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Sources

Every figure,with its source and date.

Every statistic on this site comes from this page: 61 figures, each with its source, the date we last opened it and the caveat that travels with it. Choose a block to go to its row.

The evidence bank61 figures
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  • The need
  • Reliability
  • Failures and stakes
  • Markets and labs
  • Review and privacy
  • Regulation and market size
  • Our own arithmetic

01 · How we keep them

Five rulesfor every figure.

  1. 01

    Source and date, every time

    Every figure is quoted with its source’s name and the date, and linked wherever the page allows.

  2. 02

    Re-opened before it joins

    A statistic joins this page once someone has opened its primary source and recorded the date.

  3. 03

    Leaderboards, every quarter

    Benchmark scores move faster than any other figure here. We re-open them before each quarterly report and campaign.

  4. 04

    Pairs stay together

    Where a figure needs its other half (two years, two models, two outcomes), we always quote both.

  5. 05

    Caveats travel with it

    Reported, conditional and forecast figures say so wherever they appear.

0201 · The evidence bank

Every figure we quote.

Grouped as the argument runs: the need, how reliable agents are, what failure costs, the markets, review and privacy, the rules, and our own arithmetic.

Each figure carries its source, the date we last opened it and its caveat, and leaderboard scores are re-opened every quarter.

61 figures

The need8

  1. 1

    A quarter of AI initiatives delivered the expected return on investment, and fewer scaled across the enterprise.

    A survey of 2,000 CEOs in 33 countries, self-reported; fieldwork February to April 2025.

    25% met ROI; 16% scaled

    IBM Newsroom6 May 2025

  2. 2

    Six in ten companies get hardly any material value from AI.

    The page gives no sample size. 5% of companies are “future-built”.

    60%

    BCG30 Sep 2025

  3. 3

    One in five firms has mature governance for autonomous agents.

    3,235 leaders in 24 countries, surveyed August to September 2025.

    1 in 5

    Deloitte AI Institute2026 edition

  4. 4

    Three in four enterprise AI use cases are now purchased from vendors rather than made in-house, and spending tripled.

    495 US enterprises. The 2024 base excludes inference, so the growth rate is overstated.

    76% purchased (53% in 2024); $37B spent in 2025

    Menlo Ventures9 Dec 2025

  5. 5

    62% of organisations are building, testing or deploying agents, and a quarter run several together.

    The page does not state the sample size. Multi-agent use was 6% two quarters earlier.

    62% agents; 25% multi-agent

    KPMG AI Quarterly PulseSep 2026

  6. 6

    US firms with more than $1bn in revenue plan large AI budgets for the next 12 months.

    237 US leaders, over a third at firms with $10bn+ revenue, which lifts the average. It describes $1bn+ companies alone.

    $207m average

    KPMG31 Mar 2026

  7. 7

    Nearly half of UK financial firms report a partial understanding of the AI they use, and a third of their use cases are third-party.

    118 firms responded. The third-party share was 17% in 2022.

    46%; one third

    Bank of England / FCA21 Nov 2024

  8. 8

    Fewer than half of people are willing to trust AI, and 56% have made mistakes at work because of it.

    48,000+ people in 47 countries; fieldwork November 2024 to January 2025.

    46% trust; 56% mistakes

    KPMG / University of Melbourne2025

Reliability12

  1. 9

    An agent that succeeds 75% of the time on one try passes three tries in a row less than half the time.

    Assumes independent tries. Illustrative arithmetic, not a measured result.

    ~42%

    Anthropic Engineering9 Jan 2026

  2. 10

    On τ-bench retail, a widely used model’s success collapsed when all 8 tries had to succeed.

    A 2024 model: it shows why repeated runs matter, not a current score.

    Re-checked each quarter

    61.2% on one try; under 25% on all 8

    τ-bench, arXiv17 Jun 2024

  3. 11

    On APEX-Accounting, models score well on average, yet almost none pass a task on all 8 tries.

