Newsletter · · Ashutosh Agarwal

Salesforce and HubSpot Ship Pay Per Result Pricing at Two Dollars a Resolution - Is SaaS Broken? - Week of July 31, 2026

A synthesis of what software podcasts said for the two weeks ending July 31, 2026, including Salesforce and HubSpot shipping outcome-based pricing at about two dollars per resolved issue, enterprise token-spend caps hardening into governance, Datadog's consumption tailwind, and a detailed bear teardown of Atlassian.

Is SaaS Broken?

Week of July 31, 2026: Now You Pay Per Result, Not Per Seat

Coverage window: July 18 to 31, 2026.

TL;DR

  • The big one: Both Salesforce and HubSpot shipped real, outcome-based pricing this fortnight, you pay roughly $2 per issue resolved, and nothing if the AI fails. Last week this was a Bret Taylor slogan ("you pay for outcomes, not tokens"). Now it's a live product at two of the seven names we track. The per-seat model isn't dead, but it's officially no longer the default.
  • The token panic has a governance answer. The scary "enterprises are throttling their AI bills" story from mid-July calmed into something more structured: spending caps ($1,500/month at Uber, $200/week at Tesla), FinOps dashboards, and vendors like HubSpot choosing to eat inference cost rather than pass it on. The bill shock is real; the response is management, not retreat.
  • Datadog keeps winning the "usage goes up" trade; Atlassian became the sell-side's designated victim. A software analyst named Datadog among the few names up on the year because AI drives more data to meter. In the same window, a value-investing podcast used Atlassian as its case study of a "bad business AI made worse," 80%+ gross margins and 98-99% retention, but negative operating margins and a $600M browser deal they think is a write-off.

What's new

Ranked by what actually moves a position.

1. Outcome pricing stopped being a slide and became a SKU, at both Salesforce and HubSpot

This is the development of the fortnight. For weeks this newsletter has tracked the idea that AI would blow up the "pay per seat" model and that the endgame might be paying for outcomes. This is the two weeks it got real, at two in-scope names, at almost the same $2 price point.

Salesforce. On Insights for IT Negotiations (July 23), UpperEdge's Salesforce practice leader Adam Mansfield (analyst, a sourcing and negotiation advisor, not a Salesforce insider) decoded the new Agentforce "help agent" pricing. The mechanic: $2 per resolution (versus the old $2 per conversation). You pay nothing unless the AI resolves a customer issue start-to-finish on its own. "If in the service agent interaction… that customer doesn't feel they got the answer and they flag it, I'd like to speak to someone, a human, not resolved. Any type of escalation, you don't pay for that." There's still a consumption engine humming underneath, you prepay in packs of at least 1,000 resolutions and draw the pool down, but Data 360 and Agentforce themselves stay unmetered.

Why it matters for the numbers: Mansfield's read is that consumption pricing was slowing Salesforce's sales cycles. Customers were stuck asking "how much will I use? what's the ROI?" and stalling. Outcome pricing removes that friction, and, in his words, "they are a publicly traded company. They do need to increase their revenue. They have significant investor pressure." He also pinned down why Salesforce moved: customers "see the horror stories that are out there about OpenAI and Anthropic… companies that started using ChatGPT Enterprise and all of a sudden have a bill for $4 million that they weren't expecting." Salesforce is selling bill certainty as a feature.

HubSpot. On Tech Disruptors (July 30), HubSpot's CTO (operator) described essentially the same move, and went further on the margin question everyone actually cares about. HubSpot's Customer Agent is priced per issue resolved: "if customer agent… for some reason is unable to resolve the customer issue, you pay us nothing." Current average resolution rate is "in the low 70s" (71-72%), with some customers at 85-90%. Its Prospecting Agent is priced per qualified lead delivered. Overall the company is moving to "a mix of seats and then credits… aligned to value-based consumption."

The line that should make every SaaS margin analyst sit up: on who pays for the tokens, the CTO said, "we see that as our responsibility on behalf of our customers", HubSpot is absorbing inference cost, not passing it through, and calling that part of the value it delivers to small businesses "who don't have the time or the expertise to get into AI." It manages cost by acting as "the Switzerland of models," auto-routing each task to whichever vendor, model, and size is cheapest for the needed quality. That's the whole gross-margin debate in one sentence: HubSpot is betting it can eat the roughly 50%-margin AI cost, hide it behind an 80%-margin subscription, and route its way to something acceptable.

