The Coordination Layer Market Map (2026–2036)

Over the next decade, the dominant constraint in the AI-native economy will not be how much intelligence you can access, how many agents you can spin up, or how much workflow you can automate.

It will be how precisely you can steer that abundance: which user intents you capture, which supply you orchestrate, and which outcomes you consistently deliver.

Execution is becoming a commodity service the system can assume. Coordination is becoming the scarce function the system must earn.

This is the quiet but decisive shift underway. The last decade rewarded companies that could execute complex logic at scale — SaaS platforms, vertical software, and infrastructure providers. The coming decade will reward those that sit above that execution layer and decide what gets done, in what order, with which counterparties, and under which semantic terms.

In other words: the coordination layer is becoming the economic power center of the AI-native economy.

Execution Is Abundant. Allocation Is Not.

Across domains, we are watching the same pattern repeat. Tasks that once required bespoke software and specialized teams are now being handled by shared models, composable tools, and generalized agents. Code is generated on demand. Integrations are scaffolded automatically. Agents call APIs, monitor state, and retry until completion.

As this happens, the marginal cost of additional execution collapses. The question “can we build this?” — once the central gating factor for new products — is replaced by “where should we aim this?” and “what should we build at all?” The expensive part is no longer doing the work. It is deciding which work is worth doing, and how to direct vast pools of cheap intelligence toward high-value outcomes instead of noise.

If you assume that any well-specified task can be handled by a sufficiently capable agent swarm, then the location of value shifts structurally. It moves upstream into three related functions:

First, interpreting messy human intent into executable demand. Second, allocating that demand across competing suppliers and strategies. Third, establishing the semantic and institutional rails — the “namespaces” and trust boundaries — through which that demand consistently flows.

This is the coordination layer. To understand why it is becoming the new center of gravity, we need to zoom out and reframe the AI economy itself.

The Three-Layer AI Economy

The AI economy is often described in terms of models, data, and applications. That framing made sense when models were scarce, training runs were the main bottleneck, and the primary question was “who will own the foundation models?” It is less useful now that model capability is diffusing rapidly and execution is saturating every category.

For the next decade, a more structurally accurate way to think about the system is as a three-layer stack:

Execution. Interface. Coordination.

The execution layer is where work actually happens. Models run, code executes, robots move, workflows complete, documents are generated, transactions are settled. This layer is dominated by infrastructure providers, model labs, cloud platforms, and the vast array of SaaS and micro-services that agents call behind the scenes.

The interface layer is where humans and agents express intent. These are the surfaces through which people talk to the system — chat interfaces, voice assistants, embedded copilots, AR overlays, email-like threads. At this layer, the constraint is bandwidth and usability: how much context can a user provide, how quickly can they iterate, how safe does it feel to delegate.

The coordination layer sits between these two. It is not where the raw intelligence lives, nor where the pixels render. It is where intent is interpreted, evaluated, decomposed, prioritized, and routed into specific execution paths. It decides which models to call, which suppliers to involve, which contracts to instantiate, and how to sequence actions over time given risk, regulation, and resource constraints.

In a non-AI world, these three layers were tightly coupled inside applications and institutions. The insurance agent both captured intent (interface), interpreted risk and product fit (coordination), and submitted the application (execution). The travel site both presented options (interface) and decided which supply to surface (coordination) while booking flights and hotels (execution).

AI-native architecture pulls these functions apart. Execution migrates into generalized models and API-callable services. Interfaces become lighter, more conversational, more ubiquitous. What remains as the locus of differentiation is the structured decision-making between intent and action.

That structured decision-making does four things:

It controls authorization — who is allowed to act on whose behalf, with what limits. It controls routing — which counterparties receive which demand under which semantics. It controls flow — how volume concentrates over time as feedback loops compound. And it controls semantics — the language and categories through which the system understands and prices reality.

These are not cosmetic details. They are the levers that determine where margins accumulate.

The Execution Layer: Commodity Intelligence

The execution layer is undergoing a classic commoditization process. Once, application logic and custom code were scarce assets that justified high multiples. Increasingly, they are auto-generated artifacts sitting on top of shared models and infrastructure.

As foundational models expand in capability and compress marginal cost, and as orchestration frameworks standardize how agents call tools and APIs, the differentiation at this layer erodes. Everyone has access to roughly similar cognitive primitives: summarization, planning, pattern recognition, code generation, multimodal perception, and so on.

