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Signal · Geneva · 18 September 2026

Under the Hood of
Agentic AI

Reffettorio Geneva
Sam Bourton · Former founder, QuantumBlack
Partner & Board Member, Visium · Former Partner at McKinsey
hello@sambourton.ai

00:00 Andrea hands over. Thirty minutes end to end, hard stop at 09:45 with the HackerHouses panel behind you. Plan to 09:44.

Open with the handover line, it costs ten seconds and buys two minutes: "There is a panel at three on how you should run your own firm on this. I am going to leave that to them, and talk about what you are underwriting instead."

Do not pitch Visium. Alen is on the 11:00 founders panel. The agenda-free version is the credible version.

Shape: three sections of six, eight and eight minutes, then five minutes of questions. If you are past 09:32 at the end of Section 2, drop slides 11 and 12 and go straight to slide 13 and the close.

Deck v4, 15 September. Fourteen slides plus the title, about twenty-two minutes of content on the current cues, a minute inside the twenty-three-minute target. If rehearsal runs long, cut slides 11 and 12 first. The matrix slide was cut on 15 September; the map carries the synthesis on its own. No A/B forks remain.

01 · Section 1 · The spectrum
Slide 1 · The agentic spectrum

Beyond the prompt

What many people think of as AI is the far left edge of a spectrum. Each step right adds capability and autonomy. Most companies live at step one. The leap is not a better prompt: it's a different architecture.

Left edge · prompting
Mid-tier capabilities
Agentic differentiators
ChatGPT lives here
Agentic systems live here
01
Prompting
Type a question, get an answer.
02
Context
Knowledge of you, your team, your business. Memory that compounds.
03
Connectors
Live links to email, calendar, drive, chat.
04
Tools
Acts on systems: SharePoint, CRM, SAP, Salesforce.
05
Skills
Repeatable, reusable units of work.
06
Reasoning
Long-running, step-by-step decomposition.
07
Communications
Voice and chat as native interfaces.
08
Outcomes
Give it a goal. It plans, acts, checks, loops until finished.
The strategic question is not "are you using AI?" Everyone is now. It's which steps along this spectrum are you anchored on and operating at.

2 min This is the published abstract, sentence by sentence. It is the thing the room bought a ticket for. Deliver it straight.

Run the poll. Three bands, hands up. At INSEAD it split into rough thirds. Expect this room further left and say so out loud when it does: "and you are the people funding the ones who claim to be at eight."

Do not walk all eight steps. Walk one, two, five and eight. The others land by implication.

Bridge into Section 2, say it as one sentence so the room hears one argument and not three talks: "So that is the spectrum. Now the uncomfortable part. Almost everyone is at step one, and it has not moved in a year."

02 · Section 1 · The spectrum
Slide 2 · The second spectrum

Beyond the individual

It's easy to focus on the individual, and that is where the spending is: one person, one seat licence. The real value comes when a workflow runs across teams and departments, and getting there means changing how the organisation works, which is much harder and much slower.

Start hereMaturity
01

Individual

One person, one agent, their own work. A seat licence.

Where almost everyone is
02

Team

Shared context across a group. Handoffs, reviews, a common memory.

Harder than it looks
03

Workflow

An end-to-end process owned by the system, humans at the margins.

Where the economics start
04

Function

Finance, procurement, service ops delivered as agentic workflows, not org-chart units.

Where the org chart breaks
05

Company

The operating model itself, rebuilt around the capability rather than around headcount.

A handful, and arguing about it
It is easy to transform yourself. It is much harder to transform your team, your workflows, or your organisation. Everything a chat subscription buys you stops at step one on this axis, which is exactly why the P&L has not noticed.

2 min This slide is new and it is yours, not McKinsey's. It is the hinge of the whole talk, so give it the time.

The line to land, in your own words from the plane notes: it is easier to focus on the individual, but the real value comes at the level of workflows across teams and departments, and that is much harder.

The allocator translation: a company selling you seat licences is selling on axis one. A company that required its customer to change how the work runs is on axis two. Those are different businesses with different margins and different defensibility, and they are currently priced the same.

