Fortt Knox: Innovation Curated

If This AI Works, It Could Put the Brakes on the Health Cost Spiral

Jon Fortt — Tue, 10 Feb 2026 12:03:29 GMT

This is an AI-assisted summary of my Fortt Knox Update with Included Health CEO Owen Tripp. View the full interview here:

In this Fortt Knox update, Jon Fortt sits down with Owen Tripp, CEO of Included Health, to unpack a new employer health plan that aims to flip the traditional insurance experience. Tripp argues employer-sponsored healthcare is at a crossroads: costs are rising at double-digit rates, employers are nearing a breaking point. The old approach, go cheap first and escalate later, often backfires by dragging patients through the system as bills pile up.

Included Health’s new direction? A “member-first, clinically intensive” plan that pairs AI-driven navigation with a dedicated, real physician. From day one, Tripp says, members get a concierge-style experience: an AI assistant (“Dot”) that integrates claims, EMR, and pharmacy data. They also get a consistent doctor relationship, including an extended 45-minute onboarding visit covering medical, financial, and life stressors. The goal is to get people to the right provider faster and reduce total episode costs.

Economically, Tripp says the only durable way to save money is doing the right thing more quickly. Included Health has spent years building proprietary provider-quality models, analyzing tens of millions of claims and outcomes to match members with providers most likely to deliver results at good value. That data foundation now allows the company to do something rare in healthcare: underwrite outcomes and cost trends for employers, standing behind multi-year pricing commitments. Tripp points to early contracts like CalPERS as proof that better outcomes, lower costs, and higher member satisfaction can align at scale.

Key takeaways

Employer healthcare costs are rising 12%+ annually, pushing many companies into “red alert” territory.
Included Health launched a new employer plan with immediate concierge and clinical engagement.
Members get a dedicated, real doctor plus AI support from day one.
AI gathers goals and concerns members may not share in traditional settings.
The strategy prioritizes episode cost, not just unit cost.
“Highest quality” is defined as best outcome at best value for the individual member.
Included Health has built proprietary provider-quality models from 15M+ claims.
High quality does not always mean highest price; volume and outcomes can lower costs.
The company now underwrites cost trends, standing behind multi-year pricing.
Tripp positions this as a viable alternative to traditional PPO-style insurance models.

China's Beating the U.S. in Robotics. He Has a Plan to Catch Up.

Jon Fortt — Mon, 09 Feb 2026 12:03:01 GMT

This is an AI-assisted summary of my Fortt Knox Update with Standard Bots CEO Evan Beard. View the full interview here:

Evan Beard frames Standard Bots as a response to a national competitiveness problem: the U.S. cannot rebuild its manufacturing base without dramatically increasing robotic automation. While China deploys roughly ten times as many robots as the U.S., Beard argues the real issue isn’t robot count, it’s capability.

Today’s industrial robots are hard to program and limited in what they can automate, leaving most factory tasks untouched. Standard Bots’ breakthrough is training robots through human demonstration, through VR headsets or handheld devices, rather than brittle point-by-point programming. This approach allows robots to generalize across tasks and object variations, unlocking far higher automation rates.

Beard positions this moment as a rare reset point: “physical AI” can leapfrog older paradigms just as software did decades ago. Standard Bots designs its own actuators, motors, firmware, and interfaces, mostly manufactured in the U.S.. This allows the company to lower costs, simplify deployment and scale automation across factories and warehouses. Standard Bots has raised roughly $77M and is now pushing toward broader deployment and clearer output metrics.

Today’s Toughest Problem

The core problem Standard Bots is tackling is that modern industrial robots are too limited and too difficult to use to meaningfully impact U.S. manufacturing competitiveness. Even common automation tasks like welding have surprisingly low penetration rates, because programming robots requires specialized expertise and rigid setups. As a result, factories still rely heavily on human labor for repetitive, dangerous, or dirty work that should be automatable.

Beard argues that this constraint has real geopolitical consequences. Chinese manufacturers can quote parts at six to ten times lower cost, largely because they deploy far more robots and extract more productivity from them. Without a step-change in automation, the U.S. will continue importing goods, exporting value, and losing industrial capacity. Standard Bots’ answer is a new training paradigm that lets robots learn the way humans do, by being shown. That dramatically expands the range of tasks they can perform.

Origin Story

Beard’s path to robotics began with software. Growing up in Annapolis, Maryland, he was drawn to building things early: first websites, then businesses. He studied computer science at Duke, paid his way through college by building and optimizing websites, and later co-founded a startup that went through Y Combinator and was acquired by Salesforce as a talent deal. That experience gave him both confidence and credibility. But it was exposure to robotics at Y Combinator, watching engineers solder boards and build humanoids, that reframed his ambition.

Inspired by mentors who were largely self-taught, Beard realized robotics wasn’t an unreachable domain. He began teaching himself mechanical and electrical engineering and, crucially, talking to manufacturers. After 100 customer conversations, a pattern emerged: robots were too hard to program and too limited. Standard Bots was founded to solve exactly that.

Death Valley

The company’s lowest point came from an early, risky conviction: to make robots affordable and capable enough, Standard Bots had to build them from scratch. That meant designing custom motors, actuators, and firmware. It also meant the work produced no revenue and few visible wins for years. Progress was slow, estimates slipped. Skepticism mounted. Friends staged an intervention. Venture investors warned against hardware altogether. Even Beard’s wife joked about him as an “artist in a garage,” endlessly tinkering.

What sustained Beard was learning to measure progress differently. Instead of external metrics like customers or revenue, he focused on internal input metrics: torque ripple improving, motor speed increasing, control algorithms advancing. Month-by-month technical gains convinced him the thesis was sound. The experience taught him to trust disciplined perseverance instead of blind stubbornness. And it taught him to distinguish between being early and being wrong.

Core Belief

Beard’s core belief is that the hardest problems require earned perseverance. Not every struggle is worth pushing through. But when three conditions are met: a massive opportunity, the right team, and measurable technical progress? Entrepreneurs must commit with near-superhuman persistence. In robotics, that means accepting long timelines, ambiguous feedback, and skepticism from people who focus only on surface-level outputs.

He also believes simplicity is the true unlock for automation. Most industrial robots fail not because of physics, but because of interfaces that are too opaque, rigid, and intimidating. By making robots intuitive to train and deploy, Standard Bots lowers the cognitive barrier to automation. That shift, Beard argues, is as important as any single technical advance.

Strategic Imperative

In 2026, Standard Bots’ mandate is clear: prove at scale that AI-trained robots can transform manufacturing economics. Beard wants the company to be recognized as the world’s leading deployer of AI-powered industrial robots, with undeniable output metrics to match the vision. With an annualized run rate approaching $24M, the focus now is less on explaining the future and more on demonstrating it.

Strategically, this means expanding deployments, tightening execution, and making it impossible to dismiss the idea that an American robotics company can compete globally. For Beard, success would validate a broader thesis: that the U.S. can regain manufacturing leadership by pairing physical AI with thoughtful system design rather than chase humanoid spectacle. The goal isn’t just robots in factories. It’s a renewed industrial foundation.

This CEO Knows Why Software Stocks are Suffering

Jon Fortt — Fri, 06 Feb 2026 12:02:23 GMT

This is an AI-assisted summary of my Fortt Knox Update with HUMAIN CEO Tareq Amin. View the full interview here:

Of all the CEO conversations I had about this week’s tech stock rout – and there were several – this one stood out. A global tech CEO is deploying billions of dollars to launch an AI enterprise, and he’s not buying traditional software.

HUMAIN CEO Tareq Amin frames the company as a national-scale bet to convert Saudi Arabia’s energy abundance into a durable advantage in artificial intelligence. His core analogy: where the Kingdom once led global energy exports via oil, HUMAIN aims to lead exports of AI “tokens” by building the full AI value chain: power, land, connectivity, compute, models, applications and advisory, at unprecedented scale and cost efficiency. The company is building gigawatt-class AI factories on vast master-planned sites, pairing low-cost energy with diversified semiconductors from leading U.S. chipmakers to attack the biggest constraint facing AI-native companies today: infrastructure cost.

Amin argues that AI shifts the innovation equation toward energy and scale without eliminating the need for talent. Saudi Arabia’s investments in education are paying off, he says, with a young, digitally fluent workforce returning home and blending with global hires. HUMAIN claims strong inbound interest from international talent and plans satellite offices in the U.S., while keeping Riyadh as its foundation.

