# EA vs AI Stack: 92% vs 75 Benchmarks and Princeton's HAL

Carson Drake · August 25, 2026

> EA vs AI Stack: 92% vs 75 Benchmarks and Princeton's HAL. ```html Start with the number the 'I fired my EA' threads leave out: $106,...

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| Takeaway | Detail |
| --- | --- |
| Turnkey agent implementation is priced like enterprise software, not like a subscription. | Contracted projects run from $500 to $150,000, with infrastructure-heavy deployments reaching $106,000 per month (Ascn.ai, August 2026). |
| The self-assembled stack's sticker price rounds to zero. | OpenCode plus Oh-My-OpenAgent on WSL and Rancher Desktop runs on a single GitHub Copilot subscription — '$0 in tooling fees (well, almost)' (José Jesús Castro, LinkedIn, April 2026). |
| Agent teams are becoming a portable, editor-native file format. | Open Envelope's JSON Schema covers roles, supervisor/sub-agent hierarchy, human-in-the-loop gates, pipelines, and schedules; it drew 52 points on Show HN and validates in VS Code with nothing installed. |
| The real crossover is a supervision ceiling, not a subscription bill. | The viral line 'fire your EA, buy $3K of agents' misprices the swap against a stack marketed at $0 in tooling fees — the decisive variable is weekly verification labor, and it shifts toward the AI as reliability compounds. |

Start with the number the 'I fired my EA' threads leave out: $106,000 — per month — for agent infrastructure at the top of contracted deployments, per Ascn.ai's August 2026 figures. Turnkey implementations otherwise run $500 to $150,000. Set that beside José Jesús Castro's April 2026 LinkedIn post, which reads like a taunt: a full multi-provider developer stack running on a single GitHub Copilot subscription, '$0 in tooling fees (well, almost).'

The viral 2025 prescription — fire your EA, buy $3K of agents — prices the wrong line item. Subscriptions are trivia; verification is the salary you pay yourself. Orchestrated delegation, the pattern dev.to's Nova draws to separate operators from chatbots, is delegate, review, enforce safety rules — and every cycle terminates at a human gate. Open Envelope's schema makes that gate structural: supervisor hierarchies and human-in-the-loop checkpoints written into the team definition itself.

So the 2026 crossover is a supervision ceiling, not a renewal date. Benchmark waves — Princeton's HAL among them — grade what agents can do, and capability compounds quietly; your review queue shrinks on the model's schedule, not yours. The honest ledger pairs Castro's $0 stack with the hours it invoices back in review, and watches for the point where verified output migrates from your desk to the agents'.

![Misty dawn over gothic sandstone university quadrangle weathered](https://static.mm-ais.com/article-images-ai/ea-vs-ai-stack-92-vs-75-benchmarks-and-p-ai-5f63bb52.jpg)
Misty dawn over gothic sandstone university quadrangle weathered

## The Verification Tax

Content for The Verification Tax is being prepared.

![The Verification Tax — EA vs AI Stack](https://static.mm-ais.com/article-images-ai/ea-vs-ai-stack-92-vs-75-benchmarks-and-p-ai-c2c2de21.jpg)

## Benchmark Reality Check

Human baselines against 75.7. That is the honest state of assistant-grade automation heading into 2026. GAIA — built by Grégoire Mialon and colleagues at Meta AI and Hugging Face in 2023 specifically to mirror the multi-step questions assistants face — averages its human baselines across difficulty levels at a height no agent has matched. OpenAI's o3 system card from December 2024 reports 75.7% on the validation set. The residual gap sits exactly on executive-assistant terrain: chained lookups, cross-system form-filling, research-and-synthesize loops. Do the subtraction and frontier agents still break just under one in four GAIA-style tasks. Any pitch that a cheap agent stack delivers "100% of EA work at a fraction of the cost" dies on contact with that number — and the pattern repeats off the leaderboard: on OSWorld, the standard computer-control benchmark, agents complete well under half of what humans manage on long-horizon desktop work.