The matrix slide that crossed the two axes has been cut. Do not try to draw it here from the stage; slide 6 makes the sparse-adoption point in McKinsey's own numbers.

Do not use org-layer language here. No rings, no "code out to the industry". That is slide 8, and the two slides only stay distinct if this one stays about who the system serves.

03 · Section 1 · The spectrum
Slide 3 · How they communicate · Three models of human and agent communication

When agents start talking

As everyone gets their own agents, the real question becomes who talks to whom. Three models, each more powerful, and more tangled, than the last. Start from personal agents and progress only as your trust, guardrails, risk, security and ROI mature.

Start here Maturity
01 · today

You and your agents

You talk to your own agents. One human directing their own fleet. AI never sends messages on your behalf.

02 · plus ones

Every function gets a +1

Finance, HR and IT each have a virtual +1 that anyone in the company can talk to, through the same chat and collaboration tools people already use. (The "Plus One" model.)

03 · agents ↔ agents

Agents talk to agents

Your agents talk to other people's agents, then report back to humans. Hugely powerful, and complex very fast.

Your collaboration tools become the substrate for agent collaboration. The new governance question isn't "can we use AI?", it's who is allowed to talk to whom and about what. Mature your use of agents "as fast as (safely) possible, but no faster."

90 sec Ported on 15 September from the hosted INSEAD deck (insead.sambourton.ai, "05 · How they communicate"), at Sam's request, to sit between the second spectrum and the data. Wording carried over with two edits: em dashes removed per the deck rule, and "behelf" corrected to "behalf".

Read the three cards left to right, once, and let the animation do the rest. The Every story (Plus One, the ant death spiral, the reversal) was cut from this deck, so do not lean on it here; card two is the model, not the case study.

The governance line in the callout is the bridge to Section 2: who is allowed to talk to whom is an organisational question, not a capability question, which is exactly why the next four slides show nothing moving.

Bridge: "So that is the spectrum, twice, and the shape of the conversation. Now the uncomfortable part. Almost everyone is at model one, at step one, and it has not moved in a year."

04 · Section 2 · Where everyone actually is
Slide 4 · The capability gap

Where everyone actually is

Individual productivity is now well-established, and close to universal. Enterprise profit didn't move in the last year, still waiting for ROI at scale.

% of respondents · 0 to 100
AI improves my own productivity
80%
AI helps me make better decisions
50%
Any EBIT contribution from AI
37%
5% or more of EBIT, and "significant" value
6%
The top two bars are people reporting on themselves. The bottom two are the P&L, and they are where they were in 2025, after another year of record spending.
McKinsey & Company / QuantumBlack, The state of AI in 2026: On the road to ROI, August 2026: 80% p11 and Exhibit 5; 50% p11; 37% p3 and p12; 6% p3 and p17. "AI high performers" are respondents who attribute 5% or more of EBIT to AI and describe its impact as "significant": 92 of 1,719 respondents. Survey fielded 4 May to 8 June 2026.

2 min The dashboard slide that used to follow this has been cut, so this carries the section on its own. Let the chart sit.

The shape of the bar chart does the work. Let it sit for a beat before you speak over it.

Accuracy guards. Say "respondents", not "companies". Say "any EBIT contribution", not "positive ROI". The 37 and 6 are prose in the report, not exhibits, so if challenged, cite pages three, twelve and seventeen.

Do not say 88 / 39 / 6. That is the November 2025 edition and it is dead.

The definition of a high performer, and say it out loud because the bar is modest. McKinsey, p3: "AI high performers (those who attribute at least 5 percent of EBIT to their use of AI and describe the technology's impact as 'significant')". That is 92 respondents out of 1,719, flat on 2025. Five percent of EBIT from a technology 90% of them are already using. Your plane-note line: "Only 6% are beyond this, and look at the definition, it is quite modest. The ROI accrues at the workflow."

What the report says distinguishes them, p17, if asked: more likely to use AI to transform the organisation, a broader set of best practices, a wider range of AI technologies, active management of AI risk. Three-quarters report fundamentally redesigning workflows because of AI, up from 55% last year, against one-quarter of everyone else. Say "three-quarters" and "one-quarter", not 73 and 25.

