Brian Peterson

💡 AI doesn’t just change how code is written – it changes expectations and outcomes. In the final section of Jellyfish’s AI Adoption Guide, we explore how expectations for engineering leaders are evolving as AI becomes embedded across the SDLC. Because the organizations pulling ahead are treating AI as a transformation, not a tooling upgrade. Inside, you’ll explore: - Why AI adoption is quickly becoming a leadership mandate, not an option - How to balance ambitious goals with realistic expectations - What cultural investments are required to sustain long-term gains - How data can be used to align executives and engineering teams The teams that win in the AI era won’t just have better tools – they’ll have leaders who know how to guide change. Download today: https://lnkd.in/dut_6WpQ

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the best way to filter/proxy if someone is not salesy is if they've sold to developers or technical audiences before awesome pod this week on GTMnow where Cristina Cordova talked about how they hired GTM at Stripe and now at Linear one filter they used in the early days - has this person sold a technical product to a technical audience? it's not that Stripe/Linear's product is super complex but it's because they wanted to make sure that they didn't have a sales-y culture with mercenaries on the front lines - that was a deliberate cultural decision they made they wanted cerebral sellers who were problem-first rather than money-first completely agree here, and we're seeing this with some of our AI-natives we work with at VibeScaling in some GTM motions, being a little more sales-y/go-for-the-kill is totally fine (and needed) - sales tech is one space where you need to match your buyers sometimes but in selling to devs/technical audiences or working with founders who are averse to sales this is typically the best filter to hire the right people + bridge post-founder-led sales developers have zero tolerance for traditional sales tactics - they'll sniff out BS immediately and shut down if someone has successfully sold to this audience before, it's a good signal they can lead with value, work with ambiguity, and build trust through product knowledge rather than pressure or outdated sales tactics well done with some of these recent guests GTMnow & Sophie Buonassisi

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CPTO Revolution: Why SaaS Companies Are Merging Product and Tech Leadership I’ve lived the pain of siloed leadership and the inefficiencies it creates many times. And I’ve also seen how tightly integrated product and tech can transform not just delivery, but outcomes. Traditional org charts are no longer keeping up. The most competitive SaaS companies aren’t hiring separate CTOs and CPOs anymore - they’re bringing both roles under one roof with CPTOs. This isn’t about headcount optimization. It’s about reducing friction and moving faster in a world where complexity is climbing and customer expectations evolve by the week. Here’s the core issue: when product and engineering leadership are separate, you’re always playing a game of telephone. Product defines what’s needed. Engineering estimates and negotiates. Product pushes. Engineering pushes back. Handoffs everywhere. Every step is a delay. A CPTO owns the entire journey - from insight to shipped product. One person accountable. One vision. One continuous flow. And it works. Companies running this model are seeing up to 40% faster go-to-market. Less tech debt. Clearer priorities. Higher team velocity. What Actually Changes 1. Aligned Metrics You stop optimizing for isolated outcomes. Product isn’t chasing adoption while engineering shields uptime. Both roll into shared business goals. 2. AI Gets Strategic With unified leadership, AI isn’t a bolt-on. It becomes core to how you design, build, and validate products - through data, feedback loops, and automation. 3. Developer Experience Matters Platform engineering gets the attention it deserves. Internal tools align with product delivery, and suddenly teams aren’t waiting - they’re shipping. UX maturity, platform architecture, technical scalability - all of it becomes a single continuum. You’re not just improving “process”; you’re aligning every decision to what users need and what’s feasible to build. The CPTO Skillset This isn’t a hybrid role - it’s an integrated one. You need the technical depth to guide architecture and the product judgment to say no to overengineering. You talk to engineers about latency and reliability, and to the board about market traction and differentiation. As AI accelerates software development, what matters is orchestration—of systems, teams, and strategy. You need to see the whole board. Cloud infra spend is growing ~10% yearly, mostly due to AI. That spend needs direction. CPTOs are the ones who can match innovation speed with platform efficiency. Companies clinging to the old CTO/CPO split are playing a slower game. Integration is no longer optional - it’s a competitive advantage. So—what’s keeping your org from making the leap?