On strategy, HUMAIN is pragmatic about where to build versus partner. Rather than designing chips outright, it invests heavily through a venture arm with more than $21B under management, largely in the U.S., to gain exposure to breakthrough inferencing, latency, and total-cost-of-ownership innovations. Amin is also outspoken about enterprise software’s future: HUMAIN has avoided buying traditional SaaS, instead building AI-first enterprise systems internally, using AI agents to generate databases, middleware, and interfaces at speed. He believes “results as a service,” not seats or licenses, will define enterprise sales. Platforms slow to adapt will suffer.

Highlights

Positions HUMAIN as an end-to-end AI value-chain builder anchored in low-cost energy
Building gigawatt-scale AI factories on massive, master-planned sites
Diversified compute strategy with leading U.S. chip partners
Focus on inferencing economics: latency, speed, and total cost of ownership
Strong global talent pull; diverse team spanning 29 countries
U.S. seen as core innovation partner; satellites planned in Silicon Valley
Venture arm manages $21B+, prioritizing U.S. AI infrastructure startups
Avoids traditional SaaS internally; builds AI-first enterprise software
Advocates “results as a service” over licenses or seats
Warns legacy software vendors must re-architect for AI agents or risk disruption

Why the 'Godmother of AI' is Tackling Physics Now

Jon Fortt — Thu, 05 Feb 2026 12:03:40 GMT

This is an AI-assisted summary of my Fortt Knox Update with World Labs CEO Fei-Fei Li. View the full interview here:

In a wide-ranging conversation, Fei-Fei Li, CEO of World Labs, frames artificial intelligence not as a single product cycle but as a “civilizational technology” whose impact will touch every sector of society. Li reflects on how quickly modern AI has advanced, exceeding even optimistic expectations from longtime researchers, and explains why that acceleration carries both excitement and responsibility.

A central theme is inclusion. Li argues that AI must be shaped by the participation of the entire global population, not just a narrow set of voices. AI systems are increasingly modeling the world through language, perception, and decision-making; in that process, culture, context and lived experience matter. Localization isn’t just about translating language models; it’s about embedding cultural nuance and situational understanding so that entire regions and communities are not left out of the AI future.

Li then turns to World Labs’ core mission: moving beyond text-centric AI toward spatial intelligence. Drawing on her roots in computer vision, she describes spatial and perceptual intelligence as foundational to how humans live and work. This is reasoning about the physical world, interacting with objects and understanding cause and effect in three dimensions and over time. World Labs is building frontier models that can reason, generate and interact in 3D and 4D environments, enabling applications from robotics and simulation to design, education, healthcare, manufacturing and gaming.

Gaming and interactive experiences are an early proving ground. Li notes that developers are already using World Labs’ first released model to prototype new kinds of interactive worlds, offering a glimpse of how spatial AI could reshape creativity and embodied digital experiences over the next decade.

Highlights

Li describes AI’s recent progress as breathtaking, even for longtime insiders
She emphasizes responsibility alongside innovation as AI reshapes society
Inclusion means global participation, not just representation at the margins
Localization goes beyond language to culture, context, and shared experience
AI’s reach is compared to computing itself: everywhere there is a chip, there will be AI
World Labs focuses on spatial intelligence, not just language models
Spatial AI reflects how humans reason, act, and interact in the physical world
Applications span robotics, simulation, design, education, and healthcare
Gaming is an early, natural fit for spatial and embodied AI models
Early developer adoption signals creative momentum for World Labs’ approach

A Counter-Narrative in the Software Rout?

Jon Fortt — Wed, 04 Feb 2026 13:43:33 GMT

This is an AI-assisted summary of my Fortt Knox Update with Box Data CEO Aaron Levie. View the full interview here:

As software stocks belly flopped, I was interviewing CEOs at Cisco’s AI Summit in San Francisco on Tuesday.

A couple lines of argument continued to emerge. If you were a software CEO, you acknowledged the disruptive power of AI but claimed to operate in a category shielded from its destructive force. If you were a software customer or platform provider, you were more likely to revel in the shakeup.

Box co-founder and CEO Aaron Levie pushed back on the market narrative that AI will commoditize application software and weaken incumbents. Levie argued the opposite: as AI agents proliferate, systems of record become more critical, not less. In a world with “100 times more software” and autonomous agents taking consequential actions, enterprises will need trusted platforms to control access, permissions, workflows, and accountability. Box positions itself as that system of record for unstructured data, including contracts, marketing assets, research and financial documents – a place where agents can securely operate.

Levie framed AI economics as a two-part model: a base software fee (which may evolve beyond seat-based pricing) plus consumption tied to how much work agents perform. The key margin question, he said, will hinge on how much value the application layer adds through context engineering, domain expertise and workflow orchestration, especially as AI model inference pricing becomes more competitive.

On execution, Levie described AI as the biggest shift in software development he’s seen in 25+ years. Internal examples show features built five times faster, enabling teams to pursue far more ambitious projects. While that raises concerns about technical debt, he suggested future agents will increasingly clean up and refactor code written by earlier agents.

Addressing investor skepticism and stock volatility, Levie emphasized strong enterprise demand, particularly for Box’s Enterprise Advanced plan that bundles workflow automation and AI agent capabilities. Strategically, Box remains focused on tuck-in acquisitions for teams and point solutions, but AI has made building internally faster and, in some cases, preferable to buying.

Ultimately, Levie argued that in an attention-constrained world flooded with software, enterprises will gravitate toward trusted incumbents with distribution, ecosystems and proven platforms. In the long run he argues that will strengthen companies like Box rather than displace them.

AI drives more software and agents, increasing the need for systems of record
Box positions itself as the system of record for unstructured enterprise data
Agent-heavy workflows raise the stakes for security, permissions, and governance
AI economics likely split between base software fees and agent consumption
Application-layer margins depend on context, domain expertise, and workflow value
AI model inference pricing should compress with increased competition
Internal teams are building features up to 5× faster using AI coding tools
Future agents may clean up and refactor earlier AI-generated code
Enterprise Advanced plan shows strong demand as customers automate knowledge work
AI reduces reliance on large acquisitions by accelerating in-house development

Are Hard Drives Making a Comeback in AI Infrastructure?

Jon Fortt — Tue, 03 Feb 2026 15:04:02 GMT

This is an AI-assisted summary of my Fortt Knox Update with WD CEO Irving Tan. View the full interview here:

Irving Tan, CEO of Western Digital (now rebranding as WD), joined Jon Fortt to explain why hard disk drives (HDDs) remain central to cloud and AI infrastructure—even as flash storage continues to advance. Tan outlined Western Digital’s transformation into a data-center-centric company, including a refreshed brand identity that reflects its focus on hyperscale customers, which now account for roughly 90% of its business.

Tan pushed back on the idea that flash will soon displace HDDs, noting that hard drives still store about 80% of data in hyperscale environments and are expected to do so for at least the next five to ten years. Their advantage lies in economics, durability under heavy write cycles, and scalability. As AI workloads drive data growth at an estimated 25% CAGR, customers prioritize capacity at scale and reliability over the underlying recording method.

A key focus was Western Digital’s roadmap from energy-assisted PMR (ePMR) to HAMR. Tan emphasized that hyperscalers are largely agnostic to the technology itself; what matters is seamless transitions, predictable reliability, and rapid capacity increases. Western Digital plans to ship 40TB ePMR drives with a path to 60TB, while HAMR is already in qualification with customers, targeting 100TB-plus drives later in the decade.

Performance and energy efficiency are also central. Western Digital is working to narrow the bandwidth and throughput gap with enterprise SSDs while reducing power consumption—critical as data centers run hotter and AI inference workloads increase. Tan also highlighted growing opportunities from data sovereignty initiatives and “neo-cloud” providers, supported by more turnkey storage platforms.

Highlights

Western Digital rebrands to WD to emphasize its shift to a data-center-first company.
Roughly 90% of revenue is now tied to cloud and hyperscale customers.
HDDs still store ~80% of hyperscale data and remain core to AI storage.
Storage demand is growing at an estimated 25% CAGR driven by AI and cloud.
Customers prioritize capacity at scale and reliability over recording method.
ePMR roadmap includes 40TB drives moving toward 60TB.
HAMR is in customer qualification with a path to 100TB-plus drives.
Focus on improving HDD bandwidth, throughput, and energy efficiency.
HDDs retain advantages in heavy write durability versus enterprise SSDs.
Data sovereignty and emerging “neo-clouds” create new growth opportunities.

