaliser but one that also widens the gap between top performers and the mediocre. Young people who come to the workplace curious, conversant in AI, and ready to contribute will find that the tools available to them are extraordinary and the opportunity set is opening up. The teacher, as Qiao puts it, has appeared — for anyone willing to learn and hit the ground running.