That does not mean this layer becomes unimportant. It means that its economics begin to resemble utilities. Capital intensity stays high. Margins experience pressure. Competition is relentless. The winners are large, but the power-law curve is dominated by scale efficiencies, not by unique control of demand.

For investors, this is the layer where you will see enormous companies with significant revenue, but structurally constrained pricing power. For builders, it is a layer you integrate and occasionally extend, not the primary place you attempt to capture durable economic rents unless you have very specific capabilities or structural advantages.

The Interface Layer: Abundant Expression

The interface layer is where expressive intent becomes the default mode of interaction. Users do not fill forms or tick boxes; they describe situations, preferences, constraints, and goals in natural language, voice, and other rich modalities. The system elicits clarifying questions, probes edge cases, and transforms the resulting narrative into machine-readable state.

Importantly, this layer does not, by itself, decide what to do with that state. It collects signal. It may perform lightweight validation. But its economic power depends on whether it is tightly coupled to a coordination environment that can turn expression into resolved outcomes.

There will be many interface surfaces — first-party assistants, embedded enterprise copilots, dedicated devices, and a long tail of vertical tools. The competition here looks like consumer product competition always has: who provides the least friction, the highest trust, and the most usable experience.

But because the cost of building interfaces is dropping, and because agents can be embedded in almost any surface, this layer alone is unlikely to become the main point of value capture. Without a differentiated coordination backend, an interface is just a prettier inbox.

The Coordination Layer: Scarce Allocation

The coordination layer is where the scarcity reappears. Coordination is not expensive in terms of compute. It is expensive in terms of position, data, semantics, and trust.

A coordination environment that consistently receives high-fidelity intent, maintains durable identity and context over time, integrates deeply with execution providers, and encodes the domain’s rules, risks, and constraints becomes a choke point. It is where every meaningful transaction passes on its way from expression to outcome.

Crucially, coordination is not just a technical function. It is an institutional one. Someone has to own the liability model, the regulatory integration, the dispute resolution mechanisms, the incentive structure for suppliers, the risk-sharing terms. That ownership is what turns a technical routing layer into an economic control point.

This three-layer model clarifies the core thesis: as AI makes execution and interface abundant, the dominant source of power shifts to the coordination layer. To see why, it helps to look backward.

Historical Precedents: From Rails to Routers

History is full of episodes where execution capacity expanded dramatically, only for economic power to migrate upstream into coordination and allocation. When the cost of moving things, computing things, or communicating things drops, value tends to accumulate at the points that decide what to move, what to compute, what to communicate, and in which order.

Two precedents are particularly instructive: the railroad era and the internet era.

Railroads to Logistics Platforms

The railroad was the canonical execution layer of its time. It turned geography into a solvable engineering problem. Lay tracks, build locomotives, move goods. Early on, the builders of rails captured enormous value as they opened up new territory and enjoyed temporary monopoly control.

Over time, however, the economics shifted. Track mileage expanded. Competing lines connected similar routes. Regulators intervened. Railroads became capital-intensive infrastructure businesses with cyclical returns and constrained pricing power.

Where did value go?

It moved into coordination layers sitting on top of rail, road, air, and sea. These were the logistics platforms, freight forwarders, and supply-chain orchestrators that decided which routes to use, how to consolidate shipments, where to hold inventory, and how to guarantee delivery windows across multiple carriers.

These coordinators didn’t own the trains. They owned the flows.

From a distance, this looks obvious. When transport becomes abundant, the scarce function becomes deciding what should be transported, when, and along which paths. The winners are those who control the routing logic and information symmetry, not necessarily those who supply the steel.

Internet to Search to Marketplaces

The internet followed a similar pattern. In the early days, ISPs and telcos built the pipes. They owned the execution layer of connectivity. As bandwidth expanded and competition increased, their economic leverage normalized.

Meanwhile, a new coordination layer emerged: web search. Search engines did not own the content or the networks. They controlled how attention was allocated across an effectively infinite supply of pages and services.

By becoming the default entry point for online intent, search engines turned a technical problem — indexing and ranking — into an economic choke point. They captured significant margins not because they did more work than anyone else, but because every other participant’s demand depended on their routing decisions.

Layered on top of this, marketplaces and aggregators began coordinating specific verticals: e-commerce platforms, travel booking engines, local service marketplaces, app stores. They interpreted user queries, aggregated supply, and enforced standardized terms of engagement.