Why Do Stablecoins Lead the Crypto Innovation Conversation?

Jon Fortt — Mon, 02 Feb 2026 13:09:33 GMT

This is an AI-assisted summary of my Fortt Knox 1:1 with Rain CEO Farooq Malik. View the full interview here:

Farooq Malik, co-founder and CEO of Rain, argues that the real breakthrough in stablecoins isn’t speculation, it’s efficiency. In conversation with Jon Fortt, Malik frames Rain’s mission clearly: Make tokenized money interoperable with existing payment rails while keeping the crypto complexity invisible to users.

The payoff is practical, not ideological. Stablecoins move money in a non-fungible, cash-like way. That sharply reduces the hidden working-capital costs embedded in today’s fintech flows. Many “instant” payments still rely on layered IOUs and prefunding. Stablecoins collapse that structure.

Rain started with cards for a reason. Card payments are the most complex surface in payments, involving authorizations, settlements, refunds, and disputes. By solving cards first, Rain made stablecoin balances spendable anywhere consumers already transact. Rain sells to platforms, not consumers. Its customers include fintechs, employers, and remittance providers. The company positions itself as authorization-and-settlement infrastructure that connects any payment type to any network.

Malik traces his conviction back to immigrant experiences with cross-border friction and later to seeing outdated financial plumbing firsthand at a development bank. Rain survived crypto’s deepest downturn by staying lean and regulation-forward. Now, with clearer rules emerging globally, the company is expanding features, licenses and geography. Malik’s bet is straightforward: As money becomes programmable, stablecoins will quietly power cheaper credit, global payroll and embedded finance – without users ever needing to know they are using crypto.

Today’s Toughest Problem

Rain is solving how to make stablecoins spendable everywhere: cards, ACH, and wires. The challenge isn’t consumer demand, it’s interoperability and regulatory complexity. Malik argues that today’s fintech experiences hide massive balance-sheet costs. Stablecoins reduce those costs by moving value directly, like cash. Rain’s task is to deliver that efficiency inside familiar rails and compliant frameworks.

Origin Story

Malik’s worldview was shaped by immigration and cross-border family finance. It sharpened later while running treasury and capital markets operations where billions of dollars moved via emails, PDFs and wires. The inflection point came when a customer received investment funds instantly via USDC. The contrast with week-long ACH delays was stark. Stablecoins stopped looking speculative and started looking inevitable.

Death Valley

Rain launched just before crypto’s systemic crises, including Luna and FTX, followed by bank failures that directly affected its accounts. Regulatory uncertainty was existential. Clarity could legitimize the model or shut it down. Rain survived by staying scrappy, keeping the team lean and continuing to grow modestly. The company engaged regulators early and kept building despite uncertainty.

Core Belief

Efficiency wins. Malik emphasizes humility and scrappiness as operating principles. You can be right today and wrong tomorrow. Stablecoins matter not because they oppose fiat currency, but because programmable money lowers costs, expands access and enables products that legacy rails cannot support sustainably.

Strategic Imperative

With regulation catching up, Rain’s mandate is expansion. That means deeper card and payment features, more licenses, and broader geographic reach. The pitch is simple: one API and global coverage. As markets formalize, Rain aims to be the quiet infrastructure layer that helps stablecoins move from niche to default.

A Data Traffic Jam Looms: Can A Scrappy Networking Company Race Ahead?

Jon Fortt — Tue, 27 Jan 2026 12:03:15 GMT

In this Fortt Knox 1:1, Extreme Networks CEO Ed Meyercord frames enterprise networking as one of the most underestimated yet strategically critical layers of modern business. What looks like “plumbing” is now the foundation for AI workloads, real-time analytics, security, and customer experience across stadiums, hospitals, casinos, logistics hubs, and corporate campuses.

This is an AI-assisted summary of my Fortt Knox 1:1 with Extreme Networks CEO Ed Meyercord. View the full interview here:

Meyercord argues that Extreme’s edge comes from simplifying inherently complex environments while extracting intelligence from the network itself. He says Extreme can deliver visibility, analytics, and control that competitors often can’t because of fragmented platforms and legacy architectures.

The conversation weaves between product strategy and personal history. Meyercord traces a path from Wall Street investment banking into telecom and tech operations, culminating in a CEO role forged during moments of regulatory shock and private-equity conflict. Those experiences shaped a leadership philosophy centered on people, culture, and long-term trust, not just capital efficiency. Today, with AI reshaping IT buying decisions and industry consolidation creating disruption, Meyercord positions Extreme’s smaller size as a structural advantage. The company’s focus on a unified, AI-infused platform positions it to punch above its weight as enterprises rethink how networks power security, automation, and real-time decision-making.

Today’s Toughest Problem

Enterprise networking has become more complex just as expectations have risen to “it should just work.” Meyercord describes Extreme’s core challenge as simplifying environments that span on-prem data centers, public and private clouds, and thousands of connected devices. Meanwhile, it must still deliver zero-latency performance, airtight security and continuous uptime. Stadiums illustrate the problem vividly: tens of thousands of fans, concessions, security systems, broadcast infrastructure, betting integrity, and AI-driven analytics all run on the same network fabric. Hospitals raise the stakes further, with life-critical systems, sensitive patient data, and an explosion of IoT devices creating a constant attack surface. Extreme’s response is to treat the network as a strategic system of intelligence, not just connectivity, using deep visibility and analytics to anticipate failures, isolate threats, and help customers focus on outcomes rather than infrastructure.

Origin Story

Meyercord’s leadership DNA comes from a family history of entrepreneurship and risk-taking, followed by a traditional Wall Street apprenticeship. After studying economics and competing in team sports, he entered investment banking. Covering telecom during its formative years pulled him toward operations, where he wanted to be “in the mix” rather than advising from the sidelines. A pivotal jump from banking into a fast-growing telecom operator put him on the front lines of deregulation, pricing wars, and infrastructure build-outs. That transition from analyst to operator set the pattern for a career defined by stepping into complexity, making high-stakes decisions, and learning by doing rather than observing.

Death Valley

Meyercord’s lowest professional moment came after clashing with private-equity owners over strategy, recapitalization, and the treatment of employees. Removed from his role despite strong performance, he experienced an unfamiliar personal reckoning. He’d been forced out not for results, but for refusing to compromise core values. The episode clarified his priorities: companies succeed through people first, capital second. That conviction carried forward into his later roles, shaping how he evaluates culture. Instead of becoming more cautious, the experience hardened his resolve to build organizations where trust, transparency, and fairness aren’t slogans but operating principles.

Core Belief

The central belief Meyercord brings to Extreme is balance among stakeholders. Shareholders matter. Returns matter. But sustainable value comes from motivated, respected teams. He emphasizes listening to employees and creating an environment where people believe their work has impact. That philosophy shows up in Extreme’s low turnover and high customer-satisfaction scores. In Meyercord’s view, culture isn’t a soft advantage; it’s a competitive one. Energized employees build better products, serve customers more effectively, and ultimately deliver superior financial outcomes.

Strategic Imperative

Looking ahead, Meyercord’s mandate is clear: drive adoption of Extreme Platform ONE. The company is betting on a unified, cloud-managed platform with AI at its core – combining predictive analytics, automation, and conversational interfaces to dramatically reduce the time and expertise required to run complex networks. Rather than stitch together dozens of loosely integrated systems, Extreme offers a smaller, more coherent stack that produces cleaner data and more reliable AI outcomes. Industry consolidation and AI adoption create tailwinds, as customers seek simplicity and partners seek better economics. Meyercord sees Extreme’s focused scope and integrated architecture as decisive advantages in an AI-driven enterprise reset.

Forget PowerPoint; the Future of Business Explainers is Like a Video Game

Jon Fortt — Mon, 26 Jan 2026 12:20:15 GMT

In this Fortt Knox 1:1, Jon Fortt speaks with Victor Riparbelli, co-founder and CEO of Synthesia, about how AI is transforming video from a passive medium into an interactive, adaptive interface for work. Riparbelli frames Synthesia’s next chapter around “AI-native video”: content that talks back, tests understanding, role-plays scenarios and generates data rather than just views.

The conversation spans product vision, economics and personal origin. Riparbelli explains why enterprise use cases such as training, sales enablement and recruiting are the first to justify AI inference costs. Photo-realistic avatars, he says, are only one tool in a broader visual-communication stack. He traces his path from a childhood immersed in books, games and music in Denmark to early entrepreneurial experiments and the difficult early years of Synthesis, including a nail-biter fundraising moment.

