BlackRock published its Spring 2026 Hedge Fund Outlook, which explores how a more differentiated market environment — shaped by supply shocks, AI-driven disruption, and the breakdown of traditional portfolio diversification — is expanding the opportunity set for hedge fund strategies.
Key themes from the report:
- Investor appetite for hedge funds is at its highest level in five years: Preqin data shows nearly one-third of investors plan to increase allocations, with industry AUM projected to exceed $6 trillion by 2030.
- Diversified hedge fund allocations are becoming increasingly important for portfolio resilience: As dominant mega-forces such as AI cut across asset classes and regions, the diversification properties of traditional assets are weakening – making hedge funds a critical tool for accessing differentiated return streams and building portfolios that can withstand faster regime shifts. Diversification across the hedge fund universe is also proving its worth, as certain strategies outperform at different times, further strengthening portfolio resilience.
- AI is the defining force across strategies: Rising cross-sectional dispersion – as capital allocates more selectively between AI beneficiaries and those at risk of disruption across sectors – is creating a rich environment for systematic and fundamental managers to generate alpha through disciplined security selection.
Systematic multi-strategy: Dispersion, disruption and discipline
As differentiation across companies accelerates, cross-sectional dispersion has risen meaningfully. Returns are becoming more company-specific rather than broad market beta, reinforcing the importance of selectivity for systematic investors.
Year-to-date, headline equity indices have held up better than underlying breadth would suggest. Cross-sectional dispersion has widened materially, with individual companies trading more distinctly on their own merits. Capital is no longer moving in broad synchronized waves; it is being allocated with greater discrimination.
This shift does not reflect a retreat of macro forces. Instead, it reflects a change in how those forces are transmitted into markets. Structural drivers such as artificial intelligence, geopolitics and industrial policy are no longer lifting or lowering markets uniformly. They are reshaping competitive dynamics at the company level, creating sharper distinctions across business models, balance sheets and execution capabilities.
As differentiation rises, so too does the opportunity set for active strategies. Periods of elevated dispersion reward disciplined security selection, cross-asset flexibility and the “safety engineering” embedded in systematic portfolio construction. When returns are increasingly driven by relative positioning rather than broad beta, skill and process matter more.
AI: From promise to structural integration
At the center of this recalibration is artificial intelligence. AI is transitioning from experimentation to broad structural adoption across industries. Model performance continues to improve, costs are declining and increasingly capable agentic systems are automating multi-step workflows. What was once conceptual is becoming operational. As adoption accelerates, markets are repricing accordingly, most visibly in software and other human-capital-intensive services.
The repricing is inherently cross-sectional. In our view, assessing AI requires more than considering exposure alone; it involves evaluating how companies rank along three dimensions:
- Their ability to harness AI-enabled growth
- Their exposure to disruption or redundancy
- Their capital discipline in executing large-scale infrastructure build-out
Some firms are positioned to benefit from productivity gains and rising demand for compute and automation. Others face long-term challenges around the durability of asset-light, human-capital-intensive models. Many occupy a middle ground, simultaneously exposed to both opportunity and risk.
Systematic signals identifying firms at risk of AI-driven displacement have helped navigate this repricing. Complementary signals targeting AI and data-center infrastructure beneficiaries informed allocations as leadership rotated. The divergence between long/short AI baskets underscores how decisively markets are distinguishing between perceived winners and those facing structural headwinds.
Evidence of this transition is increasingly visible beyond financial markets. Job-posting data since the release of large language models provides a useful real-economy lens. Comparing occupational demand growth between the pre-generative pre-trained transformer (GPT) period (2019–2022) and the post-GPT period (2023–2026) reveals a clear pattern: roles with higher AI replacement risk are seeing materially weaker demand growth.
The implication is not that automation eliminates work, but that it reshapes where demand appears. Investment, infrastructure and technical implementation roles continue to expand, while tasks more easily replicated by generative systems are experiencing slower growth.
At the occupational level, the divergence is even clearer. Based on our data, job postings for telemarketers, a role heavily exposed to automation, have declined by more than 40%. By contrast, postings for computer and information research scientists, who design and build the systems powering AI itself, have increased more than five-fold.
Markets often price disruption faster than productivity gains. This dynamic has been especially evident in software, advisory, and payment-related business models. Even strong earnings have occasionally been met with muted reactions, reflecting recalibration rather than deterioration in fundamentals.
Importantly, AI’s influence is broadening leadership across sectors and geographies. Infrastructure, industrial capacity, energy transmission, semiconductors, and advanced manufacturing are all increasingly central to the next phase of technological expansion. The widening dispersion we observe reflects markets processing this transformation in real time.
Read the full report here by BlackRock