Positioning read: This is the first hard evidence that the "outcome pricing" thesis is a real product decision, not a conference-stage idea, and it's landing at two of our seven names simultaneously. Bullish for the narrative that incumbents can re-rate their pricing to AI. Ambiguous for margins: pay-per-resolution transfers execution risk onto the vendor, and someone still pays the inference bill.

2. The token-throttling scare matured from panic into plumbing

Two weeks ago the market was spooked by enterprises slamming the brakes on AI spend. This fortnight that story got specifics and a shrug. On The AI Daily Brief, "A Field Guide to AI Market Freakouts" (July 23), Nathaniel Whittemore (analyst) laid out the actual caps: Uber is capping users at $1,500/month; Tesla is allowing $200/week with the ability to request more. His counter: caps "matter far less than the realization that we're using a vanishingly small portion of the intelligence we'll ultimately demand." (Same episode: momentum stocks had their worst month on record, down 40% per Morgan Stanley, and Goldman flagged hedge funds selling tech in record size, so the software tape is ugly regardless of fundamentals.)

The scale of the underlying spend, from AI to ROI, "AI is a Compensation Scale Expense" (July 21) (analyst): one unnamed company hit a $500 million-a-month AI bill; Uber imposed a $1,500/engineer/month cap; Microsoft revoked thousands of Claude Code licenses at fiscal year-end as a budget move. Anthropic's run rate went from $9B at end of last year to $47B by mid-May, with 1,000+ enterprises paying $1M/year for Claude; OpenAI is at ~$33B and now processes 15 billion tokens a minute, up 50x in three years. And the punchline for anyone modeling SaaS: AI software and token spend was ~$100B in 2024 (under 2% of global IT spend) and Gartner projects ~$2 trillion by 2030, while the entire SaaS market, built over 25 years, is only ~$250B.

Where does that money come from? The hosts argue there are really only two budgets big enough: the software budget (as SaaS turns into AI software) and the labor budget. And they see labor moving: 113,000+ tech layoffs across 179 companies by mid-May, 48% explicitly attributed to AI, while public-SaaS revenue per employee has climbed from ~$300K to ~$400K (+33%) as companies decline to backfill attrition.

The plumbing response showed up on Business of Tech (July 23), where CompTIA's Seth Robinson (analyst) described the model shift bluntly: "from per seat or per user to per seat plus consumption," because "tokens aren't free… there's a component of a running meter." He expects the FinOps discipline that grew up around cloud to migrate into DevOps, with the twist that "machines can consume it at machine speed," so bills balloon faster than in the cloud era.

Positioning read: The throttling is real and it caps near-term upside for pure usage-metered revenue. But the framing shifted from "AI budgets are collapsing" to "AI budgets are being governed." That's less bearish for consumption names than the mid-July panic implied, and it's precisely why Salesforce and HubSpot are selling bill-predictability.

3. Datadog is still the cleanest "AI raises my meter" trade

On What's Next For Markets, "Software's AI Reckoning" (July 26), sell-side software analyst Billy Fitzsimmons (analyst) gave the most useful map of where to hide in software right now. He says investors have retreated to the harshest possible yardstick, GAAP enterprise-value-to-free-cash-flow, excluding stock comp, and that the whole group de-rated on February 3 ("SaaS Pot Club's day") when Anthropic dropped vertical plugins. His three hiding spots: (1) hyperscalers (Microsoft, Oracle, but their free cash flow has gone to zero or negative on capex); (2) non-hyperscaler names that already bill on consumption, "Datadog, Snowflake, or Twilio do very well year to date because they charge on a consumption basis as AI has led to more data or usage or volumes"; and (3) select vertical software (his top pick is ServiceTitan). Those consumption names are "flat up for the year," which he says he "can't say for most of my list."

Positioning read: This is the structural bull case for DDOG in one paragraph. When AI creates more machines, logs, and traffic, Datadog meters all of it. The catch is item 2 in this issue: the same token-throttling reflex that pressures OpenAI can eventually pressure a customer's observability bill too. So far, volume growth is winning.