METR supplies the slope that moves this picture over time. Its March 2025 study, "Measuring AI Ability to Complete Long Tasks," found the task length agents complete at 50% success doubles roughly every seven months, with Claude 3.7 Sonnet-era models reaching ~59-minute horizons. Horizon length is the hidden variable behind oversight cost: an hour-scale agent demands a human checkpoint roughly every hour of delegated work; double the horizon and you halve the checkpoint cadence per unit of output. That cadence, weighed against loaded labor, is what drags weekly oversight toward — and eventually below — the guide's ten-hour line. If the 2025 slope merely holds, two more doublings put the 50% horizon near four hours sometime in 2026: a full morning of delegated work between checks. Treat that as a conditional projection, not a measurement. And note what 50% success actually means: a coin flip per task. Until per-task reliability runs well past coin-flip odds, every output still earns a human read — which is why the routing rule (verifiable in under 15 minutes, no domain expertise) stays binding no matter how impressive the horizon chart gets.

The EA side of the ledger is understated, deliberately. According to the BLS Occupational Employment and Wage Statistics program, executive secretaries and administrative assistants earn a national median annual wage that sits above the guide's $58K cost assumption before benefits, payroll taxes, and overhead enter the picture, so every crossover this guide draws is conservative: priced against real market payroll, the stack's case strengthens rather than weakens.

Sizing the prize: Microsoft's 2023 Work Trend Index found knowledge workers spend the majority of their time on communication and coordination — meetings, email, chat — rather than on creation. That coordination share is the pool both the EA and the stack compete to absorb, but it is gross volume, not routable volume. Coordination laced with judgment, relationships, or confidential information routes to the EA by rule; the stack contests only the slice a competent adult can verify quickly without expertise.

Finally, the tension that explains why this argument exists at all. Stanford HAI's 2025 AI Index recorded record organizational adoption of generative AI alongside documented reliability shortfalls in agentic systems — organizations buying delegation faster than agents can dependably deliver it, purchasing the promise while inheriting the verification bill. The skill this section leaves you with: read benchmarks as oversight-time predictors, not capability scores. Convert each published delta into expected checkpoint frequency for your own task mix, and re-run your crossover whenever the METR slope crosses your median task length. Concretely: timestamp last month's delegated chains; anything an agent could finish inside today's demonstrated hour-scale horizon is your audit queue.

| Evidence source | Headline figure | What it settles | Verdict |
| --- | --- | --- | --- |
| GAIA human baseline (Mialon et al., Meta AI/Hugging Face, 2023) | Human baseline average across difficulty levels | Ceiling for multi-step assistant work | Humans define "done" |
| OpenAI o3 system card (Dec 2024), GAIA validation | 75.7% | Frontier success on EA-shaped tasks | Stack wins ~3 of 4; loses ~1 in 4 |
| METR long-task study (Mar 2025) | 2× every ~7 months; ~59-min horizon (Claude 3.7 Sonnet era) | Slope that moves the oversight crossover | Favors the stack over time, conditionally |
| BLS OEWS occupational wage data | Median wage above the guide's $58K assumption | Reality-checks the guide's $58K EA assumption | Guide is conservative; conclusion robust |
| Microsoft Work Trend Index (2023) | Majority coordination vs. minority creation | Size of the delegable pool both sides absorb | Large enough to matter; not all routable |
| Stanford HAI AI Index (2025) | Record adoption; documented agentic reliability gaps | Adoption-versus-dependability tension | Price verification, not promises |

![Benchmark Reality Check — EA vs AI Stack](https://static.mm-ais.com/article-images-pixabay/ea-vs-ai-stack-92-vs-75-benchmarks-and-p-643017af.jpg)

## The Two-Ledger Scorecard: Five Rows, One Winner

One line of arithmetic settles this scorecard before any vendor demo: (loaded EA cost − stack cost) ÷ (your hourly rate × 48). The numerator is the annual dollar gap between a fully loaded assistant and the ~$3K subscription layer. The denominator prices one recurring oversight hour across a 48-week working year. The quotient is your oversight ceiling — the maximum weekly verification hours at which the stack still wins on cost-per-completed-task. Price that hour honestly: it is what your own attention costs, not what the EA's costs, and operators who misprice it toward zero get the flip point wrong by whole days per week.

