Macquarie Profit Tops Estimates on Commodities, Markets Activity

Macquarie CEO Shemara Wikramanayake said one concern in private markets is the potential for AI disruption to software companies. The comment highlights an emerging valuation and business-model risk for software assets rather than a specific company event. Market impact is likely limited, but the takeaway is cautious for private-market investors exposed to software.

Analysis

The important read-through is not just “AI hurts software,” but that private-market marks are more vulnerable than public equity to a slower, more ambiguous impairment cycle. In venture and growth portfolios, the biggest damage often shows up first in follow-on financing terms, not headline revenue misses: higher discount rates, down-round risk, and elongated time-to-exit as buyers demand proof that AI-native competitors cannot commoditize the core workflow. That creates a lagged but potentially nonlinear hit to private-market IRRs over the next 12-24 months if software efficiency gains compress retention, pricing power, and cohort expansion.

Second-order winners are the AI infrastructure and enablement layers, not necessarily the model vendors alone. If software application multiples compress, capital tends to migrate toward picks-and-shovels areas where monetization is tied to compute usage, security, orchestration, data tooling, and workflow automation that directly improves labor productivity. Public-market beneficiaries are likely to be large horizontal platforms and enterprise incumbents with distribution and data moats, while smaller point-solution software names face the most acute substitution risk because they lack scale to absorb AI feature parity.

The key catalyst to watch is whether AI adoption moves from experimentation to budget reallocation inside enterprise IT over the next two reporting seasons. If customers can replace seat-based licensing with outcome-based or usage-based purchasing, that compresses recurring revenue visibility and can force a reset in private SaaS valuations by 20-40% in the weakest cohorts. The contrarian view is that the market may already be over-penalizing legacy software while underestimating the implementation friction, compliance burden, and data integration costs that slow full AI substitution; this argues for dispersion rather than a blanket short on software.

Key Decisions for Investors

• Short a basket of weaker private/software proxies via listed analogs or public comps with high seat-based revenue exposure; look for rallies to fade over the next 4-8 weeks, targeting 15-25% downside if AI feature parity headlines accelerate.
• Long AI infrastructure/enablement leaders versus application-layer software over 3-6 months; pair the most commoditization-prone SaaS names against semis, cloud, or cybersecurity beneficiaries to isolate the AI spend shift.
• For venture/growth exposure, reduce underwriting on new software rounds until post-Q2 earnings visibility improves; prioritize structures with ratchets/liquidation protection because down-round probability rises materially if net retention decelerates for two consecutive quarters.
• Buy downside protection on software-heavy indices or sector ETFs into any valuation-driven bounce; 6-12 month puts are attractive if implied vol stays below realized as the market re-prices long-duration cash flows.
• Maintain a selective long book in enterprise incumbents with embedded distribution and AI cross-sell optionality; these names can gain share as buyers prefer vendors that bundle AI rather than add standalone tools.