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The future of GTM isn’t headcount. It’s infrastructure. You don’t need more SDRs. You need better systems. The companies that are scaling right now aren’t hiring faster. They’re building smarter. → Enriched data flows → Instant routing logic → Agent-led follow-up → Continuous learning loops This is the shift from manual ops to GTM engineering. From reacting to events, to designing for them. It’s not just automation, it’s autonomy. The sooner you invest in this foundation, the sooner your reps stop chasing forms and start chasing revenue.

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Love it or hate it, being in the SF Bay Area is still one of the biggest hacks for First Builders to discover huge opportunities before anyone else. But what can you do if you’re not here? Cecilia for example moved here in 2001 and instantly joined Yahoo after feeling the palpable energy around early internet. And it’s been nonstop ever since as she later joined Amazon, Cruise, Replit, and her own breakout company GC AI. She credits a lot to the emerging tech density and optimism in the Bay Area. But she also mentions additional ways people can identify these opportunities early on: - Follow intuition around big waves - Follow your envy - Never lose curiosity - Watch the teen hackers - Talk to peers early and often (builds long term networks/insights)

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We just helped Airbnb hire their first GTM engineer in Austin Solomon. Now, we want to help you do the same! Last year, I went to Natalie Ho with a problem: too many enterprise companies were asking me how to hire GTM engineers and I needed help finding them. So, we launched Clay's Talent Marketplace for GTM Engineers, which now has over 1,500 candidates across the ecosystem!! But soon we realized there was still a gap between the number of companies looking for great GTMEs and the number of people who can do the job at the highest levels. Teams like Decagon, Notion, and Lovable are quickly adopting the role, and the demand is outpacing the supply. That’s why next month we’re launching Clay’s first GTME Career Prep Bootcamp: Alpha Forge. It's an intense 4-week, 20-hour-per-week program designed to teach you how to think and operate like a world-class GTM engineer. This is not about learning Clay or any one tool. We'll teach you the mindset, core concepts, the full tech stack you could use, workshop creative strategies, and help you execute plays at scale. Graduates get priority introductions to top companies that are actively hiring GTM Engineers. For our first cohort, the bootcamp is 100% free. If you want to break into one of the fastest-growing roles in tech, this is your chance. The bootcamp starts April 6th. Learn more and apply here: https://lnkd.in/eFfbe-uK

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Imagine you could ask Claude anything about how your customers use your product, how much they pay, and how close they are to their limits. Ok, wish granted. We just shipped a Schematic MCP server for Claude Code. It connects your customer usage, billing, and entitlement data to Claude so you can query it in natural language from your terminal. "Show me how Customer X uses Feature A" returns their plan, current consumption vs. allocation, access status, and whether they've hit their limit. What used to require digging through three different dashboards now takes one question. Off to the races.

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We signed a new customer yesterday that heavily uses Stripe for billing and has some complex multi-entity and multi-currency challenges. They tried to develop an integration with a couple other accounting solutions and while doing so, asked Claude Code what other systems they should consider. Claude suggested they explore SoftLedger and by the time we got on our first call, he already had a pretty good integration specification. So he got access to his account yesterday. This morning, this is the message received by our customer success team: "This is unbelievable. I would estimate I am 60-70% of the way through what I need to do. Brought in 23,000 journal entries overnight." Something has changed in the buying journey for companies seeking an integrated accounting solution and I love it. Accounting technology should be an API driven solution that conforms to a companies processes. Tools like Claude Code enable companies to build bespoke integrations for their internal processes like never before.