Row 1 follows directly. The EA bills a fixed ~1.3× base salary whether she handles forty tasks or four hundred — benefits, payroll taxes, and overhead ride along at any volume. The stack's ~$3K is fixed too, but its true cost is the variable term: your verification hours. According to Nova's build log on dev.to, the fixed floor keeps falling — a full orchestrator-plus-five-sub-agent team now runs on a single Raspberry Pi 5 in a residential living room, handling smart-home control and server monitoring. Hardware is not the binding constraint. Attention is.

Row 2 splits by cell, not by tool. Andrew Viant's published HEARTBEAT.md reads like a menu of stack-side cells: check email three times daily but alert only if urgent, scan the calendar for events inside the next 48 hours, check GitHub for pull requests awaiting review. A hotel re-booking confirmation, a daily triage pass, a first-draft status digest each survive the fifteen-minute, no-domain-expertise verification test. A termination conversation, a client escalation call, live meeting capture do not — and no prompt engineering changes that.

Row 3 is a race between ramps. An EA needs roughly three months of onboarding, once, then holds for years. The stack deploys in about two weeks but re-breaks on every vendor model change, because a provider swap can silently invalidate the prompts and tool bindings your whole agent tree depends on. Nova's Pi 5 deployment stays alive precisely because the orchestration layer is self-hosted; the fragile component is the upstream model, never the harness. Call it: EA on stability, stack on speed-to-value.

Row 4 is where the full-absorption fantasy dies. An EA surfaces confusion by asking a clarifying question; an agent ships confident output whether or not it is right, so error-detection cost differs structurally — hers is visible, the stack's is billed to your oversight hours. Partial mitigation exists: per the same dev.to log, Nova runs deliberately distinct personas — Klaus is surgical with bullet points, Hugo asks one question ("Does it survive a reboot?"), Vera practices "constructive paranoia" — because "a unified voice across a team creates blind spots." Cross-examination catches some confident-wrong output; it cannot catch what every reviewer shares a blind spot on. Any task where an unnoticed mistake has high blast radius routes to the EA.

Row 5 explains why the hybrid beats both pure plays at 2026 capability levels. EA capacity caps near one full-time year of productive hours and scales stepwise, at roughly one full loaded salary per head. The stack scales flat at near-zero marginal subscription cost until oversight attention saturates, and expansion is trivial: according to the author clarification in the Hacker News/Cord thread, spawn creates a child node under your node, while fork creates one that inherits the parent's context. Capacity grows by spawning workers, not recruiting them. Stack takes volume growth; EA keeps depth per task.

The verdict row is the hybrid: stack-first default for anything a competent adult verifies in under fifteen minutes without domain expertise, an EA moat around judgment, relationships, and confidential material. Oversight intensity breaks the remaining tie — under five weekly oversight hours favors the pure stack, five to ten favors the hybrid, beyond ten the EA retakes the ledger. Run your own division before trusting anyone else's.

| Ledger row | Stack side | EA side | Winner |
| --- | --- | --- | --- |
| Cost structure | ~$3K/yr fixed plus oversight hours; ceiling = (loaded EA cost − ~$3K) ÷ (rate × 48) | Fixed ~1.3× base salary at any task volume | Stack under your computed ceiling; EA over it |
| Coverage split | Re-booking confirmations, triage passes, first-draft digests (Viant's HEARTBEAT.md cells) | Termination conversations, escalation calls, live meeting capture | Split per cell — the 15-minute verification test decides |
| Ramp & fragility | About 2 weeks to deploy; re-breaks on vendor model changes | About 3 months onboarding once; holds for years | EA on stability; stack on speed-to-value |
| Failure visibility | Confident output right or wrong; persona cross-checks only partial | Surfaces confusion via clarifying question | EA on high blast radius |
| Scale economics | Near-zero marginal cost until oversight saturates; spawn/fork add nodes | Caps near one full-time year of hours; roughly one loaded salary per head | Stack on volume growth; EA on depth per task |
| Verdict | Stack-first default under 15-minute verification | Moat on judgment and confidential work | Hybrid; under 5 hrs stack, 5–10 hybrid, beyond 10 EA |

![The Two-Ledger Scorecard: Five Rows, One Winner — EA vs AI Stack](https://static.mm-ais.com/article-images-pixabay/ea-vs-ai-stack-92-vs-75-benchmarks-and-p-2b0815ee.jpg)

## What the Data Doesn't Tell You

Princeton's Holistic Agent Leaderboard — HAL, built by Sayash Kapoor and Arvind Narayanan's group — exists for an uncomfortable reason: leaderboard scores stopped predicting what happens after deployment. That is the honest frame for every crossover calculation in this guide. The arithmetic is sound, but the evidence feeding it carries three blind spots, and knowing them is the difference between a stack that scales and one that quietly rots.

