Could pixels hold the keys to training useful agents?

The race to scale language models — and the agent ecosystem around them — is white-hot. Coding agents, which reason through problems and write code to solve them, have already taken us very far.

But one ambitious young team is making a different bet: that the most promising path to general computer agents may not run through language, screenshots, and tool calls, but through scaling raw video.

Standard Intelligence’s thesis is that the best way to build a general agent is through full video pre-training on computer use, because it is the only approach that can truly scale action data. Instead of predicting text tokens, the model learns to use a computer from raw screen data, predicting the next mouse movement, click, and keystroke from the pixels in front of it. 

It is the Tesla FSD approach applied to knowledge work on computer screens.

That makes the bet both deeply contrarian and deeply “bitter lesson”-pilled. Rather than hand-engineering workflows or wrapping language models in increasingly elaborate harnesses, Standard Intelligence is betting on a new pre-training paradigm: feed the model the raw stream of computer use, scale it aggressively, and let the generality emerge from the data.

“We’re not video people”

Video is unwieldy. It is computationally expensive, economically expensive, and technically unforgiving. Prior attempts to scale video toward AGI have often died on the vine.

The Standard Intelligence team is emphatically “not video people.” They did not arrive with a decade of inherited assumptions about how to work with video as a medium. Instead, they have had to reason through each challenge from first principles, and have met those challenges with unusual optimism, creativity, and scrappiness.

The results are striking. An 11-million-hour computer action dataset — the largest in the industry. A video encoder that is roughly 50× more token-efficient than competing approaches, enabling nearly two hours of 30 FPS video to fit inside a 1-million-token context window. A 30-petabyte storage cluster racked in San Francisco for under $500K, roughly 20× cheaper than hyperscaler alternatives.

FDM-1, their first foundation model trained directly on computer-use video at scale, offers an early glimpse of what this paradigm could become. It is a general model that can extrude a CAD gear in Blender, drive a car around a San Francisco block after an hour of fine-tuning, and find bugs in software by exploring its state space the way a curious human might.

Conscientious young founders

Founders Galen Mead and Devansh Pandey met as teenagers during the Atlas Fellowship in 2022, a selective fellowship for high-school students interested in AI alignment and AGI. 

Galen and Devansh are unusually serious about reaching AGI, and unusually conscientious about doing so safely. Both founders are wise beyond their years (21 and 20 respectively), and both left their undergraduate programs out of a sense of urgency to work on this problem.

Galen and Devansh stand out for their combination of taste, scrappiness, technical courage, and ambition. It shows up in the product thinking, in the research direction, and in the FDM-1 report itself.

The full team of six is small but mighty. Neel, Yudhister, Ulisse, and Ryan are each quirky and exceptional. They have chosen to turn down the conventional path (fancy degrees and offers from big token) and pursue this courageous mission together. 

A new pre-training regime

Video has long been a powerful training ground for AI. DQN showed that agents could learn rich behavior directly from pixels in Atari environments. Tesla scaled video models to make self-driving cars and robots navigate the physical world.

But in the race toward general knowledge agents, video-first pre-training remains an unconventional idea.

Standard Intelligence is betting that it will not stay unconventional for long.

We are thrilled to lead Standard Intelligence’s Series A alongside Miko and Yasmin from Spark Capital.

What does it mean to build a mind that is not a copy of our own?

Today, we are excited to announce that Sequoia is partnering with David Silver and Ineffable Intelligence, a new AI research lab based in London with a singular mission: to make first contact with superintelligence.

Ineffable is building what David calls a superlearner: a system that discovers all knowledge directly through its own experience, from elementary motor skills to profound intellectual breakthroughs. No pre-training. No imitation. Just an agent learning endlessly from the consequences of its own actions in a world built to teach it.

A Reinforcement Learning-based superlearner has the potential to rediscover and then transcend the greatest inventions in human history: language, science, mathematics and technology. Imagine a machine that derives the laws of physics from first principles. That invents new branches of mathematics we never thought to ask about. That designs materials, medicines and computers we don’t yet have the vocabulary to describe. This is the prize David is reaching for.

A Different Path

The current generation of AI was built by training on the entirety of the human internet. It is an extraordinary achievement. But a system trained on human data may also have fundamental limitations.

