**About** page of the **tokeneconomics.com** site, presenting the **Tokenomics Foundation** — a **Linux Foundation** project announced on **June 3, 2026**, operated in **close partnership with the FinOps Foundation**. **Stated mission**: *« establish open industry standards, benchmarks, and best practices for the economics of AI infrastructure »* — linking token **production, consumption, and monetization** to **business value**. **Framing definition of tokenomics**: *« Tokenomics is not just about the cost of tokens, it's about the entire layer of AI that they drive from production, to consumption to monetization »* — that is, **the entire economic layer of AI**, from infrastructure cost through model selection to value optimization. **Phase thesis**: early AI adoption prioritized **capability**; the current phase is shifting toward **efficiency and value**, which requires systematic cost management and **visibility**. **5 founding principles**: (1) ***« Efficiency is a design choice. AI cost is shaped by architecture, not just usage »*** ; (2) ***« Bigger is not always better. The best AI system is not always the one using the most expensive model »*** (right-tool / routing) ; (3) ***« Visibility comes before optimisation. Teams cannot manage what they cannot see »*** ; (4) ***« Value matters more than volume. More tokens, more calls, and more automation do not automatically mean better outcomes »*** ; (5) ***« Open knowledge benefits everyone »*** (shared standards, community learning, transparency). **Governance**: a **Governing Board** (industry direction + fund deployment) and a **Technical Committee** (open specifications + benchmarks). **Deliverables**: extension of the **FOCUS specification** (FinOps), open specs, benchmarks, frameworks and shared metrics. **Target audience**: CAIO, CTO, CIO, CFO, engineers, product teams, FinOps practitioners, researchers, startups, enterprises, public sector. **Stated goal**: moving organizations *« from experimental AI adoption to sustainable AI operations »* by extending the discipline of **variable technology spend** into the token era. **Relevance to the watch**: institutionalization/standardization of **agentic FinOps** at the level of an industry foundation — converges head-on with the notes [[finops-foundation-finops-for-ai-overview-2026-02-17]], [[finout-finops-ai-agents-four-step-allocation-framework-2026-04-27]], [[orq-ai-finops-ai-agents-cost-per-outcome-hosseini-2026-04-15]], [[gupta-token-budget-wars-marginal-token-utility-2026-05-28]] (allocation layer, token-to-outcome) and with the **token → outcome** shift (Salesforce/Tallapragada, Sierra/Greenwald). The 5 principles map exactly onto the levers already captured: architecture > usage, **Haiku/Sonnet/Opus routing**, observability before optimization, value ≠ volume.
#Tokenomics Foundation#tokenomics#token economics
**Tokenomics Foundation** (entité collective, projet de **The Linux Foundation**, en partenariat avec la **FinOps Foundation**). Page institutionnelle *About* — **aucun auteur individuel nommé**. Annonce datée du **3 juin 2026**.
Post from the **Dropbox Tech blog** (*culture* section), published on **May 28, 2026** by **Kazuaki Okumura** (Dropbox, role unspecified in the article), recapping a talk at the **DX Annual 2026** conference (developer productivity). **Pivot thesis**: engineering productivity must move beyond *code generation*. *« Accelerating code generation simply shifted some bottlenecks downstream »* — AI has massively increased code throughput, but *« the faster code moves, the more pressure it puts on review queues, CI systems, validation workflows, release coordination, and production operations »*. The real challenge is no longer writing code faster, but enabling the entire SDLC to **absorb, validate, and ship safely** a much larger volume. **From copilot to agent**: the first wave (code explanation, snippets, Q&A) operated *« as copilots alongside the engineer »*; the agent, by contrast, *« can take a scoped task, inspect the codebase, edit files, run tests, iterate on failures, and return an artifact for human review »* — with the engineer remaining *« accountable for intent, architecture, quality, and release decisions »* (more parallel work, more options, offloading repetitive execution). **Nova** = Dropbox's **internal** coding-agent platform: describe a task in natural language, execution in a controlled environment with codebase context. Canonical datapoint: ***« Nova's value comes less from the model itself than the systems surrounding it »*** (codebase context, internal practices, safe execution, workflow integration, human review); Nova