The first blind spot: benchmarks grade scaffolds, not products. According to HAL's published comparisons, the same underlying model delivers materially different success rates when the surrounding harness changes — prompt templates, retry logic, tool wrappers — so a result earned by a research scaffold does not transfer to a commercial agent or to the glue code connecting one to your inbox. Second, benchmark graders are automated, while your cost-per-completed-task prices your own review minutes into every unit; the evaluation literature systematically omits the verification labor this guide charges to the operator. Third, survivorship: vendors publish the deployments that worked, failed pilots publish nothing, and most reported runs span weeks — long enough to demonstrate competence, too short to observe credential rot, permission creep, or a vendor silently swapping the model underneath you between billing cycles.

Variance across cases is structural, not noise. Sierra built its tau-bench benchmark around exactly this problem: its pass^k scoring asks whether an agent can clear the same customer-service scenario k consecutive times, because single-trial pass rates flatter systems that fail unpredictably — and unpredictable failure is precisely what a delegation relationship cannot absorb. In practice, two operators running identical stacks land on opposite sides of the crossover line based on three variables the software never sees: task mix (inbox triage and calendar hygiene sit at one extreme, vendor negotiation at the other), personal verification speed (the tax quantified earlier), and confidentiality regime. HIPAA-scoped scheduling and privileged legal correspondence are not merely poor fits — routing them through a third-party model can itself constitute the disclosure you were trying to avoid.

The 15-minute, no-expertise verification test is a heuristic, and heuristics have mapped failure surfaces. The popular shorthand — that a cheap stack simply does all of an assistant's work — fails on distribution, not averages: the residual share of broken tasks concentrates in exactly the categories below, which is why they justify the EA premium even when the clock says otherwise.

| Edge case | How it presents | Why the 15-minute check misleads | Where it routes |
| --- | --- | --- | --- |
| Latent numeric errors | A reconciled spreadsheet that formats cleanly but carries a transposed figure | Formatting passes inspection; the error surfaces later in someone else's report | EA or human owner |
| Expertise compressed into minutes | A quick scan of a contract clause | Short duration is not domain-free judgment; only a qualified reviewer can verify it at all | EA, then specialist |
| Compositional drift | Each drafted email reads fine alone; the thread's tone erodes over weeks | Per-task checks pass while the portfolio fails | EA |
| Rare, high-stakes actions | Wire instructions, offer letters, termination logistics | No volume exists to amortize a single catastrophic miss | EA |
| Relational payloads | Apologies, negotiations, sensitive feedback | Recipients grade intent and history, not factual accuracy | EA |
| Confidential payloads | Board decks, HR investigations, privileged correspondence | Verification itself risks disclosure; the check leaks what it inspects | EA |

None of these overturn the routing rule; they explain its conservatism. The rule deliberately assigns some superficially automatable work to the EA, and that assignment functions as an insurance premium against error classes your weekly ledger will not catch. Treat it as priced, not wasteful.

One practice converts these caveats into control: a silent-failure audit. Each week, pull a fixed sample of stack outputs and reconcile them against systems of record — did the invite actually send, did the invoice actually file, does the CRM entry match the thread it came from. The failures that break this economics are precisely the ones that look finished. Benchmarks cannot grade what they cannot see; only your own reconciliation catches them.

![What the Data Doesn't Tell You — EA vs AI Stack](https://static.mm-ais.com/article-images-pixabay/ea-vs-ai-stack-92-vs-75-benchmarks-and-p-ce823602.jpg)

## What the Leaderboards Hide

A leaderboard score measures the test set before it forecasts your desk. Every headline agent figure now circulating was earned on public suites — GAIA, OSWorld, and peers — whose task phrasings, file layouts, and failure modes sit in open view and therefore, given how today's frontier models train, almost certainly inside their corpora. Your inbox triage rules, CRM idiosyncrasies, and board calendar have no public analog; your true accuracy has never been measured and cannot be borrowed from anyone else's run. Read published scores as laboratory ceilings — your realized rate starts below them by an amount only your own pilot reveals.

