The Coming Tokenomics Reckoning: Why Companies That Don't Build a Token Strategy Now Will Pay for It Later

By Adri Research Forum | Co-Author - Bedabrata Bagchi | May 2026

The Pattern We Have Seen Before

Every major commercial technology platform has followed the same arc. Build a market on underpriced access, condition behaviour at scale, then shift to a model where usage is priced for profitability.

Amazon operated at near-zero profit or at a loss for almost two decades, using penetration pricing to capture market share first in books, then electronics, then everything. By the time Amazon raised prices, the switching costs were structural: Prime membership inertia, purchase history lock-in, and seller ecosystem dependency made exit practically impossible for most customers. Uber subsidized rides heavily from 2009 to 2022, burning billions in investor capital to eliminate taxi competition and normalize app-based transport. The 2024 breakthrough of consecutive profitable quarters validated a strategy that had absorbed years of losses. In both cases, the companies that adopted these platforms during the subsidised era became dependent on them long before the pricing terms changed.

Artificial intelligence, right now, is in that identical phase. The difference is that the transition to a profit model will not just change what companies pay. It will change how they are measured, how they justify AI investment, and whether the infrastructure they built during the cheap era is actually fit for what comes next.

This article argues that companies need to start building a tokenomics strategy today, not when per-token pricing becomes the industry standard. By the time that shift is fully visible, the preparation window will already be closed.

The Cheap Era: What Is Actually Happening and Why

Between March 2023 and August 2025, the cost of running AI inference at a fixed capability level fell by 99.7%. GPT-4 launched at a blended cost of $37.50 per million tokens. By mid-2025, the cost-efficiency frontier had reached $0.14 per million tokens. Data from Ramp, drawn from aggregated transactions across more than 30,000 businesses, shows that one year prior to March 2025, enterprises were paying $10 per million tokens on average. By March 2025, that figure had dropped to $2.50 -- a 75% reduction in twelve months.

The rational interpretation is that AI was getting dramatically cheaper for businesses. The actual result was the opposite. Enterprise AI cloud expenditure tripled in a single year, growing from $11.5 billion in 2024 to $37 billion in 2025. Google's internal token processing grew 130-fold over eighteen months, reaching 1.3 quadrillion tokens processed monthly by 2025. The top five hyperscalers collectively committed $602 billion in AI infrastructure capital for 2026.

What explains this paradox? Consumption volume grew far faster than unit costs fell. Cheaper tokens did not reduce the bill. They expanded what companies chose to do with AI, and that expanded consumption more than offset every pricing efficiency gain.

This is the loss-leader phase working exactly as intended. AI providers are making access affordable to condition enterprise behavior, build workflow dependence, and normalize AI as infrastructure. The organizations that now have hundreds of AI workflows running, teams that depend on AI outputs daily, and procurement processes anchored to AI tooling are not going back. The switching cost is already embedding itself.

Gartner has formalized what that next phase looks like. In March 2026, Gartner published a forecast stating that by 2030, performing inference on a large language model with one trillion parameters will cost providers over 90% less than in 2025, and that LLMs will be up to 100 times more cost-efficient than comparable models developed in 2022. Then Gartner added the critical caveat that most enterprise finance and technology leaders have not absorbed: these savings will not pass through to enterprise customers. Per-token costs will fall. Total enterprise AI spend will continue rising because agentic workloads consume disproportionately more tokens per task than the chat interfaces most companies started with.

As Gartner Senior Director Analyst Will Sommer stated: "CPOs who mask architectural inefficiencies with cheap tokens today will find agentic scale elusive tomorrow."

The cheap era is a temporary subsidy. Tokenomics is the conversation that comes after it ends.

The Counterarguments That Need Addressing

A Goldman Sachs-level critique of the tokenomics thesis would raise three objections. All three are legitimate. All three, examined closely, strengthen rather than weaken the core argument.

Objection 1: Open source models will keep token prices near zero permanently, eliminating the pricing pressure this article predicts.

