Each Monday, a short list of the sharpest sales + GTM thinking from the past week. Not summaries — read the originals, they're better. Just what's worth your time.
Four pieces this week.
1. Sam Senior — "By the time the buyer calls you, they've already decided 70-80%"
Sam Senior (Founder & CEO, TestBox) joined GTMnow to break down what's actually happening inside the modern B2B software buying process. His central observation: buyers are doing the majority of their research via LLMs before they ever talk to a vendor. By the time that first call happens, they've already formed strong opinions about your product and your competitors. The discovery call, as it was designed, is dead — your job on that first call is to validate (or correct) what they already believe, not discover their needs together.
The downstream pressure from this: the shortlist has compressed from 3–4 vendors to 1–2. If you're not on it before they make contact, you've already lost. And the mid-funnel is getting longer, not shorter — buyers come in with expectations shaped by what they've already TestBoxed in ChatGPT or Claude, and closing the gap between the 70% they understood in 30 seconds and the 100% real-world value takes more time and trust-building than it used to.
→ How Buyers Now Decide Before Talking to Sales (GTMnow #186, April 2026)
2. Jon Addison — "Specialization in the GTM motion was the unlock — not the AI products"
Jon Addison (CRO, Okta) recently walked through how Okta went from an $812M operating loss in fiscal 2023 to $766M in non-GAAP operating income in fiscal 2026 — one of the more dramatic GTM turnarounds in recent enterprise SaaS. The headline is striking. The mechanism is more interesting: it wasn't a new product that did it. It was a restructured GTM motion built around specialization.
Deals that included Okta's new products had 40% higher average contract value — but Addison is explicit that the ACV lift came from how the team was organized to sell them, not from the products alone. Similarly: 95% of Okta's top 100 deals last year were partner-led, not because they launched a great partner program, but because of three years of consistent direction-setting, incentive alignment, and friction removal. The AI governance gap — 91% of enterprises deploying AI agents, only 10% with a security strategy for them — is a real wedge, but a wedge you have to be organizationally capable of selling into.
→ Okta's CRO: From $850M in Losses to $760M Profit (GTMnow, April 2026); newsletter write-up
3. David Zhu — "The Frankenstein stack is an architecture problem, not a feature problem"
David Zhu (Co-Founder & CEO, Reevo) came out of stealth with $80M raised — $70M as a Series A co-led by Coastal Ventures and Kleiner Perkins — and a thesis that most companies are operating on what he calls the "Frankenstein stack": 15+ sales, marketing, and support tools stitched together, leaking institutional knowledge every time a top rep leaves or a tool gets swapped. His argument is that AI-native companies aren't going to fix this with better integrations; they're going to replace the stack with a vertically integrated AI revenue operating system.
Zhu spent years at DoorDash scaling engineering through a $75B IPO. The problem he kept seeing: fragmented tooling creates fragmented institutional knowledge. When you bolt AI onto fragmented infrastructure, you amplify the fragmentation rather than resolve it. The right answer, in his framing, is a single AI-native system where every customer interaction, every rep behavior, and every sales pattern compounds into a unified intelligence layer — rather than disappearing the next time you migrate to a new tool.
→ Inside Reevo's $80M Bet to Kill the $10B Frankenstein Stack (GTMnow #190, May 2026)
4. Keith Putnam-Delaney — "LinkedIn and Google are hitting yield ceilings — B2B paid is shifting whether you're ready or not"
Keith Putnam-Delaney (Co-Founder & CEO, Primer) was on GTMnow this week with one of the more practically useful takes on the state of B2B paid in 2026. His core claim: LinkedIn and Google Search are hitting yield ceilings for most B2B advertisers — audiences exhausted, CPCs up, incremental returns compressing. The teams pulling ahead are using CRM conversion data to build high-match-rate audiences on Meta (80% match rate) and Reddit (70%), applying B2C channel mechanics to B2B audiences.
The point that stuck: the only moat competitors can't copy is your targeting data. The brands building durable paid efficiency aren't winning on creative or bidding — they're winning because they've been feeding CRM conversion signals back to ad platforms long enough to build audiences that can't be replicated. And the LLM advertising question, which most teams have been treating as a future problem, just became a present one: Perplexity's ad program has been in beta, OpenAI just launched theirs.
→ Ads in ChatGPT Are Coming. What B2B Marketers Should Do Right Now (GTMnow #195, June 18, 2026)
Common thread this week. All four are arguing, from different vantages, that the operating layer for B2B revenue has to start earlier than most tools are built for. Buyers are deciding before the discovery call (Senior). Wins came from GTM structures built three years ago, not AI tools deployed last quarter (Addison). The infrastructure problem is architectural, not feature-level (Zhu). And demand is already moving upstream into LLMs (Putnam-Delaney). The common shape: the question isn't whether AI changes GTM. It's that the work begins earlier than current tooling assumes.
Next Monday: a new four.
If you've read something this week worth flagging in next week's list — hello@mallin.io.