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Innovation Policies: The Australian Lesson Focusing Startup Investments On A Few Strategic Sectors
By Alberto Onetti For some time now, I’ve been pointing out how Australia has been making significant progress, positioning itself as a strong competitor among the innovation ecosystems of the Asia-Pacific region. The Scaleup Summit Australia, which Mind the Bridge organizes every October with the support of Investment NSW, offers a good opportunity to take stock — also thanks to the data and analysis from the “Tech Scaleup Australia 2025” report, published with the support of Crunchbase and Acciona. The numbers Australia is home to 1,582 scaleups — almost six scaleups per 100,000 inhabitants, a remarkable number considering the country’s relatively small population — that have collectively raised more than $36 billion in capital (around 2% of national GDP). Aside from the major Asian economies (the 2 billion-plus-people nations of China and India, which play in a different league with 12,403 and 4,112 scaleups respectively), Australia’s numbers are not far behind South Korea (2,127) and Japan (2,268), roughly on par with Singapore (1,660), and 3x larger than emerging ecosystems like the UAE (503). Notably, Australia also stands out as fertile ground for large tech companies — what we call scalers. We identified 71 Australian scaleups that have each raised over $100 million, a number comparable to Japan (86) and South Korea (96). This can be explained by the relative isolation of the Australian ecosystem, which has encouraged the creation of national champions in strategic sectors for the continent such as construction, mining and energy — collectively referred to as “infratech.” Infratech: Australia’s house specialty A closer look at the infratech landscape shows steady growth over the past five years, with venture capital investments rising from $100 million in 2020 to nearly $500 million in 2025. Among Australian scaleups, about 1 in 10 (107) operate in this vertical, covering the entire value chain: from critical resources (21%) to construction (57%) and energy systems (22%). Australia’s dominance in mining AI As highlighted in the “Unlocking the Future of Mining” report developed by Mind the Bridge with support from BHP, Austmine and Hub de Innovación Minera del Perú, and based on dozens of interviews with mining industry experts, large infrastructure projects promoted by local and international corporations (including ACCIONA, BHP and Rio Tinto) are increasingly integrating new technologies such as AI, advanced robotics, computer vision and digital twins. Therefore, it doesn’t come as a surprise that Australia leads globally, accounting for nearly three quarters of all investments in AI for mining, far ahead of China (12%) and the United States (9%). Australia’s dominance in mining AI reflects structural strengths that are tough to match. The country combines large-scale mining operations, supportive regulations and a mature ecosystem of mining tech vendors and talent — which made it the global hub for mining innovation. For companies abroad, this means access to mining AI capabilities now largely depends on partnering with Australian platforms and experts. Investment landscape: industrial roots, corporate muscle Investors are also moving into tech areas like space tech, UAVs, drones and autonomous mobility, especially when these intersect with construction and mining applications. Although Australia’s investor landscape is broad — with 491 active VCs and CVCs currently managing around $32 billion of dry powder available for local scaleup investments (see map on MTB Ecosystem) — it remains largely focused on seed- and early-stage funding (the majority of funds — 73% — are under $50 million). However, what’s particularly interesting — and consistent with the industrial drive of the Australian ecosystem — is that most of the mega funds (over $1 billion) are corporate venture capital vehicles. Leading examples include Rio Tinto, as well as banks like Macquarie Group, ANZ, National Australia Bank and Commonwealth Bank of Australia. Specialization attracts international players, dispersion does not The Australian strong specialization in specific verticals is attracting the interest of global corporates: 26 large international companies have set up innovation outposts Down Under (map available on MTB Ecosystem). This sends a powerful message in today’s innovation world, where the concentration of investments in a handful of major ecosystems has effectively reduced the visibility of nearly all others, pushing them toward marginalization and irrelevance. Specialization, whether in technological domains or industrial applications, can help some of these ecosystems