The interview closes with a major milestone: a $200M Series E led by Google Ventures, underscoring Synthesia’s ambition to make interactive video a core layer of enterprise software, not just a content format.

Today’s Toughest Problem

Synthesia’s central challenge is redefining video in an AI era. Riparbelli argues that traditional video – recorded once, watched identically by everyone – is limited. AI enables video that adapts to the viewer. It can answer questions, role-play customers, test comprehension, and branch based on responses. Solving this requires breakthroughs across the stack, from model reasoning and controllability to rethinking the video player itself. If done right, video begins to resemble a game, structured yet interactive. That unlocks far more value than passive viewing.

Origin Story

Riparbelli grew up in Copenhagen, the oldest of three brothers, in a household shaped by media and business. His mother was a journalist who later became CEO of a Danish lifestyle-media company; his father ran traditional, non-tech businesses. Partially deaf in one ear as a child, Riparbelli retreated into books, computers and games, cultivating deep curiosity and imagination. Early entrepreneurial instincts showed up in gaming arbitrage (selling World of Warcraft accounts) and later building low-cost websites and e-commerce stores for local businesses. These experiences honed a blend of product intuition and commercial pragmatism that would later define his leadership style.

Death Valley

Synthesia’s lowest point came after its first $1M raise, when the company struggled to secure a follow-on round. Generative AI was not yet a hot category, and the team initially asked for too much capital at too high an implied valuation. After widespread rejections, they were set to run out of money within months. The breakthrough came by resetting expectations and raising a smaller $3M round from believers who trusted the team’s ability to figure it out. That capital funded the first real product iteration, launched in 2020, which became the company’s inflection point.

Core Belief

Riparbelli’s leadership philosophy centers on staying calm under pressure. He believes panic is wasted energy; leaders should focus on what they can control and maintain optimism even in crisis. Humor and shared laughter, he argues, build trust and cohesion when stakes are highest. This mindset – staying composed and positive – became a durable operating principle forged during Synthesia’s most precarious moments.

Strategic Imperative

With $200M in new capital, Synthesia’s imperative for 2026 is to push video beyond communication into lightweight process automation. Interactive video can validate learning, generate performance data, and even conduct first-round interviews, turning content into an operational asset. Riparbelli sees enterprise training and recruiting as the beachhead, where AI video delivers ROI by saving time and creating new insight. Longer term, Synthesia aims to make AI-native, conversational video as easy to create as a PowerPoint – and far more powerful.

Can CEOs Stay Silent on Minneapolis?

Jon Fortt — Sun, 25 Jan 2026 21:51:28 GMT

A tragic news story this weekend also became a business story when major Minneapolis-area corporations called for a de-escalation of the tensions and conflict that led to the killing of Alex Pretti, a U.S. citizen and nurse with no criminal record who immigration officers gunned down in the street on January 24.

Federal officials claimed in public statements that Pretti has threatened officers with a gun, but multiple videos from people on the scene contradict that assertion. The videos show that while Pretti was initially carrying a holstered weapon, he never reached for it. Videos show him holding only his phone, and show that one officer took his gun from him before another appeared to shoot him from behind.

The incident poses a major question for CEOs who are disturbed by these events: Do they take a stand against the Trump administration’s approach to immigration enforcement, and risk backlash from a president who openly uses economic leverage to punish his detractors? Or do they stay silent and risk backlash from customers and partners who see CEOs as one of few constituencies that could sway policy?

Why Silence is Harder than Before

This challenge for leadership is particularly intense this time because, one year into President Trump’s four-year term, he is making it difficult for CEOs and their companies to keep a distance from his policies.

When Apple CEO Tim Cook did not join the president’s entourage on a trip to the Middle East in May, Trump threatened Apple with a 25% tariff on iPhones. Cook joined a presidential trip to the United Kingdom in September. The president has similarly flexed his power with semiconductor businesses to push them to embrace his economic policies, or risk economic pain.

The Trump administration’s presence at the World Economic Forum in Davos last week was another example. While the president has questioned the relevance of forums like the United Nations and alliances like the North Atlantic Treaty Organization, he has leaned into Davos, where economic power is front and center. The U.S. delegation was a record size, and included a “USA House,” as opposed to the typical U.S. pavilions.

Parallels to Another Tragedy

Taken all together, with this backdrop, the killing of Alex Pretti puts CEOs in a position with some echoes of the one they faced five and a half years ago when George Floyd was killed just three miles away. But there are also some key differences.

Like with the George Floyd killing, smartphone video of the incident is proving to be a pivotal factor shaping public opinion, more than the official statements. Like with the George Floyd killing, the dead man was not brandishing a weapon. Also similar: Public outrage is pivoting toward the corporations that have developed such influence over the flow of our daily lives.

Unlike with the George Floyd killing though, the country is not in the throes of a COVID lockdown and CEOs today are less inclined to bend to public opinion.

It leaves them without easy options.

More than 60 CEOs of Minnesota-based companies on Sunday did sign a letter released by the Minnesota Chamber of Commerce calling for an “immediate deescalation of tensions and for state, local and federal officials to work together to find real solutions.”

It’s not clear whom the chamber thinks has the most influence over that deescalation, and what the members will do if it doesn’t happen.

Winter Storm to Test AI Travel Support

Jon Fortt — Fri, 23 Jan 2026 14:22:11 GMT

In this Fortt Knox Update, Jon Fortt speaks with Ariel Cohen about how Navan is using AI to fundamentally reshape business travel, especially when things go wrong. The conversation opens with a looming winter storm, a real-world stress test that highlights Navan’s core value proposition: proactive, automated support at scale. Cohen explains how Navan alerts travelers before disruptions hit, helps them rebook without fees, and deploys its AI assistant, Ava, to handle a majority of support interactions when human agents alone would be overwhelmed.

Cohen describes Navan’s broader AI architecture, built on an internal platform called “event cognition,” which blends external large language models with Navan’s proprietary data and long-term memory of traveler preferences, company policies and loyalty programs. This foundation powers both Ava and Navan’s next major product, a more autonomous “virtual travel agent” designed to plan, book and adjust trips end-to-end based on an individual’s habits and priorities.

The discussion also touches on the resurgence of business travel. Cohen argues demand has surpassed pre-pandemic levels because in-person meetings remain essential for trust, dealmaking and leadership. He notes a shift toward higher-quality travel experiences rather than purely functional trips.

Finally, Cohen reflects on Navan’s life as a newly public company, emphasizing the need to balance regulatory discipline with long-term innovation, and shares how AI has improved his own effectiveness as a CEO, particularly in communication and strategic clarity.

Highlights

Navan uses AI to proactively manage large-scale travel disruptions like major storms.
Ava, Navan’s AI assistant, handles over half of customer interactions.
The company’s “event cognition” platform combines external LLMs with proprietary travel data.
A new virtual travel agent product aims to automate trip planning and changes end-to-end.
Business travel demand is now above pre-Covid levels, according to Navan data.
Travelers are prioritizing experience quality, not just loyalty programs.
Cohen argues AI compresses experience gaps, benefiting motivated entry-level workers.
Navan is bringing new talent into roles like travel agents by augmenting them with AI tools.
As a public company, Navan is committed to long-term innovation over short-term optics.
Cohen personally uses AI to improve clarity, alignment, and communication as CEO.

Can You Make More Money Letting AI Answer Email?

Jon Fortt — Thu, 22 Jan 2026 12:02:34 GMT

If you’d like to join me – and peers – for deeper conversations on innovation and leadership, get on this list for Fortt Knox Executive Communities, launching soon: mba.fortt.com.

This is an AI-assisted summary of my Fortt Knox Update with Podium CEO Eric Rea. View the full interview here:

In this Fortt Knox Update, Jon Fortt speaks with Eric Rea, co-founder and CEO of Podium, about the company’s rapid acceleration in AI agents, surpassing $100 million in annualized recurring revenue in under two years. Rea traces Podium’s journey from early experiments with GPT-3 in 2020 to a breakthrough moment in late 2023, when more capable models enabled real, production-ready conversational agents.

Podium initially deployed AI agents (“Jerry”) in car dealerships to handle inbound leads and schedule test drives. Early pilots revealed two challenges: trust and customization. While one customer churned quickly, others were stunned to find real customers arriving for appointments set by an AI. That experience reframed Podium’s strategy. Customers don’t love software; they love outcomes. AI agents, Rea argues, are fundamentally better than traditional software at making local businesses money because they replace scarce human labor.

