QQQM just handed investors a hard truth: the Nasdaq-100 isn't really a tech index anymore, it's a concentrated bet on a handful of AI winners staying ahead of the pack. Yes, QQQM is still worth holding in 2026, but only if you understand exactly which of its top holdings are widening their AI lead and which ones are quietly losing pricing power to cheaper, faster competitors.
What is the Concept
QQQM is the Invesco NASDAQ-100 ETF, the lower-cost sibling of QQQ, charging roughly 0.15% versus QQQ's 0.20% while tracking the same 100 largest non-financial companies listed on the Nasdaq. On paper it looks diversified across 100 names. In practice, its top ten holdings, Apple, Microsoft, Nvidia, Amazon, Broadcom, Meta, Alphabet, and Tesla among them, drive the overwhelming majority of the fund's return.
That's the part the "beware the rising AI competition" warning is really about. QQQM's performance is no longer a bet on "technology" broadly. It's a bet that today's AI leaders keep their pricing power, their compute advantage, and their customer lock-in as dozens of well-funded challengers, from open-source labs to Chinese AI startups to in-house hyperscaler chips, try to close the gap.
Why It Matters Now (2025–2026 Context)
Hyperscalers have spent hundreds of billions of dollars on AI infrastructure over the past two years, and investors have underwritten the story that this spending converts into durable, compounding profit. Rising AI competition directly threatens that story. If AI capability becomes commoditized faster than expected, the companies with the biggest capex commitments could see margins compress exactly when the market expects margin expansion.
Here's the contrarian part most investors miss: an index-tracking ETF like QQQM is often sold as "diversification," but it has quietly become one of the most concentrated single-narrative bets in market history. When ten AI-exposed companies determine the fate of a fund holding 100 names, buying QQQM for diversification is a mistake, you're really buying conviction in one theme continuing to win.
How AI Is Changing This
Cheaper and increasingly capable open-source models are compressing the margin advantage that early AI leaders once assumed was permanent. When a well-trained model can be replicated or approximated at a fraction of the original training cost, the moat isn't the model anymore, it's what a company builds around it: distribution, proprietary data, and switching costs.
There's also a structural shift happening from the infrastructure layer to the application layer. Value is moving up the stack toward companies that use AI effectively to solve real business problems, not just the companies that built the largest foundation models. That shift matters because several of QQQM's largest holdings are infrastructure and platform plays, not application-layer businesses, which is exactly where competitive pressure is building fastest.
Real-World Examples
Nvidia's dominance in AI chips, long treated as an unassailable moat, is now being challenged by custom silicon from its own biggest customers: Google's TPUs, Amazon's Trainium chips, and Microsoft's in-house AI accelerators are all attempts to reduce dependency on a single supplier and reclaim margin that would otherwise flow to Nvidia.
The emergence of highly efficient open-source models, including releases from Chinese labs like DeepSeek that demonstrated frontier-level performance at a fraction of typical training cost, rattled the market's core assumption that scale alone equals a lasting moat. That single data point wiped out real market value in a matter of days and forced a re-pricing of AI leadership risk across the Nasdaq-100.
Practical Insights / Actions
Use what we call the AI Moat Score before trusting any AI leader's index weighting: rate each company on four factors, proprietary data advantage, distribution and switching costs, compute or supply-chain leverage, and pace of independent revenue growth outside AI hype cycles. Companies that score low on data and switching costs are the most exposed to rising competition, regardless of how large their market cap looks today.
The most common founder and investor mistake is treating current market leadership as a permanent moat instead of a temporary lead that must be actively defended. The hidden opportunity sits with businesses, public or private, that build effective, defensible products on top of increasingly commoditized AI infrastructure rather than betting everything on owning that infrastructure themselves.
Future Outlook
Expect continued rotation risk inside the Nasdaq-100 through 2026: capital will keep chasing AI application companies with real revenue and defensible data, while pure infrastructure plays face pressure as competition narrows their pricing advantage. Investors holding QQQM should watch capex-to-revenue trends and margin commentary from its top holdings as the earliest warning signs of a broader re-rating.
For long-term holders, diversifying exposure beyond the top ten names, whether through equal-weight alternatives or direct allocation to application-layer AI companies, is a more resilient strategy than assuming the current leaderboard stays fixed for the rest of the decade.
Conclusion
QQQM isn't a bad fund, but it is a much more concentrated AI bet than most investors realize, and rising competition is the single biggest risk to that bet playing out as expected. If you're a founder or operator trying to make sure your own company doesn't end up on the losing side of that same competitive shift, RP SoftTech helps SMEs and startups architect AI-native products with real, defensible moats instead of dependency on someone else's infrastructure.