This is the most serious counterargument and the data is real. DeepSeek R1, released in January 2025 with a reported training cost under $6 million, achieves performance comparable to frontier proprietary models on many benchmarks. The DeepSeek V3.2 Speciale variant is available via API at as little as $0.07 per million tokens with cache hits. Over 60% of frontier model releases since early 2025 incorporate the Mixture-of-Experts architecture that produced DeepSeek's economics, and these techniques have diffused across the industry. Open-weight models from Meta (Llama 4), Alibaba (Qwen 3), and Google (Gemma 4) narrow the quality gap with proprietary models to 5 to 7 quality index points on benchmarks as of early 2026. The NavyAI cost report states plainly: "There is no credible scenario in which token prices meaningfully increase."

So does the tokenomics thesis collapse if proprietary pricing pressure never arrives?

It does not, for two reasons. First, the self-hosting of open-source models is not free. Enterprise-scale deployment of a 671-billion-parameter model requires H100-class infrastructure where cloud rental prices, though down 64 to 75% from their 2023 peak, remain a substantial ongoing operational cost. The NavyAI analysis documents that for organizations running AI in production, the inference invoice represents only 20 to 40% of actual total AI infrastructure cost. Engineering overhead, data pipeline management, model maintenance, evaluation tooling, and security infrastructure compose the remainder. Organizations that switch to self-hosted open models to avoid per-token pricing will still have a total cost of intelligence that requires measurement and optimization. Tokenomics as a discipline does not assume any specific pricing model. It assumes that token consumption is a first-class operational cost that should be measured against output.

Second, and more fundamentally: the competitive pressure created by open-source models strengthens the strategic case for tokenomics rather than undermining it. When any sufficiently capable model can be deployed at commodity inference costs, the competitive advantage shifts entirely to the quality of the architecture built on top of it, the precision of model routing across a portfolio, and the efficiency of workflows. Enterprises that have built the measurement infrastructure to know exactly how many tokens each workflow consumes, and what each workflow produces, will be positioned to arbitrage between proprietary and open-source providers in real time. Enterprises that have not built that infrastructure will be unable to make those decisions intelligently, regardless of how cheap individual tokens become.

Objection 2: AI is not a monopoly-forming market like ride-hailing. Token pricing may never consolidate into a standard commercial model, so the Uber/Amazon analogy does not hold.

This objection is partly correct. The ride-hailing analogy does have limits: AI inference is not subject to geographic network effects in the same way that a taxi network is, and the market for AI compute is structurally more competitive than urban ride-hailing. Uber achieved near-monopoly positions in many cities specifically because supply and demand were geographically colocated. AI inference is not.

However, the analogy does not depend on market consolidation. It depends on behavioral conditioning. The relevant parallel is not "AI will become a monopoly" -- it is that organizations are currently building deep operational dependencies on AI tooling during a period of subsidized access, and those dependencies will persist when pricing structures mature. Goldman Sachs has already named the commercial model that matures from this: firms will shift from billing by hours worked to billing clients by tokens consumed. That transition does not require a monopoly. It requires only that AI-dependent workflows become sufficiently embedded in operations that buyers cannot easily exit, and the data on workflow adoption suggests that threshold is being crossed in most large enterprises right now.

Objection 3: Most organizations are not measuring AI ROI at all. If the measurement baseline does not exist, how can a tokenomics framework be built on top of it?

This objection does not undermine the tokenomics argument. It restates it. Deloitte's 2025 US Technology Value survey found that only 28% of global finance leaders report clear, measurable value from their AI investments. In a separate 2026 report published in CIO Journal titled "The pivot to tokenomics: Navigating AI's new spend dynamics," Deloitte formally named token economics as the emerging framework that will replace cost-center AI accounting. Organizations lacking formal cost-tracking systems are, by Deloitte's own analysis, 41% less confident in their ability to evaluate AI ROI.

The measurement baseline is absent for most organizations precisely because the flat-subscription pricing model they currently operate under obscures per-workflow costs. This is not an argument against building tokenomics capability. It is the argument for why that capability needs to be built before consumption-transparent pricing arrives.

