Go-to-market is a space our team knows intimately well: many of us were previously buyers and users of these solutions, and Primary spends thousands of hours each year working with our portfolio companies on their sales, marketing and customer success rhythms. When we published our last GTM thesis, AI was just beginning to shake things up; today, that shakeup has accelerated dramatically. We have gained clarity on what truly matters for building a category-defining GTM company and are excited to hear your feedback.

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GTM startups must fundamentally transform the company P&L
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Our thesis: AI has finally made it possible to transform the GTM P&L, and the market is demanding companies act accordingly. Today’s best-in-class GTM solutions embrace these four market realities:
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1. Gross margin pressure requires companies to radically reduce operating expenses
   The best companies are investing heavily in compute to stay competitive and eroding gross margin in the process. They simply must get more efficient with their operating expenses, but GTM financial metrics such as CAC payback and net magic number have been stagnant for years.
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2. AI-native companies are prompting an evolution to leaner org design
   Growing revenue has historically been expensive and wildly inefficient due to the number of people required: human BDRs can only handle so many touches per week even with the newest tools, companies are forced to hire another middle manager for every X new AEs, and so forth. AI-native companies are achieving staggering productivity per employee, and are setting new expectations for what’s possible. Every company must reconsider the human intensity of their GTM motion.
   
   
3. Function-specific software silos are collapsing
   It’s finally possible to break down the barriers across functional software categories (e.g. marketing software vs. sales software vs. customer software).
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4. Companies will collapse and integrate traditional GTM roles
   We are on the brink of a new “full-funnel GTM” role, removing the traditional functional specialization between Marketing - BDR - Sales - SC - CS - AM. It’s conceivable to have one person – backed by powerful agents – cover pipeline generation, sales, and ongoing customer management. 
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We are excited to back founders building AI-native solutions to enable this exciting new world.

Introducing PRIME

We are looking for founders or aspiring founders building GTM businesses with a direct-line impact on the P&L.

We've codified this as having demonstrable impact on PRIME:

• Productivity (Revenue per Employee)
• Retention (NRR or GRR)
• Investment Efficiency (Net Magic Number / S&M Efficiency)
• Momentum (Top-Line Growth)
• Expense Reduction (Headcount and Cost Eliminations)
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This is our non-negotiable filter for GTM investing. Companies who explain ROI through opportunity cost, marginal efficiency gains, or indirect metrics don't fit our mold.

Consider a category such as digital deal rooms: they may accelerate cycle times and marginally improve win rates, but there are multiple steps of math required to translate that to the P&L. Marginal gains are certainly valuable, but we are committed to backing the companies that seek to deliver an order of magnitude impact.

Founders: we thrive with serial entrepreneurs who are obsessed with their customers
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We've learned that second-time founders are where we add the most value. Our strongest fit tends to be with serial entrepreneurs who value what we bring beyond a logo on the cap table: our network connectivity and operating experience. Exceptional talent is exceptional talent and we’re excited to meet any high-slope founder who shares our category conviction. But founders such as Amanda Kahlow at 1mind, Ganesh Ramakrishna at Lyric, Richard Harris at Black Crow AI and Jon Sherry at Alium exemplify the type of builders we're excited to partner with: they have unfinished business. 
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The founders we're drawn to are aggressive about their own GTM and deeply customer-obsessed. They understand you cannot build enduring enterprise value without enduring customer value, and they're constantly challenging their own assumptions based on what they learn in the market. They obsess over the risk of the gross retention apocalypse. At the same time, they're Challenger sellers—teaching the market where it should be headed. Importantly though, the founding team must marry this customer obsession with world-class technical acumen. 
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The path to full-stack GTM 
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We love hearing a founder’s articulation for how they will bring this new GTM operating system to life. We’ve thought extensively about wedge vs. compound startup tradeoffs and are excited to share our own thoughts: 
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Wedge Strategy
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The wedge approach starts narrow but has a strategic vision to become a platform. Wedge products must still meet PRIME criteria and demonstrate clear P&L impact because that guarantees the right to ultimate share-of-wallet expansion. But beyond financial impact, wedge products need two additional qualities:
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• Obvious right to win – whether it’s a “Zero CAC” founder with a rolodex of prospects who already trust them, a team of 10x engineers shipping products at unprecedented speed or a former operator with an earned secret, the companies we back must have a clear right to win the lane of their wedge.
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• Own the context – the best wedges embed into the bloodstream of workflows and data, earning the right to capture net-new signals or refine existing data into decision-grade context. If a wedge cannot maintain and resolve context, it will struggle with durability regardless of the initial P&L impact. Playing the context angle correctly also makes launching subsequent SKUs more seamless and more defensible.
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1mind is a great example of a successful, context-rich wedge approach. When we led the Seed round, we were investing in Amanda’s vision to build an end-to-end AI brain for all GTM functions from first prospect engagement through ongoing support, but we recognized they needed to start with a wedge. 1mind carefully selected an inbound AI BDR wedge that facilitated thousands of customer conversations, enabling impressive context. The data ingestion needed to train their AI “Superhumans” combined with that conversational context positioned them to expand into adjacent workflows with an unfair advantage. 1mind’s right to win was Amanda’s track record: she had previously built a category-defining unicorn with 6sense, and had an incredible rolodex of prospects ready to take her at her word.
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1mind also exemplifies PRIME: it makes sellers more efficient by having AI handle upfront conversations so reps can manage bigger deal loads (Productivity), increases efficiency through reduced hiring needs, thereby reducing CAC and increasing net magic number (Investment Efficiency), and drives obvious expense reduction because teams can hire fewer people (Expense Reduction). 
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"Wider" / Compound Strategy
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The compound approach goes full-stack from the onset. The core question: why are customers trusting you for this risky, transformative move? Because these plays command higher switching costs, we believe founder domain fluency is essential. 
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We recognize compound businesses take longer to build, particularly in the 0-1 GTM phase. During that extended building cycle, we're looking for a best-in-class approach to design and development partners as well as evidence of off-the-charts engineering and roadmap velocity. We love it when we hear comments along the lines of, “I can’t even keep up with our engineering team because we’re shipping so fast” (a recent verbatim from Amanda at 1mind!).
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If you’re transforming the GTM P&L, get in touch
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The GTM category is at an inflection point. AI has created both tremendous opportunity and tremendous noise. The winners will be the companies that don't just bolt on AI capabilities to existing workflows, but fundamentally reimagine how companies acquire, retain, and grow their customer base.
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We're committed to finding category-defining plays early and supporting them relentlessly. If you're building something that transforms the GTM P&L, we want to hear from you.

We are at a crucible moment in cybersecurity, one in which AI is changing everything. Just last week, Anthropic caught a near-successful cyber espionage attack that was 80%-90% executed by AI agents. These AI agents ran many parallelized attacks at thousands of requests per second, the kind of speed and sophistication a human would only dream of. The world is getting scarier, and CISOs wake up every day confronting this reality.

At the same time, AI is a new technology platform that presents internal risks. Top-down mandates from C-suites and boards mean that AI experimentation is ubiquitous, creating new threat vectors. Additionally, new hardware and configurations in the data center mean that the physical infrastructure to protect is different too, necessitating new solutions.

This change is scary, but also exciting. For too long, cyber has been bogged down in the SPM craze—“posture management” and dashboards. Of course, visibility is necessary, but the dashboard sprawl has become comical. A CISO’s main problem is no longer blindspots, but rather an excess of data. Alert after alert, and security teams seemingly get farther from actual solutions. In the words of a CISO I know well, many tools are just “more software for software.”

The evolution of the market in this way makes sense. Some visibility-focused startups have become category-defining, generational companies. In pursuit of the next Wiz, VCs have over-funded visibility. Despite some successes, most of these companies have contributed to the most fragmented, sprawling, and redundant stack in the history of cybersecurity.

AI has the potential to break this pattern by automating workflows, reducing costs, and finding better signals in the noise. We see this happening across all security categories, from identity to data to endpoint, and have made investments accordingly. The time to build in cyber is now—to give security leaders less, not more; to think of cyber from a blank slate with AI at the center; to use the data the SPMs of the last decade have generated to deliver unique value.

And New York is a great place to build. Although Primary is much bigger than we once were and we now invest globally, the firm began with a bet on New York. A bet that New York is the greatest city in the world, with the highest density of customers and talent, and that founders should be here. This is true for cyber. New York is home to the largest Israeli community outside of Israel, where a disproportionate number of cyber companies are born. And, as the center of the financial world, New York is home to some of the biggest, most sophisticated cyber customers in the world.

We are seeing the gravity around New York strengthen, as companies like Wiz, Cyera, and Axonius have all relocated their headquarters here. Today, there are thousands of security operators in this city, and that number is growing. Undoubtedly, many of these operators will start great companies of their own. We believe we are on the verge of an explosion of the New York cyber ecosystem.

Today, I am humbled that my promotion to Partner is being announced to the outside world. Primary is a deeply special organization for me. I first met the team when I was 25, before going to business school, having spent my prior years working on a startup in NYC. As a born and bred New Yorker who knew how hard the earliest days of startup building were, Primary stood out. “Startups are hard; founders deserve better” has been and is always at the core of what we do, and our ability to execute on that truth and deliver value for founders has grown exponentially since I joined the firm.