4. Atlassian became the sell-side's designated "AI made a mediocre business worse" case study

The most detailed single-name teardown of the fortnight was The Synopsis, "AI SaaS Risk (again), Atlassian, Duolingo & Uber" (July 23), from the Speedwell Research investors (analyst). They picked Atlassian specifically as a lower-quality software company to stress-test the AI-risk thesis. The financial picture they laid out (all their figures):

  • 80%+ gross margins and 98-99% net revenue retention, genuinely best-in-class on the top of the P&L. (Notable: this is the first concrete NRR figure on any in-scope name in roughly seven weeks, though it's their estimate, not a company print.)
  • But operating margins are negative (~-4%), and the company (founded 2002, so 24 years old) hasn't been GAAP-profitable since 2022.
  • R&D is 50-52% of revenue, and was also ~50% back in 2017, so zero operating leverage despite growing revenue 10x (from ~$600M to over $6B). Their jab: customers say "the product has barely changed," so "I don't understand how you're spending 50% on R&D."
  • Stock-based comp is ~25% of revenue, versus Salesforce, which cut SBC to ~6-7%.
  • The stock is down ~75%, taking EV/sales to 4x from a mid-teens-to-20x trailing multiple.

The AI-specific argument is the one worth internalizing for the whole group: they don't think AI broke Atlassian, they think AI widens the distribution of bad outcomes. The mechanism: the user's starting point moves from the Jira dashboard to a company-wide AI agent (Copilot, Claude, Agentforce). If work gets orchestrated at that AI layer, Atlassian's own agent, Rovo, looks like a sub-agent, not the orchestrator, "reduced visibility, reduced customer interaction, the potential for your workloads to basically be circumvented over time. Maybe you don't actually need Jira." Their evidence that Atlassian itself is scared: it spent over $600 million buying a browser company to try to own the AI interface, which they predict "is going to be a write-off." (They also flagged co-CEO Mike Cannon-Brookes stepping down.)

Positioning read: The bones of a paired trade, long the names with real AI usage tailwinds and pricing power (Datadog; arguably Salesforce), short or avoid the legacy work-management names where an orchestration layer can sit on top. TEAM is the sell-side's chosen short expression of the seat-erosion thesis right now.

The debate: is per-seat SaaS structurally broken, or are incumbents re-rating to consumption?

Steel-manned, both sides, using this fortnight's material.

The bear case (per-seat SaaS is structurally broken).

  • The pricing model is being abandoned by the incumbents themselves. When Salesforce and HubSpot both move to pay-per-outcome in the same two weeks, that's not a defensive tweak, it's an admission that "charge per human seat" doesn't survive a world where one agent does the work of several people. Fewer seats, and the ones that remain are re-based to results.
  • "Price increases only" has hit the wall. On 20VC's July 30 episode, Jason Lemkin (analyst) argued legacy SaaS is "five years into no net new customers and price increases… I don't think there's another five years of those knobs and dials left." His own war story: Marketo raised his price from $22,000 to $80,000 since 2020; he churned, and he bets they've lost ~20% of customers. He notes Wix trades below 1x revenue, "you're paying 10x for Base44's revenues, and you're getting the core business for free."
  • The margin math is genuinely worse. On 20VC, July 20, Fireworks CEO Lin Qiao (operator) gave the cleanest structural warning of the fortnight: "During SaaS time, product-market fit and a durable business almost are equivalent… Now, product-market fit and a durable business are two separate concepts." Companies can "scale into bankruptcy," and incumbents with huge traffic face it worst, they "cannot afford" to roll AI features to their whole base because "cost forecasting… [there's] just no way you can justify this." Her own gross margin sits at 30-40% versus traditional SaaS at 80% (she insists that's a hyper-growth choice, not a ceiling).
  • The multiple, not the earnings, is the problem. On The Pomp Podcast, July 18, Jordi Visser (analyst) argued earnings stay fine but multiples compress for three years as the market prices in disruption of all public companies, "you can't view Adobe and Salesforce past the next three years" because their terminal value is now a live question. Cheaper tokens don't kill the model makers; they arm AI-native competitors against every incumbent.

The bull case (incumbents are successfully re-rating).

  • They're capturing the new spend, not losing it. Outcome pricing is how incumbents tax the agent economy. HubSpot resolving 71-72% of support issues and charging per resolution is a brand-new revenue line that didn't exist under pure seats, and it can grow even as seats shrink.
  • Consumption names are the ones actually up. Fitzsimmons' point stands: Datadog, Snowflake, Twilio are flat-to-up on the year because AI drives metered volume. The tape is punishing the group, but the businesses tied to usage are outperforming.
  • The token-throttling scare is a governance story, not a demand story. The AI Daily Brief argument: caps at Uber ($1,500/mo) and Tesla ($200/wk) barely scratch how much intelligence enterprises will eventually buy. And investor Nick Carter's framing (quoted there) is that the pain lands on the labs, "the hyperscalers will be fine. It's just OpenAI and Anthropic that won't be, in their current forms," not on the application incumbents.
  • Stickiness is real and switching is bureaucratic. Even Atlassian's harshest critics conceded Jira is "very hard to rip out." Visser adds that Fortune 500 procurement won't move to Chinese or open models quickly. Incumbents get years to adapt.