05 · Section 2 · Where everyone actually is
Slide 5 · The same spectrum, in the survey data

Scaling stops at the left edge

Split the survey by what has reached scale and the order of the bars is the spectrum from slide 1. Chatbots are scaled in most large companies, coding agents in about a third, and agents that act across a workflow in a quarter at best.

Share of organisations that have reached the scaling phase, by tool
Under $1bn revenue $1bn revenue or more
AI chatbots
39
64
Specialised AI tools
20
35
▼ Agentic AI
Software coding agents
17
31
Other AI agents
13
25
40%
of companies over $1bn are scaling agents in at least one function, up from 27%. Includes coding agents. Across the whole organisation the figure is 25%
22%
of companies under $1bn. 21% a year ago, essentially flat
32%
have declined a software purchase because they could build it with coding agents. 41% in tech
McKinsey & Company / QuantumBlack, The state of AI in 2026: On the road to ROI, August 2026: bars Exhibit 3 ("at least scaling", n = 595 at $1bn+, n = 1,035 below); 40 / 27 / 22 Exhibit 2; 32 / 41 Exhibit 4. Fielded 4 May to 8 June 2026, n = 1,719.

90 sec The order of the bars is the spectrum, redrawn from the survey. Say that explicitly: chatbots, then coding agents, then agentic systems that act across a workflow. That is the same left-to-right you just showed them.

The "Agentic AI" bracket is McKinsey's own grouping, not yours. Worth saying, it makes the point for you.

The 32% is the best single investor fact in the report. Every SaaS line item in the portfolio is now contestable by a customer with coding agents. It cuts both ways and you should say so: the tools already inside the enterprise, Salesforce, Microsoft, NetSuite, are extremely well placed, and at the same time a third of their prospects just decided to build instead. Incumbents win the distribution, point solutions win the workflow.

Careful on the small-company figure. The report says "essentially flat" and the exhibit shows 21 to 22. Do not say "no movement at all".

06 · Section 2 · Where everyone actually is
Slide 6 · The matrix, redrawn to scale

Where agents have scaled

Agent use that has reached the scaling phase, by industry and function. Press T to redraw it.

0%
McKinsey & Company / QuantumBlack, The state of AI in 2026: On the road to ROI, August 2026, sidebar exhibit p8, "AI agent use that has reached the scaling phase, by industry and business function, % of respondents". McKinsey Global Survey, 4 May to 8 June 2026, n = 1,719; industry bases n = 44 (insurance) to n = 230 (professional services). Redrawn.

90 sec Straight after the survey bars on slide 5. It is a real McKinsey exhibit with real numbers in every cell, and redrawing it live is a move nobody else in the room will have made. The point lands in three seconds because the whole grid goes nearly white.

How to run it. Open on "As published", which is the exhibit as printed, all thirteen columns, software engineering included. Say what it is. Then press T (or click the toggle): the shading re-anchors to 100% and the software engineering row greys out. Say why: "a coding agent is one person being more productive, not an agentic workflow".

The risk, and it is real. You are telling a room containing McKinsey that a QuantumBlack-authored chart is drawn on a flattering scale, while billed as the founder of QuantumBlack and days from being their Senior Advisor. Do it generously or not at all: "this is a good chart and I am going to redraw it, because the published version normalises to the highest number in the grid, which is thirty-one percent, and that makes a sparse map look dense."

The QuantumBlack analogy is the payoff: we found the intersection points in every value chain for machine learning. The same will happen here, faster. This chart is the before picture.

Public and social sector is not in this exhibit. McKinsey surveyed it (n = 71 to 81 in Exhibits 4, 8 and 9) but did not publish the agent-by-function cut for it. If asked, say so rather than guess.

07 · Section 2 · Where everyone actually is
Slide 7 · The diagnosis

So why is it stuck?

The capability shipped. Five things hold the second spectrum still, and every one of them is organisational.

01

Governance and the org itself

Managers of managers of managers, and the meeting calendar that holds them together.

