The reasoning effect: why the coding AI gold rush is meeting a token shock
A rapid run of revenue and fast-growing ARR benchmarks in 2024–25 has been powered by a shift from autocomplete to reasoning-capable models that can plan and execute multi‑step coding tasks. That capability—popularized by Anthropic’s Claude releases, including Claude 3.5 Sonnet and the February 2025 Claude 3.7 Sonnet reasoning mode—enabled so‑called “vibe coding,” where high‑level goals can be delegated to an agent that performs multi‑file edits and tool calls. The market reacted: coding AI agents generated an estimated $1.1B in revenue in 2024, and several companies reached $100M+ ARR in months, including Anysphere, Replit, and Lovable.
The token shock and its economics
A core technical-economic problem emerged as reasoning models became common: output token volumes can swell by roughly 20×, multiplying inference costs because cloud providers bill per token and output tokens are often priced higher than input. Price increases on next‑generation models—Anthropic’s May 2025 step‑ups on Sonnet 4 and Opus 4 are an example—have compounded the pressure by raising base model costs.
That dynamic exposes a mismatch in traditional enterprise contracts. Annual, per‑seat fixed fees leave vendors responsible for uncapped compute while revenue remains static. What once supported 80–90% margins can become deeply unprofitable under sustained reasoning workloads. The strain has already surfaced in operational measures such as rate limits, overage charges, and throttling on high‑usage accounts.
Pricing and go‑to‑market adjustments
Vendors are shifting toward models that align revenue with compute:
- Seat‑plus‑usage hybrids, with stricter per‑seat compute guardrails.
- Outcome‑based task pricing, where agents quote a fixed rate for a defined deliverable (e.g., “add error handling across this service”), bundling planning, tool calls, and verification with pre‑estimated caps.
- Model tiering that reserves high‑reasoning models for high‑impact work.
Data shows companies adopting usage‑aligned pricing are displaying stronger momentum (median Momentum Mosaic of 683 vs. 671 for the broader market), even as enterprise buyers resist variable monthly bills and prefer predictable budgets.
Talent consolidation and deal structures
Margin pressure has reshaped exits and M&A. Rather than full takeovers, reverse acqui‑hires and acqui‑hires—where buyers hire teams and license technology while leaving customer contracts and compute liabilities behind—have become prominent. Big tech and leading AI firms have pursued these transactions to secure talent without inheriting costly infrastructure commitments. CB Insights highlights several companies with high momentum but constrained exit prospects that could be targets in this environment, including Sourcegraph, Augment Code, JetBrains, Qodo, Lovable, Cognition, and Harness.
Open models and the enterprise tradeoffs
Two levers stand out as mitigations: open models and usage‑aligned pricing. Lower‑cost models—examples include Moonshot AI’s Kimi K2, Alibaba’s Qwen‑Coder, Z.ai’s GLM‑4.5, and OpenAI’s gpt‑oss—offer substantial cost savings and, in some cases, the ability to run on local hardware. For enterprises, however, adoption requires new security reviews, SLAs, extended agent testing, and infrastructure to self‑host or integrate third‑party hosts, slowing uptake for high‑value contracts that expect Claude‑level reliability.
Broader implications beyond coding
The reasoning token dynamic is likely to ripple across agent categories where reasoning‑heavy workloads scale. Customer service pricing has already evolved—Salesforce’s Agentforce moved to a hybrid Flex Credits approach in May 2025, and Zendesk pursued pricing changes in late 2024—but reasoning‑intensive work in legal, healthcare, and sales agents will require similar contract and pricing recalibrations to preserve unit economics.
The market is therefore entering a phase of recalculation: growth that once masked generous unit economics will lead vendors to reprice, adopt new guardrails, or consolidate talent to survive sustained reasoning costs.
Original source: https://www.cbinsights.com/research/reasoning-effect-on-ai-code-generation/?