I am especially excited about leading the cyber practice for Primary, at a time when security is so existential and great upstarts so needed, and in a place that is poised to become a hotbed for cyber activity. We are working on a lot of interesting things here, and have big dreams. We do not invest in many companies—I will make maybe two investments a year—but the companies we do invest in will be big swings, and we will try to help them in a differentiated way unmatched by any seed firm on the planet.

The challenge of building a cyber seed investing practice is real, but we believe our strategy and positioning is unique. If you are reading this, and intrigued to learn more about our approach, please get in touch. Today I am kicking off a search for an associate to help me with these efforts. This person will be my partner-in-crime, and together, we will define the strategy and investment decisions of the cyber practice at Primary.

The reinvention of cyber in an AI age starts with startups, and amazing customers, advisers, and investors who help them on their journey. If you want to be a small part of this transformation, let’s talk.

The Biological Computing Company (TBC) is ready to tell the world what they’re building. Founders Alex Ksendzovsky, CEO, and Jon Pomeraniec, COO, have led the company to use living neural networks to develop and optimize AI architectures in a commercial setting. This investment pushed the boundaries of our compute thesis for three reasons: one, Alex and Jon live in that founder sweet spot of brilliant, on a mission, and wildly different; two, their tangible results are the stuff of sci-fi but very real; and three, it’s exactly what we’re looking for —  the belief that radical breakthroughs in compute are needed to meet AI’s demand for efficiency, performance, and scalability and that outlier founders with perspectives born of unique life experiences will build that future. 

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To understand TBC from first principles, we start with the fact that the brain is a million times more energy efficient than the best silicon today. It operates on roughly 20 watts of power to deliver an exaflop of computational power. You’d need 120K watts of power for NVIDIA’s Blackwell to deliver the same throughput. 

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For decades, neuroscientists have tried to translate the brain's functionality into digital systems. What if, instead of translating the brain, we let neurons compute on their own? If we can harness the computing power nature intrinsically provides us, we can unlock capabilities that silicon alone cannot. 

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These facts have been driving Alex and Jon for two decades. In the early 2000s, Alex was studying philosophy of mind in college and saw a visiting professor demo a robot powered by a neuron. He immediately intuited the potential to extract the immense power of the brain for computation. Since then, Alex and cofounder Jon – both neurosurgeons from UMD and Penn – have had a decades-long partnership through medical school, surgery roles, and research positions. After making rapid progress in their research around the brain’s computing capabilities in 2021, as covered by Fortune’s Term Sheet, they started The Biological Computing Company to focus on building computers for the future – out of neurons. This is not neuromorphic or “brain-inspired” chip design. This is harnessing the intrinsic and evolutionary power of the brain for compute. 

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The Biological Computing Company is building an organic computing platform that connects real living neurons with modern AI, making frontier models more stable, scalable and dramatically more efficient. TBC is able to harness intelligence, connecting neurons to electrodes, which then demonstrate superior efficiency to silicon for specific computational tasks. With its first product, TBC is showing a 23x retained improvement in video model efficiency – their team encodes real world data into living neurons, then decodes the neural activity into richer representations that have been mapped to state-of-the-art AI. 

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TBC is also using biological compute to extract principles from living neural networks, informing the development of novel AI architectures. In doing so, the company is not only augmenting today’s models, but shaping how future models will be built. This marks the first true commercialization of biological computing – and a critical step toward a world where biological and digital compute operate together. 

We’re proud to have led The Biological Computing Company’s $25M Seed round, joined by Builders VC, Refactor Capital, Wonder Ventures, E1 Ventures, Proximity and Tusk Ventures. This is entirely new technology and we don’t know exactly how the market around it will develop. We believe in founders shaping the arc of history by doing something that people previously thought to be impossible. We look forward to supporting them in their journey. If you are interested in building at this frontier, TBC is hiring.

"Move fast. Speed is one of your main advantages over large competitors."

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This quote from Sam Altman encouraged us to venture into the daunting battlefield that is the semiconductor industry and lead Etched’s seed round about 15 months ago.
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And it is speed of execution that gives us confidence in the future of superintelligence that Etched will enable. Indeed, we believe they will set a world record for fastest time to tape out for such a complex chip. This is fitting for a company building a chip that will be orders of magnitude faster than NVIDIA’s latest GPU, when running inference on transformer models.

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Today, Etched is announcing a $120 million Series A to bring the same vision they pitched over a year ago to reality.

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Hardware, not software, is the biggest bottleneck to truly magical AI experiences: artistic masterpieces like the next Titanic or Beethoven’s 9th produced by AI; agents that perform tasks on the web at the speed of thought, planning and booking honeymoons and preparing memos. What is impossible today will be possible tomorrow, but only with better hardware. To understand the Etched thesis, we encourage you to check out a post from the team at Etched discussing their bet. 

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The Etched team epitomizes the greatness of Silicon Valley, right down to the 'silicon.'

The CEO, Gavin Uberti, is brilliant, mature beyond his years, visionary, committed to the craft of being a founder, obsessed with the details, and insanely passionate. We are believers in him, and think that what Sam Altman is to software in AI, Gavin will be to hardware. You can listen to his podcast on Invest Like the Best with Patrick O’Shaughnessy, whose firm Positive Sum is co-leading this round with us, here. Gavin is joined by his cofounders, Robert Wachen and Chris Zhu, both equally remarkable in their own rights. This year, they became one of the first teams to collectively receive the Thiel Fellowship.

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The founders are joined by a powerhouse roster of semis professionals who are all driven to break the rules while leveraging their expertise. Mark Ross, the CTO, was previously the CTO at Cypress Semiconductor, which eventually sold in 2019 for almost $10 billion. Ajat Hukkoo, the VP of Hardware Engineering, was at Intel for nine years and Broadcom for 14 before joining Etched. Saptadeep Pal, the Chief Architect, cofounded Auradine. The bench at Etched goes deep and every single person on the team is driven by an ethos of speed.

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Most silicon teams are composed of people from the same networks and companies. Etched is the opposite of that: people with different skills and perspectives who want to be part of something special and change the world. It’s a team that embodies the creativity and optimism that makes startups exciting—a group of engineers, coming together with intense, uncanny ambition to build something that industry insiders believe is impossible. This is the only way that radical progress ever happens. This is how we get to superintelligence.

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Today, more than anything, we’re proud of the team at Etched and humbled to be a part of their journey. 

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We’re also excited to be collaborating with such an excellent group of investors, including not just Positive Sum, but also Hummingbird, Two Sigma, Skybox Data Centers, Fundomo, and Oceans as well as angels like Peter Thiel, Thomas Dohmke, Amjad Masad, Jason Warner, and Kyle Vogt.
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Interested in joining this stellar team? Check out the more than a dozen open roles here and help build the future of superintelligence.

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Senior living communities are at the heart of one of the most important demographic shifts of our time. An aging population, rising care costs, and persistent staffing shortages are forcing operators to do more with less—without compromising safety, dignity, or quality of life for residents. Yet most of the tools available today are point solutions: basic fall detectors, clunky emergency call buttons, or fragmented EHR systems that fail to keep pace with day-to-day realities.

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Inspiren is changing that.

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Their AI-powered hardware and software integrates real-time motion awareness, staff coordination, emergency response, and resident engagement into one cohesive ecosystem. From preventing falls to accelerating emergency response to improving care planning, Inspiren is redefining how senior living communities operate—while delivering measurable ROI to operators.

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From Fall Prevention to Full-Stack Care Coordination

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When we led Inspiren’s Seed round, the company had already built a best-in-class fall detection device. But CEO, Alex Hejnosz, and founder, Michael Wang, were thinking much bigger. They envisioned a full “intelligent ecosystem” for senior living, one that combined multiple hardware devices with a single software brain to give operators unprecedented visibility and control.

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That vision is now a reality. In the past year, Inspiren has expanded from its core device to a comprehensive hardware and software product suite:

• AUGi for in-room activity sensing and fall detection
• Sense for high-risk bathroom coverage
• Staff Beacon for staff location and workflow optimization
• Help Button for residents to request help without wearing a pendant
• Inspiren HQ and Inspiren Mobile App, an AI software suite to give operators and staff real-time insights into the overall health of their communities
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This unified approach does more than replace outdated systems—it collapses the need for multiple vendors into one platform, eliminating integration headaches and increasing staff efficiency. Dedicated clinicians partner closely with communities, ensuring clinical insights are communicated and applied in ways that maximize care quality, outcomes, and staff support. And, most importantly, keeps residents safer.