The Scale of What Is Coming: Numbers Worth Internalizing

Goldman Sachs published its report "Decoding the Agentic Economy: The Coming Inflection in AI Usage and Margins" in May 2026. Its core projection is that agentic AI will drive a 24-fold increase in global token consumption by 2030, reaching 120 quadrillion tokens per month. By 2040, enterprise agents alone are projected to increase token consumption 55 times from current levels and to account for over 70% of all token usage globally.

The Goldman model provides granular operational data that is directly useful for thinking about what per-workflow token budgets will look like. A programming agent could consume 7 million tokens per day. A data entry agent might consume 25 million. Consumer-facing agents are projected to drive a 12-fold increase in token use by 2030. At current API prices, the cost of these agents remains well below the cost of human labor performing equivalent tasks, which is the economic incentive for adoption. The math changes materially once token pricing becomes explicitly consumption-based rather than bundled in flat subscriptions.

Goldman's separate 2026 outlook on AI in the enterprise makes the commercial model shift explicit: companies will shift from calculating billing by hours worked to charging clients by the amount of tokens consumed. Goldman's Chief Information Officer framed it directly: "In my 40 years in technology, 2025 saw the biggest changes I have seen in my career. And what's crazy is we haven't seen anything yet."

A peer-reviewed study submitted to ICLR 2026 analyzing agentic coding tasks on SWE-bench provides independent empirical data that supports Goldman's projections. The research found that agentic tasks consume 1,000 times more tokens than standard code reasoning and code chat interactions. Token usage in agentic workflows is also highly variable and stochastic: runs on the same task can differ by up to 30 times in total token consumption, and higher token usage does not reliably translate to better task outcomes. This variability is itself a strategic risk. Companies that do not measure token consumption at the workflow level cannot distinguish a well-run agent from a wasteful one.

The OpenAI State of Enterprise AI Report 2025 adds confirmation from a different angle: API reasoning token consumption per organization increased 320 times year-over-year. This is not a trend that bends back. It compounds.

Why Today's Evaluation Frameworks Are Already Broken

Most companies evaluating AI success today are measuring the wrong things. The dominant metrics remain time saved per employee, cost reduction in specific functions, and qualitative assessments of output quality. These are not wrong metrics. They are incomplete ones for a world moving toward consumption-transparent pricing.

The NavyAI cost analysis documented a structural measurement gap: the inference invoice from AI providers represents only 20 to 40% of actual total AI infrastructure cost for organizations running AI at production scale. The remaining 60 to 80% sits in engineering overhead, data pipeline costs, evaluation tooling, security and compliance infrastructure, and the human capital required to maintain AI systems. Organizations that track only the provider invoice are not measuring their AI economics. They are measuring a fraction of them.

By late 2025, a growing body of practice and research had placed tokens at the center of how AI is measured, priced, and optimized. Providers increasingly expose token-based limits, routing rules, and pricing tiers. The Stanford AI Index 2025 explicitly calls for token-normalized benchmarks for comparing inference cost, efficiency, and environmental impact across model versions. This is not a hypothetical future direction. It is the direction the measurement community has already named.

The 70 to 85% AI project failure rate documented in enterprise AI research is not primarily a model quality problem. It is an architecture and measurement problem. Broadcom's ValueOps analysis explicitly frames tokens as the emerging standard for AI cost accountability, noting that "when the use of AI and tokens expands, cloud computing can become more costly and difficult to contend with" for organizations without structured token tracking. Organizations that do not build measurement infrastructure during the cheap era will attempt to retrofit it under pricing pressure, with production-scale AI that was designed without efficiency in mind.

When the market shifts to explicit token-based billing -- whether through per-token API pricing, outcome-based consumption contracts, or the token billing model Goldman Sachs describes as the emerging standard for agentic service delivery -- the evaluation framework used during the cheap era will become operationally insufficient. Companies will need answers to questions they have not yet started building the infrastructure to answer.

The Tokenomics Framework: What Companies Should Build Now

Tokenomics, as a strategic discipline for enterprise AI, is the systematic practice of measuring token consumption against business output, setting consumption benchmarks by workflow type, and building the organizational capability to optimize the ratio between the two. It is the unit economics discipline applied to AI-native operations.