    The two figures come from different models, so they are shown together. Made by Mercor with Ramp.

    Re-checked each quarter

    56.4% top average; 2.6% top all-8 pass rate

    APEX-Accounting, arXiv29 Jul 2026

  4. 12

    The top agent completes about one in five real computer workflows of around 1.6 hours.

    108 workflows; the top run was a frontier model at 500 steps. Re-checked each quarter.

    Re-checked each quarter

    20.6%

    OSWorld 2.0, arXiv28 Jun 2026

  5. 13

    The top agent’s share of real freelance projects automated rose more than eightfold in a year.

    A live leaderboard with no update date, so both years are shown.

    Re-checked each quarter

    2.5% (Oct 2025); 21.25% (Oct 2026)

    Scale AI, Remote Labor Indexfetched 25 Oct 2026

  6. 14

    Terminal-Bench 2.0’s top score rose on the same tasks in four months.

    A like-for-like task set: the figure to use to show tasks expiring.

    Re-checked each quarter

    62.9% to 76.0%

    Terminal-Bench6 May 2026

  7. 15

    Even heavily reviewed benchmark tasks break: nearly a third of Terminal-Bench 2.0 needed fixes within six months.

    Each task had about 3 reviewer-hours plus an exploit agent.

    Re-checked each quarter

    28 of 89

    Terminal-Bench6 May 2026

  8. 16

    A frontier lab stopped using SWE-bench Verified after judging that 59.4% of the hard tasks it audited were broken.

    Reported by Decrypt. The lab audited 138 tasks, six engineers each.

    Reported second-handRe-checked each quarter

    59.4% broken

    Decrypt24 Feb 2026

  9. 17

    The model that tops one test trails on another: a frontier model that leads the Remote Labor Index earns less than its predecessor on Vending-Bench 2.

    Averages over 6 runs. Rankings depend on the test world.

    Re-checked each quarter

    $9,235 vs $11,182

    Andon Labs24 Sep 2026

  10. 18

    Higher reasoning effort reduced accuracy in a majority of runs.

    Princeton, 9 models, about $40k of compute. Tested on 2025 models; current ones may differ.

    Re-checked each quarter

    21,730 rollouts

    HAL, arXiv13 Oct 2025

  11. 19

    Hosted models are not deterministic: identical temperature-0 requests give different answers.

    A large open-weight model. Batch-invariant kernels made the outputs identical.

    Re-checked each quarter

    80 distinct outputs from 1,000 runs

    Thinking Machines Lab10 Sep 2025

Failures and stakes13

  1. 22

    Court cases involving AI-hallucinated content keep mounting.

    A live count, so it grows; a few entries are flagged as alleged.

    Re-checked each quarter

    2,217 (UK 76)

    Damien Charlotin database25 Oct 2026

  2. 23

    Documented AI incidents hit a record, and agents still fail about one in three structured benchmark attempts.

    The page is undated.

    Re-checked each quarter

    362 in 2025 (233 in 2024)

    Stanford AI Index 20262026

  3. 24

    One in five organisations had a breach caused by shadow AI, and heavy shadow-AI use raised breach costs.

    $670k compares high shadow-AI use with low or none; it is not a per-breach figure.

    1 in 5; +$670k

    IBM Newsroom30 Jul 2025

  4. 25

    78% of people who use AI at work bring their own AI tools.

    31,000 people in 31 markets; fieldwork February to March 2024.

  5. 26

    Klarna’s AI handled two-thirds of its customer chats in its first month; a year later Klarna rehired people because quality suffered.

    The two halves belong together: test quality and escalation, not cost alone.

    Quoted together

    two-thirds

    Klarna27 Feb 2024CX Dive9 May 2025

  6. 27

    AIG says underwriting reviews ran faster and data accuracy rose in early rollouts.

    A customer claim: a CEO quote on a vendor page.

    A customer claim

    more than 5x faster; 75% to over 90%

    Anthropic15 Jul 2025

  7. 28

    US physicians handle about 40 prior authorisations a week, which take them and their staff 13 hours.