stand out and claim their space on the global innovation map. For more insights on startup and scaleup ecosystems, see Mind the Bridge’s reports (available for free download here). Alberto Onetti, Mind The Bridge Alberto Onetti is chairman of Mind the Bridge and a professor at University of Insubria. He is a serial entrepreneur who has started three startups in his career, the last of which is Funambol, among the five Italian scaleups that have raised the largest amount of capital. He is recognized among the leading international experts in open innovation and has wide experience in setting up and managing open innovation projects — venture clients, venture builders, intrapreneurship, CVCs — with large multinational companies, as well as advising and training on this subject. Onetti has a column on Sifted (Financial Times) and several other tech blogs. Related reading: Illustration: Li-Anne Dias
Edtech-Specific Startup Funding Stays Low
Funding to startups specifically focused on education technology remains at depressed levels relative to a few years ago. However, the tallies — which exclude general-purpose AI platforms popular with educators, students and investors alike — may understate enthusiasm at the intersection of tech and education. So far this year, global edtech-focused startups have raised around $2.8 billion in seed- through growth-stage funding, per Crunchbase data. That’s roughly flat with 2024 levels, pointing to stabilizing investment, albeit at a fraction of the peak a few years ago. In the U.S., this year’s funding numbers are a bit stronger relative to 2024, with $1.2 billion invested in edtech startups so far. While still far off of the pandemic-era highs, 2025’s funding figures puts this year roughly on par with 2023. What’s in and what’s out Edtech is a vast space, covering everything from preschool lesson-planning to corporate upskilling. Given this, it’s not uncommon to see a downturn in one subcategory while another remains a funding favorite. If we were to generalize trends looking at this year’s larger rounds and exits, it appears investors are particularly keen on opportunities in healthcare education and training. At the K-12 level, VCs are also backing startups deploying AI tools to customize lessons for individuals and free up teachers from routine, repetitive tasks. As for what’s not hot, we’ve seen a movement away from coding academies and teaching platforms, with the rise of coding automation tools. We’re also seeing a paucity of jumbo-sized funding rounds and not a lot of deals that look like pre-IPO financings. What the biggest rounds tell us So who is getting funded? Amboss, a Berlin-based startup offering a tool to learn about and research medical information, raised the largest round, securing nearly $260 million in a March financing. The company started with a focus on medical students but now also markets to practitioners. Lingokids, a provider of content and online learning activities for young children, secured the next-biggest funding, a $120 million September round led by Bullhound Capital. Other larger rounds this year include an $80 million financing for EdSights, developer of a chatbot to help students navigate college life and boost retention, and a $45 million Series B for MagicSchool AI, a provider of AI-enabled time-saving and productivity-enhancing tools for educators. For a slightly broader view, below we put together a list of eight of the larger funding recipients in the education sector this year. Buyers too Edtech is also seeing some exit activity. This is coming in the form of M&A, as the IPO market has been quiet this year. Most recently, CareAcademy, a platform for healthcare workers to learn new skills and obtain certifications, sold to Activated Insights, a software platform for senior living and home care providers, for an undisclosed sum. Founded in 2013, Cambridge, Massachusetts-based Care Academy previously raised at least $33 million in known venture funding. The company, founded and led by Harvard-trained educator Helen Adeosun, carved out a niche offering upskilling opportunities to health workers like home care aides and nursing home staffers, opening a path to advancement for what are typically lower-paid positions. Also in the health sphere, OnlineMedEd, an Austin startup focused on online learning tools for medical students and educators, sold this spring to exam prep provider Archer Review, a portfolio company of private equity firm Leeds Equity Partners. Previously, 11-year-old OnlineMedEd had raised at least $30 million in venture funding. And in the post-secondary education space, Modern Campus, a Toronto-based provider of software tools for colleges to attract and retain students, sold a majority stake to PE firm Providence