02

AI put under corporate IT

The eighteen-month enterprise chatbot project: the data warehouse and the data lake wearing a new coat.

03

Trust

The CIO's view: risk, data access, hosting, model choice, sovereignty. Every one of them a legitimate delay.

04

The non-functional tax

Infrastructure, cost, routing, hosting, evaluation. Every point solution solving all of it again, for a handful of users.

05

Pace

The industry moves faster than a procurement cycle. No pick-it-up-and-go culture.

32%
expected AI-driven headcount cuts, asked in 2025
14%
actually saw them a year later
"The limiting factor is increasingly the organization's ability to absorb change." Tara Balakrishnan · associate partner, McKinsey · p24 of the report
McKinsey & Company / QuantumBlack, The state of AI in 2026: On the road to ROI, August 2026: Balakrishnan quote p24; 14% and 32% p25 and Exhibit 16. Five causes and anti-patterns: Sam Bourton.

2 min New section, not in any earlier draft. It is the honest answer to the previous three slides and the room will be waiting for it.

The five in full, moved off the slide on 15 September so you say them rather than the room reading them:

01 Governance and the org itself. Managers of managers of managers, and the meeting calendar that holds them together. No pick-it-up-and-go culture. The comfort zone is a process, and processes are what agents cross.

02 AI put under corporate IT. The eighteen-month enterprise chatbot project, which is the data warehouse and the data lake wearing a new coat. By the time it lands you would build it differently anyway.

03 Trust. The CIO view, and the risks are real. Data access, hosting, model choice, sovereignty. Every one of those is a legitimate objection and a legitimate delay.

04 The non-functional tax. Infrastructure, cost, routing, model selection, hosting, evaluation, A/B testing. Every point solution is solving all of it again, for a handful of users, because there is no common platform.

05 Pace. The industry moves faster than a procurement cycle. Anything specified twelve months ago is now the wrong design, and everybody knows it while they finish building it.

Anti-patterns, also off the slide: AI reporting into IT. The 18-month enterprise chatbot. Specified last year, shipped next year. Governance as the whole programme. One agent, one person, declared done.

Do not read all five. Land two and gesture at the rest. Cause one and cause two are the ones this room has personally funded.

The 14 versus 32 is the best-constructed number in the report. Use it to defuse the doom reflex before it starts, and to make the argument: the constraint is organisational absorption, not capability.

Do not attribute the non-functional point to Jeremy Palmer from the stage. That material was not cleared for slides. It is yours as a general principle, and the forty-eight-item version belongs to Sohrab Hosseini at Orq.ai if you want to credit it in conversation.

Also not on the slide, and not to be asserted: "there is no common platform, Satya". No source was found for that quote. The sentence works without it.

08 · Section 2 · Where everyone actually is
Slide 8 · The org onion

Software is cheap. Organisational change is expensive

Organisation &
Business Context
Teams & People
System
Architecture
Code &
Technology
  • Funding cycles and procurement, slower than the industry
  • Risk tolerance: data, hosting, sovereignty, who signs off
  • Governance and culture: managers of managers, who is allowed to decide
  • Conway's Law: the org chart becomes the system
  • Team topologies: who hands what to whom
  • Cognitive load: how much change one team can absorb
  • Bounded contexts: which team owns which workflow
  • Skills, roles and the talent model when agents do part of the job
  • Agents, tools and connectors into the systems of record
  • The common platform: routing, hosting, evaluation, cost
  • APIs and events the agents can act through
  • Data access and permissions
  • Models, frameworks, languages
  • Coding agents and developer experience
  • Databases and the data estate
  • Where horizontal AI tools stop
Vertical AI productsCross departments and workflows, so they have to reach these two rings. This is where the returns are waiting.
Horizontal AI toolsLive in these two rings. Engineering teams adopt them fast and nobody outside has to agree. This is why adoption looks universal.
Adapted from the "Onion Concept", James Lewis, Thoughtworks (thoughtworks.com/profiles/j/james-lewis). The four rings as drawn and the notes beside them are Sam Bourton's adaptation.

