One live score for how skillfully every employee — and every agent — uses AI. The waste named in dollars in week one, the gap trained closed by day 90, and AI employees built from your own proven patterns after that.
Headcount is no longer the unit of output — behaviour is. Two companies with identical spend, tools, and talent will diverge, because one can measure how AI is actually used and lift everyone to its own best pattern, while the other caps the bill and hopes. The proof is already public — tiny teams with giant output — and the shift lands on every desk: CEO, CTO, CFO, and board.
This isn't a prediction anymore — it's the operating reality of the best companies of this cycle. The only question left is which side of the divergence your company is on.
Headcount is no longer the unit of output. Behaviour is. For a century, capacity meant hiring. Now a 40-person company can out-ship a 400-person one — if, and only if, its people and agents use AI at the top-decile level. The leverage is real, but it isn't evenly distributed inside any company: it lives in a handful of people whose patterns nobody can see.
That creates a brutally simple physics: two companies with identical spend, identical tools, and identical talent will diverge — because one can measure how AI is actually used, lift everyone to its own best pattern, and compound; the other caps the bill and hopes. The gap isn't visible in quarter one. By quarter eight it's a different company.
Roughly forty people, roughly two hundred million dollars in reported annual revenue — around $5M per employee, built almost entirely on AI leverage. The old ratio of headcount to revenue didn't bend; it broke.
Widely reported · 2023–24Anysphere reportedly reached $100M ARR in about a year with a few dozen employees — a pace that used to require hundreds of salespeople and a decade. AI-native operating leverage is the whole story.
Widely reported · 2024–25Sam Altman has said openly that a one-person billion-dollar company is coming. Whether it's one person or forty, the direction is the same: output per person is about to be the most important ratio in business.
Altman · public remarks, 2024The lean-AI shift isn't an IT project. It changes what each seat at the table is accountable for — and VibeSea is the instrument each of them is currently missing.
Your competitors' boards are already asking why a 40-person company ships more than your 400. The honest answer is behaviour, not budget — and today you can't see it. VibeSea gives you the number, per team, calibrated to real outcomes.
You fought for the tools, the gateway, the budget. What you can't show is skill: who right-sizes models, who verifies, whose agents earn their keep. VibeSea turns your existing telemetry into a defensible engineering metric — no installs, no content read.
AI is your fastest-growing spend line and the only one with no yield metric. Week one, VibeSea hands you a waste report reconciled to provider billing — then a quarterly before/after your auditors can follow to your own Git, Jira, and invoices.
Boards that missed cloud or mobile lost years. This shift is faster, and it comes with obligations — the EU AI Act's literacy duty is already in force. VibeSea gives the board an audited index, a training record, and an agent ledger: oversight, not anecdotes.
Two companies buy the same AI tomorrow. Only one measures how it's used, trains to its own best pattern, and promotes what works into agents. In eight quarters they are not the same company. VibeSea exists so yours is the one that pulled ahead.
Be the one that measures »AI spend is exploding and easy to measure; skill is invisible. So operators cap the bill and hope. A cap limits the invoice — it does nothing for the skill behind it. The market's biggest names are already saying so on the record.
AI spend is exploding and it's easy to measure. How skillfully people actually use AI is invisible — so operators cap the bill and hope. A cap limits the invoice. It does nothing for the skill behind it.
Uber exhausted its FY2026 AI budget in ~4 months and capped every employee at $1,500/mo per tool. Walmart, Amazon, Cisco, Meta, and Microsoft are tightening AI spend against the same blind spot.
TechCrunch · Jun 2026MIT found roughly 95% of pilots show no measurable P&L effect — usually a usage-skill problem, not a model problem. The teams winning aren't spending most; they can see how AI is used and lift everyone to the best pattern.
MIT via Fortune · 2025Gartner projects $206.5B of agentic AI software in 2026 — from $86B in 2025, to $376.3B in 2027 — with 40% of enterprise apps embedding task-specific agents. The fastest-growing budget line, and the least observed.
Gartner · May 2026"It's highly ambiguous whether increased AI token spend actually translates into tangible outcomes."
