Introduction: Markets Within Cycles
Every generation feels as if it is living through unprecedented change. For us, that feeling is justified. The 2020s are unfolding at the intersection of rapid technological breakthroughs and equally dramatic macroeconomic swings. Zero interest rates and quantitative easing gave way to the fastest tightening cycle in decades. Artificial intelligence has gone from science fiction to everyday productivity tool. Blockchains are moving from speculative playgrounds to serious settlement infrastructure.
The question hanging over investors, builders, and policymakers alike is whether these forces are working with each other or against each other. Are we still in a bull market, or are we in a liquidity-driven echo of 2020 to 2021? More importantly, what happens when AI and blockchain, two of the defining technologies of our time, begin to interact with the cyclical rhythms of credit, capital, and confidence?
Macro Backdrop: Rates, Credit, and Cycles
No discussion of markets in 2025 can ignore the backdrop of monetary policy. The last decade conditioned investors to expect low rates forever. The COVID crisis accelerated that assumption. Yet in response to the inflationary aftermath, central banks reversed course with breathtaking speed.
The Federal Reserve raised rates from near-zero to over 5 percent. The European Central Bank and the Bank of England followed. Global liquidity contracted. Risk assets whipsawed. Crypto, which had thrived in the era of abundant liquidity, was forced to confront scarcity.
Charles Kindleberger’s Manias, Panics, and Crashes provides a reminder that credit cycles are not new. They are the recurring heartbeat of financial history: expansion, euphoria, crisis, and retrenchment. You will see these phases with slightly different labels but the consensys draws a recognisable chart.
The twist in our era is that the contraction coincides with a pair of technological breakthroughs, AI and blockchain, that thrive on cheap capital for experimentation and rapid scaling.
High rates slow risk-taking. They make speculative narratives harder to fund. Yet they also increase the premium on efficiency. Institutions are far more willing to experiment with cost-saving technologies like tokenized settlement when the cost of capital is not trivial. Blockchain, in this environment, is not just a playground. It becomes a productivity tool.
Lessons From History: Liquidity and Innovation
The link between liquidity cycles and technology adoption is not new. The railroad boom of the 19th century was funded by credit expansion in Britain and the United States. The internet bubble of the late 1990s coincided with loose monetary policy and global capital flows. The housing bubble of the 2000s saw securitization technology reach its zenith in a world drowning in cheap credit.
In each case, innovations that were real and durable were mixed with speculation and excess. Railroads revolutionized trade routes, but many lines went bankrupt in the first wave. The internet transformed communication and commerce, but it took years after the dot-com crash for the sustainable business models to emerge. Securitization lowered funding costs for homeowners and businesses, but its misuse created systemic fragility.
Blockchain and AI now sit at a similar juncture. Loose money amplifies their adoption but risks overheating. Tight money suppresses their growth but forces discipline. Over time, the durable elements survive and reshape the economy. The challenge is distinguishing between what is cyclical froth and what is structural transformation.
Blockchain as Financial Infrastructure in Tightening Regimes
If money is expensive, waste is intolerable. The existing financial plumbing is full of waste: T+2 settlement delays, reconciliations across fragmented ledgers, costly intermediaries extracting rents.
Blockchains offer faster, cheaper, and more transparent rails. In zero-rate regimes, the value of those improvements was easy to dismiss. When capital was abundant, shaving a few basis points from settlement costs was less urgent than chasing the next growth story. But when rates are high, efficiency compounds.
Tokenization therefore finds itself paradoxically advantaged in tighter conditions. Treasury departments under pressure to deliver yield and reduce friction are more likely to adopt new rails. This is why we see treasuries, money market funds, and other low-risk instruments leading the charge into tokenization.
The parallel is instructive. The railroad boom of the 19th century did not begin in times of abundant liquidity but in periods when efficiency in trade routes became strategically necessary. In the same way, tokenized markets may scale fastest when global finance is most desperate for efficiency.
AI as Probabilistic Intelligence, Blockchain as Deterministic Memory
Artificial intelligence operates in probabilities. Large language models do not deliver certainty, they deliver the most likely answer based on training data. That probabilistic nature is both powerful and dangerous. It enables creativity and flexible reasoning, but it resists verification.
Blockchain, in contrast, is deterministic. Transactions either occur or they do not. Code executes as written. Consensus provides certainty. The marriage of these two forces, probabilistic cognition layered on deterministic settlement, may be one of the defining structures of 21st-century finance.
Cathy O’Neil’s Weapons of Math Destruction warned of the dangers of opaque algorithms shaping economic outcomes without accountability. Blockchain offers a partial antidote. By providing verifiable audit trails and transparent execution, it ensures that AI-driven decisions in credit scoring, trading, or settlement are not black boxes but recorded events.
This is why AI and blockchain are not competing revolutions but complementary ones. AI interprets and acts, blockchain records and enforces. Together, they create markets where machines can participate directly, but under rules that are transparent and enforceable.