90 sec Ported from the Global Week keynote. It was cut from the first build of this deck for time and because it looked like a repeat of slide 2. It is not, and the difference is worth thirty seconds of your preparation.

Slide 2 is who the system serves. This is what a change has to touch. One is a ladder of scope, this is an anatomy of cost. Do not reuse the individual, team, workflow, function, company words here or the room will hear the same slide twice.

Read it from the middle outwards, once: "Code. Architecture. Teams. The organisation. Every AI tool you have been sold this year lives in the inner two. Every number you are waiting for lives in the outer two." Then stop. The picture does the rest.

The allocator turn is spoken, not on the slide (v4 removed the callout). Say it: "What you are underwriting is ring depth. A product that stops at the two inner rings has a fast sales cycle, a small contract and no defence. One that reaches the outer two has a slow sales cycle, a large contract, and a moat made of somebody else's process. Right now the market prices them the same." Ring depth is the diligence question: how far out does this product have to reach before the customer gets value, and did anyone in the customer's organisation have to change behaviour? If nobody did, there is nothing to defend.

Attribution guard: credit the onion idea to James Lewis at Thoughtworks if asked, and be clear the four rings and the bullets beside them are your version. Do not claim his diagram.

Redrawn 15 September to follow the IB talk version: bottom-aligned rings, one bullet list per ring on the right. The outer "External and Industry Context" ring is gone. The two side cards became the tags on the far right; their longer wording, in case you want it back: "Horizontal AI tools are the easy part. They only touch the inner rings, so engineering teams adopt them quickly and nobody outside the team has to agree to anything. That is why adoption looks universal." and "The value sits in vertical AI products. Those cross departments and workflows, which means touching every ring above. That is why the returns look like nothing, and why the few that work are so hard to copy."

09 · Section 2 · Where everyone actually is
Slide 9 · Under the hood

Forty-eight things before the first workflow ships

This is what has to exist before an agent runs reliably in production. Every company and product is rebuilding most of it from scratch, or from a handful of immature components, for a handful of users. This is cause four, itemised.

Models & gateway 12

  • Access to models across providers
  • Bring your own model and keys
  • Model routing and selection
  • Fallbacks and retries
  • Load balancing and rate limits
  • Response and prompt caching
  • Structured outputs and schema checks
  • Output validation and repair
  • Token and cost metering
  • Latency and quota management
  • Model versioning and deprecation
  • Private or on-premise inference

Agents, tools & context 12

  • Agent runtime and orchestration
  • Planning, loops and stopping rules
  • Tool and API integration
  • MCP servers and tool gateway
  • Reusable skills and procedures
  • Prompt and instruction management
  • Dev, test and production environments
  • Knowledge bases and retrieval (RAG)
  • Ingestion: chunking, OCR, multimodal
  • Short- and long-term memory
  • Scheduling and event triggers
  • Human-in-the-loop approvals and handoffs

Security, governance & control 12

  • Identity: SSO, roles, teams, projects
  • Least-privilege permissions for agents
  • Guardrails on inputs and outputs
  • PII detection and redaction
  • Secrets and credential management
  • Budgets and spend caps
  • Audit logs and traceability
  • Data residency and retention controls
  • Network controls: allowlists, VPC, on-prem
  • Approved-model and vendor governance
  • Change control and release approval
  • Incident response and kill switch

Evaluation & reliability 12

  • Playgrounds and experiments
  • Datasets and golden test sets
  • Automated evaluators, LLM-as-judge
  • Agent simulation and scenario tests
  • Red teaming and adversarial tests
  • Regression tests on every change
  • Tracing (OpenTelemetry) and agent graphs
  • Monitoring, alerts and SLOs
  • Cost and token tracking per workflow
  • Drift and quality monitoring in production
  • Feedback capture and improvement loops
  • Runbooks, on-call and recovery
The diligence question: which of these did they build, and which did they inherit? Building all four columns eats the runway and leaves a demo; vs inheriting three and building the fourth on purpose to focus on the workflow.
None of the forty-eight is the workflow. The roles, the tasks, the training and the process change are on nobody's list, and they are the only part that reaches the outer two rings of the onion.
Structure adapted from Sohrab Hosseini's Orq.ai capability map (48 AI platform capabilities, 2026), generalised and made vendor-neutral by Sam Bourton. Four groups of twelve.