Again, the pattern held: as raw connectivity and hosting became abundant, the economic center of gravity shifted to entities that sat between users and suppliers, arbitraging information, trust, and allocation.

The AI-native coordination layer is structurally similar, but with two amplifying differences. First, the intent signal it receives is far richer — not “cheap flight NYC–SF,” but a narrative about risk tolerance, time constraints, family situation, and broader goals. Second, it is not limited to retrieval and ranking. It is able to decompose intent into multi-step plans, orchestrate agents, and directly complete transactions.

If search was an index for a static web, coordination is an operating system for an agentic economy.

The Coordination Layer Market Map (2026–2036)

If coordination is the emerging power center, what does its market structure look like? Over the next decade, we can expect several distinct but interlocking sectors to crystallize. They will not all appear at once, and their boundaries will blur. But as a map, four categories are useful: intent routers, outcome engines, agent allocators, and semantic namespaces.

Intent Routers: From Expression to Direction

Intent routers are systems that ingest rich, high-dimensional user input and decide where it should go. They sit directly behind interfaces, turning messy human narratives into structured demand and dispatching that demand into appropriate downstream coordinators or execution environments.

A mature intent router does several things in sequence.

First, it performs identity resolution and contextualization. It links the current expression to a persistent profile: past interactions, preferences, risk tolerance, constraints, permissions, and entitlements. Second, it parses the semantic content of the interaction into machine-understandable goals, sub-goals, entities, and relationships.

Third, it performs triage: which domain does this belong to (insurance, health, lending, travel, productivity), what is the urgency, what is the likely complexity, do we need human oversight. Finally, it dispatches: routing the intent to a domain-specific outcome engine or to a specialized agent workspace that can handle the case.

Intent routers do not, by themselves, resolve the user’s need. They are the air traffic control system of the coordination layer. Their economic value derives from being close to the point of expression, from integrating horizontally across domains, and from accumulating a deep distribution of user context.

Because they operate at the first point of contact, these routers are well positioned to shape default flows. Which outcome engines they call, in what sequence, under which terms, becomes an indirect way of steering value toward preferred partners, internal services, or owned verticals.

Outcome Engines: From Demand to Completed Transactions

Outcome engines are domain-specific coordination environments that own the full stack from intent interpretation through to transaction completion in a given vertical. Where an intent router decides “this belongs to small business lending,” the outcome engine handles everything that follows: eligibility assessment, product configuration, counterparty selection, risk evaluation, contract generation, and fulfillment.

Each outcome engine encodes the rules, regulations, and best practices of its domain. A lending engine understands collateral, covenants, servicing, and regulatory capital. An insurance engine understands underwriting, claims workflows, reinsurance. A healthcare engine understands triage, referral, diagnostic pathways, and consent.

Critically, these engines integrate deeply into execution providers via APIs and standardized schemas. They do not simply send leads or provide comparison tables. They programmatically instantiate and manage transactions on behalf of the user and the counterparties.

Economically, outcome engines sit at the junction of demand and highly regulated supply. They see the full picture of options. They determine which suppliers receive which volume and at what terms. Over time, this allows them to shape market structure: nudging toward certain product designs, risk standards, and servicing models.

Because each domain has its own complexities, we should expect a small number of large outcome engines per regulated vertical, rather than a single monolithic controller across everything. However, the few that emerge in each sector are likely to command significant bargaining power over suppliers and to capture a disproportionate share of coordination rents.

Agent Allocators: From Agents Everywhere to Agents Somewhere

In popular narratives, autonomous agents roam the open web, negotiating with services on users’ behalf. In reality, the legal, security, and economic constraints of transacting at scale make that picture unlikely. Agents will not be allowed to operate freely across arbitrary sites with unclear liability and permissions. They will cluster inside permissioned coordination environments where identity, authorization, compliance, and payment flows are standardized.

Agent allocators are the systems inside those environments that decide which agents do what for whom, and under which guardrails. They manage agent selection, resource allocation, escalation policies, and conflict resolution. They monitor performance and adjust routing based on observed outcomes.

From an economic perspective, agent allocators are the schedulers of the AI-native workforce. They are the ones deciding which model or agent gets access to which user context, which tools, and which opportunities to earn. In environments where agents represent different vendors, services, or strategies, the allocator effectively controls distribution.