Equity Partners in August. The optimist case Looking ahead, the optimist case is that founders, investors and acquirers alike will find plenty of appealing opportunities in ed tech. Longtime education startup investor Owl Ventures considers the education and training market to be one of the fastest-growing sectors in the global economy. In its 2025 report, the firm projects the global education market is on track to surpass $10 trillion by 2030. In terms of growth, Owl unsurprisingly points to AI as the largest ed tech driver. In recent years, the report notes, AI in the classroom has moved beyond the experimentation stage and is already proving vital in saving educators hours of work, providing personalized tutoring to students, and helping craft compelling lesson plans. Eventually, it’s likely we’ll see the impact of AI innovation in edtech also showing in the form of more funding for startups in the space. Related Crunchbase queries: Related reading: Illustration: Dom Guzman
Exclusive: Founded By Ex-Nvidia Researchers, Flexion Lands $50M To Build The ‘Brain’ for Humanoid Robots
Flexion, a startup that’s “building the brain for humanoid and human-capable robots,” has raised $50 million in funding, it tells Crunchbase News exclusively. Founded in January by former Nvidia researchers, CEO Nikita Rudin and CTO David Hoeller, along with Julian Nubert and Fabian Tischhauser, Zurich-based Flexion has now raised a total of $57.35 million in funding. The company plans to use its new capital in part to open a U.S. headquarters in the Bay Area. DST Global Partners, NVentures, Redalpine, Prosus Ventures and Moonfire Ventures participated in the Series A financing. Flexion co-founders Fabian Tischhauser, Nikita Rudin, David Hoeller and Julian Nubert Flexion was born out of years of research at ETH Zurich and Rudin’s work at Nvidia. The executive argues that today, most robots rely on scripts, tele-operation or brittle task-specific code. “That doesn’t scale,” he said. Flexion’s platform, according to Rudin, replaces that with a full autonomy stack including language-level reasoning, vision-language-action motion generation, and transformer-based whole-body control “so robots can understand instructions, move through the world, and adapt to new situations with minimal human involvement.” He added: “Unlike companies focused on a single robot form factor or on narrow behaviors, our system is built to work across morphologies and tasks, making it a true general-purpose intelligence layer for robotics.” Put simply, Flexion’s mission is to build the AI foundation “for the next era of robotics” by giving humanoid robots “the intelligence they need to transform industries and daily life, making them safe, capable, and indispensable partners to humans.” Its funding comes amid what looks like a robust year for robotics-related venture investment overall, with more than $10.7 billion invested globally as of Nov. 19, per Crunchbase data, already topping every full year since 2021. All kinds of robots, all kinds of tasks Flexion differs from other efforts around foundational models for robotics in a couple of ways, Rudin said. First, it does not rely on hand-engineered behaviors or teleoperation, in which a human operator controls and trains a robot remotely. Rather, Flexion primarily uses synthetic data generated from high-performance physics simulations to train its models. Second, it claims to leverage reinforcement learning techniques with the goal of delivering software “that is robust to the high diversity of the real world.” This gives Flexion an advantage, claims Rudin, in that its data generation is not constrained by human labor, and its models can “generalize and perform beyond the limits” of teleoperation setups. The startup is initially focused on humanoid and human-capable robots, because, as Rudin puts it, they represent the highest-value opportunity, performing “useful work” in applications across industrial settings, logistics, manufacturing, and eventually, areas like disaster response and planetary exploration. Because Flexion’s platform is morphology-agnostic, the company also sees opportunity in wheeled platforms, multiarm systems, and other complex robotic forms. Over time, Flexion’s goal is to be relevant anywhere robots need to perform long-horizon tasks autonomously. Expansion plans Presently, Flexion has 31 employees. Besides expanding to the U.S., the company plans to use its new capital to expand its Zurich-based R&D team, scale compute and robot fleets, and accelerate commercialization of its autonomy stack. Rudin says the startup is already working with major OEM partners and the funding will help it to scale those partnerships globally. Flexion licenses its software with an annual per-robot software