    1,000 physicians, fielded December 2025. US figures.

    40 a week; 13 hours

    American Medical Association2026

  8. 29

    US customer service is a large pool of jobs, projected to shrink.

    Automation is cited. A US figure, quoted for scale, not as a UK one.

    2.67m jobs; −5% over 2025–35

    US Bureau of Labor Statistics2026

  9. 30

    The EU’s database of suspected side effects took in about 1.77 million individual case safety reports in 2025.

    Up 0.5% on 2024. One case can carry several reports (an initial report and follow-ups); the database held over 31.2 million in all at the end of 2025.

    1,765,581 reports in 2025 (about 1.77m)

    European Medicines Agency, 2025 annual report on EudraVigilance12 Mar 2026

  10. 31

    The Lloyd’s market in London wrote £57.9bn of gross premium in 2025, up 4.2% on the year before.

    Gross written premium across the whole Lloyd’s market, from its results release. The major claims ratio fell to 5.8% (2024: 7.8%).

    £57.9bn gross written premium in 2025 (2024: £55.5bn)

    Lloyd’s, full-year results 202519 Mar 2026

  11. 32

    District planning authorities in England received 314,200 planning applications in the year to June 2026.

    Applications for planning permission. The same authorities decided 74,500 in the quarter.

    314,200 a year; 78,700 in April to June 2026

    MHCLG, Planning applications in England: April to June 202624 Sep 2026

  12. 33

    UK management consultancies earned about £21.8bn in fees in 2025.

    An estimate made with the Management Consultancies Association: its member firms earned £10.9bn and make up about half of the UK industry, so the whole is roughly double.

    about £21.8bn in 2025; MCA members £10.9bn

    Oxford Economics, MCA Annual Industry Report 202610 Jul 2026

Markets and labs7

  1. 34

    Almost half of 2025 tech deals had an AI component.

    AI deal value in January to September 2025 more than doubled all of 2024.

    ~50% (about 1 in 4 in 2024)

    Bain & Company27 Jan 2026

  2. 35

    The SEC fined two investment advisers for “AI washing”.

    Delphia $225k, Global Predictions $175k.

    $400k in total

    US SEC18 Mar 2024

  3. 36

    AI labs typically pay a few hundred to a few thousand dollars per training task, and much more for exclusivity.

    From 18 interviews; typical prices, not list prices. A $20k task is rare.

    $200–$2,000 per task; 4–5x for exclusivity

    Epoch AI12 Jan 2026

  4. 37

    Leaders at a frontier lab reportedly discussed spending over $1B on training environments in a year.

    A reported discussion, relayed from The Information; not a confirmed budget.

    Reported second-hand

    over $1B

    TechCrunch21 Sep 2025

  5. 38

    A large technology company bid for one airline’s emails and Teams chats in a bankruptcy auction.

    A bid, not a payment: no closing has been reported, and 121 lawmakers objected.

    $10M bid

    The Record8 Oct 2026

  6. 39

    On 23 October 2026 a pound bought $1.3387, the Bank of England’s daily spot rate: the rate used to set dollar licence income against a fee in pounds.

    The latest daily rate published when the page was checked (26 Oct 2026). It sets the dollar figures on the data program and on Pricing beside Butterfly’s fees in pounds. Rates move every day, so a comparison made with it is approximate.

  7. 40

    Healthcare AI spending reached $1.4bn in 2025: $450m on coding and billing, and over $100m on prior authorisation, growing tenfold in a year.

    An investor’s estimate. Health systems are about 75% of the spend ($1bn), payers about 5% ($50m).

    $1.4bn; $450m coding and billing; $100m+ prior authorisation

    Menlo Ventures, The state of AI in healthcare21 Oct 2025

Review and privacy9

  1. 41

    Expert graders often disagree, so serious benchmarks review each task several times.

    71% is agreement when grading model deliverables. Every task had at least 3 reviews.