For example, imagine a B2B finance coordination environment where multiple underwriting agents, pricing agents, and servicing agents compete to handle parts of a loan lifecycle. The allocator learns over time which combinations produce optimal outcomes, and steers volume accordingly. Agents that perform well receive more calls and usage, while underperformers are starved.

This creates a second-order power dynamic: agents compete to integrate into high-traffic allocators, just as app developers compete to be featured in app stores. The allocators, in turn, can set terms, fees, and prioritization logic that reflect their own strategic objectives.

Semantic Namespaces: From Brand Real Estate to Intent Infrastructure

Underneath intent routing, outcome engines, and agent allocators sits a more subtle but equally powerful layer: the semantic namespaces through which expressive intent is channeled. This is where linguistic territory becomes economic infrastructure.

In an AI-native economy, users will rarely navigate through explicit URLs or multi-step menus. Instead, they will express intent once in a context that feels natural: “Handle my business’s cash flow and lending so we never fall below a 90-day runway,” or “Design a vacation that matches this vibe: low-key, nature-heavy, minimal logistics, no social media pressure.”

As this behavior scales, certain words, phrases, and domains will become the canonical containers for expressive delegation in different categories. Just as “search” became the default label for information retrieval, and “stream” for media consumption, new linguistic anchors will emerge for agentic delegation in insurance, finance, health, travel, design, and beyond.

These anchors are not just marketing assets. They are namespace control points. If a particular phrase or domain becomes the culturally understood gateway for a type of delegation — “vibe travel,” “vibe health,” “vibe insurance,” to use one current emergent pattern — then it accumulates several advantages simultaneously: user mindshare, model alignment (as systems learn to map that phrase to a set of actions), and supplier integration (as providers treat that namespace as a distribution channel).

Because linguistic territory is finite and culturally sticky, early consolidation of high-signal semantic namespaces can create durable leverage. Software logic can be replicated indefinitely. Models can be retrained. Interfaces can be redesigned. But once a word or domain becomes the shorthand for a behavior — and the coordination infrastructure behind it is built — dislodging it is structurally difficult.

From 2026 to 2036, this semantic layer will quietly become one of the most important arenas of competition. Who controls the names through which billions of dollars of expressive intent are channeled? Which portfolios of domains and categories have been assembled in advance of the behavioral shift? Which corporate and financial actors recognize that these aren’t speculative assets, but coordination-layer primitives?

The Value Capture Model: Where Margins Accumulate

Once you see the three-layer stack and the emerging market map, the value capture logic becomes clearer. Economics migrate away from the tasks that are becoming infinitely reproducible, and into the points of constrained control.

There are four primary mechanisms through which the coordination layer captures margins: demand aggregation, outcome-based pricing, compounding context, and namespace gravity.

Demand Aggregation and Routing Power

First, coordination environments aggregate and normalize high-intent demand. Instead of fragmented, low-fidelity leads trickling through multiple channels, suppliers connect to a small number of engine-like environments that deliver pre-qualified, context-rich, transaction-ready users.

This aggregation lowers acquisition costs for suppliers, but it also shifts bargaining power. When a coordination layer controls a significant share of inbound demand in a category, suppliers have limited outside options. They either integrate under the coordinator’s terms or face structurally higher acquisition costs elsewhere.

This is where margin begins to accumulate: coordinators can charge suppliers for access, take performance-based fees, or capture spread between wholesale and retail pricing, all while remaining modular with respect to the underlying execution providers.

Outcome-Based Pricing and Risk Arbitrage

Second, because coordination environments see the full journey from intent to outcome, they are unusually well positioned to price on outcomes rather than inputs. A traditional SaaS tool charges per seat or per usage metric. A coordination layer can charge for resolved cases, improved conversion, reduced churn, or optimized risk.

In domains like lending, insurance, and healthcare, coordination systems that see both expressive intent and long-term performance can underwrite demand more accurately than any individual supplier. They can construct synthetic risk pools, segment users in ways incumbent scoring systems cannot, and arbitrage between different suppliers’ risk appetites.

This confers another source of margin: the ability to retain a share of the value created by better matching, lower default rates, or more efficient care paths. Over time, some coordination environments may take principal risk onto their own balance sheets, further blurring the line between platform and institution.

Compounding Context and Switching Friction

Third, coordination layers accumulate context over time. Each interaction deepens the system’s understanding of a user’s preferences, behavior, constraints, and outcomes. This compounding context improves routing accuracy, reduces friction, and enables increasingly personalized orchestration across domains.