The company has since invested heavily in deep customization through an “AI Studio,” allowing owners and managers to train agents with real-time feedback, policies and procedures. This infrastructure supports text, email, messaging, and increasingly voice, which is critical in local industries where most customer interactions still happen by phone. Financially, roughly half of the $100M ARR comes from existing customers upgrading, and half from new customers buying agents first. Notably, Podium’s gross margins have improved with AI; the value delivered far exceeds model and infrastructure costs. The next phase: expanding specialized agents across automotive, home services, and health and wellness.

Highlights

Podium crossed $100M in AI-agent ARR in under two years
Early AI work began with GPT-3 experiments in 2020
First production agents launched in late 2023 for car dealerships
Trust and customization were the biggest early adoption hurdles
“Jerry” agents now handle text, email, messaging, and voice
AI Studio lets owners directly train and tune agents
~10,000 locations (“rooftops”) use Podium agents globally
About 50% of AI revenue comes from existing customer upgrades
Some customers expand their spending up to 5× versus software alone
Gross margins have improved despite heavy AI usage

If you’d like to join me – and peers – for deeper conversations on innovation and leadership, get on this list for Fortt Knox Executive Communities, launching soon: mba.fortt.com.

Emil Eifrem: Neo4j’s Graph Bet for the AI Era

Jon Fortt — Wed, 21 Jan 2026 13:11:03 GMT

If you’d like to join me – and peers – for deeper conversations on innovation and leadership, get on this list for Fortt Knox Executive Communities, launching soon: mba.fortt.com.

This is an AI-assisted summary of my Fortt Knox 1:1 with Neo4j CEO Emil Eifrem. View the full interview here:

In this Fortt Knox 1:1, Emil Eifrem, co-founder and CEO of Neo4j, lays out why the AI era is fundamentally about relationships, not just data volume. Eifrem argues that nearly every AI problem is a data problem; more specifically, a problem of understanding how things connect across disparate systems. Traditional relational databases, built for a more static and tabular world, struggle to model these connections at scale. Neo4j’s graph database treats relationships as first-class citizens, enabling enterprises to turn fragmented data into coherent “knowledge graphs” that AI systems can reason over.

The conversation moves from technical foundations to personal history: Eifrem’s upbringing in Sweden, early fascination with computers and music, and formative experiences wrestling with relational databases in early SaaS startups. Those frustrations seeded Neo4j’s creation, and a belief that better data models can unlock better decisions, from enterprise AI to investigative journalism. Eifrem also reflects on existential early-company moments after the 2008 financial crisis, when Neo4j nearly ran out of cash. Persistence, trust, and shared purpose carried the team through. Looking ahead, he frames 2026 as a turning point when AI moves decisively from experiments to production, powered by knowledge graphs that make sense of the world’s complexity.

Today’s Toughest Problem

Eifrem says the hardest problem Neo4j is solving is helping organizations make sense of their data in an AI-driven world. While large language models appear magical, they are only as useful as their data. Most enterprise data lives in silos spread across incompatible systems, making it difficult for AI agents to reason accurately. Neo4j’s answer is the knowledge graph: a model that captures entities, their attributes and the relationships between them, allowing AI to infer context rather than hallucinate.

Origin Story

Neo4j grew out of repeated encounters with the limits of relational databases. As a young CTO building multi-tenant SaaS systems, Eifrem watched teams spend enormous energy fighting database schemas instead of building products. That mismatch between real-world complexity and tabular data models convinced him and his co-founders to attempt something audacious: build a new kind of transactional database where relationships are native, not an afterthought.

Death Valley

The company’s lowest moment came in 2009, in the shadow of the financial crisis. After signing a term sheet, Neo4j’s lead investor walked away mid-process, leaving the company with $2,000 in the bank and days before payroll. The team survived by consulting on the side, factoring invoices, and sheer grit. That bought them time until customers and new investors emerged. It was an existential test that nearly ended the company before it began.

Core Belief

From that period, Eifrem internalized three enduring beliefs: persistence matters more than polish; deep relationships within a team can outweigh financial incentives; and intrinsic passion is the ultimate survival advantage. These ideas mirror Neo4j’s own technology ethos of durable systems built on strong connections.

Strategic Imperative

Looking to 2026, Eifrem sees a decisive shift from AI pilots to production deployments inside Global 2000 enterprises. The strategic imperative for Neo4j is to be the backbone of those systems, transforming live enterprise data into knowledge graphs that AI agents can trust. As companies chase real ROI from AI, Eifrem believes graph-based knowledge will move from optional to essential.

If you’d like to join me – and peers – for deeper conversations on innovation and leadership, get on this list for Fortt Knox Executive Communities, launching soon: mba.fortt.com.

Walmart’s AI Platform Strategy: Owning the Shopping Journey Across AI Interfaces

Jon Fortt — Tue, 20 Jan 2026 12:03:04 GMT

Earlier this month I interviewed Hari Vasudev, Chief Technology Officer for Walmart U.S., at the National Retail Federation’s Big Show in New York. I wanted to press him not only on the day’s news, a new open approach to AI for commerce in partnership with Google; but also on the impacts he sees for Walmart’s massive in-store workforce.

This is an AI-assisted summary of our 30-minute conversation.

If you’d like to join me – and peers – for deeper conversations on innovation and leadership, get on this list for Fortt Knox Executive Communities, launching soon: mba.fortt.com.

Walmart’s AI strategy starts with a platform-first view of commerce rather than a bet on any single AI interface. Vasudev describes an evolution from predictive AI to generative and now agentic systems, unified by internal platforms that serve customers, associates, sellers, and developers at scale. That architecture underpins Walmart’s partnerships with both OpenAI and Google, but with a key distinction: Walmart wants the entire shopping journey, not just item-level handoffs.

The Gemini integration announced with Google embeds a full Walmart experience inside the AI interface, allowing customers to move from intent to basket-building to fulfillment with less friction. By contrast, current ChatGPT integrations focus more narrowly on single-item fulfillment. Vasudev frames this not as a platform war, but as customer-centric design: whoever reduces friction, preserves trust, and accelerates completion wins.

Crucially, Walmart is pairing openness with control. A hybrid, multi-cloud and multi-model approach gives Walmart leverage over cost, data governance, and speed of innovation. Identity and context — who the customer is and what they’re trying to do right now — become the new “currency” of commerce, replacing older constructs like cookies alone. Throughout, Vasudev emphasizes guardrails, transparency, and human-in-the-loop systems as foundational, not optional.

Walmart has embedded AI across search, personalization, forecasting, and now agentic workflows
The company built internal AI platforms to serve customers, associates, sellers, and developers
Gemini enables a full Walmart experience inside AI, including basket-building
ChatGPT integrations today focus more on single-item fulfillment
Walmart prioritizes reducing friction over controlling a single interface
Its AI platform strategy mirrors its hybrid, multi-cloud architecture
Customer identity plus real-time context drive agent behavior
Human-in-the-loop design is central to trust and governance

As the conversation moves from AI platforms and abstract agents to the tangible realities of retail, the question becomes less about where intelligence lives and more about how it shows up. For Walmart, that means translating AI from digital discovery into physical stores — without losing the human connection that defines in-person retail.

AI in the Physical Store: Empowering Associates Without Replacing Them

Vasudev is clear that Walmart’s physical-store AI strategy is not about replacing people with robots, but about making associates more effective. He frames associates as customers of Walmart’s software, deserving tools that reduce friction and surface the “next best action” based on role, permissions, and real-time location. AI helps route tasks to the right associate at the right moment, minimizing wasted movement and improving responsiveness on the sales floor.

Managers, in turn, move up the stack from micromanaging tasks to higher-level planning, staffing, and inventory flow. Walmart pairs these tools with training and continuous feedback, avoiding the common enterprise failure of overwhelming workers with disconnected systems. Engineers regularly spend time in stores, using the same tools as shoppers and associates to refine design.

On automation and robotics, Vasudev draws a sharp line between purpose-built technology and humanoid replacement. Computer vision, for example, detects spills and enables frictionless exit at Sam’s Club, removing uncomfortable interactions rather than human warmth. But robots won’t clean the spills, people will. The lesson from self-checkout is balance: transactional friction can be automated, but emotionally positive moments still belong to people.