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Why Now
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The timing for Inspiren couldn’t be better. Over 10,000 people turn 65 every day in the United States. The senior living market is fragmented, underserved, and under increasing operational pressure. Regulatory scrutiny around resident safety is increasing, while financial risk from early move-outs and under-documented care is pushing operators to modernize.
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At the same time, Inspiren’s combination of AI-powered sensing and integrated software is hitting an inflection point in cost and capability:

• Affordable hardware means mass deployment is now possible.
• AI-powered embedded workflows make adoption easy and ROI immediate.
• Regulatory and operational tailwinds are pushing toward data-driven, documented care.
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In a market where speed of deployment is critical, Inspiren’s ability to get devices “on walls” faster than anyone else is a decisive advantage.
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Why We Backed Inspiren—Again
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We’re doubling down in Inspiren’s $100M Series B led by Insight Partners because we believe they are building the category-defining operating system for senior living. Our conviction comes down to four key beliefs:

1. The market is ripe for a platform that replaces fragmented point solutions with an integrated, AI-driven ecosystem.
2. Inspiren has proven product-market fit with industry-leading retention, rapid ACV expansion, and clear ROI for operators.
3. The economics work at scale, with a recurring software model, short payback periods, and a massive market opportunity.
4. This team can execute, having consistently delivered growth and product velocity ahead of plan.
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As the aging population grows and care demands intensify, senior living communities will need more than incremental tools. They’ll need a system that makes care safer, faster, and more efficient. We believe Inspiren is poised as the market leader in this space.
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We’re proud to continue our partnership with Alex, Mike, and the entire Inspiren team. The future of senior living is intelligent, connected, and compassionate — and Inspiren leading that future.

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Bad design is one of the most persistent, overlooked, and expensive problems in construction. Change orders caused by coordination issues, missed specs, or incompatible designs aren’t just costly—they’re preventable.
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In the U.S., hundreds of billions of dollars are spent on construction every year. Projects are chronically delayed and over budget, and with interest rates where they are, new development has become even more difficult in many markets. The majority of change orders could be caught in the pre-construction phase, but today’s review process is slow, expensive, and imperfect.
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Large developers often outsource drawing reviews to third-party firms, paying hundreds of thousands of dollars for reports that take 6–20 weeks to deliver—and still contain errors. These delays and oversights ripple through every stakeholder, from architects to subcontractors, costing valuable time and eroding margins.
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LightTable solves this. By applying AI-powered coordination and peer review, they can eliminate up to two-thirds, improving project IRRs by 3–4 points. For real estate owners, this means delivering projects faster, on budget, and with fewer costly surprises. For architects and subcontractors, it means less time revisiting old projects to fix preventable mistakes.
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The Right Wedge: Peer Review
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We backed LightTable in 2024 based on a simple premise: peer review today is manual, expensive, and error-prone—and AI can do it better. Starting with coordination issues across disciplines creates a powerful wedge to expand into broader design optimization and collaboration.
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Three things gave us conviction:

1. Acute pain: Developers feel the cost and time impact most directly.
2. Adoption leverage: They can push better tooling across architects and engineers.
3. Workflow ownership: Controlling the design review process opens the door to downstream automation, from value engineering to inventory-aware specs.
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Since funding in November, LightTable has signed product development and design partnerships or pilots with five of the top ten developers in the U.S., including Hines, The Related Group, Greystar, Mill Creek, and Alliance. These pilots are already driving real-world feedback and product iteration, setting the stage for enterprise contracts with annual values that could reach seven figures.
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Built With—and For—the Industry’s Best
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LightTable’s go-to-market strategy is rooted in deep collaboration. Through these design partnerships, the team is running real projects and building in lockstep with enterprise users.
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Paul Zeckser, Dan Becker, and Ben Waters didn’t just set out to build better design tools—they’re rethinking how buildings get designed from the ground up.
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Paul, a product-first leader with sharp commercial instincts, spent over a decade shaping HomeAdvisor before leading product at Sealed. He’s known for turning big product visions into real market wins.
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Dan, a seasoned AI and simulation engineer, founded a startup that was acquired by DataRobot and helped enterprise teams adopt AI. Frustrated by how slowly legacy players like Autodesk move, he decided to build LightTable: an AI-native company already shipping faster than most incumbents can prototype.
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Ben, a trained architect that has worked at SOM and Gensler, knows and has lived the pain of coordinating large drawing sets, only to see RFIs and Change Orders pile up. He was convinced AI could transform this archaic practice of human-only review and deliver massive ROI for builders.
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Together, they’re the team with the perfect set of backgrounds to build for speed—and for a smarter, more intuitive future in design.
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Why We’re Excited
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LightTable is starting where the pain is most acute—pre-construction peer review—and building a platform that could become the coordination layer for the entire industry. The combination of early traction with top developers, a pragmatic and valuable product, and a sharp, fast-moving team makes this exactly the kind of founder-led business we want to partner with.
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We’re proud to be backing Paul, Dan, Ben,and the team as they redefine how better buildings get built, long before anyone breaks ground.

Electricity costs are rising fast. One midstream energy CFO in Texas told us his company’s rates have climbed from $0.35/MWh in 2021 to nearly $0.70MWh today. “This is getting ridiculous,” he said. “We used to say the price at the pump decides elections. Going forward, it’s going to be the price at the meter.”
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Similarly, an executive responsible for the data center buildout at Microsoft asks, “If every producible watt is already committed for the next five years, where can I get more watts?”
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That question has become the defining obsession of this moment. AI companies, hyperscalers, and industrial manufacturers are all competing for the same scarce resource: firm, dispatchable electricity. Yet, turbines, the single most important piece of equipment required to make that power, are suddenly the most scarce.
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There are effectively only three relevant manufacturers that can produce large-scale gas turbines: GE Vernova ($162B market cap), Siemens Energy ($91B), and Mitsubishi Heavy Industries ($84B). These machines sit at the center of the modern power plant. They are the hardware that converts fuel into motion and motion into electrons. Today, they are booked solid for years. GE, Siemens, and Mitsubishi are each reporting order backlogs that stretch roughly five years, with many delivery slots already sold into the next decade.
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Rumors are circulating in Washington about just how acute this has become. A contact close to the Department of Energy’s new venture fund (a kind of “In-Q-Tel for energy”) told us the administration is even considering whether to pressure allies with outstanding GE turbine orders to release them back to U.S. buyers. It sounds extreme, but that’s the level of urgency building around this bottleneck.
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What a Gas Turbine Actually Is
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A gas turbine is, at its core, an air compressor, a combustion chamber, and a spinning shaft. Air is compressed, mixed with fuel (usually natural gas) and ignited. The expanding gases turn the turbine blades, which drive a generator to produce electricity. In a simple-cycle setup, that’s the whole process. In a combined-cycle plant, the hot exhaust is captured to make steam and power a second turbine, pushing overall efficiency and reducing emissions.
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These are enormous machines. A single heavy-duty turbine can weigh more than 400 tons, stretch nearly 50 feet long, and generate anywhere from 100 to 400 megawatts, enough to power a mid-sized city. Smaller aeroderivative models, adapted from jet engines, are used for distributed or peaking power. Each large turbine costs roughly $50-100M depending on its class and configuration. It is elegant, brutally engineered machinery, and the workhorse of global baseload power.
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How We Got Here
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The roots of the shortage go back two decades. In the early 2000s, gas plants were being built everywhere. Then the 2008 crash hit, followed by another glut in the mid-2010s. Orders evaporated. OEMs shut factories, laid off skilled labor, and merged their supplier bases. The “painful overbuild” became a corporate cautionary tale as the turbine market crashed in 2018.
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By the late 2010s, renewables dominated investor attention. Gas turbine divisions were treated as cash cows, not growth engines. Capacity stayed flat even as demand for electricity quietly began to climb again. Then came AI.
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Starting in 2023, data-center power requirements exploded. Hyperscalers that once drew tens of megawatts suddenly needed hundreds. Natural gas looked like the fastest, most practical bridge to new capacity, but the world only had three major manufacturers left, and none had invested to scale. With the fresh memory of 2018 and some uncertainty about how much energy the AI buildout would actually require, they weren’t rushing to ramp capacity again.
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Siemens Energy now reports a €136 billion backlog, the largest in its history. GE Vernova has roughly 55 gigawatts of gas orders in its queue and plans to expand from about fifty to eighty heavy-duty units a year by 2026. Mitsubishi Power says it will double production, but is already sold out into 2028. Even if all three deliver on their expansion plans, total output might rise only twenty to twenty-five percent—nowhere near enough to meet demand.
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Anatomy of a Shortage
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Every layer of the supply chain is fragile. The high-temperature blades and vanes inside the turbine are cast from exotic nickel alloys by a few companies such as Howmet Aerospace ($76B market cap) and Precision Castparts (acquired by Berkshire Hathaway for $37B). The massive forged rotors that hold everything together are produced by a handful of plants worldwide, including Japan Steel Works. The heat-recovery steam generators that make combined-cycle plants efficient are backlogged. Transformers, switchgear, and control systems are just as scarce.
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The result is a perfect storm of concentration and pricing power. One energy CFO told us that total project capex costs have doubled in just fifteen months, from roughly $1,000/kWh to more than $2,000/kWh. “It’s cartel pricing,” he said. “There’s nowhere else to go.” Reuters now cites installed combined cycle gas turbine costs moving from ~$1,000/kW to $2,000–$2,500/kW on recent projects, driven by turbine scarcity, HRSG/transformer backlogs, and EPC/labor constraints. Some developers are scouring Alberta and the Gulf Coast for refurbished turbines from the 1990s because new ones are unavailable. Others, including major industrial conglomerates, are buying units on spec simply to guarantee they have equipment for future plants. Koch Industries is rumored to be doing the same.
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Implications for the AI Buildout
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Natural gas was supposed to be the fast path. Today, its build timelines look a lot like those of emerging nuclear small-modular-reactor projects. It’s no surprise that Microsoft, Google, and others are signing enormous power-purchase agreements with nuclear and geothermal developers such as Oklo, Kairos, TerraPower, and Fervo.
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A chief strategy officer at one hyperscaler told us he’s signing these LOIs freely, while assuming most will never reach operation. Even among those that do, he expects only a fraction of the current terms to survive to commissioning. But that hardly matters. For hyperscalers, the act of signing unlocks investment flows, de-risks sites, and signals seriousness to regulators. It’s a rational strategy in an irrationally tight market: spray capital at every potential source of electrons, then back the few that deliver.
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Our Perspective
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The turbine shortage is not just an isolated equipment problem. It exposes the fragility of the entire industrial stack: the transformers, EPC labor, HRSGs, and switchgear that all depend on long, brittle supply chains. Turbines just happen to be where the pressure shows first.
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For startups, there are enormous opportunities across this landscape. Predictive-maintenance networks for turbine fleets. Software that helps EPCs and utilities coordinate procurement. Financing models that let developers pre-buy critical equipment. Materials or manufacturing breakthroughs that reduce lead times. Even the idea of a fuel-agnostic turbine that runs on gas today and integrates with a nuclear reactor tomorrow feels less like science fiction than a commercial inevitability.
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Still, solving this likely won’t be as simple as building another “Anduril for turbines” (though we’d entertain it). Heavy industrial capacity takes years, patient capital, and government partnership to stand up. The payoff is profound. Whoever helps unjam these bottlenecks will sit at the center of the next trillion-dollar buildout and power the world’s compute.
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We’ll continue exploring these supply-chain chokepoints in future issues, from turbines to transformers to the EPC networks that knit them together. As always, we’re early in our journey into energy and learning in public. If you’re building, financing, or operating anywhere in this bottleneck, whether you make turbines, HRSGs, or just want to power the next wave of AI, we’d love to hear from you.