    71% agreement; 5 reviews per task

    OpenAI GDPval, arXiv5 Oct 2025

  2. 42

    Physicians agreed with each other on grading rubrics part of the time.

    Measured by macro F1 across 262 physicians. The authors call disagreement inherent.

    55–75%

    OpenAI HealthBench, arXiv13 May 2025

  3. 43

    Attacks on synthetic data recover a large share of outlier records, even when similarity-based privacy tests pass.

    A black-box attack. Rare, costly cases are exactly the outliers that matter.

    78–100% of outliers

    Ganev & De Cristofaro, arXiv2025 (IEEE S&P)

  4. 44

    Italy fined IQVIA over health data it claimed was anonymous.

    Decision of 23 September 2026. A persistent ID plus birth year, sex, diagnoses and location let people be singled out.

    €7m

    EDPB9 Oct 2026

  5. 45

    On Amazon Bedrock, model calls run in accounts the Bedrock team owns and operates; model providers have no access to them, to their logs, or to customers’ prompts and completions.

    So a model call leaves the client’s own network for AWS, which processes it. That is why Butterfly agrees a residency policy per model before the first test.

    One deployment account per model provider, per Region

    AWS, Bedrock data protectionfetched 26 Oct 2026

  6. 46

    Retention on Bedrock depends on the model: a frontier model and its point release require AWS human review and keep prompts and completions inside AWS for up to 30 days; zero retention can be set and enforced across an organisation.

    Content is not shared with the model provider. Under cross-Region routing, retained data is stored in the Region that served the request.

    Re-checked each quarter

    Up to 30 days (a model and its point release); zero retention elsewhere by policy

    AWS, Bedrock data retentionfetched 26 Oct 2026

  7. 47

    From London, a large frontier model and a mid-size model can run in-Region; a newer mid-size model runs through the EU profile; another vendor’s frontier model is reached through global routing alone.

    Availability changes with each release, so it is re-checked before every comparison. The EU profile from London routes to Frankfurt, Stockholm, Milan, Spain, Ireland, London and Paris.

    Quoted togetherRe-checked each quarter

    In-Region: 2 models · EU: 1 · Global: 1

    AWS, Bedrock model card: a large frontier modelfetched 26 Oct 2026the older mid-size releasefetched 26 Oct 2026the newer mid-size releasefetched 26 Oct 2026a widely used model from another makerfetched 26 Oct 2026

  8. 48

    European data protection authorities judge anonymisation on three tests: can a person still be singled out, can records about them be linked, and can anything be inferred about them.

    Adopted 10 April 2014, under the Data Protection Directive that the GDPR replaced. It also says pseudonymisation is not a method of anonymisation. Re-opened 26 Oct 2026.

    Three tests: singling out, linkability, inference

    Article 29 Working Party, Opinion 05/2014 on Anonymisation Techniques (WP216)10 Apr 2014

  9. 49

    Pseudonymised data is personal data in the hands of anyone who holds the additional information that links it back to people.

    UK guidance, under review since the Data (Use and Access) Act. Shared without the additional information, the same data can be anonymous in the recipient’s hands.

    Personal data

    ICO, Pseudonymisationfetched 26 Oct 2026

Regulation and market size11

  1. 50

    The Netherlands fined Uber over fully automated decisions about drivers.

    171 drivers were deactivated without human review. Uber has appealed.

    €824.99m

    EDPB8 Oct 2026

  2. 51

    EU AI Act high-risk duties for workplace AI apply from 2 December 2027.

    Set by the AI Omnibus. Annex I moves to 2 August 2028; transparency duties applied from 2 August 2026.

    2 Dec 2027

    European Commissionin force 27 Jul 2026

  3. 52

    The UK AI assurance market is about £1bn today and could grow eighteenfold by 2035.

    £18.8bn assumes barriers to adoption are removed; DSIT’s trend case is £6.53bn.

