Anthropic has partnered with Indian IT services giant Tata Consultancy Services (TCS) in a bid to accelerate adoption of its artificial intelligence models at enterprises.

The partnership will see TCS creating a business unit focused on deploying Anthropic’s AI models to its customers. TCS will also gain early access to new model releases, which it says it will use to build expertise, and it will provide Anthropic’s Claude AI assistant to its employee base of more than 50,000 people.

The companies said they would develop solutions for sectors like financial services, healthcare, telecommunications, and aviation.

Frontier AI companies have been securing enterprise distribution channels by partnering with firms like TCS in India. Earlier this year, Anthropic teamed up with Infosys, and OpenAI roped in Infosys and HCLTech to do something similar.

Beyond enterprise deployments, the partnership extends to several TCS businesses and platforms. Diligenta, TCS’s U.K.-based life and pensions business with over 22 million customers, plans to use Claude for customer service and process automation. Similarly, TCS iON, the company’s digital learning platform, will offer training and certification programs on Anthropic’s models.

TCS said it would contribute capabilities to Anthropic’s Claude Code ecosystem, including tools for claims adjudication and lending advisory.

Anthropic has been working to expand its footprint in India, which the company has described as its second-largest market. Over the past year, the startup has opened an office in the country, hired for leadership roles, and expanded ties with major IT services firms.

The deal comes as investors and tech companies alike have begun doubting the viability of India’s $315 billion IT services amidst the rise of AI. Shares of TCS and Infosys have fallen about 34% and 31%, respectively, so far this year.

TL;DR

Enterprise AI bills are tripling despite a 98% drop in per-token prices, as agentic tools drive consumption 18.6x higher per developer. The Linux Foundation is launching the Tokenomics Foundation to bring cost discipline to AI spending.

Uber blew through its entire 2026 AI coding budget by April. Microsoft revoked its developers’ Claude Code licences six months after enabling them. One company reportedly ran up a $500 million Claude bill in a single month after forgetting to set usage limits. A Priceline employee told TechCrunch that a routine Cursor contract renewal came back four to five times more expensive.

The pattern is the same everywhere. Per-token prices have collapsed, but the push for autonomous AI agents has sent consumption through the roof. Companies that gorged themselves on all-you-can-eat subscriptions in early 2025 are now scrambling to understand where the money went, and whether any of it produced a return.

The paradox in numbers

GPT-4-equivalent performance now costs roughly $0.40 per million tokens, down from $20 per million in late 2022. That is a 98% reduction. Yet enterprise AI bills have risen by an estimated 320%, according to multiple industry analyses. The average enterprise AI budget has grown from $1.2 million per year in 2024 to $7 million in 2026.

The culprit is volume. Agentic AI tools released since November 2025, including Anthropic’s Claude Opus 4.5, OpenAI’s GPT-5.1, and Google’s Gemini 3 Pro, have multiplied token consumption per task. A simple linear workflow in 2023 cost about $0.04 per interaction. An orchestrated agentic system in 2026 costs roughly $1.20, about 30 times more. Individual engineers at Microsoft were reportedly spending between $500 and $2,000 a month on tokens before the licences were pulled.

Nicholas Arcolano, head of research at engineering management platform Jellyfish, told TechCrunch that per-developer consumption has risen roughly 18.6 times in nine months. Engineers who used the most tokens were about twice as productive as lighter users, but they spent 10 times the tokens to get there. “Whether extreme spend pays off comes down to the ultimate business value of shipped code, which most companies still can’t measure,” Arcolano said.

From tokenmaxxing to guardrails

“Six months ago, I would have a conversation with a customer and it would be all about ‘What can it do? Is it good enough?’” Alexander Embiricos, OpenAI’s head of enterprise, told TechCrunch. “Now the conversations are about, ‘We’re spending so much. What visibility do you have? What token controls do you have?’”

J.R. Storment, executive director of the FinOps Foundation, described the shift bluntly. “In April and May, I started hearing from companies: ‘Oh my god, we are 3x over our entire 2026 token budget and it’s only April.’ The whole conversation shifted from tokenmaxxing and ‘go fast’ to ‘we need guardrails, how do we control this?’”

Priceline’s senior director of IT finance, Chris Reed, drew a comparison to the telecom billing era. “It’s like the crack-cocaine epidemic. They let you try it to get you hooked, and now you’re kind of beholden to it.” The company has begun placing token limits on certain groups. Reed said he is already seeing discrepancies between vendor-reported usage and Priceline’s internal data.

The Tokenomics Foundation

It is against this backdrop that the Linux Foundation this week unveiled plans for the Tokenomics Foundation, a new standards body aiming to bring the same cost discipline to AI tokens that FinOps brought to cloud spending.

The Foundation plans to build a canonical definition of “tokenomics,” open standards for AI token usage and billing, and new metrics including cost-per-intelligence and tokens-per-watt. A formal launch is planned for July. Nishant Gupta, chief availability officer at Salesforce, said in a statement that “token economics is fundamentally more abstract and opaque than anything we’ve managed at this scale before.”