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Supply chains weren’t built for the world we live in now.

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Pandemics, geopolitical risk, climate shocks, volatile demand, and an AI-fueled productivity race have put operators under more pressure than ever. But while everything around them is changing, the software they rely on hasn’t kept up.

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Legacy supply chain tools like SAP, Blue Yonder, and Coupa were built for static environments. They offer rigid templates, brittle implementations, and expensive services arms that struggle to adapt to real-world variability. Even Gen 2 “smart” platforms like Palantir lean heavily on forward-deployed engineers to tailor models to each customer, resulting in long timelines, high cost of ownership, and limited reach.

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In a world where operational agility is the difference between growth and chaos, the software that powers our supply chains can’t be static. It has to be composable, context-aware, user-extendable and fast.

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In 2023, we led the seed round in Lyric because we believed the services-heavy playbook for enterprise software was breaking down, especially in supply chain. We saw a world where bespoke AI tooling was finally colliding with business reality: long-tail complexity, fragmented data, and a crippling reliance on forward-deployed engineers. Lyric offered a new path: a composable platform that could absorb complexity without defaulting to custom work. We were honored that a sophisticated repeat founder like Ganesh Ramakrishna opted to partner with us on what we do best: seed-specific operational partnership.

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Today, we’re proud to double down in Lyric’s $44 million Series B alongside Insight Partners, VMG Catalyst, Permanent Capital, and other insiders.

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Why? Because Lyric isn’t just delivering on the original thesis—it’s blowing past it.

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The Product Problem Nobody Solved

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Ganesh Ramakrishna knows this world inside and out.

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Before Lyric, he built Opex Analytics, a services business that delivered custom data applications to Fortune 500 supply chains. But every win came at a cost: each solution was a one-off. The team shipped bespoke code for every engagement.

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That’s the problem with most enterprise AI today. It generates project-level IP useful for one customer, once. Not product-level IP that scales across use cases and companies.

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Ganesh started Lyric to change that.

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Lyric is a composable, AI-powered platform that turns operational complexity into decision intelligence. The platform layers domain-aware data infrastructure, a rich library of optimization and ML models, a no-code sequence builder, and intuitive UIs, all wrapped in agentic AI tools that accelerate development and deployment.

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It doesn’t just solve problems. It productizes the solutions.

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From Six Months to Six Hours

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What does this mean in practice?

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At a major beverage company, Lyric started with just two use cases: out-of-stock forensics and production planning. These weren’t problems the customer’s existing stack (Palantir, Blue Yonder, Oracle) could solve. But Lyric could. The first few apps saved $8 million in value. Soon the customer had built 20+ apps on their own with no consultants involved. Today, Lyric powers more than $40 million in ongoing ROI inside that enterprise.

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At another global CPG, Lyric replaced a $2 million, six-month failed implementation with a working solution in four weeks. That app saved $1 million per quarter and led to a wave of expansion across planning, logistics, and risk analytics.

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The POC Factory deserves special mention. Lyric can go from raw data to a full-featured production-grade application in as little as six hours. Customers aren’t pitched with decks or mockups. Instead, they’re handed a live app tailored to their business. That experience doesn’t just accelerate sales. It sets a new bar for customer expectation.

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It’s no surprise that Lyric’s go-to-market motion, which until recently was entirely founder-led, has scaled with remarkable efficiency and unprecedented ACV expansion.

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The Platform Is the Product

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Lyric’s true innovation is about speed and scale. Most enterprise AI platforms still operate like bespoke consultancies. They promise flexibility, but that flexibility is serviced, not shipped. Lyric’s composable architecture flips that model. It allows customers to build once and scale infinitely. Applications are modular, models are extensible, and integrations are already in place across the dominant enterprise data and planning systems.

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What we’re seeing now is the platform unfolding across use cases far beyond what it was originally built for. Customers who began with core supply chain planning and network design are now solving for fleet and workforce optimization, predictive maintenance, sustainability forecasting, warehouse layout, and more. These weren’t on the roadmap a year ago, but because Lyric’s platform is composable by design, these apps aren’t exceptions. They’re natural extensions.

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This is how platforms grow. Not by narrowly optimizing a single workflow, but by adapting to the evolving needs of the enterprise. Lyric isn’t just flexible software. It’s infrastructure for operational intelligence.

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What’s Next

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Lyric now has the team, product, and momentum to scale into the broader vision it was always meant to fulfill. With seasoned GTM leadership in seat, the company is ready to go from founder-led selling to full GTM scale. The POC Factory remains a superpower, dramatically shortening the distance between interest and impact, and will continue to be a cornerstone of the buyer experience.

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Under the hood, Lyric is pushing aggressively into agentic AI. New copilots are being trained to help customers test scenarios, build workflows, and deploy models even faster. As the product expands, so does the community: more customers are building their own apps, repurposing existing ones, and contributing to a growing library of reusable solutions.

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Most importantly, Lyric’s market is expanding. What began as a bet on supply chain planning is now becoming a horizontal platform for any operationally complex, data-rich decision in the enterprise. In a world where cost structures are shifting, AI tooling is democratizing, and operational resilience is a board-level priority, we believe Lyric has the chance to define a new category.

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Lyric isn’t just rewriting how enterprise software is built. It’s redefining how real-world decisions get made. We’re proud to be along for the ride—again.

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More and more money continues to flow into the private markets. Today, private funds manage over $13T and continue to grow rapidly—averaging 20% annually since 2018. This explosive growth reflects a fundamental shift in how institutional and private capital is being deployed, with private market capital becoming an increasingly critical pillar of our global financial systems.
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As funds grow, so does complexity. The bespoke nature of how deals are structured—both for individual investments as well as agreements with limited partners—means it historically has taken an army of accountants and excel spreadsheets to manage. Fund CFOs are faced with two not-so-great options:
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1. Try to pay top dollar to grow a large team in-house in the midst of a massive accounting talent shortage and bear all of the associated costs.
   
2. Pay a third-party fund administrator like SS&C ($18 billion market cap) or SEI ($9 billion market cap) 6 or 7 figures annually. and then run a shadow system to double check everything in-house).
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The challenges facing fund CFOs today are more acute than ever before. When we spoke to 20+ fund CFOs ranging from $1B first time fund managers to the most sophisticated finance teams with hundreds of billions under management, we heard the same pain points over and over again. Dissatisfaction with fund admins. Poor data quality and access. Lack of visibility into how things are calculated. Multi-day long turnaround times on simple questions. Frequent mistakes. High staff turnover.
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All this against a backdrop where pressure is mounting on both sides. On one hand, their Managing Partners are pushing them to be more strategic—i.e. evolve beyond the operations of running the back office and deliver insights on portfolio construction and management, cost optimization, and fundraising strategy, and deliver better insights, more quickly. On the other side, LPs and internal IR teams are asking for faster, more in-depth, and more frequent reporting—leading to a constant time-suck of information retrieval and bespoke analyses.