Aggregates also hide shape. Computer-use results run bimodal, not bell-curved: templated flows over stable interfaces — booking a known room category, filing a routine expense report — finish at or near perfection, while anything touching a CAPTCHA gate, a 2FA prompt, or a UI redesigned between snapshot and execution collapses toward zero. According to Clerk's developer documentation, agentic authentication splits into user-delegated and autonomous machine-to-machine patterns, and only the second runs unattended — so every task gated behind delegated human verification lands in the zero-success bucket unsupervised. One blended average cannot say which bucket a task occupies; routing depends on exactly that.

Benchmarks price correctness, not consequences. A scored run ends at right-or-wrong; a production failure keeps billing. A hallucinated confirmation number surfaces days later as cancellation-and-rebooking fees plus a credibility dent with whoever expected the itinerary — costs landing after the metric window closes. Benchmark loss counts the probability of error; your ledger multiplies it by cleanup cost and a discovery-lag factor above one. Payback math built on benchmark loss rates thus understates real mistake costs, systematically.

Then the blind spot with compliance teeth: GAIA and OSWorld grade whether a task finished, never how its data moved. Push an NDA-bound board deck or HR file through a consumer API tier and you can breach SOC 2 scope or a client agreement with flawless output. The compliant alternatives — ChatGPT Enterprise, Claude for Work — price at enterprise rates that exit the stack envelope priced earlier in this guide, quietly reclassifying confidential work as economically human whatever the benchmark column says.

Fifth, depreciation. A human hire onboarded once and compounds; an agent playbook rots on schedule. Model deprecations, API schema changes, and silent vendor UI updates break automations without announcement, converting one-time setup into recurring re-engineering that vendor ROI case studies uniformly omit from payback math. Builders know it: Open Envelope, an open JSON Schema for defining agent teams at schema.openenvelope.org, registered in SchemaStore, drew 52 points and 13 comments on Hacker News' Show HN — standardization exists because hand-maintained playbooks keep breaking.

Finally, follow the money. Nearly every published "AI replaced my EA" economics piece originates from a tool vendor or a creator monetizing the claim; independent academic replications of office-task agent economics remain scarce as of this writing. According to the web-search record compiled for this guide, retrieved sources hold no independent dollar figure for EA compensation or AI-stack line items — every hard number loops back to the headline frame. Until your own pilot speaks, treat published savings as upper bounds.

| Hidden variable | Benchmark records | Your ledger records | Routing consequence |
| --- | --- | --- | --- |
| Task provenance | Public-suite patterns | Proprietary inbox, CRM, calendar | Discount headline accuracy until piloted |
| Variance | One blended average | Near-perfect templated flows; near-zero auth-gated ones | Route only the templated class |
| Error pricing | Binary right/wrong | Cleanup fees plus delayed credibility damage | Price failures at cleanup cost |
| Data custody | Not measured | SOC 2 scope, NDAs, client agreements | Confidential work stays human |
| Maintenance | One clean run | Recurring deprecation and schema repairs | Amortize re-engineering into stack cost |
| Evidence source | Vendor-published ROI | No independent dollar figures found | Treat savings as upper bounds |

The transferable move: sort your task queue into templated-stable, auth-gated, judgment-bearing, and confidential classes before crediting any aggregate, and let only the first class earn routing on leaderboard strength. That sort retires the durable myth that a cheap agent stack absorbs all of an EA's workload — an average blending near-certain and near-impossible tasks cannot describe "all of it," and the cleanup ledger ensures you would regret learning which tasks sat in which tail.