The challenge is enormous. “Tracking cloud costs is a hundreds-of-millions-of-rows-a-month data problem,” Storment said. “Tracking token costs is a trillions-of-rows-a-month data problem.”

A market forms around the problem

Startups and established vendors are racing to fill the gap. Pay-i tracks and optimises AI spending. Paid lets developers bill based on actual value rather than subscription fees. Jellyfish, Waydev, and Faros AI provide agent monitoring to prove the ROI of developer tools. Ramp has moved into AI spend management. Datadog and New Relic have added token-level observability.

Model routing is emerging as the primary cost lever. Factory, an enterprise AI coding startup, launched a model router this week that automatically picks the cheapest adequate model for each task. Vitaly Gordon, CEO of Faros AI, said frontier labs are already doing this internally. “The financial report for how much you spend on Anthropic, even if you call the Opus model, some of the spend will be on Sonnet or Haiku, because they are smart enough to do it,” he said.

Goldman Sachs projects global token usage will multiply 24 times by 2030. The companies already over budget need solutions now, and the Tokenomics Foundation’s first deliverable is still months away. As Gordon put it: “Maybe we created a steam engine, but we still haven’t figured out the assembly line.”

TL;DR

OpenAI expanded Codex from a coding tool into an enterprise work platform with Sites (hosted web apps), Annotations, and role-specific plugins connecting 62 business apps. Non-developers now make up 20% of 5 million weekly users and are adopting 3x faster than engineers.

OpenAI announced a major expansion of Codex on Tuesday, transforming its AI coding agent into a broader enterprise work platform with three new capabilities: Sites, a feature that lets users create and share hosted interactive web applications; Annotations, an in-place editing tool; and six role-specific plugins that aggregate 62 popular business applications including Snowflake, Figma, and Salesforce with 110 automated skills built in. The update signals OpenAI’s ambition to make Codex the default interface for knowledge work, not just software development.

The most telling data point is the user composition. Non-developers, including financial analysts, marketers, operations staff, and researchers, now constitute approximately 20% of Codex’s 5 million weekly users and are adopting the platform three times faster than traditional engineers. The vibe coding phenomenon, in which non-technical users build applications through natural language prompts, is no longer a curiosity. It is becoming a measurable share of a product used by millions.

Sites: from spreadsheet to web app

Sites, launching in preview for business and enterprise customers, lets Codex create interactive, hosted web applications that users can share via secure workspace URLs. The practical implication is that a financial analyst can take a static spreadsheet, describe what they want in natural language, and Codex will generate a live web application, a scenario planner, a dashboard, or an interactive model, that colleagues can use without downloading files or navigating spreadsheet tabs.

This directly threatens the workflow layer that tools like Tableau, Power BI, and even internal business intelligence teams currently occupy. AI-native enterprise spending is surging precisely because these tools can collapse the gap between wanting an interactive application and having one, from weeks of development to minutes of prompting.

Plugins and the SaaS integration play

The six role-specific plugins are OpenAI’s most direct assault on horizontal SaaS. By connecting 62 business applications and bundling 110 automated skills, Codex is positioning itself as an orchestration layer that sits above existing enterprise tools rather than replacing them. A marketing manager who currently switches between Salesforce, Figma, and Snowflake could theoretically manage workflows across all three through Codex’s natural language interface.

The strategic logic follows a pattern established by Salesforce’s Agentforce and Microsoft’s Copilot: build the AI agent layer that connects to everything, and capture the value of orchestration rather than competing with each individual tool. Every SaaS company is building AI features, but OpenAI is betting that the orchestration layer, the thing that connects them all, is more valuable than any single application’s AI capabilities.

The SaaSpocalypse accelerator

The Codex update arrives in the middle of the SaaSpocalypse debate over whether AI will destroy or enhance the SaaS industry. The answer from OpenAI’s product direction is clear: Codex is designed to let users build custom solutions that replace off-the-shelf software. AI coding platforms like Cognition are already producing software at a fraction of the cost and time of traditional development. Codex’s expansion to non-developers accelerates this dynamic by removing the last barrier: the user no longer needs to think of themselves as a developer at all.

The 3x adoption rate among non-developers is the statistic that should concern SaaS companies most. It suggests that the market for AI-powered work tools is expanding faster outside the engineering function than within it, which means the revenue opportunity, and the competitive threat, is broader than the coding use case alone.

Defenders of traditional SaaS argue that enterprise software’s value lies in domain knowledge, compliance, and integrations that AI tools cannot easily replicate. The plugin architecture in today’s Codex update is OpenAI’s response: if the domain knowledge lives in the connected applications, Codex only needs to orchestrate it. Whether that orchestration layer can match the reliability, security, and auditability that enterprises require is the question the preview period will answer.