This shift from operational CFO to strategic CFO is one that we’ve seen play out in the corporate CFO tooling world where software has emerged to streamline and automate day-to-day operational work of finance teams to allow time to be freed up to focus on strategic decision making. Many large winners have emerged from this trend including Anaplan, Blackline, Workiva, and OneStream, just to name a few. Private fund accounting however isn’t governed by standardized GAAP principles. Instead, they’re governed by their limited partnership agreements (LPAs) which not only can take many shapes and forms but are also oftentimes accompanied by negotiated side letters with preferential terms for certain investors. This complexity meant that previous attempts to build software solutions for this problem were either limited to much simpler use cases (e.g. small venture funds) or requiring tons of customization with multi-year implementation timelines.
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Enter Maybern
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Maybern is revolutionizing private funds with the first true operating system for fund finance. Purpose-built in partnership with the most complex funds managing billions in AUM, Maybern's core product centralizes and automates essential operations, saving finance teams hours of headache. More importantly however, the software becomes the single source of truth for all fund flows regardless of the complexity of the underlying legal structure. The core engine, designed for maximum flexibility, adapts to each fund's unique structures and workflows, handling even the most complex edge cases.
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What makes Maybern particularly special was the decision to start with real estate private equity—arguably the most complex segment of the market. By solving for the most challenging use cases first, they've built an incredibly robust and flexible platform that can handle virtually any fund structure or requirement without writing a single line of custom code. They’ve quickly leveraged this advantage to win the hearts and minds of CFOs across traditional private equity, growth equity, and private credit.
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There is no team that is better suited to tackle this problem. The Maybern cofounders Ross Mechanic and Ashwin Raghu initially met at Cadre, a tech-enabled private equity platform for individual investors. There, they built the internal system that allowed Cadre to multiply AUM without needing to scale headcount. Upon our first meeting with Ross, it was clear that he was deeply obsessed with this problem and determined to change this market. Although an engineer by training, he’d both personally read hundreds of LPAs and set out to learn everything there was to know about fund finance before coming to realize that it was possible to build software that could address all the edge cases and complexities, it just wouldn’t be easy. Since then, Ross and Ashwin have assembled a team of world-class engineers and fund finance specialists to tackle a problem many thought was unsolvable.
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When we spoke to early customers, it became clear that Maybern would fundamentally transform how funds are run. Fund CFOs are very rarely effusive, so hearing effusive praise of the solution’s immediate transformation of internal operations and the deep expertise and credibility of the team suggested that this was clearly something special. With an internal buyer in house, it only made sense for us to put Maybern in front of our CFO, MIke Witowski, who previously had spent time at one of the largest fund admin providers. In the span of one meeting, Ross and Ashwin managed to transform his wary skepticism into astonished enthusiasm. It was at that moment that we knew this was something special.
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As the private markets just get bigger and more complex, Maybern is at the center building a true enterprise grade central nervous system for fund operations. Soon, the most sophisticated private funds will run on Maybern. We’re thrilled to be leading the Series A and to be partnering with the team on this journey.

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Revenue collection is the biggest opportunity in the CFO stack, and Tabs is poised to own it. CEO and Co-Founder Ali Hussain is fierce in all the right ways: urgent and thoughtful, yet a true servant leader. Alongside co-founders Deepak Bapat and Rebecca Schwartz, and an extraordinary team of passionate builders, Tabs is creating one of the most promising vertical-AI companies in New York City.

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Today, we’re excited to announce our continued support of Tabs, the AI-native revenue platform for modern finance teams, as a part of its $55 million Series B led by Lightspeed Venture Partners. Tabs is automating the revenue engine for the AI-Native CFO, powering fast growing companies like Cursor, Statsig, and Cortex.

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Accounts receivable, the all-important task of collecting revenue, is breaking free from the ERP. There are incredible decacorns associated with every layer of the CFO stack—Ramp for spend, Rippling for payroll, Bill.com for AP—but no company owns AR. The ERP is unbundling as enterprises are modernizing, with 50% of CIOs expecting to upgrade their ERP in the next two years. The ERP historically dominated the CFO stack, but lengthy implementations, expensive pricing, brittle architecture, and antiquated product experiences created an opening to build best-in-breed solutions for each category.

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We believe revenue is the biggest prize in this unbundling. Revenue collection sees the biggest flow of money. It commands the most time from experienced, senior members of your team. Given the complexity and criticality of revenue, the market opportunity for Tabs swells to over $1 trillion given in senior labor spend required to manage revenue and billing operations at companies of all sizes.

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Tabs transforms AR from a manual, ops-heavy burden into an automated revenue engine. By making contracts—not CRMs or spreadsheets—the system of record, Tabs enables CFOs to say “yes” to growth, experimentation, and new monetization models without getting trapped in their legacy ERP.

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The Finance Stack Wasn’t Built for the AI Era

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Accounts receivable has historically been a burden finance teams haven’t been able to engineer themselves away from. Legacy approaches to AR force companies to map CRM and billing data through tools like NetSuite or Stripe, creating endless edge cases. Contractual deviations—discounts, custom usage, multi-year contracts—becomes a painful manual exercise, ballooning the size of finance ops teams. It could take three days to reconcile contracts in an Excel doc with what’s in your CRM, manually cross-reference materials, feed that data back into your ERP, then automate a billing flow. The painful result is delayed collection of revenue.

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AI has created an incredible opportunity to transform AR, improving accuracy on contract parsing, reducing time spent on reconciliation, and making the platform flexible to new pricing models. AI is the unlock that enables Tabs to convert a contract into cash. By treating the contract—the same artifact auditors already rely on—as the source of truth, Tabs eliminates manual reconciliation, using AI to collect unstructured contracts and automatically turn it into revenue.

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Pricing innovation makes this shift even more urgent. AI-native companies are experimenting with consumption billing, usage-based pricing, and hybrid models at a pace legacy systems can’t support. Even if you solved the data mapping problem, introducing a new pricing model renders it useless in moments. Traditionally, this made the CFO the brake on sales creativity, prioritizing revenue clarity over pricing innovation. Tabs changes that: finance becomes a team of “yes,” with revenue systems that adapt as quickly as sales. For CFOs navigating the AI era, this flexibility is crucial to keeping up, as every company knows its pricing will evolve, and AR must keep pace.

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The result with Tabs is faster closes, leaner teams, and higher confidence for both finance and auditors.

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Contract-to-cash and beyond

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Tabs is built for this moment. Its wedge is contract-to-cash automation: ingesting messy contracts, extracting commercial terms, generating invoices, chasing payments, and recognizing revenue with minimal human involvement. Tabs’ ingestion engine transforms static PDFs into bill-ready logic, while Slack-native workflows and API-first integrations embed directly into how finance teams already operate. Tabs isn’t just digitizing invoicing—it’s reimagining the entire revenue cycle.

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Billing and revenue often require senior team members tackling mundane tasks. Their work also exists across many solutions, referencing the contracts, CRM, ERP, payments processor, and other systems to recognize revenue. This cross-functional workflow that requires domain expertise makes it perfect for agentic automation, freeing up teams to do higher value work. Tabs makes it possible to automate everything from edge cases and revenue rules that used to require armies of finance professionals.

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CEO Ali and the Tabs team believe the future of finance looks like this: lean senior teams powered by swarms of AI agents managing revenue at scale. From your first dollar to your billionth, the platform automates billing, collections, revenue recognition, audit, and reporting – with an auditable trail across the CFO stack.

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That vision unlocks more than AR. With the launch of Tabs Agent, its Slack-native AR agent, Tabs is taking the first step toward building a true agentic revenue platform.

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Why we invested

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Since incubating Tabs, Ali and his team have blown past every target, quarter after quarter, while shipping products at remarkable speed. With $55 million in fresh funding led by Lightspeed, Tabs is positioned to lead the shift away from ERP-bound finance and toward an AI-native, best-in-breed revenue platform.

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At Primary, we believe Tabs will be one of the defining companies of the AI era—a generational compounder that fundamentally reshapes the economics of finance. That’s why we’re proud to deepen our partnership in this $55 million Series B. Tabs isn’t simply automating AR—it’s creating the revenue operating system for the AI era, unlocking a trillion-dollar labor market and turning CFOs from bottlenecks into accelerators. We’re proud to back Ali, Deepak, Rebecca, and the entire Tabs team again for this next phase of their product.

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Tabs was incubated at Primary Labs. If you’re interested in learning more about incubations, becoming an Operator-in-Residence, and building with us, please reach out to labs@primary.vc.

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Primary has, from our beginnings, never looked like a traditional VC firm. My co-founder Ben and I always liked it that way, and as we’ve scaled, we’ve leaned ever-harder into those differences. Today, I’m thrilled to share that we have taken the next step in scaling our firm and in breaking the norms of our industry as we announce our fifth set of funds. At $625M, this represents the largest standalone seed firm in the market and a ringing endorsement by our LPs of our ambition to build the biggest, most impactful, and best performing seed firm on the planet. 

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Today, Primary leads pre-seed and seed rounds across tech sectors, backing exceptional founders from San Francisco to Tel Aviv. We practice the craft of seed investing at institutional scale. On our way to 80 full time employees, we are led by a team of eight exceptional, sector-focused investing partners, each running their own strategies and partnering with the very best founders in their sectors: Financial Services, Healthcare, Vertical AI, Infrastructure/Compute, Cybersecurity, Consumer, GTM Tech, and the Industrial Evolution. Our partners bring deep insights, extensive networks, and curious, prepared minds to their craft.