accounts for **~1 in 12 PRs at Dropbox** today (adoption growing), and extends beyond features to **migrations, flaky-test remediation, bug investigation, dependency updates** (high-toil work). **Measuring product velocity, not code output**: *PR throughput*, a useful signal when coding velocity was the constraint, *« was no longer sufficient »*. A **4-stage** measurement model: ***Fuel*** (are AI tools being used?) → ***Adoption*** (how workflows are changing across teams) → ***Output*** (is AI contributing to production work?) → ***Impact*** (*« improving product velocity and reducing the time it takes to move from idea to customer value »*). Quality signals tracked: **code review turnaround time, first-run test pass rate, defect ratio, rework rate**. *« Quality and trust matter as much as speed »* — the core of the shift: *« moving from local activity metrics toward broader system outcomes »*. **Workflows have to evolve too**: this is *« not just a tooling shift »* but a change of **operating model** — the engineer's role shifts toward *« defining intent, mapping problems, reviewing generated changes, and making higher-context architectural and quality decisions »*. **Enablement** is as crucial as the tool itself (hands-on learning, hackathons, workflow spotlights, bootcamps, peer-led examples); adoption proceeds at varying speeds across teams; *« The goal is not to force every workflow through an agent »* — the goal is to make it *« useful, safe, measurable, and repeatable where it creates meaningful leverage »*. **What we learned**: ***« AI doesn't eliminate bottlenecks in software development, but it does move them »*** (downstream: review, validation, testing, release, prod ops) → optimizing the old bottleneck no longer creates the same leverage. *« The advantage will not come from access to the same foundation models everyone else can use. It will come from the systems built around those models: context, internal tooling, quality controls, and the workflows that connect them together. »* Pressure also builds **upstream** (product & design): structured specs, design clarity, sharper problem framing. Closing: ***« The future of engineering productivity will not be defined solely by who has the best models. It will be defined by who builds the best systems around them »***; *« The real challenge is no longer just generating more code, but building engineering systems that can reliably turn AI-assisted output into valuable experiences for our customers »*. Direct convergence with **Salesforce/Tallapragada** (Effective Output: measuring value, not volume; no speed/quality tradeoff), **Gupta** (token-to-outcome attribution, cost of a completed outcome), **DORA** (beyond throughput), and the shift of the KPI toward **system outcome** (idea→customer value).
**Kazuaki Okumura** — Dropbox (rôle non précisé dans l'article ; le billet reprend une intervention présentée à la conférence **DX Annual 2026** sur la productivité développeur, ce qui suggère un profil engineering leadership / platform, sans confirmation). Publié sur le **Dropbox Tech blog** (dropbox.tech) · rubrique *culture* · le **28 mai 2026**.
Viral X thread (**230.5K views**, May 28, 2026, 1:51 AM) by **Jaya Gupta** (@JayaGup10, investor — likely Foundation Capital, author of the *Context Graphs* framework) titled ***"Token Budget Wars"***. **Pivot thesis**: ***"Enterprise AI has moved from adoption to allocation"*** — phase 1 of enterprise AI proved that models can work; phase 2 will decide **how much of that work is worth it**. The new currency at the top of the enterprise is the **ability to quantify AI ROI**: *"show me the value"*. Canonical concept: ***marginal token utility*** = *"the business value created by each additional dollar of inference"* — the number that matters at scale, and that **most companies cannot see**. Timeline: **Claude shipped November 2025**, after the 2026 annual budgets were locked → as early as **Q1**, companies *"running multiples ahead of plan"* → inference stops being an experimentation line item and becomes a **recurring operating cost**. Shift from **experimentation (a few $100K) → infrastructure (seven figures, $1M+)**: at infrastructure scale, **technical variance produces material P&L swings — two runs of the same workflow on the same input can differ by 5-10× in token cost** with nothing visibly broken, *"a number the CFO has to explain to the CEO"*. **AI competes with labor**: 3 types of budget requests (replace outsourced work / replace internal work / generate revenue) → shift toward the ***cost of a completed outcome*** (cost per resolved ticket, processed claim, reviewed contract, completed invoice, avoided hire, retained customer, dollar of revenue moved). **BPO = the easiest baseline to benchmark against** (already priced in completed units); internal work is much harder (multi-skilled employees, diffuse gains, HR resistance to headcount reduction). **Why it's different from SaaS**: SaaS learned to treat usage as a proxy for value; AI breaks that proxy — *"the signal and the noise share the same unit"* (the token), *"SaaS usage told you the software had been adopted. AI usage tells you the meter is running. It doesn't tell you whether your company is cooking."