Jigar Desai

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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Cloud cost uncertainty isn’t a fluke; it’s by design. Cloud providers profit from complexity: 💸 Granular billing 💸 Shifting discount programs 💸 Opaque pricing for AI workloads 💸 Exploding SaaS and shadow IT The result? Engineering and FinOps leaders are left scrambling to explain unpredictable cloud bills while innovation can’t slow down. Traditional dashboards and static budgets won’t save you. You need adaptive cloud cost management that embeds cost awareness into engineering workflows, detects anomalies early, and drives shared accountability. We break it all down in our latest post, including 5 steps to start leading through the chaos. Read it here: https://lnkd.in/ge4jadxr #FinOps #CloudCost #EngineeringLeadership #CloudComputing #AI #SaaS #Ternary

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AI Is Rewriting the Rules of Go-to-Market (GTM) As someone who’s spent years aligning strategy, systems, and outcomes across product, engineering, and GTM teams — this article by Dave Birckhead really hits home. In his latest Full-Stack Growth post, “Why AI Requires a New Kind of GTM Role and Org Structure,” Birckhead explains why traditional functional silos — marketing ops, sales ops, customer success ops — can’t keep pace in the AI era. AI doesn’t thrive in isolation. It thrives on shared context, unified data, and connected workflows. Most enterprise AI projects fail not because the models are weak, but because the systems they live in are fragmented. The takeaway: AI value is created when organizations reimagine entire workflows that span the customer journey — using AI as connective tissue, not as point solutions. Birckhead makes a compelling case for a new leadership role: Head of GTM Systems — the orchestrator of data, tools, and AI workflows across marketing, sales, and customer success. The payoff? ✅ Faster innovation ✅ Higher ROI on AI investments ✅ Seamless customer experience ✅ Shared measurement across the funnel Just as SaaS created Marketing Ops and RevOps, the rise of AI now demands GTM Systems Leadership — a discipline that connects, aligns, and scales the way growth truly happens. 📖 Full article here: https://lnkd.in/gU2fa5Jn By Dave Birckhead, Full-Stack Growth (Oct 14, 2025) #AI #GoToMarket #ProductOperations #GTMSystems #RevOps #DigitalTransformation #Leadership #Strategy #Innovation #FutureOfWork #OperationalExcellence #ProductManagement #SalesOps #MarketingOps #CustomerSuccess #B2BLeadership

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When a CEO publicly spits out their own product, they're revealing something far more damning: that they never saw the product as the point. To the MBA-trained executive, the product is just a vehicle. A means to margins, multiples, and an exit. The thing itself — what it does, who it serves, the craft behind it — is almost beside the point.

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Every PMM team needs to do this now! Get access to the source code repo. Here is why: 1. Ask questions in Claude or Cursor 2. Uncover how the product works 3. Answer technical and high level questions quick 4. Format outputs by audience 5. Even draw flow diagrams Now there is never a new question you can’t answer in minutes. No need to ask a PM. No need to wait.

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AI is creating a new execution risk: two communication protocols AI is not being “adopted.” It is being embedded into the workforce. That turns every company into a hybrid org, humans + agents, and creates a new execution risk: everyone becomes a manager. Design for it and you scale execution. Ignore it and you scale rework + cultural erosion. Hybrid orgs need a hybrid operating model. Two challenges show up immediately: 1) Management becomes a baseline skill Every employee will need to assign work, set standards, review output, and improve performance because agents become integral to deliverables. 2) Communication splits into two lanes Agents need precision: context, constraints, acceptance criteria, escalation paths, measurable feedback loops. Humans need trust, nuance, psychological safety, and space for disagreement and learning. The analogy that keeps coming to mind: global scaling. When teams span cultures and time zones, one communication style stops working for everyone. You have to be intentional about how you communicate, what belongs in writing vs live conversation, what needs specificity vs nuance, how you avoid misread tone, and how you keep collaboration strong when you can’t rely on real-time back-and-forth. Hybrid human + agent teams create the same requirement except the “other culture” is machine logic. If you don’t clearly separate the modes, people start pushing agent-style communication onto humans (or expecting human-like intuition from agents). Both are failure modes. I loved how Bethany Ayers discussed this on The Operations Room podcast (Episode 91, AI in the Future of Work): agent management becomes a core capability for every employee, and people will increasingly be measured by the outcomes their agents produce. In some orgs, AI adoption is already showing up in promotion and incentive conversations. I have built operating models across startups and global orgs, this is the same pattern: when the communication protocol changes, you have to design the norms or you pay later. Awareness is step one. Step two is building muscle: what “good agent management” looks like by role, how employees are trained, and how leaders reinforce the two-lane communication model. This is the work I do as an operator: designing operating models that scale without cultural drag. If you are a CEO/COO/CHRO navigating this shift (especially in a scaling org), I am happy to compare notes and share a simple way to operationalize it without adding bureaucracy. #COO #Operations #OrgDesign #FutureOfWork #AI #Leadership #Culture