1. Map Every AI Workflow to a Token Budget

Before consumption-transparent pricing becomes standard, companies should know what each major AI-powered workflow costs in token terms. This is not a one-time audit. It is an operational instrumentation practice. The goal is a live view of token consumption by workflow category: customer-facing interactions, internal knowledge retrieval, autonomous agent tasks, content generation pipelines, and code generation or review workflows. Building this view while tokens are cheap is strategically preferable to building it under pricing pressure.

Anthropic's API provides usage-tracking endpoints that enable this directly. For teams operating across multiple providers, AI FinOps platforms such as Finout now offer unified visibility, cost allocation by team or feature, and real-time anomaly detection across the full AI and cloud spend without requiring custom instrumentation. The tooling exists. The organizational will to use it is what is absent.

2. Define Output-to-Consumption Ratios

The central metric of tokenomics is not total tokens consumed. It is the ratio of measurable business output to tokens consumed to produce it. The specific output metric will vary by workflow type: revenue influenced per million tokens in a sales AI workflow, cases resolved per million tokens in a customer support deployment, lines of reviewed code per million tokens in a developer tooling context.

A February 2026 analysis of developer AI productivity published in Medium put this precisely: "Developers who use fewer tokens to produce higher-quality code represent maximum ROI. Those who consume heavily but produce mediocre results represent negative ROI." The same principle scales to every AI workflow in an organization.

Without this ratio, companies cannot distinguish a productive AI workflow from a wasteful one. Both will look similar on a flat subscription invoice. They will look very different on a consumption-transparent bill.

3. Build Model Routing Logic

Not every workflow requires a frontier model. Routing high-complexity, high-judgment tasks to frontier models while routing structured, repetitive tasks to smaller, cheaper models is the foundational efficiency principle of enterprise tokenomics. The token cost differential between a frontier model and an optimized smaller model can exceed an order of magnitude. DeepSeek V3.2 Speciale at $0.07 per million tokens with cache hits versus GPT-5.5 at $5 per million input tokens represents more than a 70x spread. Companies that have not built model routing logic into their AI architecture are leaving that entire differential on the table.

The OpenAI Enterprise AI Report 2025 shows that workers who consume the most AI report the highest time savings, but that task complexity varies enormously across an organization's AI footprint. A blanket frontier-model policy is both architecturally and economically indefensible at scale.

4. Instrument for Variability, Not Just Average Cost

The ICLR 2026 finding that agentic task token consumption can vary by up to 30 times across runs on the same task is an operational risk disclosure, not an academic observation. Companies running autonomous agents without token usage monitoring at the run level are exposed to cost variance they cannot see and cannot control.

Sparkco AI's 2025 analysis documented 30 to 40% token reductions in real deployments through retrieval optimization, context pruning, batching, and improved memory management -- none of which require model changes, and all of which are only findable through instrumentation. Run-level monitoring is a prerequisite for managing agentic AI economics responsibly.

5. Build a Token Benchmark Library by Workflow Type

The market does not yet have established, published benchmarks for what efficient token consumption looks like by sector and workflow type. That is simultaneously a gap and an opportunity. Organizations that begin benchmarking their own workflows now, and that build internal reference libraries of token-to-output ratios across their AI deployments, will hold a proprietary cost intelligence advantage when industry benchmarks do emerge. They will also be better positioned for vendor negotiations, build-versus-buy decisions, and board-level reporting when AI becomes a defined cost center.

The Stanford AI Index 2025's call for token-normalized benchmarks signals that external benchmarking frameworks are coming. The organizations that have been measuring internally for two or three years before those frameworks crystallize will be positioned to shape them rather than be evaluated against ones they had no hand in creating.

Sector-Level Benchmarks: Where the Data Currently Points

While sector-wide tokenomics benchmarks do not yet exist as a published standard, enough deployment data has accumulated to define where efficient and inefficient consumption is likely to concentrate by industry.