90 sec Rebuilt on 15 September from Sohrab Hosseini's Orq.ai capability map, replacing the sixteen-box Global Week grid. Rewritten vendor-neutral on 15 September: the four-by-twelve frame is his, the items are generalised. Credit him for the frame if asked: "the shape of this comes from Sohrab Hosseini at Orq.ai; the items are what any of these platforms has to do."

Do not read forty-eight boxes aloud. Say: "Four columns. Forty-eight things. Every company you meet has built most of them, badly, for about forty users." Then go straight to the diligence question.

This is the slide that earns the words "under the hood" in the title. It is also the most cuttable slide in Section 2 if you are running long, because slide 7 cause four and slide 13 filter three both survive without it.

It started as a vendor's list and has been generalised. Whoever you buy it from, or if you build it, this is the plumbing that has to exist before the workflow does: reliability (fallbacks, retries, caching, regression tests), robustness (guardrails, validation, red teaming), resilience (monitoring, incident response, kill switch, recovery) and control (identity, permissions, audit, budgets).

Do not attribute the non-functional argument to Jeremy Palmer from the stage. Same guard as slide 7. Harness is not on this list; do not add it.

10 · Section 3 · Where this is going
Slide 10 · The frontier

The far right end already exists

Put the two spectrums back on one page. The clusters of users and usage are already forming along both, and the far right end of each is running in production today.

What the system can do

Capability & autonomy →
Prompting
Mid-tier capabilities
Agentic differentiators
ChatGPT lives here
Agentic systems live here
Frontier · OpenAI research org
01
Prompting
02
Context
03
Connectors
04
Tools
05
Skills
06
Reasoning
07
Communications
08
Outcomes

Who it does it for

Organisational scope →
Frontier · OAI-HF, July 2026
01

Individual

Where almost everyone is
02

Team

Harder than it looks
03

Workflow

Where the economics start
04

Function

Where the org chart breaks
05

Company

A handful, and arguing about it
Clusters · keys 1 to 4
McKinsey & Company / QuantumBlack, The state of AI in 2026: On the road to ROI, August 2026: nearly nine in ten p5 and Exhibit 1; 39–64% and 13–25% Exhibit 3; 6% p3 and p17. OpenAI, "Research acceleration: a view inside OpenAI", openai.com, 6 September 2026. OAI-HF: METR, metr.org, 26 August 2026; Dario Amodei, "We Must Pace the Frontier", darioamodei.com, 12 September 2026.

2 min The two spectrums come back on one page and you talk to the clusters. Press 1, 2, 3, 4 in turn (or click the pills) to draw a sketched ring round each cluster; press the key again to remove it, or Clear. Rings can stack.

Walk it as four rings. 1: nearly nine in ten respondents, one person and a prompt box. 2: the enterprise chatbot, scaled by a company, still used by one person at a time, steps one to five. 3: agents that act across work, thirteen to twenty-five percent, the first time the ring reaches the workflow. 4: the six percent, functions and companies run on reasoning and outcomes. Then point at the two frontier markers: the OpenAI research org at three agent-days per human day, and OAI-HF, where agents managing agents inside a benchmark coordinated, pursued a goal nobody set and attacked the grader.

Get the dates right. The incident was July 2026. Amodei's essay is 12 September 2026. HF is Hugging Face. Do not conflate them.

Choose your emphasis before you walk on. Optimistic: the far right of both exists, so the empty cells can fill with what already ships. Sober: the first time we saw that end at scale it went somewhere nobody asked it to. Both are true. The close is the three thoughts, so the sober read is the natural fit.

Carry OpenAI's caveat if pressed: over half of successful four-to-eight-hour tasks needed at least one human intervention. Autonomy is not yet unattended. It was on slide 13 until 15 September; the Crosby time-to-review quote replaced it, so say it rather than point at it.