* **Three causes of marginal token utility's invisibility**: (1) ***retry tails*** — tokens per resolved workflow ≈ **T/p**; going from 90% to 70% completion increases effective cost by ~**28%**, not 20%, because failures compound; (2) ***context inflation*** — inference cost ≈ **O(n²)** in context length (attention), doubling the context **quadruples** reasoning cost (over-retrieval: 50 docs when 5 would do); (3) ***routing*** — by default the most powerful model is used (basic classification run on a complex reasoning model); across millions of calls, the difference between routing easy tasks to a small model and sending everything to the frontier model = *"the difference between a manageable bill and a board-level problem."* **Sector split**: **software** companies = a **productivity measurement** problem (already instrumented: PRs, commits, deploys, incidents, cycle time, MTTR — tracks *"AI layoffs"*); **non-software** companies = a **transformation** problem (operational work: claims, underwriting, support, compliance reviews, supply chain exceptions, payment disputes — *right under audit, not just right on average*). **The missing layer = token-to-outcome attribution**: a conversion layer linking inference spend → work performed → business outcome, answering 3 questions (real cost including retries/corrections; which parts of the trace mattered vs. thrashing; did the work change the operating model). ***Measurement becomes memory***: linking a token to an outcome requires capturing **decision traces** (what the agent saw, retrieved, called, ignored, where it retried, when a human overrode it) — *"decision rationale is one of the most perishable assets in a company"* (lives in Slack, emails, escalation calls, people's heads). Agents **create** these traces; captured first to justify the spend, they become *"more valuable than the cost report"* → a **context graph** (*"although I am so tired of that word these days"*). **The allocation layer is the prize**: whoever owns token-to-outcome attribution makes the **allocation calls** (which workflows deserve more compute, which are capped, which move to cheaper models, which stay human, which replace BPO). Companies won't do this on their own — they'll **buy it as a transformation** (Fortune 500 playbook: McKinsey + Palantir alumni + top-down CEO, in the manner of ERP/BI/digital transformation, a *"program"* with an executive sponsor and infrastructure that becomes the **new source of truth**). Framed by **Charlie Munger**: *"show me the incentive and I will show you the outcome."* Organizational sub-thesis: the decades-old executive instinct that *big teams = big jobs/scope/power* → once intelligence becomes the **scarce resource**, the new marker is *"how much of it you're orchestrating."* Direct relevance to the **Cost Optimization / agentic FinOps positioning**: empirically confirms the levers (model routing, prompt caching, context hygiene, sub-agents) and shifts the KPI toward **cost per completed outcome**. Strong convergence with Bain's *cross-system labor* (execution data moat, Cursor), Ng's *No AI jobpocalypse* (pricing anchored on the replaced employee's salary), DORA ROI (cost per feature), Mensch/Mistral (electron→token), Ensarguet (economics of computation), Foundation Capital's *Context Graphs* (decision traces, same author), Wescale's *Token Burning*, BFM/Girard (token = value fuel).
**Jaya Gupta** (@JayaGup10) — investisseuse / VC. Très probablement **Foundation Capital** (le thread s'auto-réfère au cadre ***Context Graphs*** — *« ahem, context graph, although I am so tired of that word these days »* — concept porté par Foundation Capital, cf. fiche `bain-100b-saas-opportunity` qui cite *Foundation Capital — Context Graphs trillion-dollar opportunity, 2025-12-22*). Thread publié sur X le **28 mai 2026 à 1h51** · **230 · 5K vues** · format essai long en un seul post. Une réponse notable de **@tuning_engines** (*« DevSecFinOps for the Agentic Era »*) : *« Tokens will basically have to be managed like headcount […] model hierarchies too »*.
