Crypto Outlook Bright as AI Disrupts Software Firms

Context

Kraken-backed KRAKacquisition Corp.'s CEO Ravi Tanuku told Coindesk on March 28, 2026 that the latest crypto bear cycle is "a mere blip" compared with the existential threat that artificial intelligence poses to traditional software services (Coindesk, 28 Mar 2026). The quote crystallizes a shifting narrative among certain public-market investors: cryptocurrencies and blockchain infrastructure are being reframed not as speculative oddities but as alternative layers of resilience against rapid product substitution driven by AI. While market participants debate timing and valuation, the structural question is whether decentralized systems can capture demand and revenue displaced from incumbent software providers.

This piece situates that commentary in market data, sectoral comparisons and downside scenarios. We anchor the discussion in three dated reference points: the Coindesk interview (28 Mar 2026), historical crypto market stress during 2021–22 (CoinGecko reported total crypto market capitalization contracted roughly 70% from Nov 2021 to Nov 2022), and Fazen Capital's proprietary scenario analysis projecting up to 20% structural revenue risk to legacy enterprise software by 2028 under aggressive AI substitution. Those three datapoints frame a comparative assessment of risk, opportunity and timeline for allocators weighing crypto exposure versus software equities.

Our approach is intentionally comparative and empirical. We examine transaction-level flows and capital allocation signals into AI and blockchain, assess the relative vulnerability of SaaS revenue models to algorithmic substitution, and quantify plausible adoption pathways for crypto-native infrastructure. Where public data are thin, we flag assumptions explicitly. Readers should treat Fazen Capital numbers as scenario estimates, not market certainties; this article is informational and not investment advice.

Data Deep Dive

Public and private capital activity demonstrates a bifurcation between AI and crypto. Venture investment into AI-first companies grew materially through 2024–25, with multiple global funds citing multiyear commitments; by contrast, venture capital into blockchain protocols has been more episodic, driven by niche infrastructure and L2 scaling plays. On March 28, 2026, Ravi Tanuku framed crypto’s latest price weakness as short-term relative to structural shifts in software demand (Coindesk, 28 Mar 2026). That statement aligns with observable capital flows: leading AI compute and foundation-model ventures attracted fresh capital rounds in late 2025, while many public crypto tokens remained below their 2021 peaks (CoinGecko peak-to-trough ~70% decline Nov 2021–Nov 2022).

Transaction-level indicators show heterogeneity within the crypto ecosystem. Layer-1 networks that emphasize on-chain programmability and composability report rising developer activity measured by active addresses and commits; by contrast, tokenized speculative assets show lower velocity. Fazen Capital's internal tracker registers a 35% year-on-year increase in smart-contract deployments on a selection of EVM-compatible chains from Q4 2024 to Q4 2025 (Fazen Capital internal dataset). These micro signs suggest that real economic use cases—payments rails, tokenized real-world assets, and on-chain identity—are beginning to separate from purely speculative flows.

On the AI side, adoption dynamics create two distinct threats to incumbent software providers: first-order automation that replaces tasks (e.g., content generation, code assistance) and second-order platform substitution where AI-native primitives supplant productized APIs and SaaS workflows. Fazen Capital models a scenario in which 10–30% of legacy SaaS deal value could be vulnerable to AI-native replacements by 2028; our baseline assumes a 20% revenue displacement for a cross-section of mid-market enterprise software providers (Fazen Capital scenario analysis, Feb 2026). Those figures inform the comparative risk calculus that Mr. Tanuku highlighted: if software revenues compress materially, investor appetite for alternative infrastructure (including crypto) may improve.

Sector Implications

For public market software incumbents, the most immediate implication is margin compression and churn. Companies with high gross-margin, subscription-based models but limited AI integration face a two-pronged risk: customers that adopt AI augmentation for productivity and customers that pivot to AI-native vendors offering superior functionality at lower incremental cost. The time window for incumbents to respond effectively is short—Fazen Capital's actionability timeline estimates meaningful market share shifts can occur within 24–36 months after a well-funded AI competitor scales a production-ready offering (Fazen Capital, Mar 2026).

For crypto and blockchain firms, the implication is a potential repositioning. If AI reduces demand for some centralized software layers, blockchain-based solutions that offer verifiable data provenance, decentralized orchestration, and token-aligned incentives may become relatively more attractive for applications where trust minimization and composability matter. That said, the blockchain sector must reconcile adoption frictions: transaction costs, UX shortcomings, and regulatory uncertainty continue to slow enterprise integration. A pragmatic path for crypto protocols is to target vertical use cases where decentralization solves a definable economic problem—payments settlements, cross-border remittances, and niche financial primitives remain primary candidates.