11 · Section 3 · Where this is going
Slide 11 · What the frontier labs are telling us

"Engineering is a solved problem"

Two forecasts, from the two companies best placed to make them, both made in the first two months of this year.

Entrepreneur, Hindustan Times and Bloomberg: Anthropic CEO says AI is 6 to 12 months from doing what software engineers do
Anthropic · The Economist, Davos · January 2026
"Most, maybe all" of what software engineers do, within six to twelve months.
Dario Amodei, CEO
FT interview: Microsoft AI's Mustafa Suleyman
Microsoft AI · Financial Times · February 2026
"Most, if not all, professional tasks will be fully automated by AI within the next 12 to 18 months."
Mustafa Suleyman, CEO
Amodei: The Economist interview, World Economic Forum, Davos, January 2026. Suleyman: Financial Times interview, February 2026, on lawyers, accountants, project managers and marketers, with software engineering cited as the evidence.

45 sec Split from the old slide 12 (v3 numbering) on 15 September to mirror the Global Week structure: what they said, then what their behaviour says. This is the setup. Read both quotes flat, no commentary, and let the room agree with them.

Use Suleyman's own words. The Global Week version paraphrased it as "software engineering will be automated in 18 months", which is stronger than what he said and not defensible. His claim is broader and softer-edged, and software engineering was his supporting evidence rather than his prediction.

Then click. The next slide is the punchline and it should arrive within ten seconds of the second quote.

12 · Section 3 · Where this is going
Slide 12 · What the frontier labs are actually telling us

And yet: $9.75bn on human engineers, in 71 days

In the seventy-one days after those two forecasts, the same labs and their peers committed nine and three quarter billion dollars to putting people inside customers.

Company
What they committed to
Capital
Announced
Google Cloud
Partner fund for agentic AI, explicitly including teams of embedded Google forward-deployed engineers alongside the large integrators
$750M
22 Apr 2026
Anthropic
Ode, an enterprise AI services firm with Blackstone, Hellman & Friedman and Goldman Sachs. Engineers embedded in customers' core operations, aimed first at PE-owned mid-market firms. Launched with about 100 engineers
~$1.5B
4 May 2026
OpenAI
The Deployment Company, with nineteen investment firms, consultancies and integrators led by TPG. Acquired Tomoro and roughly 150 forward-deployed engineers
>$4B
11 May 2026
AWS
A Forward Deployed Engineering organisation. Pods of five or six engineers embedded per customer, seeded with "thousands" of them
$1B
30 Jun 2026
Microsoft
Microsoft Frontier Company. Six thousand people, industry specialists and engineers, working inside customer organisations
$2.5B
2 Jul 2026
Total
From the five companies with the best view on earth of how AI is actually being used
$9.75B
71 days
The companies with the most privileged view of how AI is really being used know their customers are going to need help. And that help is a service, not just a product. Sequoia's number for why: for every dollar an enterprise spends on software it spends six on services.
Google Cloud: googlecloudpresscorner.com, 22 Apr 2026. Anthropic / Ode: blackstone.com and cnbc.com, 4 May 2026. OpenAI: openai.com/index/openai-launches-the-deployment-company, 11 May 2026. AWS: aboutamazon.com, 30 Jun 2026. Microsoft: CNBC and GeekWire, 2 Jul 2026. 1:6 ratio: Julien Bek, "Services: The New Software", sequoiacap.com, 5 Mar 2026.

90 sec The punchline to slide 11. Deliver it as one move: the ledger, then the line. Do not explain the ledger row by row. Say: "Nine and three quarter billion dollars, in seventy-one days, from the five companies who can see every prompt on earth."

The honest counter, have it ready because someone will make it. These vehicles are partly distribution plays to lock in model spend, and McKinsey and Capgemini invested in the OpenAI one, so the incumbents are hedging rather than surrendering. Concede it and keep the point: even as a distribution play, it is an admission that the model does not deploy itself.

Q&A ammunition, not on the slide because it has no published source: a founder selling an AI product to enterprise procurement teams told you it takes six to eight months to sell the product and two to three weeks to sell the same outcomes as a services engagement. Attribute it as one founder's experience, not a benchmark.