Ineffable Intelligence is scaling reinforcement learning from a clean base: no pre-training, no human data to shortcut the system. Guided by the Era of Experience as a north star, David is proving that agents trained purely from an environment can develop non-human strategies for reasoning about problems we don’t yet know how to solve.

David led some of the defining breakthroughs in deep reinforcement learning, most famously in the devilishly difficult game of Go. Go is the ultimate machine intelligence test because it cannot be brute-forced by a computer: a combinatorially explosive O(10^170) possible legal board positions vastly exceeds the O(10^80) atoms in the observable universe. It was thought to be simply too hard for machines to solve.

At DeepMind, David drove the key breakthrough that finally solved the game of Go: self-play. Self-play drove the ~800 ELO-point leap that led to the historic AlphaGo vs. Lee Sedol showdown in March 2016. David pushed the idea further still with AlphaGo Zero: removing human pre-training entirely and learning purely through self-play increased the system’s ELO rating from ~3,700 to 5,000+. The result was a system that reached decisively superhuman performance, and with somewhat non-human mannerisms.

That’s the lineage David has spent his career building. He was the lead researcher and technical force behind the Alpha series at DeepMind, where, for a brilliant period, his approach was the dominant paradigm: AlphaGo, AlphaZero, AlphaStar, AlphaProof and more.

Even with the arrival of LLMs, David never stopped believing. He is one of the very few people on earth with the conviction, the technical depth and the team to scale reinforcement learning.

What’s Ahead

The work ahead is hard, the timeline to superintelligence is uncertain, and the bet is genuinely contrarian. That is exactly what excites us. The largest leaps in AI have always come from people willing to ignore the consensus. David has ignored more consensus, more correctly, than almost anyone in the field.

We are honored to co-lead Ineffable’s first round and to partner with David on what may be the most ambitious scientific mission of our generation.

Partnering with Auctor: AI Autopilot for Software Implementation

For every dollar spent on software, six are spent on services. Why? Because buying enterprise software is easy. Implementing it is hard, and still leans heavily on labour. Across the top 10 software ecosystems (ServiceNow, Salesforce, SAP, AWS, and others), 9 million implementation consultants represent more than $500 billion in annual labour spend, still growing 10%+ a year. It’s one of the largest markets in technology, and one of the least disrupted.

Enterprise platforms have thousands of components that change daily. A single deployment can span hundreds of requirements, dozens of stakeholders, and months of back-and-forth between what a business needs and what the system can actually do. Today, consultants hold all of this together through experience and pattern recognition. But they forget context between meetings. They miss dependencies across systems. They can’t track thousands of platform updates simultaneously. Unlike LLMs, their context windows are limited by biology. 

Auctor is the autopilot for software implementation, from the moment a customer requirement is captured all the way through to delivery. It brings together the requirements, decisions, and context that typically live across meetings, documents, and dozens of disconnected systems, and translates them directly into the outputs needed to move a project forward. What used to take teams weeks of scoping now gets done in a single sitting.

We first partnered with Auctor at the Seed, shortly after the team graduated from YC. At Sequoia, each year we select a handful of portfolio companies and take their founders to meet executives at some of the most important technology companies in Silicon Valley. For an hour, these leaders effectively join the team, helping us and the founders think through strategy, positioning, and go-to-market. In our very first meeting with the C-suite of a major enterprise platform, the executives asked for a pilot halfway through the conversation, before we’d even demoed the product. 

What gave us conviction beyond the market was the team. This is a category where speed of execution determines the winner given the first mover advantage. Will, Sky, and Matt are the hardest-charging, fastest-moving team we’ve met in this space. They shipped their platform and landed their first major enterprise contract within months of graduating YC. They’ve set up shop in New York and are pulling in talent the way only a team with this much momentum can.

We’re leading Auctor’s Series A and couldn’t be more excited to partner with Will and the team to bring software implementation into the age of AI.

From Hierarchy to Intelligence

At Sequoia, we see that speed is the best predictor of start-up success. Most companies are focused on AI as a productivity enhancer. Few are focused on the potential of AI to change how we work together. Block is showing what it looks like to fundamentally rethink organization design, ultimately harnessing AI to increase speed as a compounding competitive advantage.

Two thousand years before the first corporate org chart, the Roman Army solved a problem that every large organization still faces: how do you coordinate thousands of people across vast distances with limited communication?