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It’s 2025 B2B SaaS Benchmarks Report release day! We curate everything we see/hear into what’s most relevant for $2-20M SaaS. We at also included an Interactive Quarterly Board Scorecard Tool in there, which you can use to get everyone on the same page at your next board meeting. We’ve been using it for 5 years at EVERY Jurassic Capital portco board meeting. Thought it was time to share. Here are some key highlights from this year’s report: 1. Retention Under Pressure - Gross Revenue Retention (GRR) dropped to 88% median for $5-20M ARR companies (down from 90%), with logo retention falling to 86% - Net Revenue Retention (NRR) declined to 101% showing expansion is becoming increasingly challenging - AI is making switching easier, forcing vertical SaaS to rely on unique datasets and specialized labor for stickiness ⠀2. Growth vs. Efficiency Trade-offs - ARR Growth continues declining for 4th consecutive year - $5-20M companies at 27% - Burn Multiples are stable at 1.5x while gross margins improved to 77% as companies focus on capital efficiency ⠀3. Sales Getting Tougher - Expansion ARR now 37% of total new ARR as new logo acquisition becomes harder - Sales cycles finally dropped 9% YoY in early 2025 after years of increases, showing some market softening and hopefully leading to growth later in 2025 ⠀4. Lean Operations - Teams are doing more with less: Series A companies down to 15 people (from 20 in 2023) - Hiring at historic lows with dramatic reduction in 2024 software hiring, likely due to AI improving efficiency in OpEx - OpEx % of Revenue is dropping for companies utilizing AI (with some re-deploying into Growth via Sales & Mktg), while COGS are increasing due to compute costs - Founder salaries plateau around $140k median after $5M revenue ⠀5. Market Recovery driven by AI, but cautious road ahead for equity-backed companies, driven by barbell effect - Growth/PE dry powder and re-opening of IPO window drove best first half for software M&A ever in quantity with median multiples slowly rising to 4.2x - Concentrated LP fundraising into fewer firms leading to fewer rounds at higher valuations: Seed/Series A up 30%/2% in valuations but down 48%/60% in volume - Seed and Series A companies are still ~2 years to graduate to the next round; Bridge rounds increased to record highs as Seed/Series A at 46%/36% of all rounds - AI companies command 41% valuation premium over those that aren’t defined as AI Huge thanks as always to the researchers (like Carta, Peter Walker, SaaS Capital, Benchmarkit, PitchBook, Pilot.com, Software Equity Group, ChartMogul, Maxio, and so many more) who actually find this data via surveys, their own reporting/software, etc., and then we get to curate down. You all rock. See full report below and access our Benchmarking Tool here: https://lnkd.in/exT4bkva

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A lot of newer OSS companies have communities but they are heavily virtual. Ofc makes sense early stage. That said, well capitalized businesses should invest more $ into in-person events. Plane tickets, HH, on-sites with prospects and customers. You don’t need to sponsor, but you should aim to be the “must see” team to meet anytime you visit.

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the best lever to generate pipeline with outbound is literally to just have a strong offer this frustrates people in the modern leadgen space because they want modern AI-native tools to be the best lever. a strong offer seems too simple and underwhelming

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SF AI-Native Operator Takeaway #2: In AI-native PLG, the hard part isn’t conversion... it’s discovery. Many AI-native teams are still talking about PLG using a classic SaaS mental model, but based on operator conversations in SF, that model is starting to break down in fairly obvious ways. The biggest bottleneck right now isn’t conversion. It’s discovery. In traditional PLG, users generally understood the category before they ever signed up. The problem was obvious, the product’s value was legible from the homepage, and the “aha” moment tended to show up quickly in first use. In that world, PLG meant optimizing onboarding, reducing friction, and improving free-to-paid conversion because user intent already existed. AI changes that assumption. In AI-native products, users are often curious but unclear. They don’t yet know what’s possible, value depends heavily on workflow, context, data, and role, and the product can feel abstract until it’s applied directly to their job. As a result, many users stall not because the product isn’t valuable, but because they haven’t discovered how it fits into their world and how they can't live without it. This is the real distinction people kept coming back to. PLG conversion answers, “Is this worth paying for?” PLG discovery answers, “What problem does this solve for me, right now?” What’s working best in practice is less about funnel polish and more about clarity up front: role- or workflow-specific entry points, guided examples instead of blank states, and opinionated first actions that show users a concrete outcome before asking them to explore. This also explains a broader pattern across AI-native companies. Forward-deployed teams and services-heavy delivery aren’t just implementation tools; they’re discovery mechanisms. They translate abstract AI capability into concrete workflow value, observe real use cases users wouldn’t self-discover, and feed those learnings back into what eventually becomes productized. PLG isn’t going away, but in AI-native companies it’s being redefined. Self-serve no longer means self-explanatory. Education becomes part of the product, and discovery has to come before optimization. The teams making progress aren’t obsessing over conversion rates yet. They’re focused on whether users see themselves in the product, how quickly they reach a meaningful outcome, and whether the product helps users get to a meaningful outcome for themselves quickly, without too much guesswork. Bottom line: in AI, PLG is less about removing conversion friction early and much more about creating understanding first. Once they understand, they may be hooked. Tomorrow is my last SF AI operator takeaway focusing on everyone's favorite topic du jour: 996 work schedules.