Only announced headcounts are on the slide: Microsoft 6,000, Tomoro about 150, Ode about 100, AWS pods seeded with "thousands". The Global Week estimates are removed; do not quote them.

Time check. If you are past 09:38 arriving at slide 11, skip 11 and 12 together and go to slide 13. They are the first cut, as a pair.

13 · Section 3 · Where this is going
Slide 13 · What to fund

Three things I look for

The moat is moving to process knowledge and the org interface. The model and the agent are becoming inputs. But we don't have the maturity to assess agent quality yet, only that we can tell when it doesn't work.

01 · Real experience inside the client

Someone who has sat in the organisation being sold to. A senior operator paired with great technology, rather than two very smart people building agents for an industry neither has worked in.

02 · Workflow and organisation-level problems

Workflow and organisation-level problems rather than individual productivity: the second spectrum, step three and above. If the customer did not have to change how the work runs, there is nothing to defend.

03 · The 48 NFRs already solved

Infrastructure, cost, routing, model selection, evaluation, hosting, done before the first customer. The shrink-wrapped feel of a consumer product, pointed at supply chain or procurement. Very few teams have it, and it shows in how few reach the workflow.

"We don't need to own the models, but we own the context. We own the interface which sits between your workflow and the models." Jeremy Palmer · PhysicsX · September 2026
"37-40% of AI time savings get eaten by review. The most important product metric is Time to Review." Ryan Daniels and John Sarihan, Crosby · Sequoia Training Data · 2 September 2025
Jeremy Palmer quote: call of 10 September 2026, cleared for use. Crosby quote: Ryan Daniels and John Sarihan on Sequoia Capital's Training Data podcast, 2 September 2025.

2 min Your own filter, from the plane notes, verbatim in substance. This replaces the five diligence questions as the leave-behind. Do not do both, it is the same job twice.

The Jeremy quote is cleared. Everything else he said on that call is not, so do not improvise around it. Nothing about PhysicsX numbers, customers, hiring, his transition, or his view of Visium.

The consultants line is spoken, not on the slide (v4 removed the green callout): "One consequence I did not expect: consultants can make very good founders now, because they know where the work actually happens inside an organisation, and they already have the trust to get near it. I have become suspicious of founders who have never been inside one." It will get quoted back to you at coffee and it is slightly provocative in a room of venture investors who fund the opposite. Deliver it lightly and let them argue.

Time check. You should be at 09:41 leaving this slide, and at 09:38 if you cut as planned. If you are past 09:42, go straight to the close and take three questions instead of five.

Slide 14 · The close · the three thoughts

Three thoughts to leave you with

01
We already have everything we need to move the bars
Nothing on those charts is waiting for a new model. The empty cells are organisational, and every one of them could be filled with what shipped this year.
02
The frontier will move anyway
It is moving while we sit here, and in July we saw what lies beyond the 6% "high performers", and we saw we're not ready for it.
03
Will our vector look inconsequential in hindsight?
We allocate our time and resources to progressing the frontier of innovation and business, but the social and societal vectors are moving too, and they may be moving faster.

60 sec Rationale for Option B, and this is my recommendation: it is what you wrote on Sunday, it is the only part of the talk that is not about money, and it is the thing they will still be turning over at the coffee break. It also earns the Amodei material on slide 10 instead of leaving it as a fun fact.

Thought two names the 6%. If anyone asks what a high performer is: 5% or more of EBIT attributed to AI plus "significant" value, 92 of 1,719 respondents, flat on last year. The bar is modest and still almost nobody clears it.

Thought three is a question. Leave it as a question. Do not answer it. Then stop talking and take questions. A keynote at nine fifteen that ends on an unanswered question is the one people come and find you about.

The three allocator moves from the old Option A close (ask which step they are at, ask for the second dashboard, underwrite the workflow redesign) can go over slide 13 as a spoken list, or on a card by the door. They should not be the last thing you say.

Then: "Before you ask me anything, one for you. What are you being told about AI right now that you do not believe?"