Assets, Not Apps: The Real Source of Startup Moats

When iCapital’s research team warned in September 2025 that AI is "challenging traditional enterprise software’s economics", the note crystallised a dilemma facing every young software firm.

The observation echoes a broader pattern that MIT Sloan traced back as early as 2018: knowledge embedded within state-of-the-art production and design tools "is a powerful force that is leveling the global technology playing field" and accelerates high-tech commoditization. Together, the findings underline a strategic pivot—enduring advantage stems less from code and more from assets that rivals cannot easily clone.

Software Moats Under AI Pressure

Rapid feature imitation is not new, but diffusion speed has quickened. As a result, according to the 2025 iCapital briefing.

MIT Sloan’s article "Why High-Tech Commoditization Is Accelerating" argues that sophisticated design tools embed years of tacit know-how, letting newcomers "skip years of practice." That transfer of capability erodes incumbents’ technology gaps and forces differentiation to migrate away from pure product features.

Generative models intensify the trend, compressing iteration cycles as reported in industry analyses. For firms whose only moat is novel functionality, margin pressure appears sooner and harsher. Investor behaviour is adjusting accordingly, as the iCapital note discusses pressures on SaaS pricing models.

Why Code Commoditizes Faster Than Assets

Code is inherently replicable: once published, its logic can be reverse-engineered, rewritten or recombined. In contrast, assets such as brand equity, contractual datasets or two-sided networks require time-dependent accumulation. Their value grows through user trust, behavioural habits or exclusive agreements—factors competitors cannot buy off the shelf.

Knowledge diffusion reinforces this asymmetry. The MIT Sloan piece notes that sophisticated design tools make complex processes routine, opening the door for fast followers. By comparison, building a globally recognised brand demands consistent market signals and customer experiences over many cycles.

Legal protection also differs. Copyright shields exact code, yet functionally equivalent implementations avoid infringement. Trademark and trade-secret regimes, however, defend brand identifiers and proprietary data with clearer, more enforceable boundaries.

Economic incentives accentuate the gap. Feature wars push pricing toward marginal cost, while established assets enable premium pricing or lower acquisition expense through organic pull. Over time, asset-heavy firms convert lower churn and better pricing power into cash that funds further moat expansion.

Asset Types That Compound Over Time

Brand equity tops the list. Interbrand’s 2024 Best Global Brands report ranks and values leading brands.

User communities generate a different kind of lock-in. Andrew Chen’s 2021 book The Cold Start Problem documents how early "atomic networks"—small, dense user clusters—tip platforms into self-sustaining growth. Once critical mass forms, switching costs rise because value accrues from the network itself, not the underlying tool.

Proprietary datasets work similarly. Machine-learning feedback loops improve model performance with usage, making late entrants face a moving target. Vendor contracts or exclusive data partnerships further entrench the advantage by denying rivals equal training fuel.

Content libraries form another compounding resource. Streaming services, for instance, rely on long-term rights that cannot be duplicated without negotiation and capital. The catalogue, not the playback code, keeps subscribers locked in.

Last, regulatory licences—while not strictly assets in accounting terms—can act as structural barriers. Payments platforms often need costly compliance approvals; once obtained, the licence portfolio becomes a choke point for prospective challengers.

Evidence from Unicorn Valuations

A 2020 Business Research study, based on a CB Insights list of 258 unicorns as of November 2018, found that inorganic growth via acquisitions was a strong predictor of valuations.

Case studies reinforce the pattern. Airbnb survived an early feature-copy attempt by Wimdu because Airbnb’s host and guest network was already self-reinforcing, a dynamic akin to those in Chen’s book on network effects. Similarly, TikTok’s algorithm may be imitated, but its creator community and content graph continue to widen the performance gap.

Capital allocation reflects these findings, indicating that investors may pay a premium for latent network power.

Market exits tell the same story: firms with entrenched assets fetch richer acquisition multiples because buyers value the future cash flows secured by switching costs rather than the replaceable feature list.

Together, the evidence suggests that while code remains the delivery mechanism, the economic engine sits in less tangible—yet more durable—asset classes.

Building Moats: Practical Steps for Early Teams

Start with a narrow, high-value user cohort to seed the first atomic network. Chen calls this "Flintstoning": manual hustle that proves the network’s utility before automation. The goal is density, not breadth—dense clusters accelerate word-of-mouth and data accrual.

Instrument everything to capture unique datasets from day one. Whether telemetry on workflows or labelled images, early collection compounds. Later competitors may match the feature, but reproducing longitudinal data is impossible without time travel.

Invest intentionally in brand signals even before scale. Early branding need not be lavish, but it must be coherent and repeatedly delivered.

Structure agreements that lock in exclusivity where ethical. A licensing deal for niche content or an API partnership that requires mutual integration can create time-boxed monopolies that smooth the road to broader adoption.

Finally, design pricing to reward engagement, not just consumption. Usage-based tiers that scale with customer dependence turn the network effect into recurring revenue, aligning user benefit with the firm’s margin expansion.

Where the Trend Leads

As generative AI keeps compressing build cycles, pure-software differentiation will decay even faster. Market share will flow toward players that already possess reinforcing assets, much as the personal-computer era eventually rewarded ecosystem control over hardware specs.

For founders and investors, the implication is clear: treat code as the ticket to enter a market, not the moat that secures it. The firms that win the next decade will be those converting early functionality into compounding assets—brands customers trust, networks users need and datasets algorithms cannot do without.

Sources

• Chambless, J. "Building Assets vs. Building Software." Silicon Oasis, 2025.
• Basak, S.; Repetto, P.; Weaver, N.; iCapital Investment Strategy Group. "iCapital Market Pulse: As AI Disrupts, Not All Software Is Created Equal." iCapital, 2025.
• Shih, W. "Why High-Tech Commoditization Is Accelerating." MIT Sloan Management Review, 2018.
• Interbrand. "Best Global Brands 2024 Report." Interbrand, 2024.
• Chen, A. "The Cold Start Problem: How to Start and Scale Network Effects." Harper Business, 2021.
• Bock, C.; Hackober, C. "Unicorns—what drives multibillion-dollar valuations?." Business Research, 2020.