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Every org chart tells you where the next systems of record will emerge. Look for the roles that exist because software failed. RevOps exists because someone has to reconcile sales, finance, marketing, and customer success. DevOps exists because someone has to bridge development, IT, and support. Security Ops sits between IT, engineering, and compliance. These "glue" functions are a tell. They emerge because no single system owns the cross-functional workflow. The org chart creates a role to carry the context that software doesn't capture. An agent that automates that role doesn't just run steps faster. It can persist the decisions, exceptions, and precedents the role was created to produce. Palantir Technologies built what’s now a $400B+ company on prescribed ontologies: define the schema, then map messy enterprise data into it. The next $400B+ company will be built on learned ontologies. This is what Jaya Gupta and I mean by “context graphs”: not graph databases or structured memory, but living records of how decisions get made, learned bottom-up from how the work actually happens. cc Animesh Koratana

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This CS Agent just replaced a $200,000 SaaS tool 🤯 And it’s a sign of what’s coming. GTM was built in the image of SaaS. →One tool for every function. → Dashboards everywhere. → Disconnected workflows patched together by process. But the SaaS-Native GTM model is breaking: → Too many tools. → Too many gaps. → Too much headcount. → Too many teams stuck reacting - instead of moving with intelligence. And now AI is exposing the real issue. Because it can understand, reason, act and learn. But SaaS-Native GTM misses the context, intelligence and memory. Recently, we worked with a $250M ARR SaaS business - where Customer Success was struggling to scale: → NRR was falling → Retention weak → CSAT in decline → Just 20% of customers actively engaged → No visibility into product usage → Sales handovers were non-existent → No insights flowing to Product or Marketing → Playbooks untouched since 2023 → Support inbox chaos → CSMs firefighting and burning out They were preparing to hire 8 more CSMs. And they were already spending $200,000 on a well-known CS platform. But that platform couldn’t help. → It was disconnected. → Outdated. → Siloed from CRM, product, support, and the rest of GTM. So we replaced it. Not with another tool. With GTM Intelligence Infrastructure and an AI-native CS Agent. Here’s what changed: ✅ Context was unified - CRM, usage, support, and calls all connected ✅ Signals were identified - risk and expansion surfaced in real-time ✅ Agents orchestrated action - plans, workflows, follow-ups ✅ Memory was built - learning from every customer touchpoint ✅ Slack became the interface - insights and recommendations delivered where work happens The CS Agent didn’t just alert. It acted: → Detected risk or opportunity → Explained context and surfaced reasoning → Set objectives based on account maturity and goals → Referenced ICP, persona, and past plays → Generated a dynamic plan → Guided the CSM with meeting prep, follow-up, and next best action → Reported and escalated when needed → Monitored resolution and learned continuously → Shared insights with Product, Marketing, and Sales - automatically This is what a system that thinks looks like. And the results? → 45% increase in CSM capacity (more active customer conversations, zero added headcount) → 250% increase in expansion pipeline → $200K platform fully replaced → CS team re-energised, focused, and aligned with GTM And this is just the beginning. GTM teams don’t want another disconnected tool. They need infrastructure that adapts. Agents that guide. Systems that learn. We’re now replacing legacy platforms across sequencing, forecasting, ABM, and CS - with one GTM system powered by context, intelligence and memory. If you build the infrastructure now - You gain compounding insight, action, and advantage. 📩 Tired of legacy SaaS tools in GTM? DM me. ✉️ Building GTM with AI? Get in touch. 🔔 Follow for more insights as we we build AI-native GTM.

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