Andrew Macdonald · President & COO, Uber · TechCrunch, Jun 2026
"Something has gone completely wrong."
Alex Karp · CEO, Palantir · CNBC, Jul 1 2026
Two engineers, the same refactor, invisible on any invoice — one ships in ~8 minutes for ~$0.40; the other takes ~45 minutes and ~$6.20 including rework. We think the spread between your best and average AI user is large, possibly 10×. We won't state that as fact until the founding beta measures it.
Here's the pattern we're measuring in the founding beta — two engineers, the same refactor, invisible on any invoice. We think the spread between your best and average AI user is large, possibly 10×. We won't state that as a fact until the beta measures it. Here's what the gap looks like mechanically:
Connect read-only in under a day — nothing installs on employee devices. Waste report in dollars by week one, Team Behaviour Index by day 30, targeted training by day 60, and a before/after ROI review at day 90 reconciled to your own Git, Jira, and billing records. Employees are notified before anything about them is scored.
Nothing installs on employee devices, no workflows change, and employees are notified before anything about them is ever scored. One IT admin, read-only credentials, under a day of effort.
Read-only admin APIs + your gateway. SSO identity map. No agents, no extensions, no code changes.
Coverage summaryMisrouted models, abandoned runs, duplicate spend — in dollars, every line linked to a billing line item.
CFO-forwardable PDFTeam Behaviour Index published — aggregates first — with your top-decile patterns extracted per team.
Team Behaviour IndexFlagged axes route to matching simulations the same day. Certification tracking on; the index starts moving.
Certification recordsBefore/after on spend, index, and outcomes — reconciled to your own Git, Jira, and billing records.
Day-90 ROI reviewWe don't sell a dashboard — we sell four specific returns, each tied to a document you can forward, audit, and hold us to: ① money back (the week-one waste report), ② compounding capability (training built from your own top decile), ③ evidence-based AI decisions (a quarterly capital-allocation review), and ④ agent control (one ledger for humans and agents). This is the whole pitch; the rest of the page is mechanism.
We don't sell a dashboard. We sell four specific returns, each tied to a document you can forward, audit, and hold us to. This is the whole pitch — the rest of the page is just the mechanism.
Every company we've spoken to is over-spending on AI in the same three ways: premium models on trivial tasks, abandoned generations, and duplicate seats. Naming this in dollars is the first thing VibePerform does — before anyone is scored.
The index finds your own top decile's patterns, and VibeTrain teaches exactly those patterns to everyone else — targeted to each person's weakest measured axis, re-certified as models change. Your best people become the curriculum.
Which tools deserve renewal. Which teams are ready for agents. Where the next dollar of AI budget compounds and where it evaporates. Today these are opinions in a meeting; with a behaviour index they're queries.
Agentic software is the fastest-growing enterprise category, and most companies will meet it with zero instrumentation. VibeSea puts every agent on the org's roster with a named owner, a cost per completed task, and the same five-axis score as a human.
Each product feeds the next on the same data spine. Tap a product below to bring it on stage — every screen is the actual specced product surface, sample data, real build.
Plugs into the AI tools you already run and scores how skillfully every employee and agent uses AI — one honest, rankable index, calibrated against real outcomes, with the waste named in dollars by day 7.
| Finding | What we saw | Recovery / mo |
|---|---|---|
| Misrouted models | Premium models on lookup & formatting tasks a small model completes at equal quality | $5.5K |
| Abandoned runs | Long generations discarded unused — iteration loops thrashing, not converging | $3.7K |
| Duplicate spend | Overlapping seats + parallel API & copilot access on identical workflows | $2.8K |
| Total recoverable | ≈ 3.4× the pilot fee | $14.2K |
Turns every measured gap into simulation training built from your own top decile's real patterns — with certification records shaped for the EU AI Act's Article 4 AI-literacy duty, in force since Feb 2025. Not checkbox videos.
| My queue | Routed from | Status |
|---|---|---|
| Model right-sizing lab | Model choice · 38 | IN PROGRESS |
| Prompt structure basics | Prompt craft · 44 | UP NEXT |
AI employees grounded in your outcome-labeled behaviour corpus — months of "this behaviour produced accepted work here" — held to the same five-axis rubric as your people, each with a named owner, a cost trail, and per-agent ROI.