    ConditionalQuoted together

    £1.01bn GVA; over £18.8bn by 2035

    UK DSIT3 Sep 2025UK DSIT, trend case3 Sep 2025

  4. 53

    A forecast, reported second-hand: Gartner predicts over 40% of agentic AI projects will be cancelled by the end of 2027.

    A prediction, not observed failures, as Outlook Business reports it.

    A forecast

    over 40%

    Outlook Business25 Jun 2025

  5. 54

    The EU AI Act lists as high-risk: credit scoring, life and health insurance pricing, eligibility for public benefits, recruitment, and AI that allocates work or monitors and evaluates workers.

    Point 5(b) excludes AI used to detect financial fraud. The duties apply from the dates in row 51. A summary for planning, not legal advice.

    Annex III, points 4 and 5

    EU AI Act, Annex IIIfetched 26 Oct 2026

  6. 55

    US insurance regulators in 29 jurisdictions have adopted the NAIC model bulletin on insurers’ use of AI, and 4 more have insurance-specific rules or guidance.

    Status as of 8 October 2026. The map records state action; it does not judge whether each adoption contains every element of the model bulletin.

    Re-checked each quarter

    29 jurisdictions adopted; 4 with their own rules

    NAIC, implementation map8 Oct 2026

  7. 56

    The PRA’s model risk principles apply to models made in-house or by vendors, AI included, for UK banks with internal-model approval.

    Binding for banks, building societies and PRA-designated investment firms with internal-model approval; other firms use it as a benchmark.

    In force since 17 May 2024; current version from 23 Apr 2026

    Bank of England, PRA SS1/2323 Apr 2026

  8. 57

    California’s rules on automated decision-making: a business already using it for significant decisions (credit, housing, education, jobs and the allocation of work, healthcare) must comply by 1 January 2027.

    §7200(b). “Significant decision” is defined in §7001(ddd): financial or lending services, housing, education, employment (including the allocation or assignment of work and pay) and healthcare services. A summary for planning, not legal advice.

  9. 58

    DORA, the EU’s digital operational resilience rules, applies to 20 types of financial firm and to the ICT providers they rely on, with EU oversight of critical third-party providers.

    Third-party risk management (monitoring providers, key contract terms) is one of its core areas. Banks, insurers and investment firms are among the 20 types.

    Applies from 17 Jan 2025

    EIOPA, Digital Operational Resilience Actfetched 26 Oct 2026

  10. 59

    US Medicare Advantage, Medicaid and CHIP plans must decide prior-authorisation requests within 72 hours when urgent and seven calendar days otherwise, from January 2026; a prior-authorisation API follows from January 2027.

    The decision timeframes do not apply to Qualified Health Plan issuers on the federal exchanges. US rules, quoted for US payers and providers.

    72 hours or 7 days, from 1 Jan 2026; API from 1 Jan 2027

    CMS, Interoperability and Prior Authorization final rule (CMS-0057-F)17 Jan 2024

  11. 60

    The EU moves to settling securities trades one business day after trading (T+1) on 11 October 2027; the first deadline, for allocations and confirmations, is 7 December 2026.

    The legal basis, Regulation (EU) 2025/2075, was published on 14 October 2025. The final deadline also covers sending settlement instructions early and CSD functions such as auto-partial settlement.

    T+1 from 11 Oct 2027; first deadline 7 Dec 2026

    ESMA, Statement on T+1 preparations20 Jul 2026

Our own arithmetic1

  1. 61

    A clean streak proves less than it looks: 8 clean runs still allow up to 31% failure, while 100 clean runs cap it near 3%.

    The 95% upper bound on the failure rate after n clean runs, 1 − 0.05^(1/n). 59 clean runs are needed to claim 95% reliability at 95% confidence.

    Our own arithmetic

    8 runs: up to 31%; 100 runs: about 3%

    Butterfly’s calculation25 Oct 2026

0302 · The films

Transcripts,word for word.

