From promise to plumbing: tokenisation and AI meet the coordination problem

Big Picture

What happened: The story is shifting from promise to plumbing. Whether in tokenisation or AI, the constraint is no longer vision — it’s incentives, integration, and institutional readiness. The winners will be those who align technology with how systems actually behave under pressure.

The Through-line

Across digital assets and AI, progress is bottlenecked not by capability, but by coordination: aligning incentives, upgrading infrastructure, and embedding new models into existing institutional logic. Efficiency gains are visible — but capturing them requires rewiring how organisations operate, not just adopting new tools.

1) Tokenisation meets institutional reality — infrastructure is necessary, not sufficient

What it is: Olaf Ammermann highlights how tokenisation and stablecoins are gaining traction with institutional players, promising faster settlement, reduced friction, and new liquidity models — but only when integrated into existing financial architecture.

Why it matters: Tokenisation is often framed as a clean break from legacy systems. In practice, adoption depends on interoperability with those systems — custody, regulation, compliance, and market structure. Without that, efficiency gains remain theoretical.

First move: Focus on integration points, not just issuance. Map how tokenised assets connect to custody, reporting, and liquidity flows before scaling pilots.

Source: LinkedIn

2) Tokenisation’s efficiency promise — real, but unevenly captured

What it is: A second thread questions whether tokenisation actually delivers on its efficiency narrative. While it can reduce intermediaries and streamline processes, the benefits are often offset by new layers of complexity, coordination costs, and regulatory overhead.

Why it matters: Efficiency is not automatic — it depends on who bears the transition cost and who captures the upside. Early implementations risk recreating existing inefficiencies in new technical wrappers.

First move: Identify where tokenisation removes entire steps (not just digitises them). If the process still requires the same intermediaries, the gains will be marginal.

Source: LinkedIn

3) Blockchain in practice — management, not technology, is the constraint

What it is: Anthony Day’s post reinforces a familiar pattern: blockchain initiatives stall not because the technology fails, but because organisations struggle to operationalise it — governance, ownership, accountability, and change management become the real blockers.

Why it matters: Distributed systems require coordinated adoption. A technically sound solution can fail if incentives across participants are misaligned or if no one owns the end-to-end outcome.

First move: Treat blockchain projects as organisational transformations, not IT deployments. Define governance early: who decides, who benefits, and who is accountable when things go wrong.

Source: LinkedIn

4) The coordination problem — shared systems need shared incentives

What it is: Across multiple posts, a common friction emerges: tokenised and distributed systems only work when multiple parties adopt them simultaneously — but each party has different incentives, risk tolerances, and timelines.

Why it matters: This creates a classic coordination failure. The system is valuable only when widely adopted, but no single actor has enough incentive to move first at scale.

First move: Start with closed or semi-closed ecosystems (consortia, vertical-specific networks) where incentives are naturally aligned. Expand outward only once value is proven.

Source: LinkedIn

5) The economics of AI — intelligence is abundant, value is not

What it is: Bammanath’s post explores how AI is shifting the economics of knowledge work: as intelligence becomes cheaper and more accessible, the bottleneck moves to attention, distribution, and trust.

Why it matters: When supply of “good enough” output explodes, value concentrates in curation, context, and credibility. The scarce resource is no longer content generation — it is signal.

First move: Build mechanisms that filter, rank, and validate AI outputs. Advantage will come from systems that turn abundance into trusted, decision- ready insight.

Source: LinkedIn