| Agent | Owner | $/task | Index | Status |
|---|---|---|---|---|
| pr-triage-01 | Dana W. | $0.84 | 91 | EARNING |
| test-gen-02 | Priya S. | $1.12 | 88 | EARNING |
| intake-qc-01 | Marco L. | $6.90 | 41 | RUNAWAY |
Every big claim on this page has a mechanism behind it — and a published kill criterion if the mechanism fails. These are the answers we give in every diligence call, written down. Tap any claim to open its receipt.
Six diligence receipts, each expandable: how we find the waste, whether the index is real, where "10×" comes from, why we can't read your content, why the score can't be gamed, and what we do about coverage gaps. Three carry published kill criteria; two are structural claims procurement can verify.
Three waste classes are computable from usage metadata alone, no content required: misrouted models (premium-tier models on lookup and formatting tasks a small model completes at equal outcome quality), abandoned runs (long generations discarded without use — iteration loops that thrash instead of converge), and duplicate spend (overlapping seats and parallel API + copilot access on identical workflows).
Each finding reconciles to a provider-billing line item — OpenAI, Anthropic, and Copilot admin exports — so your procurement team can verify every dollar without us on the call. The 10–30% range is a design target validated across the first ten installs, not a guarantee.
The index scores five coachable axes — model right-sizing, prompt quality, verification, cost awareness, and tool & context use — from signals your AI tools already emit: which model handled a task, iteration chains, verification actions, cost per completed task. It never reads the content of anyone's work.
Then it has to prove itself: every axis is calibrated per account against outcome joins — merged PRs, closed tickets, approved documents from your own Git, Jira, and CI. A validation report with correlations and confidence bands is published from the beta cohort.
Today, 10× is an illustrative figure, built from the mechanism above: on the same task, right-sized model + tight iteration + verification versus premium-model brute force + thrashing + rework produces order-of-magnitude spreads in cost and time (the ~$0.40 vs ~$6.20 example). It is invisible on any invoice — which is exactly the problem.
The founding beta's job is to measure the real distribution across 20 accounts and publish it. Until then, it stays labeled directional everywhere it appears — including here.
No content path exists in the build — the event schema has no content field, and a content-free validator must pass before an account can go live. The OAuth scopes we request are usage/billing exports only; the permission to read content is never asked for. Capture happens at the provider and gateway boundary — no agent, extension, or monitor on any employee device.
The pipeline is event-sourced and idempotent: replaying raw events reproduces the identical index, bit-for-bit — the audit answer in one sentence. This is a claim procurement can verify in review, not a promise you must trust. We accept the lower signal ceiling this costs us, and the coverage meter keeps that limit visible instead of hidden.
The index is anchored to outcomes, not activity — you can't game merged PRs and closed tickets by prompting differently. Axis weights are hidden and rotated; scores are cohort-relative, so mass gaming shifts the baseline, not the ranking. Gaming attempts are themselves a detectable behaviour signature.
And we deleted the feature that would have made gaming easy: the score export API is batch-only — a streaming score feed is a gaming vector and a surveillance feature, so it doesn't exist.
Every connected source is classified into a tier that's permanently visible on your dashboard: Tier-1 full signal (API/gateway), Tier-2 partial (copilot admin APIs), Tier-3 spend-only (native apps). Nobody is scored on signal we don't have, and unmatched identities stay in the spend math as unattributed cost — never guessed onto a person's score.
We will never raise coverage with a browser extension or laptop agent — that's a design commitment, not a phase-one limitation.
Move the sliders. This is the same arithmetic the tier ladder prices against — monthly AI spend × the misroute / duplicate / abandoned share. Every output is directional; the founding beta's first ten installs decide the real range.
Workforce measurement dies on privacy objections unless governance is architecture — ours is. No content path exists in the system, disclosure reaches employees before the index does, individual scorecards need Pro tier plus a signed purpose-limitation contract, and every score can be replayed bit-for-bit from raw events. Written for the CISO and the works-council representative, because they hold the veto.