From the user’s perspective, this looks like “it just knows me.” From the competitor’s perspective, it looks like a substantial barrier to entry. A rival coordinator might match on features and model quality, but it will struggle to replicate a decade of accumulated, cross-vertical behavioral data for each user.

This context compounding has two economic implications. It allows the coordinator to improve unit economics (less waste, fewer errors, higher conversion), and it raises switching costs for both users and suppliers. Leaving the environment means losing an intelligent, history-aware orchestrator. Integrating as a supplier elsewhere means starting from scratch with a thinner slice of demand.

Namespace Gravity and Linguistic Moats

Finally, semantic namespaces can convert soft cultural signals into hard economic moats. When a particular phrase becomes the default way users express a mode of delegation, models begin to anchor around it. Agents are trained to treat that phrase as an entry point into specific workflows. Suppliers align their products, content, and integration strategies to that namespace.

This creates a feedback loop: the more a namespace is used, the more models and users expect it to be meaningful. The more meaningful it becomes, the more valuable it is as an entry point. Over time, the namespace behaves like prime commercial real estate in a digital city: everything wants to be near it; most traffic passes through it.

Owning or structurally controlling this namespace — whether through domains, categories, or institutional definitions — becomes a form of demand-side infrastructure. It does not matter which specific models or interfaces sit on top at any given time. The underlying semantic territory persists.

The net result of these dynamics is that, by 2036, we should expect a landscape in which:

A small number of horizontal intent routers mediate a large share of expressive interactions. Each major regulated vertical has one to three dominant outcome engines that coordinate most meaningful transactions. Agent allocators inside these environments shape which agents and services receive usage and revenue. And a set of semantic namespaces underpins everything, acting as the linguistic and institutional rails through which expressive intent flows.

These are the layers where durable margins are likely to accumulate, not because they “do more work” but because they control how work is allocated.

The Strategic Land Grab: 2026–2036

If this thesis is directionally right, then the next decade is, fundamentally, a land grab — but not the one most teams are focusing on. The scarce assets are not incremental features or isolated models. They are coordination points: the surfaces, systems, and namespaces where high-intent, high-value demand first crystallizes and is allocated.

For builders, investors, and incumbents, this reframes what “defensibility” means.

Why Control of Coordination Points Matters

Coordination points are not just high-traffic intersections. They are the places where defaults are set. Defaults around which suppliers are considered, which options are hidden, which trade-offs are emphasized, which regulations are strictly enforced or treated as flexible. Over time, these defaults shape market structure.

In an AI-native economy, countless micro-decisions are taken by systems on the user’s behalf. Which insurer to quote first. Which lender to approach. Which hospital or doctor to route to. Which legal template to instantiate. Each individual decision looks trivial. In aggregate, they define the flow of trillions of dollars.

If you control the environment in which these decisions are encoded, you control the invisible constitution of the market.

This is why, in retrospect, app stores, search engines, and social feeds accrued such power. They defined which options were even visible. Coordination-layer systems in AI will have similar leverage, but applied to more complex, higher-stakes transactions across more verticals.

The Window of Opportunity

The structural significance of the coordination layer is implied by the trajectory of AI, but the assets that will dominate it are not yet fully claimed. Between 2026 and 2036, three things will happen in parallel:

First, user behavior will shift from query to delegation. As interfaces become more conversational and as early coordination environments prove reliable, expressing rich, narrative intent will become normal across consumer and enterprise settings.

Second, vertical outcome engines will harden. Early prototypes in lending, insurance, healthcare, and travel will give way to deeply integrated, regulated, institutionally backed systems that handle entire lifecycles rather than isolated tasks.

Third, semantic territory will consolidate. Certain phrases and domains will emerge as the natural gateways for expressive delegation in each category, and models will learn to treat them as such.

The land grab lies in shaping these three trajectories while they are still fluid. Once behavior ossifies and infrastructure crystallizes around a set of coordinators and namespaces, latecomers will find themselves integrating into someone else’s environment, not defining their own.

Implications for Builders

For founders and product teams, the strategic question is not “what can our agents do?” but “which high-intent entry points can we own?” Instead of building yet another task-specific assistant, the more interesting play is to design environments where users feel comfortable delegating entire classes of decisions — and to embed those environments at the right junctions in existing workflows.