Their answer was a nested hierarchy with a consistent span of control at every level. The smallest unit was the contubernium, eight soldiers who shared a tent, equipment, and a mule, led by a decanus. Ten contubernia formed a century of eighty men under a centurion. Six centuries made a cohort. Ten cohorts made a legion of roughly 5,000. At each layer, a named commander held defined authority, aggregated information from below, and relayed decisions from above. The structure (8 → 80 → 480 → 5,000) was an information routing protocol built around a simple human limitation: a leader can effectively manage somewhere between three and eight people. The Romans discovered this through centuries of warfare. Even today, the US Army’s hierarchical chain follows a similar pattern. We now call it “span of control,” and it remains the governing constraint of every large organization on earth.

The next big change came from Prussia. After Napoleon’s army destroyed the Prussian forces at the Battle of Jena in 1806, a group of reformers led by Scharnhorst and Gneisenau rebuilt the military around an uncomfortable truth: you cannot depend on individual genius at the top. You need a system. They created the General Staff, a dedicated class of trained officers whose job was not to fight but to plan operations, process information, and coordinate across units. Scharnhorst intended these staff officers to “support incompetent Generals, providing the talents that might otherwise be wanting among leaders and commanders.” This was middle management before the term existed. Professionals whose purpose was to route information, pre-compute decisions, and maintain alignment across a complex organization. The military also formalized the distinction between “line” and “staff” functions. Line advances the core mission. Staff provides specialized support. Every corporation still uses this vocabulary today.

Military hierarchy entered the business world through the American railroads in the 1840s and 1850s. The U.S. Army lent West Point-trained engineers to private railroad companies, and these officers brought military organizational thinking with them. Staff and line hierarchies, divisional structure, bureaucratic systems of reporting and control: all of it was developed in the military before the railroads adopted it. In the mid-1850s, Daniel McCallum of the New York and Erie Railroad created the world’s first organizational chart to manage a system stretching over 500 miles with thousands of workers. The informal management styles that worked for smaller railroads were failing. Train collisions were killing people. McCallum’s chart formalized the same hierarchical logic the Romans had used: layers of authority, defined reporting lines, structured information flow. It became the blueprint for the modern corporation.

Frederick Taylor (1856-1915), often called the “Father of Scientific Management,” optimized what happened within that hierarchy. Taylor broke work into specialized tasks, assigned them to trained experts, and managed through measurement rather than intuition. This produced the functional pyramid organization – a structure optimized for efficiency within the information routing system that the military had pioneered and the railroads had commercialized.

The first real stress test of functional hierarchy came during World War II. The Manhattan Project required physicists, chemists, engineers, metallurgists, and military officers to work across disciplinary boundaries toward a single objective under extreme secrecy and time pressure. Robert Oppenheimer organized Los Alamos into functional divisions but insisted on open collaboration across them, resisting the military’s instinct to compartmentalize. When the implosion problem became critical in 1944, he reorganized the lab around it, creating cross-functional teams unlike anything in corporate America at the time. It worked, but it was a wartime exception led by a singular figure. The question the postwar business world faced was whether that kind of cross-functional coordination could be made routine.

With the growth and globalization of companies after World War II, the scale limitations of functional design became acute. In 1959, McKinsey’s Gilbert Clee and Alfred di Scipio published “Creating a World Enterprise” in the Harvard Business Review, providing an intellectual framework for a matrix organization that combined functional specialties with divisional units. Under the leadership of Marvin Bower, McKinsey helped companies like Shell and GE implement these principles, balancing central standards with local agility. This became the “professional” or “modern” corporation that propelled the postwar global economy.

Over time, other frameworks emerged to address the complexity, rigidity, and bureaucracy of matrix structures. The McKinsey 7-S framework, developed in the late 1970s by Tom Peters and Robert Waterman, distinguished the “hard Ss” (Strategy, Structure, Systems) from the “soft Ss” (Shared Values, Skills, Staff, Style). The core idea was that structural elements alone were insufficient. Organizational effectiveness required alignment across cultural traits and the human factors that determine whether a strategy actually succeeds.

In more recent decades, technology companies have experimented aggressively with organization structure. Spotify popularized cross-functional squads with short sprint cycles. Zappos attempted Holacracy, eliminating management titles entirely. Valve operated with a flat structure and no formal hierarchy. Each of these experiments revealed something about the limitations of traditional hierarchy, but none solved the underlying problem. Spotify moved back toward conventional management as it scaled. Zappos saw significant attrition. Valve’s model proved difficult to scale beyond a few hundred people. As organizations grow into the thousands, they revert to hierarchical coordination because no alternative information routing mechanism has been powerful enough to replace it.