Official **Salesforce News** blog post (*Agentic Enterprise* section, *"Pioneering the Agentic Shift Within Salesforce Engineering"* series), published on **May 27, 2026** (6-minute read) by **Srinivas "Srini" Tallapragada**, *President and Chief Engineering and Customer Success Officer* at Salesforce. Direct follow-up to an earlier post (*"How we got our engineers to use AI — without breaking everything"*) which recounted crossing **>90% adoption**. **Pivot thesis**: Salesforce Engineering moved from a world where AI was a useful *copilot* to one where **agentic tools drive the software development lifecycle (SDLC) itself** — writing code, reviewing PRs, generating tests, updating documentation, managing deployments, coordinating work once handled through human handoffs. **Canonical signal decision**: org-wide standardization on **Claude Code** + ***"we removed all token limits"*** — *"remove every last piece of friction between our engineers and the tools that make them faster and more effective"*. **Major empirical result** (April 2026 vs April 2025): work items completed per developer **+50.8%**, PRs merged per developer **+79%**, and above all **Effective Output score** (an ML measure of the **real value of delivered code**, not volume) **+151.3% year over year**. **Flagship use case**: migration of **33 API endpoints** to a cloud-native architecture, estimated at **~231 person-days** (7 per API) the traditional way, completed in **13 days — 18× faster** — via a **rule-based framework built in Claude** (markdown files + reference implementations), with PR feedback continuously fed back into the rule set, **autonomous LLM loops (build, fix, validate)** with no manual intervention, parallelized across isolated environments → **5 PRs**, the largest delivering **21 endpoints with 100% test coverage**. **No speed↔quality tradeoff**: through the **Engineering 360** platform (centralizing engineering data from hundreds of systems), **total incidents drop by 5%** despite the rise in PRs (*"quality doesn't suffer from speed. It benefits from it"*), thanks to **security guardrails and quality standards structurally embedded** in the agentic workflow (Trust as the #1 value). **SDLC overhaul**: once AI is adopted, engineers **tear down and rebuild** workflows (which processes to eliminate? which handoffs are now unnecessary? where does a human still do work an agent could own?). **New engineering craft**: **Claude Code skills** (packaged, reusable capabilities encoding team context, naming conventions, patterns) become a shared, composable **engineering artifact**; **AI Expert Suite** + **Salesforce Foundation Plugins** = an institutionalized, curated skills library (internal benchmark: **higher accuracy and reliability, reduced unnecessary cost**); **subagents & agent teams** parallelize workstreams (*"They describe the outcome, and a set of coordinated agents figures out the steps"*). **What remains hard**: (1) **context management** in long sessions — **CLAUDE.md file quality** varies widely and weighs heavily on output quality; (2) **agentic security** = a fundamentally different model (agents that *act*, not just *suggest* → increased blast radius); (3) **evolving roles** (how do juniors become seniors if AI absorbs entry-level work? role of the designer/PM? the execution unit = scrum team → experiments with 1- or 3-person units). Conclusion: *"It changed what was economically possible"*; the stated ambition is **"the most automated, agentic SDLC in the industry"**. Directly intersects with Gupta (*cost of a completed outcome*, marginal token utility), Greenwald/Sierra (outcome-based pricing), DORA (ROI / cost per feature) and the BFM/Girard debate (token as a value fuel, not a cost to cut).
#Agentic SDLC#agentic SDLC#Claude Code
**Srinivas « Srini » Tallapragada** — *President and Chief Engineering and Customer Success Officer* de **Salesforce**. Plus d'une décennie chez Salesforce · dirige l'ingénierie mondiale de la plateforme unifiée. Auteur de la série *Agentic Enterprise* sur le blog Salesforce News ; ce billet (27 mai 2026) est la **suite** d'un premier opus consacré à l'adoption de l'IA par les milliers d'ingénieurs Salesforce (*« How we got our engineers to use AI — without breaking everything »*). Position d'autorité = **dirigeant exécutif** parlant en son nom et au nom d'une organisation d'ingénierie à grande échelle (donnée terrain à l'échelle d'un hyperscaler SaaS) · avec accès aux métriques internes (Engineering 360, Effective Output).
FinOps for AI Agents: A Four-Step Allocation Framework for Coding Assistant Costs (Claude Code, Cursor, Copilot) and Why Traditional Cloud Tagging Fails - Finout
FinOps for AI agents centered on "cost per outcome": why traditional FinOps fails against runtime behavior, guardrails, behavioral observability, and a 4-phase lifecycle - Orq.ai