Research the demand-side of the equation:

Agentic AI is driving a step-change in token consumption, with Goldman Sachs projecting a 24x increase to 120 quadrillion tokens per month globally by 2030 (from an implied ~5 quadrillion currently), fueled by both consumer and especially enterprise adoption of multi-step autonomous workflows.[1][1]

This dwarfs traditional generative use cases because agents perform chained reasoning, tool calls, memory lookups, and sub-agent orchestration—often consuming 5–30x more tokens per task than a simple query (e.g., basic chatbot: 50–500 tokens; multi-step agent workflow: 100k–1M+ tokens).[2] Enterprise inference bills are already material: the average enterprise spent ~$7 million on AI model usage in 2025 (nearly 3x the $2.5 million in 2024), with some hitting tens of millions monthly; many organizations exceed 10 billion tokens/month, and the share expecting >100 billion is projected to triple by 2028.[3]

Salesforce provides a concrete production signal: as of early 2026 (FY26 Q4), it had processed ~19 trillion tokens all-time (5x YoY) through its LLM gateway, converting them into 2.4 billion Agentic Work Units (AWUs, discrete tasks completed by agents), with strong QoQ growth.[4][5]

Broader AI software/application markets are scaling rapidly (hundreds of billions globally), but the inference/API layer (token consumption monetized by providers like OpenAI/Anthropic) represents the direct demand-side TAM for tokens. Coding and agentic enterprise use cases are emerging as the highest-volume, fastest-monetizing drivers.

Enterprise software and agentic workflows represent one of the largest and fastest-scaling sources of token demand, as vendors embed agents into core business processes (sales, service, operations) and shift from copilots to autonomous execution. Gartner forecasts 40% of enterprise apps will feature task-specific AI agents by end-2026 (vs. <5% in 2025).[6] AI agents markets are projected in the tens of billions soon (e.g., one estimate: $7.84B in 2025 to $52.62B by 2030 at 46.3% CAGR).[7]

SaaS vendors face margin pressure from rising inference costs (variable with usage) but capture value through higher ARR, usage-based pricing, and workflow lock-in. Notion’s CEO noted gross margins had been ~90% but are being impacted by inference needs that grow with task complexity.[2] Horizontal copilots still dominate spend, but agents (e.g., Salesforce Agentforce) are growing explosively in production. Vertical agents (e.g., healthcare) show high CAGR potential.[7]

For competitors: Focus on efficient orchestration, token optimization, hybrid pricing (seats + consumption), and data/workflow moats. Enterprises are willing to pay for measurable outcomes (cost reduction, headcount replacement) but scrutinize variable costs.

AI coding assistants and agentic dev tools are monetizing fastest among use cases, with a credible ~$100B annual TAM as developers and companies pay ~$2,000/developer/year for generation, reasoning, and autonomous execution layers.[8]

This expands the traditional dev tools TAM dramatically. Tools like Cursor, Claude Code, GitHub Copilot, and others are seeing rapid adoption; coding is repeatedly cited as a standout category in enterprise spend (e.g., one 2025 analysis: coding captured $4.0B of $7.3B total AI spend tracked).[9] Agentic coding platforms enable “vibe coding” and faster shipping, driving hundreds of millions in revenue already for leaders.[10]

Coding agents often involve high token volumes (complex code: 20k–100k+ tokens) and are shifting toward usage/credit-based pricing on top of seats.[2]

Implication: This sector offers quick monetization and defensibility via IDE integrations and enterprise security/compliance features. New entrants should target specific workflows (e.g., testing, refactoring) or verticals rather than general models.

Consumer applications contribute meaningfully to volume but lag enterprise/agentic in per-user monetization intensity; demand is growing via chat, personalization, and early agents, though token spend is more diffuse across subscriptions and ads. Goldman Sachs notes consumer agents (shopping, device control, etc.) will drive a 12x token increase on that side by 2030.[1] Broader consumer AI (e.g., ChatGPT-scale usage) benefits from high engagement but lower average revenue per user compared to enterprise API deals.

Productivity/value capture here often flows through platforms (e.g., subscriptions, in-app purchases) rather than raw tokens. Agentic consumer use (e.g., personal assistants handling multi-step tasks) is an emerging accelerator.

Public revenue figures for leading model/API providers show explosive growth through 2025–2028, driven by enterprise/API adoption (especially coding and agents), with Anthropic pulling ahead on run-rate in mid-2026.

• OpenAI: ~$13B revenue projected for 2025; internal targets and analyst views point to continued rapid scaling (e.g., toward $40B+ ARR mid-2027 range in some forecasts), though growth has shown some flattening and high infrastructure spend leads to losses.[11][12]
• Anthropic: ~$9B ARR end-2025; run rates reached $30B by April 2026 and ~$47B by late May 2026 (fueled by enterprise and Claude Code); company projections include $70B revenue and $17B cash flow by 2028.[13][13]

These figures primarily reflect API/inference revenue tied directly to token consumption. Coding/enterprise verticals and agent platforms are key growth engines. Note: Figures are run-rate/ARR estimates from reports and can vary by source; actual realized revenue depends on usage realization.