Highlights

Associates are treated as primary users of Walmart’s AI systems
“Next best action” tools guide associates based on role and location
Managers shift toward planning, staffing, and optimization
Training and upskilling accompany every new AI deployment
Engineers regularly work inside stores to inform product design
Computer vision improves safety and enables frictionless checkout
Walmart avoids humanoid robots in customer-facing roles
Automation is used where it removes friction, not trust

If you’d like to join me – and peers – for deeper conversations on innovation and leadership, get on this list for Fortt Knox Executive Communities, launching soon: mba.fortt.com.

Dr. Shiv Rao: Abridge and Availity Aim to Shrink Prior Authorization From Months to Minutes

Jon Fortt — Fri, 16 Jan 2026 12:03:43 GMT

If you’d like to join me – and peers – for deeper conversations on innovation and leadership, get on this list for Fortt Knox Executive Communities, launching soon: mba.fortt.com.

This is an AI-assisted summary of my Fortt Knox Update with Abridge CEO Dr. Shiv Rao. View the full interview here:

Jon Fortt sits down with Shiv Rao, founder and CEO of Abridge, to unpack a newly announced partnership with Availity that targets one of healthcare’s most painful bottlenecks: prior authorization. Rao explains that prior auth today is a slow, sequential process that forces clinicians to document, submit, revise, and resubmit information long after a patient visit. That creates delays and wasted effort for doctors, patients, and insurers alike.

The Abridge–Availity integration moves this work upstream, directly into the doctor-patient conversation. By combining real-time conversational AI with Availity’s payer APIs and longitudinal medical records, Abridge can prompt clinicians to ask the right questions, surface contraindications and generate the necessary documentation as the visit unfolds. The result is a parallelized workflow where approval steps happen in minutes instead of weeks or months.

Rao emphasizes that this is not about removing humans from care. Healthcare remains fundamentally relational, but AI can automate the invisible administrative work that distracts from patient outcomes. With Abridge already live across more than 200 health systems and on pace for roughly 80 million clinical encounters this year, the partnership enables experimentation across regions, specialties, and care settings. Over time, Rao says, the goal is not just efficiency and cost reduction, but measurable improvements in patient outcomes, using the clinical conversation as the foundation for a more connected healthcare ecosystem.

Highlights

Prior authorization is reframed as a real-time, in-visit process, not post-visit paperwork.
Abridge uses conversational AI plus payer APIs to prompt clinicians and close documentation gaps.
The approach parallelizes workflows that are traditionally slow and sequential.
Availity provides broad payer connectivity, enabling one-to-many insurer integration.
Abridge is live across 200+ health systems, touching millions of encounters weekly.
Rollout will vary by geography, specialty, and care setting.
The long-term aim goes beyond efficiency to demonstrable outcome improvement.

If you’d like to join me – and peers – for deeper conversations on innovation and leadership, get on this list for Fortt Knox Executive Communities, launching soon: mba.fortt.com.

DevRev's Dheeraj Pandey: From Distributed Work to Intelligent Teams

Jon Fortt — Thu, 15 Jan 2026 12:03:10 GMT

If you’d like to join me – and peers – for deeper conversations on innovation and leadership, get on this list for Fortt Knox Executive Communities, launching soon: mba.fortt.com.

This is an AI-assisted summary of my Fortt Knox 1:1 with DevRev CEO Dheeraj Pandey. View the full interview here:

In this Fortt Knox 1:1, Jon Fortt sits down with Dheeraj Pandey, CEO of DevRev, to explore how generative AI is reshaping work, leadership, and enterprise software itself. Pandey argues that modern work has become fragmented, tribal, and transactional, spread across departments, tools and geographies. Meanwhile, CEOs are left to manually stitch organizations together. DevRev’s core insight is that AI can only deliver real ROI if enterprise data is reorganized into a unified “machine memory” that spans sales, support, product and engineering, enabling agents to reason across the entire company rather than operate as shallow bots bolted onto legacy systems.

Pandey also traces his personal journey from Bihar, one of India’s poorest states, through elite technical education, early exposure to the internet, and formative engineering roles that culminated in founding Nutanix. He reflects on times Nutanix nearly collapsed, the mental endurance required to survive them, and how those lessons now shape his leadership philosophy. Looking ahead, Pandey frames DevRev’s ambition as nothing less than delivering a ChatGPT-like experience for enterprises: deeply customized, data-rich, and product-led, capable of serving both employees and customers at scale.

Today’s Toughest Problem

Pandey sees fragmentation as the defining problem of modern work. Talent is distributed, offices are optional. Departments have become tribes with their own data, incentives, and tooling. Earlier waves of enterprise software moved systems to the cloud but never truly solved internal collaboration or shared understanding. As a result, executives rely on human glue: managers who carry the company’s knowledge graph in their heads. DevRev’s answer is to replace that fragile, human-only stitching with AI agents grounded in a unified data model that captures customers, products, people, and processes together.

Origin Story

Pandey’s worldview was shaped early by scarcity, instability and education. Growing up in Bihar during periods of political corruption and economic strain, his family relied on extended relatives when state salaries failed to arrive. Academic excellence became a path to opportunity, leading him to IIT, early exposure to the internet, and eventually graduate study in the U.S. The combination of constraint paired with intellectual freedom instilled a bias toward systems thinking and long-term leverage rather than short-term fixes.

Death Valley

The hardest period of Pandey’s career came during Nutanix’s early years, when the company nearly failed multiple times. The challenge was making reliable software run on unreliable commodity hardware while depending on partners whose platforms could shift underneath them. Those years tested not just technical architecture but leadership. Pandey says entrepreneurship is fundamentally a mental game: resisting panic, avoiding blame and sustaining team belief through repeated setbacks until the system finally works.

Core Belief

At the heart of Pandey’s philosophy is endurance and integration. He believes great companies are built by people who keep trying without turning on one another, even when failure feels imminent. Culturally, that means stoic persistence; technically, it means building systems that integrate rather than silo. AI, in his view, should augment human work by holding institutional memory, spotting patterns and enabling self-service. People can then focus on judgment, creativity and relationships.

Strategic Imperative

For 2026, DevRev’s mandate is to deliver a consumer-grade AI experience for enterprises. Pandey is skeptical that incumbents like Salesforce or ServiceNow can fully make that leap given their legacy architectures. DevRev aims to unify agents for employees and customers on the same platform, reviving true product-led growth and making advanced AI accessible not just to large enterprises, but to startups and mid-market companies as well.

If you’d like to join me – and peers – for deeper conversations on innovation and leadership, get on this list for Fortt Knox Executive Communities, launching soon: mba.fortt.com.

Scott Woody on Metronome Monetizing the AI Era

Jon Fortt — Wed, 14 Jan 2026 12:03:06 GMT

If you’d like to join me – and peers – for deeper conversations on innovation and leadership, get on this list for Fortt Knox Executive Communities, launching soon: mba.fortt.com.

This is an AI-assisted summary of my Fortt Knox 1:1 with Metronome CEO Scott Woody. View the full interview here:

As Stripe closes its acquisition of Metronome this week for a rumored $1 billion, Jon Fortt speaks with Scott Woody, cofounder and CEO, in this Fortt Knox 1:1.

AI is forcing a generational shift in how software is priced, sold, and built. Woody argues that AI doesn’t just change product features; it fundamentally alters business models by introducing high marginal costs and agent-driven value creation that breaks seat-based pricing. Metronome sits at the center of this transition, providing real-time usage tracking, pricing, budgeting, and control so companies can safely adopt consumption- and outcome-based monetization.

Woody traces his own path from a remote childhood in Bishop, California. Because of his father’s work, he spent time surrounded by astronomers and directing his own learning. Later he studied physics at Berkeley, pivoted into software, and learned the ropes at Dropbox. That background shaped his belief in cross-disciplinary thinking, intensity, and building infrastructure that scales under pressure. The conversation culminates in Metronome’s defining stress test: supporting OpenAI through the explosive growth of ChatGPT, when Metronome’s systems (and balance sheet) were pushed to the brink. The experience reinforced Woody’s core belief that treating customers honorably during crisis builds durable companies and powerful long-term advantage.