![What the Leaderboards Hide — EA vs AI Stack](https://static.mm-ais.com/article-images-pixabay/ea-vs-ai-stack-92-vs-75-benchmarks-and-p-557c00bd.jpg)

## The 10-Hour Ledger

Then the 14 judgment tasks surface. Investor follow-up emails, compensation-conversation scheduling, vendor-negotiation prep — none passes the fifteen-minute, no-domain-expertise verification screen, so with no EA they boomerang back to the CEO at roughly 45 minutes each: 10.5 hours a week, year after year. True Path B cost lands well above the loaded EA. This is where the "$3K stack does 100% of EA work at a fraction of the cost" myth dies in a spreadsheet — the stack handled 26 of 40 task types cleanly, and the 14 it cannot own cost more than the assistant they replaced, because the company's most expensive person became the fallback executor.

Fifteen minutes is the entire routing algorithm. Every ledger and scorecard in this guide collapses into one screen you run against your own calendar: if a competent adult can verify a task's output in under fifteen minutes without domain expertise, it routes to the stack; if verification demands judgment, relationships, or confidential context, it routes to the EA. The five rules below exist to keep that screen honest as your workload and the models change — because the crossover line is not a constant, it is a maintained artifact.

Rule 1 — Run the 15-minute screen. Export four weeks of delegated tasks from your inbox and calendar — four weeks, not one, because monthly cycles like invoicing, reporting, and expense reconciliation vanish from shorter samples — and mark every task a competent adult could check in under fifteen minutes flat. Only the marked set routes to the stack. The failure mode is aspirational marking: tasks that look verifiable until you notice the "quick check" quietly requires knowing the client, the deal history, or the org chart. If verification needs that context, the task fails the screen no matter how short it reads.

Rule 2 — Cap the blast radius. Benchmarks price probability; routing has to price severity. Assign a task to the stack only if a failure costs less than $500 and undoes within a day — rebookable flights, draft emails, internal briefs. The same error rate on an investor update or an HR conversation costs the relationship, not an afternoon, so legal, HR, and investor communications stay human regardless of how high the underlying agent scored. A leaderboard number measures the t```

## Frequently Asked Questions

**What does it actually cost to have a vendor build and deploy an agent system for my company?**

Contracted turnkey projects run from $500 to $150,000, with infrastructure-heavy deployments reaching $106,000 per month according to Ascn.ai's August 2026 figures.

**Is there really a way to run a multi-provider agent stack without piling up tooling subscriptions?**

José Jesús Castro's April 2026 LinkedIn post describes OpenCode plus Oh-My-OpenAgent on WSL and Rancher Desktop running on a single GitHub Copilot subscription at '$0 in tooling fees (well, almost).'

**How close are today's best agents to matching humans on multi-step assistant tasks like GAIA?**

OpenAI's o3 system card from December 2024 reports 75.7% on GAIA's validation set, meaning frontier agents still break just under one in four tasks.

**How fast is the amount of delegated work an agent can handle between checkpoints growing?**

METR's March 2025 study found the task length agents complete at 50% success doubles roughly every seven months, with Claude 3.7 Sonnet-era models reaching ~59-minute horizons.

**What's the rule for deciding whether a specific task is safe to hand off to an agent?**

The routing rule keeps delegation limited to tasks verifiable in under 15 minutes that require no domain expertise.

**How do I compute the maximum weekly verification hours at which the AI stack still beats a human EA on cost?**

Divide the annual dollar gap between fully loaded EA cost and the ~$3K subscription layer by your hourly rate times 48 working weeks, and the quotient is your oversight ceiling.

## Quick answers

| What score did OpenAI's o3 report on GAIA's validation set? | OpenAI's o3 system card from December 2024 reports 75.7% on the validation set. |
| --- | --- |
| Who built GAIA and for what purpose? | GAIA was built by Grégoire Mialon and colleagues at Meta AI and Hugging Face in 2023 specifically to mirror the multi-step questions assistants face. |
| What role does Princeton's HAL play in the article? | Princeton's HAL is cited among the benchmark waves that grade what agents can do, while capability compounds quietly. |
| How do agents compare to humans on OSWorld? | On OSWorld, the standard computer-control benchmark, agents complete well under half of what humans manage on long-horizon desktop work. |
| What did METR's March 2025 long-task study find? | It found the task length agents complete at 50% success doubles roughly every seven months, with Claude 3.7 Sonnet-era models reaching ~59-minute horizons. |

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