Workforce measurement dies on privacy objections unless governance is architecture. Ours is — written for the CISO and the works-council representative, because they hold the veto.
The index is computed from behaviour metadata — model choice, iteration shape, verification actions, cost per completed task. No content path exists in the codebase; in the Regulated Edition this is binary-inspectable.
Aggregates first; individual scorecards only at Pro, with role-scoped visibility, access audit logs, and a purpose-limitation contract. Scores are non-exportable to HRIS without a signed policy artifact. Stack ranking isn't a setting — attempting it is a contract breach.
Axis weights are versioned and auditable; the pipeline replays bit-for-bit from raw events; export is batch-only and logged. Employees are notified before scoring begins, and re-notified before any policy expands.
The ladder maps one-to-one onto the product sequence: see the waste » trust the score » close the gap » govern the agents. All prices monthly and directional — being validated with the 20 founding partners.
Buyers and investors get the same rule: a number is either sourced (with a citation) or directional (labeled, and on the beta's proof list). Nothing on this page hides which it is — six third-party numbers hold the argument up, and six of our own numbers sit on the founding cohort's homework list.
Buyers and investors get the same rule: a number is either sourced (with a citation) or directional (labeled, and on the beta's proof list). Nothing on this page hides which it is.
Eight questions, eight straight answers — tap any question to open it.
It shows you how well every person and every agent uses AI — as one score. You already know what AI costs. VibeSea shows you what it's worth: who's skilled, who's wasting, which agents earn their keep — then trains the gap closed and deploys agents built from your own best patterns.
Week 1: a waste report that targets covering the pilot fee by itself — wrong models, abandoned runs, duplicate spend, every line linked to a billing line item. Day 90: a before/after skill index your CFO can audit, reconciled to real outcomes in your own Git, Jira, and ticketing — not vibes.
From metadata your AI tools already emit: model choice, prompt shape, iteration loops, verification actions, cost per completed task — scored on five coachable axes against your own top performers, then calibrated against real outcomes (merged PRs, closed tickets, approved documents). There is no content path in the system — a structural property you can verify, not a policy you must trust. Full receipt above.
One IT admin, under a day: read-only admin API credentials (OpenAI, Anthropic, Copilot — revocable in your own provider console), an SSO identity map, and a visibility policy you set before a single score exists. Nothing installs on any employee's machine — no agent, no browser extension, no proxy. Employees are notified before any scoring begins.
No — and not because we say so. Aggregates by default; individual scorecards require Pro tier plus a signed purpose-limitation policy, with re-notice to employees and every access logged. Stack ranking is prohibited in the contract, not hidden behind a toggle. Employees see their own score first, with the reason behind every number, and get training aimed at their exact gap. Bottom-decile flags route to coaching, not discipline — that default is in the product.
Then our wedge thesis is wrong, and we've published exactly what happens: if fewer than 8 of the first 10 installs find waste ≥3× the pilot fee — or 10 installs find waste under 2× — we stop, tell the design partners, and reprice. Beta partners are the ones who benefit from that honesty being contractual.
VibePerform and VibeTrain are in build with founding design partners now; sales open Q4 2026. VibeEmployee is in private beta and gated behind ≥6 months of measured data per account — we won't sell you a cold-start agent. Joining the beta today means a 90-day discounted pilot on your own usage data, weekly founder office hours, and roadmap influence.
Yes — the Regulated Edition exists for exactly you. Individual scoring isn't switched off, it's compiled out of the build (binary-inspectable), with region lock (US/EU/IN), DPIA and works-council artifacts shipped as product, and hard human sign-off gates on anything agent-shaped. The honest trade: aggregate-only means less granular coaching — and the coverage meter keeps every limit visible.
A 90-day discounted pilot, run personally by the founding team, on your own usage data. Twenty accounts, four cities, one bar: the week-one waste report has to earn its place — or we've published what happens next.
Target mix: mid-market & enterprise tech · Bay Area · Seattle · Salt Lake City · New York. Regulated and services organizations welcome — the Regulated and Services editions are being shaped with this cohort.