Today’s Toughest Problem

Software is being disrupted on two axes at once: product capabilities and monetization. AI agents now perform work on behalf of users, shifting value away from “who has access” toward “what gets done.” At the same time, AI introduces real marginal costs, making flat subscriptions economically misaligned. Woody’s challenge is enabling companies to set prices based on usage, outcome and value, without surprising customers or slowing innovation. Metronome’s role is to ingest product telemetry, combine it with contract data, and apply pricing in real time. That way companies can move fast and give customers transparency and control over their spending.

Origin Story

Woody grew up in a town of 3,000 people, where his father ran an observatory for Caltech. With limited internet access, he became a self-directed learner immersed in science conversations at the dinner table. At Berkeley, he discovered the power of cross-pollination: mixing physics with literature, biology, and computing. A stint recruiting for a hedge fund sparked entrepreneurial instincts, leading to his first startup and then seven formative years at Dropbox, where he became a true software engineer. That blend of rigor, curiosity, and team-building laid the groundwork for Metronome.

Death Valley

Metronome’s defining crisis came during the ChatGPT explosion in late 2022. As OpenAI’s usage surged, Metronome’s costs spiked dramatically, while revenue lagged due to flat-fee pricing. The small team ran a 24/7 “bucket brigade” for months, risking a meaningful share of their remaining cash to keep OpenAI live during its breakout moment. Cutting support might have protected short-term finances but violated Metronome’s values. Woody chose to absorb the pain, betting that integrity and partnership would pay off. It did – but only after intense stress, sleepless nights, and existential risk.

Core Belief

Two convictions emerged. First: if you act with integrity and do right by customers, you increase your odds of long-term survival and trust. Second: the hardest moments become the most important in hindsight. Woody frames startups as “type-two fun.” They can be miserable in the moment, rewarding in retrospect. That mindset helps leaders endure volatility, knowing today’s pain often becomes tomorrow’s moat.

Strategic Imperative

In 2026, Metronome’s mission is to reframe “billing” as “monetization.” Woody wants the market to see pricing infrastructure not as back-office plumbing, but as a growth engine. By showcasing customers like OpenAI, Anthropic, and enterprise acquirers such as IBM-backed platforms, Metronome aims to prove that aligning business models with AI-driven value unlocks faster, healthier growth. The goal: make modern monetization a strategic advantage, not an afterthought.

If you’d like to join me – and peers – for deeper conversations on innovation and leadership, get on this list for Fortt Knox Executive Communities, launching soon: mba.fortt.com.

Julio Martinez: Abacum’s Bet on AI-Native Finance Leadership

Jon Fortt — Tue, 13 Jan 2026 12:01:26 GMT

If you’d like to join me – and peers – for deeper conversations on innovation and leadership, get on this list for Fortt Knox Executive Communities, launching soon: mba.fortt.com.

This is an AI-assisted summary of my Fortt Knox 1:1 with Abacum CEO Julio Martinez. View the full interview here:

Julio Martinez frames Abacum as an AI-native rethink of how finance teams operate in an era of volatility, data overload, and rising expectations on the CFO. He argues that the modern CFO is no longer a back-office scorekeeper but a chief business performance officer. That shift collides with legacy systems built decades ago, siloed data, and analytics tools that surface numbers without context. Abacum’s ambition is to close that gap by creating a real-time, trustworthy data foundation and layering AI on top to surface insights, narratives, and decision support.

The conversation moves between product strategy and personal history. Martinez traces his values to a childhood shaped by parents committed to education and social impact, especially a father who dedicated decades to training workers in impoverished rural Spain. That grounding created a lifelong tension between purpose and profit. After time in investment banking, development finance, and venture building, Martinez found entrepreneurship as a “third way” that combines business rigor with societal impact.

He describes Abacum’s early years as a long, difficult build. Product maturity took far longer than expected, competitors failed, and patience became a defining discipline. That experience hardened a core belief: durable advantage comes from deep foundations, not speed alone. Looking ahead, Martinez sees Abacum’s strategic imperative as extending its AI lead while continuing to invest heavily in core engineering, even as others chase efficiency shortcuts.

Today’s Toughest Problem

Martinez defines the hardest problem as extracting usable insight from massive, fast-moving financial and operational data during a period of macro and geopolitical uncertainty. Finance teams now manage more data than ever, while being asked to play a more strategic role in guiding companies through volatility. Boards and CEOs increasingly expect foresight, not just reporting.

Traditional analytics and dashboards fall short because they surface metrics without context and require heavy manual interpretation. New AI approaches change the interface with data. Large language models help generate narratives and make information accessible to non-finance stakeholders. Large time-series models improve forecasting accuracy with fewer data points. The combination creates faster understanding, not just better charts.

The challenge is not accuracy alone. It is trust, speed, and usability. Finance leaders need to answer strategic questions in hours, not weeks, and with confidence in the underlying data. Abacum’s focus is on enabling that shift by unifying data sources and using AI to flag drivers, anomalies, and risks, while keeping humans in the loop for judgment.

Origin Story

Martinez’s origin story is anchored in purpose. He was born in Barcelona as an only child to parents whose lives revolved around education and social impact. His father grew up in extreme rural poverty under dictatorship-era Spain and later devoted decades to building training programs and businesses that created jobs for thousands in impoverished regions. That legacy of responsibility and gratitude was deeply ingrained.

Growing up in a rapidly modernizing Barcelona shaped Martinez’s worldview. The city’s post-Olympics transformation exposed him to global ambition and opportunity. As a student, he was drawn to philosophy and meaning, but gravitated toward business and finance for its practical power. That path led to investment banking and corporate finance, environments rich in learning but often distant from the impact he valued.

Over time, Martinez reconciled those tensions. He came to see entrepreneurship as a vehicle for both rigor and meaning. Building companies, creating jobs, and enabling others to perform better became his way of honoring his upbringing while operating at scale.

Death Valley

Abacum’s lowest stretch was not market rejection but time. The company underestimated how long it would take to build a truly robust FP&A platform. The product was complex, deeply integrated, and required exceptional engineering discipline. While some competitors rushed to market, Abacum stayed focused on fundamentals, even as others collapsed or sold cheaply.

This period felt like crossing a desert. Capital expectations had been set on faster timelines. Legacy vendors still worked “well enough” and had brand recognition. Internally, conviction had to outweigh anxiety. Martinez describes this phase as requiring patience, board alignment, and relentless customer focus.

A key inflection point was accepting that good things take time. Rushing would have compromised the product and the company’s long-term position. Emerging from this phase with a differentiated, trusted platform validated that discipline and set the foundation for rapid growth later.

Core Belief

The defining belief that emerged is simple: slow can be smooth, and smooth can be fast. Martinez borrows the idea from martial arts. Mastery requires repetition, patience, and attention to fundamentals. Cutting corners leads to mistakes and injury. The same applies to software.

At Abacum, this belief translates into deep investment in data foundations, architecture, and engineering talent. AI is an accelerator, not a substitute. It magnifies the impact of strong teams rather than replacing them. The goal is not to ship features quickly, but to build systems customers trust with mission-critical decisions.

This philosophy also shapes leadership. Martinez emphasizes conviction, long-term thinking, and clarity over hype. In an industry prone to rushing toward trends, Abacum’s culture prioritizes durability and customer love as the ultimate competitive advantage.

Strategic Imperative

For 2026, Abacum’s imperative is to extend its lead in AI-driven finance while strengthening its moat in core technology. Martinez sees AI as genuinely value-creating, not just marketing. Customers are already benefiting, but much more remains to be built.

The company is investing aggressively in engineering and product depth, even as others emphasize efficiency and headcount reduction. Martinez is explicit that complex problems still require the best people, supercharged by AI, not replaced by it.

At a broader level, he views Abacum as part of a larger shift in the global startup ecosystem. Remote work and cross-border talent flows, accelerated by COVID, have strengthened innovation hubs in Europe and beyond. Abacum aims to be a global leader born from that moment, helping finance teams navigate uncertainty with confidence, speed, and insight.

If you’d like to join me – and peers – for deeper conversations on innovation and leadership, get on this list for Fortt Knox Executive Communities, launching soon: mba.fortt.com.

Katherine Kostereva on No-Code, AI Agents, and Time-to-Value

Jon Fortt — Mon, 12 Jan 2026 11:58:31 GMT

If you’d like to join me – and peers – for deeper conversations on innovation and leadership, get on this list for Fortt Knox Executive Communities, launching soon: mba.fortt.com.