Financial Services - Financial services has been one of the highest-scale enterprise AI adopters. The OpenAI Enterprise AI Report 2025 notes that finance organizations typically begin with customer support automation, before moving to risk analysis, compliance monitoring, and trading analytics. Bain's 2025 survey of the financial sector found a 20% average productivity gain, with 57% of AI leaders in finance reporting ROI exceeding expectations. AI-powered loan processing has demonstrated a 70% reduction in processing times and a 90% increase in accuracy. Forty-three percent of companies using AI in financial services reported meaningful gains in operational efficiency.

In tokenomics terms, financial services AI workflows tend to be high-context and high-stakes, which pushes toward frontier model use. The efficiency opportunity lies in the large share of financial AI workloads that are structured and repetitive -- compliance document review, transaction categorization, routine customer query resolution -- which are strong candidates for model routing to smaller models without material quality loss.

Healthcare - Healthcare AI spending reached $1.4 billion in 2025, nearly tripling 2024's investment, per Menlo Ventures' 2025 Healthcare AI report. The highest-value token-intensive use cases are ambient clinical documentation ($600 million in spend) and coding and billing automation ($450 million). Healthcare organizations are generating $3.20 in return for every $1 invested in AI within 14 months. An NVIDIA 2025 Healthcare and Life Sciences survey found that 81% of healthcare organizations reported increased revenue from AI implementations, with nearly half achieving ROI within one year.

Healthcare token economics carry a constraint absent in other sectors: the cost of a model error is not financial -- it is clinical. This creates a sector-specific ceiling on model routing for clinical workflows: frontier models will remain required for clinical decision support and documentation review regardless of per-token cost pressure. The efficiency opportunity in healthcare is therefore concentrated in the administrative and operational layer -- prior authorization processing, claims submission, scheduling optimization, and patient engagement workflows -- where task structure is high and clinical risk is low. Insurers using AI agents for policy lifecycle automation are documenting up to 30% operational cost savings through AI-driven automation of claims processing and customer support.

Software and Technology - Software development AI agents are among the most token-intensive enterprise deployments documented. The Goldman data point of 7 million tokens per day for a programming agent aligns with the ICLR 2026 empirical finding that agentic coding tasks consume 1,000 times more tokens than standard code reasoning interactions. The OpenAI Enterprise AI Report 2025 shows the technology sector leading all industries in AI adoption at 11 times year-over-year growth, with API reasoning token consumption per organization up 320 times year-over-year.

The tokenomics challenge in software is the variability problem. The same coding agent run on the same task can consume anywhere from the baseline to 30 times the baseline in token terms, and higher token consumption does not reliably produce better outputs. For software organizations, the highest-priority tokenomics investment is workflow-level token monitoring combined with output quality scoring -- building the operational visibility to distinguish efficient agent runs from wasteful ones and to route work accordingly.

Legal - Legal AI has emerged as one of the highest-ROI vertical agent categories, with investor data from Finro showing that legal, healthcare, and B2B-focused agents consistently fetch higher valuation multiples than general-purpose AI tools -- specifically because the workflows are defensible, compliance-heavy, and decision-critical. Document review, contract analysis, and regulatory compliance monitoring are established high-value use cases. Legal token economics are characterized by very high context window requirements -- the need to process large documents in their entirety -- which drives disproportionate input token consumption. Prompt caching strategies, which can reduce the cost of repeated high-context queries by up to 90% (Anthropic's documented cache discount rate), represent the primary efficiency lever for legal AI deployments. For a legal team processing the same master service agreement framework across hundreds of matters, the difference between cached and uncached token pricing is the difference between scalable economics and a cost structure that breaks at volume.

The Strategic Imperative: Why Preparation Has a Window

The argument for building tokenomics capability now rather than later rests on three structural observations.

• First, the pricing transition is not speculative. Goldman Sachs explicitly describes token-based billing as the emerging commercial model for agentic service delivery. Gartner has documented the paradox that will make token efficiency a board-level concern: per-token costs will fall dramatically, but total enterprise AI spend will continue rising because agentic consumption grows faster than unit costs decline. Deloitte named the discipline in a January 2026 CIO Journal article as "the pivot to tokenomics." The question is not whether token-aware economics will matter. The question is how prepared an organization will be when full visibility arrives.