The Convergence Cycle: Technology Riding Macro Tides
History shows us that technological revolutions rarely occur in a vacuum. They are accelerated or slowed by the tides of macroeconomic cycles. Railroads expanded during credit booms in the 19th century. The internet scaled in the liquidity-rich environment of the 1990s. The global financial crisis of 2008 did not kill innovation, but it created the conditions for Bitcoin’s birth.
Neil Howe and William Strauss, in The Fourth Turning, argue that history moves in generational cycles. Each era of crisis reshapes institutions and makes room for new paradigms. The 2020s fit that mold. The combination of monetary tightening, political upheaval, and technological transformation is precisely the crucible in which new systems emerge.
The convergence cycle between AI and blockchain should be understood in that context. These technologies will not move in smooth adoption curves. They will accelerate during liquidity expansions, stall during contractions, and ultimately embed themselves when institutions realize they are necessary, not optional.
Case Study: Eurodollars and Invisible Infrastructure
One way to understand the convergence cycle is through the lens of the Eurodollar market. In the 1950s and 1960s, dollars held outside the United States created a parallel financial system beyond the reach of US regulators. It started small, grew in opacity, and eventually became the dominant global funding market.
Eurodollars were not created by legislation but by necessity. They thrived in the gaps between regulation and demand. Blockchain-based finance is similar. Tokenized assets and stablecoins already form a parallel infrastructure. For now, they sit uneasily alongside the regulated core. Over time, however, their scale and efficiency may force incumbents to integrate.
Just as Eurodollars reshaped global liquidity without anyone voting on it, tokenized systems may become the plumbing of finance by stealth. AI will accelerate this process by demanding programmable, verifiable settlement layers that traditional systems cannot provide.
Case Study: ETFs and the Path to Mainstream Adoption
Exchange-traded funds were once a niche product. Launched in the early 1990s, they faced skepticism from traditional asset managers. But their low cost, tax efficiency, and transparency slowly won converts. By the 2000s, ETFs were a mainstream tool. Today they account for trillions in assets and are often the default investment vehicle for retail and institutional investors alike.
Tokenized assets may follow the same path. Initial skepticism and regulatory uncertainty will give way to adoption once the cost advantages become undeniable. AI could accelerate this process by making it easier to manage, trade, and allocate tokenized products at scale.
The lesson from ETFs is that credibility comes from utility. It is not hype that sustains adoption but persistent efficiency gains. Tokenization must prove itself in the same way.
Bitcoin in the Convergence Cycle
Macro cycles shape adoption, and every technological wave leaves behind a financial instrument that captures its essence. Railroads had bonds. The internet had equities. The Eurodollar system created shadow money. For blockchain and the AI era, Bitcoin plays that role. It has moved beyond being just another ‘crypto asset’ and now trades as neutral collateral in global liquidity cycles.
This is why making sense of the convergence cycle requires putting forward scenarios for Bitcoin itself. Not as a speculative call, but as a guidepost for how macro tides and technological adoption intersect.
Here’s how I see the second half of the decade playing out:
2025–2026: Liquidity Return, Institutional Inflows
Fed rate cuts likely begin September 2025, with the ECB lagging. Dollar liquidity expansion should support risk assets.
Tokenized treasuries cross $500B AUM. Stablecoins process trillions in organic flows.
Bitcoin outlook:
Base case: $120k–$150k by late 2026.
Bull case: $200k+ if global easing is synchronized.
Bear case: capped below $90k if inflation proves sticky.
2027–2028: The AI–Blockchain Flywheel
AI agents become embedded in capital allocation and risk workflows. Tokenized credit markets expand significantly.
Bitcoin outlook:
Base case: $180k–$220k consolidation.
Bull case: $300k+ if convergence sparks a capital cycle shift.
Bear case: retraces toward $120k in a global recession, but holds above prior highs.
2029–2030: Stress Test and Structural Adoption
A new tightening cycle arrives, but tokenized markets and AI-driven execution are systemically embedded.
Bitcoin outlook:
Base case: $250k–$350k by 2030, with market cap above $5T.
Bull case: $500k if AI agents accelerate crypto adoption globally.
Bear case: below $200k if stagflation or regulation fragments markets, but structural role remains intact.
Conclusion: Building Through the Cycle
Markets move in cycles. Technology rides those cycles but also transforms them. AI and blockchain are converging at a time when macro conditions are unstable. Rates are high, liquidity is scarce, and investors are cautious. That is precisely why efficiency and transparency matter.
The first trillion dollars of tokenized assets may arrive in this cycle. AI agents may begin to manage real economic flows before the decade ends. What matters is not whether we are in a bull or a bear market today, but whether we are building the infrastructure that will matter in the next cycle.
The convergence cycle teaches a simple lesson: efficiency wins, transparency builds trust, and technology aligned with macro reality does not just survive, it thrives.
Cheers,
Ian
Ian Randle is a Core Contributor at Derive.xyz and Founder of SettledHere.com