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Every tech ecosystem in the US (and the world) is losing ground to SF. Network effects apply here too. Let’s stop beating ourselves up about everything we are doing wrong like any of it makes any difference at all - at best it’s a rounding error. Eg. If tax mattered SF wouldn’t be on this list as it’s literally the highest tax place to start a business in all of the US. This is no different than trying to compete against the IBM of the 80s, the Microsoft of the 90s or the Nvidia of today. How do you beat a leader that has strong network effects? Focus on what you can do that is differentiated and unique and in an area the leader cannot follow or doesn’t now care about.

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Conventional wisdom holds that startups function as a safety net in times of layoffs. As large companies shed workers, fast-growing newcomers are expected to absorb displaced talent, whether immediately or over time. That narrative resurfaced as Big Tech layoffs extended into early 2026, with more than 26,000 employees affected across 59 companies in January. Data from an Ascend survey of startups suggests the picture is more complicated. Among the VC-backed startups in our survey, nearly nine in 10 CEOs report actively using AI in the operation or building of their companies. Many say they are planning to hire smaller teams than they would’ve needed just a few years ago. To understand how those choices are being made, Ascend surveyed ~70 CEOs of VC-funded startups that are small and overwhelmingly early stage. The average company was founded in 2022, most are pre-seed or seed, and they are typically building their first 10 to 15 roles. These companies have always represented a small share of the labor market, but they are often where new roles are created first. Amazon was once a startup, too, which is why shifts at this stage matter. Opportunities certainly still exist, even in this economy! Later-stage startups continue to hire, new companies are being formed, and early-stage founders are looking for strong talent. Many people affected by layoffs will find a great landing, and some will start companies of their own, as has happened after nearly every downturn. But at the same time, hiring dynamics are changing in quieter ways. At large companies, AI-driven efficiency gains tend to surface through layoffs that make headlines. At early-stage startups, they appear to surface earlier, through hiring plans that never materialize and roles that may never be filled. Founders remain builders and job creators, while also operating under intense pressure to extend runway and move quickly with lean teams. In many cases, they are navigating the same forces as their larger peers, just earlier in the company’s life. *Layoffs and tight job markets still suck!* I’ve seen the impact firsthand, especially on people with families, visas, and very little margin. None of this should undermine the skills and humanity of those impacted. What this survey is intended to capture is a shift in how startups are being built from the beginning. AI is shaping team design long before those changes show up in headline job numbers, influencing when new roles appear and how many emerge. Startups may still create the next wave of jobs, as they always have. The path looks different this time, though, and it’s worth being clear-eyed about that, especially for the people living through the transition. If you’re a founder who is hiring, feel free to link your job board in the comments. 👉 Swipe through the carousel for the data and breakdown.

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I wrote about how product managers should deliberately decide which features should be built to an A grade and which ones should be built to a B or even only a C grade. Making the resource tradeoffs to optimize business outcomes is a core job of product management, and building every capability to an A grade is a poor use of scarce resources. https://lnkd.in/gTXBfNYE

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Jyoti Bansal joins Mudassar Malik for the Season 4 premiere of The Builders Playbook to share an honest look at scaling without losing quality, speed, or accountability. They dig into Jyoti’s journey from engineer to founder, the investor question that pushed him to go all-in on AppDynamics, and the lessons he brings to building at Harness today. You’ll hear about: ✓ Turning one focused use case into a platform. ✓ Harness’s “startup within a startup” model. ✓ Why small, AI-enabled teams move faster. 🎧 If you’re building or scaling, this one’s packed with practical takeaways: https://lnkd.in/enrHvuU3

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