An expanded view of public comparables - covering SaaS, AI Infra, and Cyber. Refreshed weekly, from The Weekly Crunch
This week’s insight
AI is everywhere in GTM. So where is the productivity?
Nir Goldstein, Partner, Head of G2M Platform
AI is now embedded across the revenue organization, from prospecting to deal management. Work that took thirty minutes takes five. What hasn’t followed is the thing that should have: shorter sales cycles, lower CAC, more ARR per rep.
Across dozens of companies I work with, the fundamental challenges of building and scaling a GTM organization look much as they did before. That isn’t evidence AI is failing. It points at something more useful: making individual tasks faster is not the same as making the organization more productive. If an AE saves twenty-five minutes preparing for a meeting and finishes the year generating the same ARR, we bought efficiency and very little else.
The gap is process, not capability. AI needs a strong process to operate inside, like who you sell to, the value you create, how your best teams actually sell. All this should be encoded into playbooks that define the objective, the context the model needs, and how success is measured. AI can help sharpen a playbook. It is not a substitute for building one. Where the underlying process is mediocre, AI executes a mediocre process faster, and at greater scale.
The largest prize isn’t a better top rep. In a study of more than 5,000 customer-support agents, AI lifted productivity about 14% on average — and roughly 34% among the least experienced. That is what it looks like when a tool spreads the practice of strong performers to everyone else. The opportunity in GTM is the same: move the average seller closer to the standard of the best. It’s not about raising the ceiling. It’s about raising the floor.
Then close the loop. Tie the playbook to a KPI, let AI surface which behaviors correlate with progression and where deals consistently break down, and feed that back in. GTM fundamentals → best practices → playbook → AI execution → KPI → feedback → improved playbook. That is when the value compounds.
Every cohort constituent is plotted. Bubble size scales with market cap. Per-name detail for each cohort is in the tables below.
AI Infra EV / NTM Revenue Multiples Over Time
AI / Data-Center Capex
This week in AI infrastructure
Large-Cap Security
Large-Cap EV / NTM Rev vs. Growth
Security Comps
Sorted by enterprise valueas of
Company
Market cap
EV
EV/NTM Rev
NTM Revenue Growth
P/E (NTM)
Gross margin
FCF margin
Macro Indicators
Macro-Economic Indicators
Sources and method. Public market data from stockanalysis.com (S&P Global Market Intelligence and Nasdaq Data Link), as of September 25, 2026. The two EV / NTM Revenue Multiples Over Time charts are built from PitchBook consensus data. TSMC's EV / NTM Revenue is taken from PitchBook rather than stockanalysis, so the figure in the Semiconductors table matches the multiples-over-time chart; its remaining columns are stockanalysis-sourced like every other name. EV / NTM Rev is enterprise value divided by consensus next-fiscal-year revenue. Growth-adjusted multiple is EV / NTM Rev divided by NTM growth. Rule of 40 is NTM revenue growth plus LTM free-cash-flow margin. P/E shows "n/m" where earnings are negative. SG&A percent of revenue is shown because the data source does not break out a separate sales and marketing line. Year-to-date trading is an ETF-style, market-cap-weighted cumulative return from January 2026, using a fixed basket at current share counts. P/E is the market-cap-weighted trailing P/E of profitable constituents, shown on a log scale. Capex is total reported capital expenditure, as companies do not disclose an AI-only figure. LTM is the sum of the four most recently reported quarters of cash capital expenditure, from each company's cash-flow statement (source: stockanalysis.com), and it updates every quarter rather than once a year. Fiscal calendars differ, so the LTM windows are not identical: Microsoft, Amazon, Alphabet and Meta run through June 2026, Oracle through May 2026. The 2026 column is company guidance or consensus (Amazon $200B, Alphabet $185B, Meta $135B). Microsoft and Oracle have already closed their 2026 fiscal years, in June and May respectively, so both show actuals rather than estimates: Microsoft $115.9B and Oracle $55.7B, which is why each matches its LTM bar. Microsoft's widely quoted FY26 figure of $190B, later revised to roughly $175B, counts capital expenditure plus finance leases; this chart uses cash capital expenditure from the cash-flow statement throughout, so the two are not comparable. Guidance for the calendar-year companies is not always stated on that same basis, so their 2026 bar and LTM bar are not strictly like for like. SpaceX issued no formal 2026 capex guidance; its bar is H1 actuals of $28.5B plus management's remark on the Q2 call that quarterly capex stays near $18.4B, so it is a derived figure rather than a company forecast. Bubbles on the EV / NTM Rev and Rule of 40 charts are sized by market cap, scaled so that area rather than width tracks it. Sizes are capped above roughly $680B and below roughly $19B, so the very largest names are drawn alike and so are the very smallest; hover any bubble for the figure. For the security chart, NTM Revenue Growth is used as a proxy for ARR growth. The 2025-26 SaaS IPOs (SailPoint, Figma, Navan, Netskope, and MNTN) are added to the scatter; none currently price above the EV / NTM Rev top ten. Macro indicators: the 10-year Treasury yield, the effective Federal Funds rate and CPI year-on-year, monthly from January 2012 (macro tracker workbook, PitchBook-sourced). This is not investment advice. Verify before acting.
Nir Goldstein, Partner, Head of G2M Platform