The swing factor. It comes down to a single question the whole sector is now organized around: when incumbents re-price to outcomes, do they keep the margin? If HubSpot and Salesforce can absorb inference cost (routing to cheap models, riding the 10x cost decline Lin Qiao expects) and still clear high-70s or 80s gross margins, the re-rate story wins and this is a pricing upgrade. If inference cost compresses AI-feature margins toward the 50s and outcome pricing hands execution risk to the vendor, the bear wins and the group's terminal multiple keeps sliding. The one number that would settle the entire debate, an explicit AI-feature gross margin, still isn't disclosed by any of the seven.

Stocks in play

Ranked by how much this fortnight's material actually moves the name.

Salesforce (CRM)

  • Bull: Shipped outcome pricing ($2/resolution) that removes the consumption-forecasting friction slowing its sales cycles; selling bill-certainty against OpenAI and Anthropic horror stories. Marc Benioff spending $300M/year on Anthropic (~3.8% of dev salaries, per Mercor's CPO on 20VC, July 25, operator) signals it's building aggressively, not defending. Returned capital: $27 billion of buybacks in Q1 (Fitzsimmons). SBC already disciplined at ~6-7%.
  • Bear: Pay-per-resolution transfers execution risk to Salesforce and keeps a consumption meter running underneath (the 1,000-resolution packs). Buyback hasn't held the stock. Visser's "can't see past three years" terminal-value worry names Salesforce explicitly. That $300M Anthropic bill is a cost line that only grows.
  • Next catalyst: Q2 FY2027 print (late August), watch for any reported Agentforce resolution volume or revenue, and whether outcome pricing shows up as a growth or a margin story.

Atlassian (TEAM)

  • Bull: 80%+ gross margins, 98-99% net revenue retention, genuinely sticky (Jira is "hard to rip out"); down ~75% to 4x EV/sales prices in a lot of pain; AI-agent users reportedly show ~2x the ARR.
  • Bear: Negative operating margin, R&D stuck at ~50% of revenue with no visible product payoff, SBC ~25% of revenue, a >$600M browser acquisition the Speedwell team calls a likely write-off, co-CEO departure, and a structural risk that Rovo ends up a sub-agent beneath Copilot or Agentforce. The sell-side's designated seat-erosion short.
  • Next catalyst: F4Q/FY2026 results (late July or early August), watch cloud revenue growth deceleration, any Rovo adoption or monetization metric, and whether the browser deal gets quantified.

Datadog (DDOG)

  • Bull: The cleanest "AI raises my meter" name, consumption billing means more AI-driven data, logs and traffic flows straight to revenue; one of the few software names up on the year (Fitzsimmons).
  • Bear: The same token-throttling and FinOps reflex that hits the labs can eventually reach a customer's observability bill; rich multiple leaves no room for a usage air-pocket.
  • Next catalyst: Q2 2026 earnings (early August), the single most important read in this whole newsletter is whether any consumption-priced in-scope name shows the mid-July "throttling" in its usage or billings.

HubSpot (HUBS)

  • Bull: Furthest along on the pricing pivot with real product (Customer Agent per-resolution at 71-72% success; Prospecting Agent per-lead; seats plus credits). Proprietary "growth context" data and two decades of go-to-market learnings as a moat versus raw frontier models; "system of action, not system of record."
  • Bear: It has chosen to absorb LLM inference cost as "our responsibility," a direct hit to gross margin if model costs don't fall as fast as Lin Qiao's 10x-in-three-years hope. Downmarket SMB base is the most price-sensitive and churn-prone.
  • Next catalyst: Q2 2026 print, watch for any disclosed AI-feature gross margin, Breeze or agent attach or credit revenue, and NRR.

Adobe (ADBE)

  • Bull: Still the deepest creative-software moat and the broadest AI feature surface (Firefly) among the seven.
  • Bear: Named explicitly in Visser's "can't value past three years" terminal-value worry; the seat and freemium-pressure and inference-margin questions from prior weeks are unresolved.
  • Next catalyst: Q3 FY2026 results (mid-September).