The constraint is the same one the Romans faced and the Marine Corps rediscovered in World War II: narrowing span of control means adding layers of command, but more layers mean slower information flow. Two thousand years of organizational innovation has been an attempt to work around this tradeoff without breaking it.

So what’s different now?

At Block, we’re questioning the underlying assumption: that organizations have to be hierarchically organized with humans as the coordination mechanism. Instead, we intend to replace what the hierarchy does. Most companies using AI today are giving everyone a copilot, which makes the existing structure work slightly better without changing it. We’re after something different: a company built as an intelligence (or mini-AGI).

We are not the first to try to move beyond traditional hierarchy. Haier’s rendanheyi model, platform organizations, “data-driven” management: these are real attempts at the same problem. What they lacked was a technology capable of actually performing the coordination functions that hierarchy exists to provide. AI is that technology. For the first time, a system can maintain a continuously updated model of an entire business and use it to coordinate work in ways that previously required humans relaying information through layers of management.

For this to work, a company needs two things: a kind of “world model” of its own operations, and a customer signal rich enough to make that model useful.

Block is remote-first. Everything we do creates artifacts. Decisions, discussions, code, designs, plans, problems, and progress all exist as recorded actions. It’s the raw material for a company world model. In a traditional company, a manager’s job is to know what’s happening across their team and relay that context up and down the chain. In a remote-first company where work is already machine-readable, AI can build and maintain that picture continuously. What’s being built, what’s blocked, where resources are allocated, what’s working and what isn’t. That’s the information the hierarchy used to carry. The company world model carries it instead.

But the capability of the system is only as good as the quality of the customer signal feeding it. And money is the most honest signal in the world.

People lie on surveys. They ignore ads. They abandon carts. But when they spend, save, send, borrow, or repay, that’s the truth. Every transaction is a fact about someone’s life. Block sees both sides of millions of these transactions every day, the buyer through Cash App and the seller through Square, plus the operational data from running the merchant’s business. That gives the customer world model something rare: a per-customer, per-merchant understanding of financial reality built from honest signal that compounds. The richer the signal, the better the model. The better the model, the more transactions. The more transactions, the richer the signal.

Together, the company world model and the customer world model form the foundation for a different kind of company. Instead of product teams building predetermined roadmaps, you build four things.

First, capabilities. The atomic financial primitives: payments, lending, card issuance, banking, buy-now-pay-later, payroll, and so on. These are not products. They are building blocks that are hard to acquire and maintain (some have network effects and regulatory permission). They have no UIs of their own. They have reliability, compliance, and performance targets.

Second, a world model. This has two sides. The company world model is how the company understands itself and its own operations, performance, and priorities, replacing the information that used to flow through layers of management. The customer world model is the per-customer, per-merchant, per-market representation built from proprietary transaction data. It starts with raw transaction data today and evolves toward full causal and predictive models over time.

Third, an intelligence layer. This is what composes capabilities into solutions for specific customers at specific moments and delivers them proactively. A restaurant’s cash flow is tightening ahead of a seasonal dip the model has seen before. The intelligence layer composes a short-term loan from the lending capability, adjusts the repayment schedule using the payments capability, and surfaces it to the merchant before they even think to look for financing. A Cash App user’s spending pattern shifts in a way the model associates with a move to a new city. The intelligence layer composes a new direct deposit setup, a Cash App Card with boosted categories for their new neighborhood, and a savings goal calibrated to their updated income. No product manager decided to build either solution. The capabilities existed. The intelligence layer recognized the moment and composed them.

Fourth, interfaces (hardware and software). Square, Cash App, Afterpay, TIDAL, bitkey, proto. These are delivery surfaces through which the intelligence layer delivers composed solutions. They are important, but they are not where the value is created. The value is in the model and the intelligence.

When the intelligence layer tries to compose a solution and can’t because the capability doesn’t exist, that failure signal is the future roadmap. The traditional roadmap, where product managers hypothesize about what to build next, is any company’s ultimate limiting factor. In this model, customer reality generates the backlog directly.

If this is what the company builds, then the question becomes: what do the people do?