Fastest-monetizing sectors are coding/agentic development tools and enterprise agent platforms (sales/service/ops), followed by verticals like healthcare. These benefit from clear ROI (productivity, cost savings, speed) and willingness to pay usage-based premiums. Broader software productivity value capture is shifting: model providers capture inference spend, while application vendors capture via embedded agents, higher pricing power, and outcome-based models—though inference costs can compress margins if not optimized.[2]

Bottom-up models (tokens per task × scale of workflows × adoption) consistently point to agentic/enterprise use as the dominant long-term driver over pure consumer chat. For market entrants: Prioritize measurable enterprise outcomes and cost controls; raw model scale alone is insufficient without workflow integration and efficiency. Data is as of mid-2026 reporting; projections carry uncertainty due to rapid evolution and infrastructure dependencies.

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Recent Findings Supplement (June 2026)

Goldman Sachs (May 2026) provides the most detailed recent bottom-up projection for agentic AI token demand, modeling real-world use cases like travel booking and call-center support. Agentic workflows multiply token use dramatically versus simple chat (often 10-50x per task due to sequential reasoning, tool calls, retries, and self-correction). The firm forecasts overall token consumption rising 24x from 2026 levels to 120 quadrillion tokens per month by 2030, with consumer agents driving a 12x increase and enterprises adding the rest. LLM query volume is modeled at 40% CAGR, reaching 11 billion daily by 2030. Enterprise adoption lags consumer (only 12% of knowledge workers by 2030) due to integration, testing, compliance, and documentation requirements, though coding agents show strong near-term economics.[1][1]

• Goldman’s simulation of agentic consumer/enterprise scenarios directly underpins the 24x/120 quadrillion figure; hyperscalers are positioned for margin inflection as inference costs fall 60-70% annually while volumes surge.
• Short-term chip shortages expected (next 12+ months) as use cases evolve faster than capacity planning.

This implies agentic workflows represent the primary long-term demand driver at scale, with coding as an early high-volume enterprise beachhead. Competitors or entrants must prioritize token-efficient architectures and governance tooling, as raw volume growth will outpace per-token price declines.

Gartner (June 24, 2026) highlights surging token consumption in coding assistants as a near-term budget shock. AI coding costs are projected to exceed average developer salaries by 2028 under consumption-based pricing, as seat-based models shift to usage and developers prioritize speed over efficiency. Many organizations lack visibility into token calculations, leading to overruns; ungoverned agent autonomy, bloated contexts, and lack of optimization exacerbate this.[2][2]

• Programming already accounts for over 50% of LLM token usage on platforms like OpenRouter (late 2025 into 2026 data).
• Recommendations include task classification (developer-led vs. fully agent-led), smaller-model routing for simple work, context engineering, token thresholds, and embedding usage reviews in sprints.

Coding assistants are driving the fastest near-term monetization and cost pressure among enterprise use cases. Vendors and enterprises entering this space need built-in cost controls and FinOps from day one; productivity gains risk being eroded without them.

Enterprise token consumption has accelerated sharply, with specific 2026 examples showing 13x growth since early 2025. Ramp data indicates AI token/API spend per firm rose 13x from January 2025 levels, shifting budgets from fixed seats to variable inference. Uber provided Claude Code access to 5,000 engineers starting December 2025 and exhausted its full annual AI budget by April 2026. A healthcare firm consumed 1 trillion tokens in six months (> $6 million unplanned).[3]

• Deloitte and others note AI as the fastest-growing IT expense (up to 50% of some digital transformation budgets), with falling per-token prices offset by rising volume and complexity (especially reasoning/agentic models).[4]

This consumption explosion validates Goldman’s trajectory and signals that enterprise software/API usage is scaling faster than budgets planned. New entrants must build real-time monitoring, caps, and ROI gates; sectors like software engineering and customer operations are leading adoption.

Global AI spending forecasts were updated in 2026, with enterprises accelerating genAI and agent investments. Gartner revised its 2026 worldwide AI spend projection to $2.59 trillion (+47% YoY) from an earlier $2.52 trillion (+44%); enterprises are expected to more than double spending on generative models and agents (adding ~$6 billion in 2026). AI agent software spend alone is forecasted at $206.5 billion in 2026 (up from $86.4 billion in 2025).[5][6]

• Broader AI market estimates for 2025–2026 range from ~$390–602 billion (various 2026 reports), growing at 29–31% CAGR toward multi-trillion figures by the early 2030s, driven by enterprise generative/agentic adoption.

Enterprise and API segments are capturing the fastest monetization growth. Public company run-rate figures reflect this: Anthropic grew from ~$9 billion ARR at end-2025 to $30 billion+ by April 2026 (estimates reaching $47 billion by May 2026), heavily via enterprise API and coding tools; OpenAI reached ~$20 billion+ revenue in 2025 with a $25 billion+ early-2026 run rate.[7][8]

China’s usage surge provides a contrasting data point on geographic demand. Official announcements and OpenRouter data (April 2026) show China reaching 140 trillion daily tokens (ByteDance’s Doubao >120 trillion/day alone), surpassing US models in tracked API traffic (e.g., one week: Chinese models ~13T vs. US ~3T).[9]

Overall, recent 2026 data shows token demand scaling rapidly via agents and coding, with enterprise spending and provider revenues rising accordingly, though governance and cost visibility remain critical gaps. Projections to 2028–2030 remain dominated by agentic growth models like Goldman’s.