Asana (ASAN)

  • Bull: Cheap, work-management incumbent with an AI Studio agent story if it can convert it.
  • Bear: Sits squarely in the category Speedwell used Atlassian to indict, horizontal work-management, crowded (named alongside Monday, Linear, Notion as Atlassian competitors), and exposed to the orchestration-layer risk.
  • Next catalyst: Q2 FY2027 (early September).

Monday.com (MNDY)

  • Bull: Fast-growing work-OS with an AI-credits model that, in theory, is already a consumption on-ramp.
  • Bear: Same seat-heavy, horizontal, orchestration-risk bucket as Asana and Atlassian; explicitly floated as a PE-style "buy cheap, lever, squeeze" target on 20VC (July 30), "can you buy a Monday or a Wix at a price where you can make a return?"
  • Next catalyst: Q2 2026 (mid-August).

Read-throughs

  • Adjacent seat-heavy SaaS (HUBS, ASAN, MNDY). The Speedwell and Atlassian teardown is really a read-through indictment of the entire horizontal work-management category, the exact bucket ASAN and MNDY live in. The orchestration-layer risk ("one company-level AI agent that orchestrates a lot of this") applies to all of them. HUBS is the counter-example that's doing something about it (pay-per-resolution, absorbing inference cost, proprietary data moat).
  • Model and inference vendors (OpenAI, Anthropic, Bedrock, Azure OpenAI, DeepMind). Pricing is compressing fast and the anchor points are now public: per million tokens, OpenAI's Fable ~$56, Sol/Claude 4.8 ~$26, Grok ~$1.50, Chinese models ~$0.50 (All-In, July 18); Claude Opus 5 launched at $5 input and $25 output; Kimi K3 at ~$15 output versus OpenAI's ~$30 and Anthropic's ~$50. The bear case for the app layer is that this arms AI-native competitors; the bull case (Nick Carter, via the AI Daily Brief) is the opposite, cheap tokens are a subsidy to application incumbents, and the pain lands on the labs, not the apps. For our seven, cheaper inference is unambiguously good if they keep serving customers on premium outcomes while routing the back end to whatever's cheapest, exactly HubSpot's "Switzerland of models" play.
  • Multiple de-rating risk (the whole group). Independent of any single name: momentum stocks had their worst month on record (down 40%, per Morgan Stanley via the AI Daily Brief), hedge funds dumped tech in record size (Goldman), and the market punished even Alphabet's beat for capex. Investors have retreated to GAAP EV/FCF ex-SBC as the yardstick (Fitzsimmons). The risk is that the "AI reckoning" rotation keeps compressing software multiples regardless of which names are actually winning, which is what makes the paired-trade framing (long usage and pricing-power winners, avoid seat-heavy laggards) more attractive than a directional bet on the group.

What changed vs. last issue

Last issue (July 17) the dominant, brand-new theme was token-spend whiplash, enterprises going from "token maxing" to slamming the brakes, with outcome-based pricing named by Bret Taylor as the theoretical endgame. Here's what moved in the two weeks since:

  • Outcome pricing went from slogan to shipped product. Last issue: Bret Taylor said "you're not paying for tokens, you're paying for business outcomes." This fortnight: Salesforce and HubSpot both launched pay-per-resolution products at ~$2/resolution. This is the single biggest step-change and the reason it's the subject line.
  • The token-throttling story matured. Last issue it was raw panic ($6,000 to $600 cuts, "doubling every 45 days"). This fortnight it got concrete caps ($1,500/mo Uber, $200/wk Tesla), a governance framing (FinOps to DevOps), and a vendor response (HubSpot absorbing inference cost). Less "the sky is falling," more "here's how it gets managed."
  • First in-scope NRR figure in roughly seven weeks, for TEAM. The Speedwell podcast surfaced Atlassian at 98-99% NRR and 80%+ gross margins (their estimate, not a company print).
  • A named single-stock bear thesis emerged: Atlassian. Last issue TEAM was mention-only. This fortnight it got a full, financially detailed teardown as the sell-side's chosen seat-erosion short.
  • Salesforce coverage shifted from "rebutting a downgrade" to "pricing strategy plus capital return." New this fortnight: the $2/resolution mechanic, the Benioff $300M Anthropic spend, and $27B of Q1 buybacks.