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Super-charging our investing work is an Impact team, led by a C-suite of seasoned operators, that delivers operational support unequaled at any stage of venture. And tightly aligned with both teams is PrimaryLabs, our incubation engine, which in recent years has launched many of the most exciting companies in our portfolio. In aggregate, Primary is a team with more scale, more muscle, and more ability to drive genuine results in support of founders than any in the market. 

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At 10x the size from our first fund, we are beyond what Ben and I imagined when we began working on Primary. We were never remotely interested in building a typical venture capital firm. We joined forces because we wanted to build something that changed the way the industry operated, putting the founder at the center. Our most important firm value has always been “Wear the Founder’s Shoes.” Guided by that, we listened to founders and invested in the capabilities they most valued across talent acquisition, revenue generation, and capital raising.

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As our early investments began to bear fruit – 9 of the 25 investments in our first fund reached unicorn status and another two achieved cash exits north of $500M – our conviction grew. The unique way we made operating resources available to founders was making a difference. With conviction came ambition to push further, for we knew that our model would get even better with scale. As we’ve scaled, we’ve resisted the temptation that unfortunately defines our industry: using new management fee resources not to execute better for founders, but to further line the pockets of the very investors whose incentives now drive them away from seed and towards larger/later deals. Instead, we’ve grown our team and capabilities in line with the growth of our funds. 

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This year our team will make hundreds of hires, drive tens of millions of dollars of revenue pipeline, and support dozens of new financings for our companies. As we all race to find and earn the right to work with the founders who are driving the AI revolution, Primary is showing up with more capability and more expertise than ever. And at a time when many firms are struggling to raise capital, our existing LPs have doubled down on our strategy and several amazing, mission-driven institutions have joined us as LPs for the first time.

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We are eleven years into an amazing journey at Primary. Every year we relearn the original lesson of venture: success is all about the founders with whom you partner. Our journey has been defined by a remarkable collection of founders and the successes they’ve delivered for our LPs. Our mission has been to support them with everything we’ve got. We feel truly blessed that the unconventional strategic bets we made early on have resonated with founders and paid off in earning us their partnership. 

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We sit today with a unique market position in the midst of what is by a vast margin the most exciting and important moment in the history of information technology. And we’re not stopping building. We never will. We’re rapidly reinventing everything about how we operate for an agentic future, building new products, new technologies, and new capabilities internally, all in service of continuing to offer something even better in support of world-class founders (more coming here soon).
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We are eternally grateful to the LPs who have entrusted us with their capital and given us the right to continue to add our own dent to the universe in service of founders. Fund V is bigger than ever, and we fully expect it to be better, as well. We can’t wait to meet the founders defining a new generation of innovation and magic in our world.

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In January 2023, semiconductors were not the rage. NVIDIA was worth $360B. Memory was a sleepy commodity. Hyperscaler capex totaled $130B+, relatively flat from the year prior. Approximately 100k H100-equivalent compute systems were deployed globally, with few having ever heard the term “H100” before.

We were post the ChatGPT moment, but the gears of the semiconductor supercycle were only beginning to churn.
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Today, NVIDIA is worth over $4.5T. Multi trillion parameter models demand staggering memory capabilities, driving sky high DRAM prices and scarce supply. Hyperscaler capex in 2025 surpassed $400B and is expected to grow to $600B in 2026. As of Q3 2025, more than 3 million H100 equivalent systems are training models and running inference. Executives now brag about their secured GPU shipments as they compete in an economy increasingly organized around AI.
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Back to early 2023.
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Our belief was that this was about to change. AI demand was set to explode, and the world did not yet have sufficient compute to meet the need. Scaling laws suggested that no amount of computation would ever truly be enough to deliver the magical experiences AI could unlock. A demand tsunami was coming.
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We intuited that a window of opportunity was opening for entrants to capitalize on the moment. In February, I shared this view with the Wall Street Journal: “There’s new openings for attack and opportunity for those players because the types of chips that are going to most efficiently run these algorithms are different from a lot of what’s already out there.”
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That same day, I met Gavin Uberti, the CEO and cofounder of Etched.
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Gavin had pitch perfect understanding of what was about to happen: transformer dominance would expand; the GPU bottleneck would intensify; inference would become the all-important battleground; and the power of purpose-built hardware would become clear.
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We led the Seed round in Etched in March 2023 and have since worked with Gavin and the team as they (as they like to say) build the hardware for superintelligence. This experience combined with studying the broader compute supercycle helped crystallize our conviction. 
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Our Compute Thesis
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Our compute thesis is simple. Alpha exists at seed for compute investing. Every major technology shift requires a rebuild of its underlying infrastructure. As compute drives intelligence, demand for computation is growing exponentially, forcing a massive capex buildout across silicon, power, networking, and cooling. Fundamental bottlenecks are emerging. Incumbents are being pushed beyond incremental improvement. New opportunities are opening for founders taking first principles approaches to rearchitect how computation is produced and scaled. Yet the majority of seed investors shy away from compute deals.
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We’ve noticed that fewer VCs spent time in the compute market, but wanted to quantify it. We analyzed the past decade of M&A across software infrastructure (defined as Dev Ops, Dev Tools, Data, etc) as compared to compute (semiconductor value chain). Since 2015, software infra saw 8,500 M&A transactions, while compute saw 1,300. Despite 15% the deal count, the resulting value creation in compute was $705B, compared to $587B in software infrastructure. It’s the power law on top of the power law – fewer opportunities but far bigger outcomes.
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We believe our thesis will only grow in strength thanks to the token exponential. Consumer adoption is accelerating as models improve and new use cases emerge. AI is moving from occasional interaction to daily workflow. Swarms of agents are running continuously in parallel. Enterprise infrastructure is migrating from CPU-based machine learning to GPU-based LLMs. Beyond knowledge work, AI will expand into science, robotics, security, entertainment, and products we have not yet imagined.
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The simplest evidence is personal. AI is not just pervasive in my life. My usage is accelerating rapidly. My wife and I just had our second baby; ChatGPT was around for the first but now Chat is like a doctor/therapist in our pocket. At Primary, Gaby and I are in deep debate with Claude around our compute thesis. The firm is restructuring our workflows around AI tools in real time. Our token usage today is easily a hundred, if not a thousand times, greater than a year ago at this time. 
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And underneath it all sits the same constraint: compute. So it should come as no surprise that we think Jensen is directionally correct when he suggests a $3 to $4 trillion buildout may arrive this decade alone.
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The Bear Case
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Of course not everyone is a Jensen bull. Michael Burry, the seer of the housing bubble, has $1.1 billion in short bets against NVIDIA and Palantir – and he's not alone. The bear case: infrastructure spend won't yield equivalent value. Anthropic and OpenAI will make over $60B in revenue, backed up by over $600B of hyperscaler capex. Google spent $24B in capex in Q3 2025, only to make $15B in their cloud business. Enterprise revenue is at risk – OpenAI, Anthropic, Google, Microsoft, and Palantir all want the entire enterprise wallet. Even early winners like ServiceNow are trading back at 2023 prices as the market sorts winners from losers.
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Naysayers are pointing to signs of "bubble behavior": circular revenue deals where NVIDIA invests into OpenAI which buys NVIDIA hardware; depreciation concerns as chip cycles accelerate; unsustainable spending with OpenAI committing to nearly $1T in compute while burning $9B a year; and questionable valuations across asset classes, with sketchy pricing practices and stressed debt markets.
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The behavior – irrational exuberance, circular revenue, fantastical projections – all just screams bubble. You can hear the echoes of the dot.com bubble when people speak of tokens as proof of value. Or the telecom overbuild. Or the 2008 financial crisis with the funky debt.
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The lynchpin to the bull case is simple: AI needs to deliver value. We believe it will, and then some.
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In Defense of the Bull Case
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The historical differences that defend Jensen's bull case matter:

• 2001: Overbuilt fiber sat at 5% utilization. Today, every GPU yearns to run at capacity. Even a 1% increase in utilization can be a billion dollar revenue opportunity.
• 2008: Money flow built on unpayable consumer debt with no cash generation. Today, hyperscalers with $500B+ annual free cash flow invest in productive infrastructure. Meta, Microsoft, Google, Amazon, SpaceX are good debtors.
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OpenAI went from $2B, to $6B, to $20B in revenue in the last three years. Anthropic went from $1B to $9B in 2025 and is on track for $30B this year. This is with only 10-20% global consumer usage, no ad monetization, and barely cracking the enterprise opportunity.
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Circular revenue becomes problematic when no value is created. Nvidia invests in OpenAI and Coreweave, then sells them GPUs with usage guarantees. But this reflects genuine scarcity and real demand – Nvidia captures value at multiple points in an actually expanding market, not recycling dollars in a closed loop.
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And perhaps most importantly, geopolitical competition removes the option to slow down. There's consensus we cannot cede AI leadership to China. That requires more compute.
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An Unprecedented Opportunity for Value Creation
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There maybe should be a rule in investing that you're not allowed to say, “this time things are different.” But ... the all important word in that sentence is “time.” We are undoubtedly in bubble territory. I got into tech in 2010 and, for 12 years, I heard talk of bubbles. And then the bubble burst. The value creation in that time was astronomical, and it will be de minimis compared to what is taking place now. Said another way, we believe years of demand, endless innovation, and cash-rich companies financing the build point to a fact that things are different this time, for now.  
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We believe the next phase of compute will require more than iteration. It will require step functions. In some cases, discontinuous jumps in architecture, efficiency, and system design that incumbents are not economically or culturally positioned to lead. In moments like this, the opportunities emerge not because incumbents are asleep, but because they are rationally bound to the status quo. In moments like this, the bottlenecks move faster than the incumbents.
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The Founders Making Building the Future
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Ultimately, this thesis is brought to life by founders ambitious enough to build where the stack is hardest. The ones we are most excited to back combine deep technical realism with a willingness to challenge what the system has accepted for decades.
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A new generation is showing up. Semiconductor and computer architecture programs are surging, and young builders want to rebuild the physical foundations of the modern economy. They bring brains, audacity, and an AI native intuition for where the world is heading.
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The teams that win, though, will pair that first principles ambition with seasoned builders who know how to ship silicon, scale systems, and execute in the real world.
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Since 2023, we have taken a high conviction approach to investing behind this view. We have backed the teams at Etched, Atero (acquired by Crusoe), Haiqu, and several more in stealth across memory, alternative computing, and other layers of the stack.
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Compute is the limiting factor in a world of accelerating intelligence. The token exponential shows no signs of slowing, and the resulting pressure continues to expose structural cracks that incumbents alone cannot repair. History suggests that moments like this create rare openings for new entrants.
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If you are building in compute, now is the moment. We want to meet you early, and partner with you as you shape what comes next.

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Today’s “modern” go-to-market motion often feels anything but modern because it is fundamentally broken for both sellers and buyers. Commercial teams struggle to navigate growth and efficiency trade-offs, and buyers invariably feel those trade-off tensions in underwhelming customer experiences. While AI promises transformation, to date it has largely delivered only incrementality. Buyers and sellers are drowning in point solution experiments that don’t materially move the needle. Nobody wins.

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Growth commands a premium over profitability even in the tightest market conditions, so SaaS companies are always charging hard at top-line ARR growth, even if that means sacrificing quality and efficiency along the way. Everyone loves to talk about the eye-popping growth of the AI darlings, but we live in a tale of two cities and in the non-AI-native world where top-line ARR growth has been compressed, companies have been forced to pay more attention to their bottom lines, meaning all eyes are on efficiency. Metrics such as “APE” (ARR/FTE) are the talk of the town.

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More often than not, getting more efficient means cutting costs. But the reality for sales-led businesses is that 50-80% of their sales and marketing burn is headcount, so when they push to make teams more efficient, it’s usually the buyer who suffers: slower response times, reduced access to answers until they can prove they are fully “qualified,” and so forth. Buyers are consistently underwhelmed and frustrated when evaluating new solutions because the process is so disjointed for them: schedule a meeting with a BDR only to get minimal information while they evaluate you, wait an entire day for an account executive to sync with a solutions consultant on technical questions before circling back, etc. As the old sales adage goes, “time kills all deals,” and many deals are lost before a qualified lead is ever even logged in the system purely on account of the bumpy buyer experience.

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Even high-growth or mature sales teams with plenty of resources haven’t delivered much better for their buyers. The larger commercial teams become, the wider the distribution in team performance and quality, thus weakening the efficiency of the overall funnel and rate-limiting growth (while still disappointing the end customer).

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The product-led growth (“PLG”) movement proved that it is possible to operate in a leaner capacity while still delighting customers, but “product-led” was the operative part of that equation. We believe that AI-led growth will democratize this potential to every shape, flavor and size of company in a way that benefits buyers and sellers alike. This is why we’re excited to introduce 1mind and announce their $40M in funding following their recent $30M Series A led by Battery Ventures. We were privileged to lead 1mind’s $10M seed round in the spring of 2024, and are excited to double-down with our support as 1mind enters their next chapter of growth.

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Today, go-to-market (“GTM”) executives are inundated with pitches for AI point solutions and co-pilots. Siloed tools lack the efficacy of a horizontal solution (i.e. a sales-facing AI would be much more helpful to a prospect if it was also trained on customer support questions, internal knowledge bases, etc.) and broader, more horizontal LLMs don’t offer the level of accuracy CROs require. And at the end of the day, humans are inherently rate-limited in how fast they can read, digest and act on information provided by co-pilots. 1mind deploys human-like AI to GTM teams to augment and ultimately replace full-time employees. As Jacco van der Kooij wrote in a recent Winning by Design research paper, “the best use of AI is not to improve people's performance through co-pilots but to create disproportionate improvement by using AI to optimize the process entirely and replace the seller.” GTM teams are hungry for a consolidated approach purpose-built for them.

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We aren’t talking about agents or AI wrappers. 1mind’s superhumans have faces, voices, personalities, knowledge, motivations and a GTM brain. They have their own AI-optimized virtual desktops that allow them to do anything a human can do on their computer, including joining impromptu video calls to give presentations or demos. 1mind promises the power of a salesforce that never sleeps, learns at lightning speed, and evolves continuously.

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Amanda Kahlow, 1mind’s founder and CEO, was previously founder and CEO of 6sense, the pioneer of B2B account-based marketing and intent data. After taking some time off following that incredible journey, Amanda sprung back into action when she identified another category-creating opportunity to transform how B2B companies grow, through what she’s coined as AI-led growth, or AiLG. As Amanda regularly says, 6sense was to find buyers and 1mind is to close them. Sachin Bhat, Amanda’s right hand and 1mind’s CTO, is an experienced enterprise technologist and former founder who most recently spent 5 years scaling complex global engineering and data teams at Rippling.

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We believe that Amanda’s reputation and reach make her uniquely suited to usher GTM teams into the next (AI-powered) era of growth and that the formidable commercial-technical duo of her and Sachin will build the definitive GTM platform and accelerate AI adoption across all flavors of commercial and customer teams.

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Indeed, sales is just the beachhead: 1mind’s vision is to be all-knowing for an organization, with targeted use cases that run the gamut from top of funnel qualification all the way through to ongoing technical support. 1mind will become the brain, face, and voice of commercial and customer organizations. And as 1mind executes its horizontal, end-to-end play, we believe they will be well-positioned to box out other categories of software, including training and enablement software, internal knowledge management tools, demo automation software and more.

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No one tells a company’s story better than its customers, and 1mind’s reviews are off the charts. To date the team has successfully partnered with incredible brands such as Hubspot, Nutanix, Owner, Seismic New Relic and Boston Dynamics, and 30% of customers expand to new use cases within 90 days of going live, and several of 1mind’s customers have joined the cap table.

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AI-led growth is the future of go-to-market. There will always be things humans will do better than AI, but there are many things AI will consistently do better than humans, and we’re excited to live in a world where that is celebrated and encouraged. As Amanda says, “if you are worried about your AI hallucinating, have you asked yourself whether your sellers hallucinate?”

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It's exhilarating to imagine SaaS companies no longer need to think about account prioritization or ticket deflection because AI does the heavy lifting for them, never mind the resulting implications for buyer satisfaction. If you’re a revenue leader committed to true transformation in your go-to-market organization, we encourage you to engage with 1mind’s superhuman (Mindy) today.

At Primary we are always thinking about the future of the Go-to-Market stack. This is partially because we spend so much time—hundreds of hours a year—working with portfolio founders on the sales, marketing, and customer success functions. It’s also a space we know intimately well from our past lives in senior operating roles, where we were both economic buyers and end users of GTM solutions.

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Cassie Young was Primary’s first GTM leader. She was previously the CRO at Sailthru and then joined its acquirer Marigold, where she became CCO and managed a $200+ million P&L and a team of 200+ people in the U.S., Europe, Australia, and New Zealand. In mid-2022 Jason Gelman joined us from Compass to further expand our GTM support; in backchannel calls, we heard he was “the best revenue strategy and operations leader in New York” several times over. And rounding out Primary’s GTM pod is Zach Fredericks, a principal on our investing team with rich product management experience on the operating side.

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Our familiarity and excitement with the GTM space has left us with strong conviction that there are still many category-defining opportunities that don’t yet exist, and we believe the recent advances in AI will accelerate—though also complicate—their creation. 90% of sales leaders say that they plan to utilize AI solutions “often” in the next 24 months; Gartner predicts that sales enablement budgets will actually increase 50% by 2027—an increase much higher than we would have expected given overall budget compression.

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We are specifically compelled by GTM SaaS businesses poised to offer a combination of integrated, ROI-rich workflows and data advantages, regardless of which comes first (but we believe the best businesses have plans to achieve both). We are also excited by companies focused on the "connective tissue" that exists between multiple GTM teams. Below, we’ll elaborate on these priorities, plus share insights from some of our notable friends and collaborators in the space.

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In case this is not abundantly clear, if you are working on something in any of these areas, we want to speak with you.