The org structure follows from this, and it inverts the traditional picture. In a conventional company, the intelligence is spread throughout the people and the hierarchy routes it. In this model, the intelligence lives in the system. The people are on the edge. The edge is where the action is.

The edge is where the intelligence makes contact with reality. People reach into places the model can’t go yet. They sense things the model can’t perceive: intuition, opinionated direction, cultural context, trust dynamics, the feeling in a room. They make the calls the model shouldn’t make on its own, especially ethical decisions, novel situations, and high-stakes moments where the cost of being wrong is existential. A world model that can’t touch the world is just a database. But the edge doesn’t need layers of management to coordinate it. The world model gives every person at the edge the context they need to act without waiting for information to travel up and down a chain of command.

In practice, this means we normalize down to three roles.

Individual contributors (ICs) who build and operate capabilities, the model, the intelligence layer, and the interfaces. They are deep specialists and experts in a specific layer of the system. The world model provides the context that a manager used to provide, so ICs can make decisions about their layer without waiting to be told what to do.

Directly Responsible Individuals (DRI) who own specific cross-cutting problems or opportunities and customer outcomes. A DRI might own the problem of merchant churn in a specific segment for 90 days, with full authority to pull resources from the world model team, the lending capability team, and the interface team as needed. DRIs may persist on certain problems or move elsewhere to solve new ones.

Player-coaches who combine building with developing people. They replace the traditional manager whose primary job was information routing. A player-coach still writes code or builds models or designs interfaces. They also invest in the growth of the people around them. They don’t spend their days in status meetings, alignment sessions, and priority negotiations. The world model handles alignment. The DRI structure handles strategy and priority. The player-coach handles craft and people.

There is no need for a permanent middle management layer. Everything else the old hierarchy did, the system coordinates, and everyone is empowered, with a role that’s much closer to the work and the customer.

Block is in the early stages of this transition. It will be a difficult one, and parts of it will likely break before they work. We’re writing about it now because we believe every company will eventually need to confront the same question we did: what does your company understand that is genuinely hard to understand, and is that understanding getting deeper every day?

If the answer is nothing, AI is just a cost optimization story. You cut headcount, improve margins for a few quarters, and eventually get absorbed by something smarter. If the answer is deep, AI doesn’t augment your company. It reveals what your company actually is.

Block’s answer is the economic graph: millions of merchants and consumers, both sides of every transaction, financial behavior observed in real time. That understanding compounds every second the system operates. We believe the pattern behind this, a company organized as an intelligence rather than a hierarchy, is significant enough that it will reshape how companies of all kinds operate over the coming years. Block is far enough along to show the idea is more than theory (though, we welcome debate and feedback to pressure test and improve our ideas).

Companies move fast or slow based on information flow. Hierarchy and middle management impede information flow. For two thousand years, from the Roman contubernium to today’s global enterprises, we have had no real alternative. Eight soldiers sharing a tent needed a decanus. Eighty men needed a centurion. Five thousand needed a legate. The question was never whether you needed layers. The question was whether humans were the only option for what those layers do. They aren’t anymore. Block is building what comes next.

Partnering with Edra: Context for Agents at Scale

Every company runs differently. Two businesses in the same industry will have their own escalation paths, and workarounds—their own tribal knowledge accumulated over years and stored, if anywhere, in the institutional memory of people who won’t always be there. When you drop a general-purpose AI into that environment, it starts from zero. The work of getting it up to speed (the forward-deployed engineers, manual documentation, consultants) is slow, expensive and has to be redone every time a process changes. Most companies are living this problem right now.

Eugen Alpeza spent seven years at Palantir, where he was instrumental in building the company’s U.S. commercial go-to-market motion, including starting Palantir’s work with AT&T, one of its largest and most complex deployments. In 2023, he took on the launch of Palantir’s AI Platform under CEO Alex Karp. Together with Yannis Karamanlakis, they created the Forward Deployed AI Engineer role at Palantir—designed to bridge AI research with real world production deployments. Yannis became the first Forward Deployed AI Engineer at the company, leading a team focused on taking LLMs from demos into production at scale. Yannis had already led a major pure AI commercial project, a recruiting search engine that increased placement rates for a staffing firm by 129%. The two left Palantir as close friends and co-founders. They had known each other for 13 years, since university, and had long planned to start a company together.