  
  

• Second, open-source competition makes tokenomics more important, not less. When any organization can self-host a near-frontier model at commodity infrastructure cost, the competitive moat shifts entirely to architectural quality, measurement precision, and workflow efficiency. The enterprises with token-level visibility across their AI portfolio will be positioned to make real-time arbitrage decisions between proprietary and open-source providers. The ones without that infrastructure will be unable to make those decisions intelligently, regardless of how cheap any individual token becomes. As Zylos Research noted in April 2026: "The competitive moat in AI agent systems will not be access to cheap inference, but rather the quality of agent architecture, memory systems, tool integrations, and organizational knowledge embedded in agent behavior."

  
  

• Third, the infrastructure cost of not being prepared compounds. Agentic AI built on architectures without token monitoring, model routing, or workflow-level efficiency controls is technical debt with a growing interest rate. Organizations that do not build measurement infrastructure during the cheap era will be retrofitting it under pricing pressure, with production-scale AI that was designed without efficiency in mind.

What a Tokenomics Strategy Looks Like in Practice

Building a tokenomics strategy does not require waiting for industry standards to emerge. It requires treating token consumption as a first-class operational metric today. The concrete steps are:

• Assign ownership. Tokenomics requires someone accountable for AI cost efficiency at the workflow level. In most organizations today, AI cost accountability is fragmented across the CTO, CFO, and individual product owners, with no single function responsible for the token-to-output ratio across the AI portfolio. This ownership gap should be closed before token pricing makes it operationally expensive.
  

• Build a token consumption dashboard. This is the equivalent of a cloud cost management dashboard for AI workloads. It should show token consumption by workflow, by model, and by time period, with the ability to correlate consumption to output metrics. Most major AI providers and cloud platforms now offer the instrumentation APIs needed to build this. AI FinOps platforms that provide unified multi-provider visibility are also available. The engineering investment is modest relative to the strategic value.

  
  

• Run a model routing audit. Map every active AI workflow against the question: is this workflow using the appropriate model tier for its task complexity? Structured, repetitive, low-judgment tasks running on frontier models represent a solvable inefficiency. An audit will almost certainly identify workflows where routing to a smaller or open-source model reduces token costs by 80% or more with no material impact on output quality.

  
  

• Define output metrics per workflow. For each AI workflow, define the output metric used to calculate the token-to-output ratio. Many current AI deployments have been justified on qualitative grounds -- "it saves time," "it improves the experience" -- without a defined quantitative output metric. The discipline of defining these metrics now creates the measurement foundation for a tokenomics strategy.

  
  

• Start sector benchmarking internally. Begin tracking token consumption per defined output unit for each major workflow. Build an internal benchmark library. When industry standards emerge -- and the Stanford AI Index and Deloitte's January 2026 CIO Journal report both signal they are coming -- having historical data will be a strategic asset for competitive positioning, procurement negotiations, and board-level AI ROI reporting.

  
  

Conclusion: The Window Is Open Now

The organizations that will lead in the agentic economy are not necessarily the ones that adopted AI earliest. They are the ones that built the measurement infrastructure to understand what their AI consumption is producing and to optimize the ratio between the two.

Token pricing will not arrive as a sudden shock. It is already embedded in API economics for organizations building on AI infrastructure. What will shift is the visibility and scrutiny applied to that pricing, as flat-subscription models that currently obscure per-workflow costs evolve toward consumption transparency. That shift is already named, already documented, and already underway.

The ecommerce parallel is instructive precisely because of what it reveals about the transition point. When Amazon and Uber moved from growth-phase pricing to margin-focused models, the companies that had built operational discipline around unit economics during the subsidized era retained their positions. The ones that had treated cheap access as a permanent operating condition did not. The open-source counterargument, taken seriously, does not dissolve the thesis. It sharpens it. When tokens become a commodity, the question of how efficiently you use them becomes the only question that differentiates you.

AI is in its subsidized era. The operational discipline required for what comes next is tokenomics. The time to build it is now, while the cost of building it is still low and the competitive pressure to have it has not yet arrived.

The companies that start this work in 2025 and 2026 will not just be more efficient when pricing shifts. They will be the ones who set the benchmarks that everyone else is measured against.

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