This is an AI-assisted summary of my Fortt Knox 1:1 with Creatio CEO Katherine Kostereva. View the full interview here:

In this Fortt Knox 1:1, Jon Fortt speaks with Katherine Kostereva, founder and CEO of Creatio, about the evolution of no-code enterprise software into the age of autonomous AI agents. Kostereva positions Creatio as a workflow-first platform built for enterprises that need speed without sacrificing governance, compliance, or control. Founded more than a decade ago and largely bootstrapped until recent growth capital, Creatio now employs roughly 900 people across 26 countries and is growing around 40% year over year.

The conversation explores why workflow automation historically has taken quarters or years, why no-code was initially dismissed by CIOs, and how Creatio’s BPM-centric architecture made it unusually well positioned for generative AI. Kostereva also shares her personal journey from Ukraine to global enterprise leadership, her belief in intuition and brutal self-honesty, and her long-term commitment to building one enduring company rather than serial startups. The through-line is time-to-value: helping enterprises adapt workflows in days, not months, as markets, regulations, and AI capabilities rapidly change.

Today’s Toughest Problem

Kostereva defines today’s hardest problem as enabling enterprises to change workflows as fast as their businesses change, without breaking compliance, governance, or security. Legacy platforms still require long implementation cycles, which is incompatible with modern volatility and AI-driven automation. As agents move from copilots to autonomous actors, workflows become the guardrails that determine what AI is allowed to do. Creatio’s challenge is ensuring agents can act independently while remaining constrained by enterprise-grade permissions, roles, and auditiung. The problem is not whether AI can act, but whether it can act safely, transparently, and in alignment with business rules.

Origin Story

Kostereva grew up in Kyiv in a highly academic household. Both of her parents were professors of electrical engineering at Kyiv Polytechnic Institute, spending their entire careers in the same faculty. That environment instilled discipline and technical thinking, but it also clarified how different her own instincts were. Kostereva was less drawn to the measured pace of academic life and more energized by speed, creativity, and execution. As a teenager, she gravitated toward advertising and marketing, launching small projects with classmates at 13 or 14. They created messages, designs, and promotional materials. By her late teens, she was financially independent, supporting herself through school. Although she went on to study computer science, those early advertising experiences revealed her natural pull toward go-to-market work: communicating value, shaping narratives, and building momentum. That blend of technical grounding and commercial instinct became the foundation for her later leadership style as a founder and CEO.

Death Valley

Kostereva does not describe a single dramatic “near-death” moment, but instead frames the hardest stretch as repeated market entry challenges. Every new geography requires rebuilding trust, brand awareness, and local proof points, essentially replaying the product-market-fit grind at a regional level. Countries like Australia, where buying decisions depend heavily on community validation and references, have tested Creatio’s patience and discipline. The lesson is that even at scale, growth remains a grind, requiring persistence, cultural sensitivity, and proof delivered one customer at a time.

Core Belief

Her core belief is that leadership demands brutal honesty with reality combined with intuition. Data and logic matter, but so does listening to gut instinct – especially during inflection points. Kostereva believes people are “wired” for specific paths, and fulfillment comes from discovering and committing to that wiring. For her, that path is enterprise software and go-to-market execution, not serial entrepreneurship. This belief also shapes Creatio’s culture: high intensity, fast growth, genuine care for people, and clarity that the company’s pace is not for everyone.

Strategic Imperative

Creatio’s strategic imperative for 2026 is autonomous agents. Kostereva sees agents not as bolt-ons, but as workflows brought to life: AI acting within structured, governed processes. Because Creatio already centers on BPM and dynamic case management, agents become a natural extension rather than a risky experiment. The goal is to compress time-to-value even further, allowing enterprises to deploy intelligent automation quickly while preserving trust, security, and compliance. In her view, this is the rare moment when platform architecture, market timing, and AI capability finally align.

If you’d like to join me – and peers – for deeper conversations on innovation and leadership, get on this list for Fortt Knox Executive Communities, launching soon: mba.fortt.com.

Jake Loosararian: Ground-Truth AI for the Physical World

Jon Fortt — Sun, 11 Jan 2026 18:37:02 GMT

If you’d like to join me – and peers – for deeper conversations on innovation and leadership, get on this list for Fortt Knox Executive Communities, launching soon: mba.fortt.com.

This is an AI-assisted summary of my Fortt Knox 1:1 with Gecko Robotics CEO Jake Loosararian. View the full interview here:

In this Fortt Knox 1:1, Jon Fortt sits down with Gecko Robotics CEO and founder Jake Loosararian to unpack how robotics, sensors, and software are reshaping critical infrastructure – from power plants and refineries to shipyards and defense assets. Loosararian argues that the AI revolution will stall without “ground-truth” data from the physical world, and that decades-old industrial sectors are dangerously under-digitized. Gecko’s robots crawl, fly, and swim across live facilities to collect high-fidelity data that feeds a central operating platform, enabling safer operations, longer asset life and higher production.

The conversation traces Gecko’s evolution from a hardware-first inspection company to a software-centric operating platform, inspired by Amazon’s Kiva robots and driven by the realization that customers ultimately care about production outcomes. Loosararian also shares a deeply personal founder journey, including his Armenian family history and faith, homeschooling, engineering, and a formative cross-country bike ride that cemented his commitment to mission-driven entrepreneurship. The interview culminates in a clear thesis: AI leadership depends on owning the right physical-world data, and the biggest returns will come from transforming the industries that underpin modern life.

Today’s Toughest Problem

Loosararian frames Gecko’s core challenge as closing the gap between AI’s promise and the reality of critical industries that still rely on manual inspection and incomplete data. Energy, manufacturing, and defense assets are aging, complex, and expensive to shut down. But they remain largely invisible to modern software. The risk isn’t just inefficiency; it’s hallucinations, missed signals, and catastrophic failures when AI systems lack reliable inputs. Gecko addresses this by using robots and advanced sensors (including ultrasound-based techniques) to diagnose the “health of the built world” while assets remain online. The goal is not exhaustive data collection, but the right data: timely, high-confidence signals that prevent explosions, reduce downtime, and unlock performance gains. In Loosararian’s view, without this foundation, AI cannot safely or meaningfully transform these sectors.

Origin Story

Gecko began with a visceral safety problem: inspectors climbing 100 feet in the air with handheld tools, sometimes with fatal consequences. As an engineering student, Loosararian became obsessed with replacing dangerous, low-fidelity inspections with robots that could see deeper and more accurately. Early success revealed a harder truth: selling robots alone captured only a fraction of the value created. Customers saved hundreds of millions, yet paid comparatively little, and often ignored warnings until failures forced action. This disconnect pushed Gecko beyond hardware into software, where insights could continuously influence operations. The inspiration came from Amazon’s use of Kiva robots: hardware that enabled an unfairly advantaged operating platform. Gecko adopted the same logic for industrial infrastructure.

Death Valley

The lowest point came during the bootstrapped years. With no knowledge of venture capital, Loosararian poured his savings into the company, dwindling to $100 while carrying the weight of a new engagement and family expectations. His mission of serving overlooked industrial workers and preventing deadly failures kept him going through moments of doubt, including breaking down alone under a desk. Salvation arrived not as an exit, but as a choice: sell early for validation or keep betting on Gecko’s mission. Encouraged by peers and a pivotal introduction to Y Combinator, Loosararian chose the harder path: remaining poor, doubling down, and relocating cross-country to build a platform company.

Core Belief

At the heart of Gecko is a conviction that production—not novelty—drives relevance in industrial markets. Safety, asset longevity, and compliance matter, but customers ultimately care about output: barrels per day, kilowatts produced, ships built faster. Technology must move these needles to endure. Loosararian also believes that who builds matters: industries powering modern life have been ignored by Silicon Valley, despite their centrality to national security and economic growth. His Armenian heritage, faith, and family history reinforce a sense of stewardship. He says he wants to use opportunity responsibly and build tools that honor human dignity by keeping workers safe and productive.

Strategic Imperative

Gecko’s strategy centers on becoming the operating platform for the physical world. Robots and sensors are means to an end: generating proprietary, first-order data that feeds AI systems capable of optimizing real assets in real time. Loosararian argues the AI race will be won by those who control unique physical-world data, not just algorithms. With deep deployments among a core set of customers and growing international relevance, Gecko aims to create a flywheel: better data enables better software, which guides better robots, compounding advantages over time. The payoff, he believes, will be transformative gains in the industries that matter most.

If you’d like to join me – and peers – for deeper conversations on innovation and leadership, get on this list for Fortt Knox Executive Communities, launching soon: mba.fortt.com.