Goldman Sachs investment banking co-head Kim Posnett recently predicted an IPO "mega-cycle" ahead, citing improved market conditions and pent-up demand from private companies. While most coverage focuses on macroeconomic factors driving this forecast, the more interesting question concerns what happens when algorithmic intermediation increasingly determines which companies actually reach public markets. The rise of AI-mediated deal sourcing and evaluation tools at investment banks represents a structural shift in how capital allocation decisions get made, and the coordination mechanisms these platforms create deserve closer scrutiny.

The Procedural Encoding of Investment Banking Judgment

Investment banks have begun deploying large language models to analyze pitch materials, screen potential deals, and even draft portions of prospectuses. These tools promise efficiency gains, but they fundamentally alter the coordination structure between companies seeking capital and the institutions that provide it. The critical issue is not whether AI can replicate existing investment banking procedures. The issue is whether codifying current decision-making patterns into algorithmic systems locks in specific schemas about what constitutes a viable public company (Kellogg et al., 2020).

When Goldman analysts develop intuitions about IPO readiness, they build adaptive expertise through exposure to varied market conditions and company types. They learn structural patterns about capital formation that transfer across contexts. An AI system trained on historical deal data, by contrast, develops routine expertise optimized for pattern matching against past successes. This distinction matters because the companies most likely to benefit from Posnett's predicted mega-cycle may be precisely those that deviate from historical templates (Hatano & Inagaki, 1986).

The Awareness-Capability Gap in Founder-Bank Coordination

Companies preparing for public offerings increasingly know that algorithmic screening shapes their fate. This creates the awareness-capability gap I have documented in platform work contexts. Founders understand that AI systems evaluate their materials, but this awareness provides no actionable guidance about how to adapt their presentation. The folklore that emerges fills this vacuum with folk theories rather than structural understanding (Gagrain et al., 2024).

Consider the founder who learns their pitch deck will be analyzed by natural language processing tools. They might respond by optimizing keyword density or mimicking the linguistic patterns of successful prospectuses. This represents a procedural adaptation to perceived algorithmic constraints. But it misses the deeper structural question: what schemas about market opportunity, competitive dynamics, and growth potential are encoded in the training data these systems use?

The power-law distributions we observe in IPO outcomes suggest that small differences in how companies navigate this algorithmic gatekeeping get amplified through the capital formation process. Identical companies with identical fundamentals may experience dramatically different outcomes based on how their materials interact with screening algorithms (Schor et al., 2020). Unlike traditional gig economy platforms where workers can experiment and adjust, companies typically get one chance at an IPO.

What Topology Reveals About Capital Market Coordination

The topology of algorithmic intermediation in investment banking differs fundamentally from consumer-facing platforms. The coordination structure is not many-to-many matching between distributed workers and customers. It is sequential gatekeeping where passage through each algorithmic checkpoint becomes necessary for advancement to human decision-makers. This creates what Rahman (2021) calls an "invisible cage" where the shape of constraints matters more than any individual barrier.

When Posnett discusses an IPO mega-cycle, she implicitly assumes that worthy companies will successfully navigate this topology. But if the algorithmic systems mediating access to investment bankers encode schemas derived from previous market regimes, structural misalignment emerges. The companies that could drive genuine innovation in public markets may be filtered out precisely because they do not match historical patterns.

This is not an argument against AI in investment banking. It is an argument for transparency about the coordination mechanisms these systems create. Banks deploying algorithmic screening tools should document what structural features their models privilege and what they ignore. Companies preparing for public offerings need schema induction about how these systems operate, not just procedural tips about keyword optimization.

The coming IPO cycle will test whether algorithmic intermediation in capital markets creates better coordination or simply encodes existing power structures into automated form. The answer depends on whether participants develop adaptive expertise about these systems or merely accumulate procedural workarounds.