This requires a different design orientation. You are not just optimizing usability or model performance. You are constructing a stable, trustworthy, and context-rich space in which users can offload the cognitive load of comparison, configuration, and coordination.

Practically, that means:

Choosing categories where intent is complex, stakes are high, and fragmentation is painful — places where users would happily delegate if they trusted the system. Building deep integrations with suppliers and regulators early, even if it slows initial velocity, because without that depth you are just another interface. And thinking from day one about the language, domains, and semantics you want to anchor your behavior to, not just the brand veneer.

The goal is not to ship another vertical SaaS tool. It is to become the default environment where a specific kind of expressive intent is resolved.

Implications for Investors

For capital allocators, the coordination thesis suggests a shift in underwriting focus. Instead of asking purely about market size and competitive moats in terms of features, the relevant questions become:

Which nodes in this ecosystem control high-intent, high-trust entry points? How defensible is their position in terms of identity, regulation, integration depth, and semantics? Are they building muscle in outcome-based pricing and risk management, or are they stuck at the level of UX improvements?

It also opens up new asset classes. Semantic namespaces — portfolios of domains and categories aligned to emerging patterns of expressive delegation — are one example. These are not speculative tokens. They are, in effect, call options on control of future coordination surfaces.

Structured correctly, investment in coordination-layer primitives behaves like infrastructure exposure rather than traditional equity risk. The returns depend less on any single app’s adoption curve and more on the broader migration of value into delegation behaviors that are already underway.

Implications for Incumbents

For incumbents in finance, insurance, healthcare, and other regulated industries, the core risk is becoming a price-taker inside someone else’s coordination environment. Many incumbents are currently experimenting with agents and generative AI inside their own products. That is useful, but insufficient.

The tougher choice is whether to:

Attempt to build or sponsor the outcome engines in your domain, taking on the integration and neutrality challenges that entails. Act as a preferred anchor tenant inside a neutral coordinator, negotiating for privileged access and data sharing. Or ignore the shift, betting that brand, distribution, and regulatory moats will hold even as users begin delegating decisions elsewhere.

The third option is, structurally, the most hazardous. When users delegate intent to a coordination environment, their relationship with individual suppliers becomes mediated. They remember who resolved their situation, not which carrier or lender sat underneath. Over time, this erodes direct brand relationships and compresses margins for those who are not controlling the coordination layer.

The Emergence of a New Economic Layer

When you strip away the surface-level novelty of AI — the chat interfaces, the talking avatars, the code that writes itself — what remains is a reconfiguration of where economic power resides. Intelligence, once scarce and human-bound, is becoming abundant and machine-provided. Execution, once the bottleneck, is becoming a background assumption.

In that world, the scarce function — and therefore the source of power — is coordination.

Coordination is where intent crystallizes into decisions, where semantics solidify into categories, where agents are authorized to act, and where demand is up- or down-weighted for different suppliers. It is where the invisible constitution of the AI-native economy is drafted and enforced.

This is not a transient UX layer. It is a new economic layer akin to logistics platforms on top of physical transport, or search and marketplaces on top of the internet. It will have its own asset classes, its own regulatory battles, its own industrial structure.

From 2026 to 2036, three battles will quietly define this layer.

The battle for expressive intent: who becomes the trusted environment where users are willing to speak freely, delegate deeply, and return repeatedly. The battle for outcome engines: which entities codify the rules and integrations of each high-value domain and become the de facto routers of its demand. And the battle for semantic territory: which words, domains, and namespaces become the default gateways through which the system understands and allocates intent.

Most of the market is still focused on the model layer — who has the largest training run, the best benchmarks, the most impressive demos. That is understandable. It is also, strategically, incomplete.

The investors and builders who step slightly ahead of the curve will treat coordination assets as infrastructure, not marketing. They will accumulate semantic territory, not just feature lists. They will design for delegation, not just assistance. And they will position themselves not only as application providers, but as the orchestrators of how demand is allocated and how outcomes are defined.

In hindsight, this will look obvious. Of course value relocated to the layer that all transactions had to pass through. Of course the economic center of gravity shifted from code to coordination. The question, from the vantage point of 2026, is who chooses to act as if that future is already implied.

The decade ahead will not be remembered as the age when AI learned to write emails more politely. It will be remembered as the decade when coordination, not computation, became the decisive variable in economic power. The terrain is already moving. The only real decision is whether you move with it — and whether you aim, not to be another node in the network, but to help define the network’s coordination layer itself.