Capital markets will price these structural changes unevenly. Software index funds and large-cap SaaS names may see higher beta to AI adoption metrics, whereas select crypto infrastructure plays could re-rate if they show persistent on-chain demand growth. Comparison to historical cyclicality is instructive: crypto experienced a severe drawdown in 2022 (CoinGecko ~70% contraction), but that event was primarily liquidity and leverage-driven. The potential AI-driven displacement of software is a fundamentally different shock—it is structural rather than solely valuation- or sentiment-driven, and it may alter long-run free-cash-flow assumptions for incumbent providers.

Risk Assessment

Material risks remain on both sides of this thematic debate. Crypto faces regulatory uncertainty that can impose binding constraints on token utilities and custody models; policy changes in major jurisdictions could meaningfully compress or reroute flows irrespective of technological fit. Likewise, AI presents durability and accuracy risks: hallucinations, model brittleness, and data-poisoning incidents could undermine business adoption if not resolved. Investors should therefore evaluate convexity of outcomes—crypto's upside from a re-rating tied to real usage is large, but downside from regulatory clampdown is similarly concentrated.

Counterparty and systemic risks also diverge. Software incumbents typically have concentrated enterprise customers and longer contract durations, which can buffer near-term revenue shocks but may hide latent vulnerability to AI substitution. In contrast, crypto-native firms often have more distributed revenue sources but rely on continuous network effects and developer activity. Our scenario overlays show that a 20% structural revenue reduction for legacy software (Fazen Capital baseline, 2028) would not translate linearly into equivalent dollar flows to crypto; instead, value accrual would bifurcate toward services and infrastructure enabling new use cases.

Operational timing is critical. If AI-native competitors can deliver enterprise-grade SLAs and compliance within 18–24 months, incumbents facing adoption gaps could experience accelerated churn. Conversely, if regulation and technical limitations slow AI's enterprise penetration to a multi-year cadence, incumbents will have more runway to integrate AI capabilities and preserve margins. Fazen Capital models a 50/50 probability that AI reaches mission-critical parity for broad enterprise workflows by 2028; the outcome split represents material dispersion in forward earnings estimates for software companies.

Fazen Capital Perspective

Fazen Capital's view is contrarian to the simplistic narrative that AI's ascent automatically sidelines crypto. Instead, we argue that AI and crypto are overlapping but not perfectly substitutable technologies. AI commoditizes certain software tasks but simultaneously increases demand for secure, auditable data channels and tokenized incentive systems that ensure data integrity—functions where blockchain adds measurable value. In our scenarios, roughly 10–15% of value displaced from traditional SaaS could translate into increased demand for decentralized services over a 3–5 year horizon (Fazen Capital scenario projection, Mar 2026).

We also caution against binary framing. The most likely near-term outcome is asymmetric: AI will disrupt specific software categories (content creation, code generation, routine analytics) while leaving verticalized, compliance-heavy, or mission-critical systems less affected. Crypto has the best near-term product-market fit where it reduces friction or enforces provenance—examples include on-chain identity attestations, programmable money in cross-border workflows, and settlement layers for tokenized assets. We highlight two practical indicators to monitor: (1) month-over-month active developer counts on major L1/L2 networks and (2) enterprise procurement cycles for AI-native versus incumbent platforms.

Finally, from a portfolio construction lens, the catalyst horizon matters. If an allocator’s horizon is short (12–24 months), the dominant risk to crypto remains price volatility and regulatory action. For longer-term investors (3–7 years), the structural interplay between AI and decentralized infrastructure becomes a more salient determinant of relative returns. We maintain an investment-research library that tracks these indicators and publishes quarterly updates; see our insights hub for the methodology and dataset summaries.

FAQ

Q: Will AI adoption automatically increase demand for cryptocurrencies? A: Not automatically. Short-term AI adoption can reduce demand for some centralized software services but does not directly create token demand unless use cases map to on-chain primitives—payments, provenance, or tokenized incentives. Historical precedent shows technology substitution often creates adjacent demand curves rather than direct transfer. Monitor on-chain activity metrics and enterprise pilot announcements for leading indicators.

Q: How should investors measure whether software revenue is at risk from AI? A: Look beyond marketing claims to customer-level metrics: net retention, average contract value changes, and pipeline conversion post-AI pilot. Fazen Capital recommends tracking realized productivity gains in customer proof-of-concepts and the timeline vendors provide for enterprise-grade model governance. These operational metrics typically precede revenue migration by 6–18 months.

Bottom Line

Crypto's current price cycle does not negate the structural value proposition that decentralized infrastructure can offer if AI materially reshapes software economics; the interplay is nuanced, time-dependent and measurable through developer activity and enterprise procurement signals. Fazen Capital estimates meaningful but not total substitution risk to legacy software—allocators should treat AI and crypto as correlated but non-identical exposures.

Disclaimer: This article is for informational purposes only and does not constitute investment advice.