What each film says to a visitor who has not named a company. Name one and the picture shows its name throughout; the voice says it too wherever we have a recording for that company. Every figure about a named company comes from our model and is illustrative.

Film 1 · Agent benchmarks

Your agent benchmark

346 words1:51 voicedNarration: AI voice

00 Introduction, for everyone

Every company runs on thousands of tasks, each one costing hours, salaries and software. AI can take on more of them every month. But which ones can you trust it with? Now let me show you, for your business.

01 Read and price

This is your company, from the outside: your website, your posts, your own words. We read all of it: what you do, how you work, and the thousands of tasks behind it. Take this one: matching a supplier invoice to its purchase order.

Each one costs about £7 in hours, salary and software. It happens hundreds of times a week, all year.

02 Test it

Your vendor says its agent can do it. But one wrong answer reaches a customer, with your name on it. So we’d set it up in your own cloud: your records, your rules, no access for our staff. It would face a hundred held-back cases.

Right three times in four sounds good. But across a hundred cases, that’s twenty-five wrong answers. We’d test every option: your own agent, and eight models, frontier and open-weight. Two clear the bar, and you’d see what each costs to run.

03 Approve

For the one you choose, you’d get its blueprint: the access it needs, where a person stays in, the test to keep passing. The task owner sets the bar, and approves it going live.

We’d test it again every week, and fully each quarter: models change. As agents take on more sensitive work, this is how your company knows which to trust, and what each job is worth.

04 What comes next

That was one task. Behind it are thousands more. We start with the twenty-five worth more to you than the rest, and test each one the same way.

Your readiness report sets them out: what each costs today, what you could save, and how it would run in your own cloud. And the tasks you choose to license could earn you income from AI labs, while your records never leave.

Agents will take on more of your work every year. With Butterfly, you’ll know which are ready. That’s your agent benchmark.

Film 2 · The data program

Licensed, not handed over

264 words1:26 voicedNarration: AI voice

00 Introduction, for everyone

AI labs need real work to train and test their models. Your company does that work every day, across thousands of tasks. Licensed the right way, your records never leave your cloud. Now let me show you, for your business.

01 Your tasks

This is your company. Every case your people close has a known right answer, and the hardest ones are what labs pay for. Data deals today are usually one-off: hand over an archive, take one payment, and it’s gone.

02 Licensed, not handed over

This works the other way. Your records stay in your own cloud, and nothing is handed over. Take one closed case: a supplier invoice that didn’t match its order.

Inside your cloud, it becomes a task: the instruction, the systems as they stood, and the answer your expert gave. Personal details are rewritten, and the rewrite is tested independently before anything moves. Then you decide, task by task. Nothing leaves without your approval.

When you approve one, a copy of the task leaves. Your records never do.

03 What it earns

One lab trains its model on it. Another tests its agent. Licences are non-exclusive, so the same task can be licensed again. Each licence earns you one to two thousand dollars.

A first pack of a hundred tasks is worth a hundred to two hundred thousand, and it’s priced to be profitable for you. It keeps going.

Your work makes new tasks every day, and as agents take on higher-stakes work, labs need the ones models still get wrong. Your own readiness tests never leave, and they are never licensed. Licensed, not handed over. That’s the data program.

0403 · Narration: AI voice

The voiceis an AI voice.

Narration: AI voice
  • The films are narrated by an AI voice, made with ElevenLabs’ Eleven v4 model. Every player says so on its bar, as the EU AI Act asks from 2 August 2026.51
  • The narration is made in advance from the words above: one take for everyone, and one for each company we recorded. Any other company hears the first take, with its name on screen; where live narration is switched on, its web address and name are all that is sent to voice its name, and nothing else you type is spoken.
  • Every film has captions, a pause button first in the tab order, and a “Read it as text” view.

Your proposal

Get your proposal.Then register your interest.

A readiness report made from your company’s public website: what your tasks cost, and the first 25 to test. If it reads right, register your interest: a person from Butterfly will call you to talk it through.

We start reading your public website as you type.