What Edra has built is elegant in its logic. Instead of asking humans to document processes, Edra analyzes the data a company already generates. Through support tickets, emails, logs, chat histories, it creates a living knowledge base that reflects how the business actually runs, not just how it was supposed to run on paper. As people use it, the system learns and improves on its own. And unlike black-box fine-tuning approaches, it is transparent and editable—you can see exactly what Edra has learned and why. From there, agent automation is straightforward. 

The early results are real. The first successful use cases are around automating IT service management and customer technical support, where the data is rich and the pain is acute. The early customers love it and are expanding aggressively. 

As always, our investments are all about people. When I first met Eugen and Yannis, what struck me was not only what they had built, but how they work together. Eugen is one of the most commercially gifted people I have met—someone who earns the trust of skeptical buyers and makes them believe. Yannis is technically exceptional, the kind of partner who makes the hardest things feel solid. Their dynamic as a founding duo is a genuine superpower.

We are thrilled to partner with Eugen, Yannis and the entire Edra team. 

Partnering with Scanner: Every Log Tells a Story—If You Can Find It Fast Enough

A while back, I was deep in research on the next generation of security infrastructure, talking to CISOs and security engineers at some of the most technically sophisticated companies in Silicon Valley. I asked them all the same question I’d asked a decade earlier when I worked in enterprise software: What’s your biggest headache? The consistency of their answers surprised me. “We drown in logs we can’t afford to keep,” as one security leader put it, “and go blind on the logs we can’t afford to search.”

Enterprise security today is a story of impossible choices. The tools that teams rely on generate enormous amounts of log data—every API call, every login event, every network connection. To investigate cyber threats, they need all of it, often going back a year or more. But storing everything in a SIEM like Splunk is prohibitively expensive; costs could easily consume 15% of a CISO’s entire budget. Instead, companies make a compromise: they keep only the most recent 10 to 30 days of logs in their SIEM and park the rest in Amazon S3, where storage is cheap, but the data is effectively frozen. When a breach, a compliance audit, or a forensic investigation happens, security teams discover too late that the evidence they need is out of reach, opaque and unsearchable. 

I first heard about Scanner from a member of the security team at Temporal, one of our portfolio companies, who called it, “crazy fast.” I looked into it, and reached out to Cliff Crosland right away.

What Cliff and his co-founder Steven Wu have built is elegant in its insight. They asked: what would a log search engine look like if you designed it from scratch for object storage? The answer was a purpose-built inverted index that maps field values directly to file regions in S3. Rather than combing through billions of rows, Scanner narrows each query to only the relevant slices of data. A petabyte of logs becomes interactive. Queries that took hours now run in seconds. And a streaming detection engine runs hundreds of detection rules continuously across tens of terabytes a day, without re-scanning the world for each one.

Cliff and Steven are exactly the kind of founders we look for. Both Stanford CS alums, they were engineering leads together at Accompany (acquired by Cisco), where they built core data infrastructure under demanding, production-scale conditions. They have an obsession with performance that borders on the philosophical; they don’t tolerate systems that feel slow. And they have the expertise to build something better.

What’s most striking about Scanner isn’t the technology—though that is genuinely impressive. It’s the customers. The companies using Scanner today read like a who’s who of the cloud native world: Notion, Ramp, Benchling, Confluent, Lemonade, BeyondTrust. And they’re not just using it—they love it. Benchling replaced another product after a forced tenfold price increase, and their head of security engineering called it one of the best technical decisions their team had made. Ramp started with security logs and then expanded to application logs, reducing their SIEM bill in the process. Notion’s detection and response team built an internal AI agent that autonomously runs security investigations using Scanner. 

That last example signals what’s to come. We are entering a new era of security operations, where AI agents will do much of the investigative work that today consumes hours of human time. But agents need to rapidly iterate, ask questions and follow threads; queries can’t take minutes, much less hours. Scanner’s speed is enabling these agentic security workflows across a wide range of companies: within weeks of their MCP release, nearly a third of Scanner’s customers were already using it in production, and agents now account for 80% of queries on the platform. That is not a prototype or a promising beta. That is the future arriving ahead of schedule.

Sequoia is proud to lead Scanner’s Series A, and we’re thrilled to partner with Cliff, Steven and their team as they work to transform a market overdue for reinvention. Scanner is winning hearts and minds among the most technically forward organizations today, and together, they will define the next decade of security infrastructure.