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‍The state of the sector

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The GTM stack—also sometimes referred to as “RevTech”—refers to solutions that service the entire customer and revenue cycle.  The space is crowded. Really crowded. And generative AI will only compound that reality. Consider MarTech, which is just one sub-category of GTM tech: that vertical is estimated to have grown 7,000% over the past 12 years (not a typo!), even after plenty of industry consolidation, wind-downs, etc.. And the proliferation of “SalesTech” is quickly feeling like Martech 2.0. Buyers of these technologies are exhausted by the number of options available to them.

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And while AI is making the GTM stack even more crowded, GTM executives are still shopping for new solutions, because technology advancements in this category are making it easier and cheaper to build and scale the GTM function. As Mandy Cole, Partner at Stage 2 Capital tells us, “One of the most significant changes we will see in GTM as a result of AI in the next year is improved efficiency and CAC payback because people will not only be able to do more in the same amount of time, but that improvement could mean less people to produce the same outcomes.” An Insight Partners survey supports this, indicating that 87% of surveyed GTM leaders anticipated that generative AI would increase efficiency by at least 16%. Companies are ready and willing to invest here.

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Budget compression in the age of doing more with less

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“Since 2015, the GTM tech space has gotten incredibly saturated,” says Max Altschuler, General Partner at GTMFund. At this point, “GTM tech is an area where CFOs are aggressively cutting budgets or just not spending. A space where tech is sold by seats and those seats are being eliminated via layoffs.” With that, “only the best one or two companies in each category will get funded. And they will have to have real proof points in order to even be considered a ‘need to have’ category.” Put another way, successful GTM solutions will address obvious and acute pains in an organization. We love The Challenger Sale as much as the next sales nerd, but GTM products that need to teach organizations why their solutions are relevant simply will have to work much harder to gain adoption and traction.

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Feature vs. platform

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The age-old “feature vs. platform” conversation is a common one in the GTM category. Today there are many GTM point solutions that are focused on a singular business application, and CFOs and most champions are eager to consolidate their vendor stacks. Moreover, businesses focused on a singular use case are inherently rate-limited on how big they can ultimately become. As such, the GTM category is ripe for both organic and acquisitive consolidation, as evidenced by Gong launching Gong Engage to compete with Outreach and ZoomInfo’s $575MM acquisition of Chorus.

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How Gen AI is playing in so far

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Generative AI has made things both exciting and challenging. Because the barriers to entry are so much lower with GenAI technology, the winners will be those who can think big. Now is not the time for point solutions.

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GTM incumbents have embraced AI technology to launch countless new products. Salesforce recently launched AI Cloud, a set of GPT-powered tools supporting multiple business functions. 6sense, the revenue intelligence platform, launched a generative AI email writing feature. The list of established GTM tech companies launching AI products is seemingly endless.

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And yet we’ve also seen waves of new startups and point solutions enter the market addressing GTM pain points. We’ve looked at more “AI-powered BDR'' businesses than we can count. The barrier to launching innovative pain-killing solutions with generative AI is very low, but in most cases, incumbents have a clear data advantage. It is difficult to see a path for a startup to win in the GTM category unless they are building something much bigger than a point solution.

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In the startup landscape, we’ve been most excited about startups that are either attacking a high ROI, integrated workflow opportunity or setting themselves up to build a defensible data moat. Elaine Zelby, cofounder of Tofu and former GTM partner at Signalfire, summarized the opportunity with workflows: “In 5-10 years, humans will be solely focused on the creative (branding, messaging/positioning, etc) and the strategic (differentiation, relationship building, channel strategy, etc), but everything else from campaign/workflow creation and execution to measurement, optimization, and expansion will be done by AI.” A Salesforce survey reiterates this conviction—Seven in 10 marketers (71%) expect generative AI will help eliminate busy work and allow them to focus more on strategic work.

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We regularly meet startups offering workflow innovation, but we’ve found it much more difficult to meet early-stage founders that have a clear path to a data advantage over incumbents. That said, these businesses certainly exist—Clay is a recent fan favorite in the ecosystem—and we have strong conviction that there are many parts of the GTM stack where generative AI could be used to ultimately develop a data moat.

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So with all the challenges with GTM software/RevTech, why are we spending time here and which early-stage companies are we paying attention to?

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Workflows to data advantages: Klaviyo

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Companies like Gong, Outreach, SalesLoft, and more have built valuable workflows for GTM teams that also ultimately unlocked data moats/advantages. One other example we love is Klaviyo. Klaviyo’s early growth was spurred by its email service; they made it dead-simple for Shopify retailers to send targeted, automated emails at scale. The initial value proposition put Klaviyo on a breakout trajectory, growing from just 1,000 customers by the end of 2016 to over 5,000 by the end of 2017 and 12,000 by April 2019. By amassing a massive customer base and powering marketing workflows for all of them, Klaviyo ultimately built a marketing system of record and managed to achieve its long-term vision of building a Customer Data Platform (“CDP”) on the back of Shopify that enabled high-ROI workflows beyond email, which then allowed them to further expand their data moat, launch even more workflow automation, and so on.

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Data advantages to workflows: 6sense

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In the reverse direction, there is 6sense, which built an early wedge with a B2B buyer intent database that GTM teams would pay for to better understand which prospects were in buying cycles. With this data, 6sense’s customers were able to increase the reach and ROI of their top-of-funnel efforts without building workflow automation tools at the application layer. That said, 6sense did ultimately layer on a host of different workflow products ranging from display advertising for ABM to BDR email tools.

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In either direction, data compounds over time

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As Daniel Chesly of Work-Bench says, “The value of data is that it compounds over time. By leveraging historical data, insights and actions can be codified into the product and used as a moat. To create a data moat, the tool must own the atomic unit for which that company conducts business. For Gong, it’s sales calls, for Clari, it’s forecasting, for Dialpad it’s customer intelligence, for Zoominfo it’s prospecting information. Given the distribution moats for many growth-stage companies, emerging startups must attack an acute, but underserved pain point as their wedge. For startups, wedges are more important than moats. Wedges help you differentiate while moats help insulate you from competition” We agree with Daniel’s take, and it’s relevant regardless of whether a company begins with the workflow component or the data itself.

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Opportunities in the GTM stack

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We like to think of the stack broadly in two sections:

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Acquisition tools (aka Sales/Marketing wedge products that help GTM teams contact, nurture and close new deals))

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On the list of enterprise applications for generative AI, automating outbound sales is one of the most obvious, so there has been ample incumbent and startup activity in this space. Multiple companies in the most recent YC batch have launched products in this realm and incumbents have all said that they plan to release generative AI-based features in the near future. One company we admire in this space is Valley, which is building a product that automatically runs conversations between SDRs and prospects. Valley can look at a list of leads, write custom outreach, nurture conversations, and eventually set meetings for AEs. Users can drop a lead list link from LinkedIn Sales Navigator, upload information on their product, and then connect an AE’s calendar. From there, Valley runs the outreach and scheduling process on its own. This product is a wedge to Valley’s broader vision of building a sales and marketing data lake. The company believes that its wedge product will allow it to capture and store customer data in a vector database, identify patterns in that data at scale, and distribute insights on that data to their customers.

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Leveraging AI for marketing automation is another common use case, but also a category that is ripe with point solutions. Tofu is an exciting new solution that is building a broader platform play by using generative AI to eliminate manual content creation for enterprise marketing. But in addition to simply offering a painkiller campaign creation tool, Tofu built its capabilities directly into the Marketo workflow, allowing them to automatically update content across each channel based on which campaigns and iterations perform best. This approach sets them up well for building a proprietary database of how and where certain leads respond to certain content and at scale, they should have a large database of lead behavior, which should make its products more valuable to marketing teams—a great example of the workflow-to-data advantage approach.

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Retention tools (aka Customer Success wedge products that help existing business teams retain and grow their installed base of customers)

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For this space, we bet on Lantern in 2022. To the naked eye Lantern may look like a customer success platform (similar to a Gainsight or Catalyst), but the core business is actually a CDP for B2B. Lantern built a novel approach to data ingestion that has allowed for an unparalleled single view of the B2B customer (a major barrier to adoption for the incumbents in the CS category). With this data intact, Lantern can offer a host of its own recipes and plays based on that customer data, and its early wedge is focused on customer success and account management teams (e.g. identifying expansion opportunities). Lantern is an example of the reverse approach of focusing on data to unlock workflow advantages.

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Another trend that has us excited in the post-sales world is the power of LLMs focused on customer data. We’ve recently looked at businesses building LLMs to make it easier to action unstructured customer data—customer calls, tickets—for a variety of different use cases.

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The more pervasive a software application is in an organization, the better the company is positioned to drive upsell and growth from its installed base of customers. So needless to say, regardless of workflows or data moats, the most powerful GTM tools will aid and abet more than one GTM team in an organization.

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Finally, we believe that regardless of the initial direction—workflow or data—there is a big opportunity for GTM superapps: “Rippling for GTM,” if you will. This has been validated by several of the GTM stack incumbents (e.g. Outreach) already layering on new capabilities, and regardless of a startup’s initial wedge, we expect to see much more of this use case aggregation. The winners will be the businesses who build early momentum by picking the most compelling use cases for their wedge products.

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