licensing model. “There is a clear appetite for a software-only intelligence layer that can generalize across robot bodies,” Rudin told Crunchbase News. But its priority for now, he said, is to stay focused on core technology development. Working at Nvidia gave Rudin “a deep appreciation” for the compute and data flywheel that enabled the leap in large language models. “Working on the fundamental tools for robot learning training provided me a window into the challenges robotics companies were facing: They are rebuilding the same components, going through the same learnings, and fighting the same challenges,” he said. ‘The toughest and most defensible part of the stack’ Philip Kneis, an investor at Redalpine, told Crunchbase News via email that after looking into the robotics space for years, Flexion stood out because in his firm’s view, it is focused on “the toughest and most defensible part of the stack: building a shared brain for robots.” “They’ve already put robots to work in the real world …” he said. “That ability to turn cutting-edge research into robust, field-tested autonomy is a big part of why we invested.” Sandeep Bakshi, head of Europe investments for Prosus Ventures, said the startup’s simulation-first approach was compelling because it hadn’t seen any other robotic model developers building with that approach. “Most players in the market today are taking teleoperations-heavy approaches, which requires hundreds of thousands of hours of manual human demonstrations — an approach we believe is fundamentally unscalable in the long run,” he added. “Robotic foundation model developers will eventually need to heavily leverage simulation-based training, and the Flexion team is best suited to win with this approach.” Related Crunchbase queries: Related reading: Illustration: Dom Guzman
Exclusive: With Customers Like Okta And Coinbase, Coverbase Raises $16M To Grow AI-Powered Procurement Platform
Coverbase, an AI-powered procurement platform, has raised $16 million in a Series A round led by Canapi Ventures, the startup tells Crunchbase News exclusively. Founded in 2024, San Francisco-based Coverbase aims to reinvent how enterprises vet and manage vendors in a “security-first” manner. Specifically, it uses artificial intelligence to automate and secure how large, regulated companies onboard new vendors and suppliers, such as software providers, consultants, contractors and service firms. Coverbase counts Nationwide, Coinbase, Okta and the Navy Federal Credit Union among its customer base. Coverbase co-founders Clarence Chio and Kao Zi Chong. Courtesy photo. Co-founders CEO Clarence Chio and CTO Kao Zi Chong have impressive backgrounds. Chio also co-founded Unit21, a startup that helps businesses monitor fraudulent activities with its no-code software that has raised $92 million from the likes of Tiger Global Management. Chong is a former engineering manager at fintech giant Stripe. The pair started Coverbase to automate the procurement process “by weaving risk, security, and compliance decision-making directly into every stage of intake, due diligence, contracting, and ongoing monitoring.” “What makes us different is that our AI agents don’t just manage workflows, they actually perform the work,” Chio, who currently teaches AI and cybersecurity at UC Berkeley, told Crunchbase News. Most competitors, he claims, build tools that help humans move through manual approval steps. (Competitors include the likes of Zip, Coupa, Ariba and Archer.) By being “AI-agents-first” instead of “workflow-first,” Coverbase allows customers to onboard vendors faster, with less friction and stronger security outcomes, according to Chio. According to Verizon’s 1 Data Breach Investigations report, breaches involving third parties reached 30% this year, up 2x compared to 2024, “driven in part by vulnerability exploitation and business interruptions.” Investors appear to be pouring more money into AI-powered procurement startups lately. Examples include: Fast growth Existing and new investors including Fika Ventures, TTV Capital, Pear VC, Valley Bank and Founders You Should Know also participated in Coverbase’s Series A round. To date, the company has raised about $20 million. While Chio declined to reveal its valuation, he said the Series A represents “a roughly 4x increase” compared to its previously undisclosed $3.5 million seed round led by Fika. Chio also declined to reveal hard revenue figures, noting that Coverbase’s customer base has grown 10x since the start of 2025. Overall, the startup has about 35 customers and operates on a usage-based SaaS pricing model that scales with the number of suppliers and risk assessments performed on its platform. Its customers range in size, with some having as few as 50 suppliers, and others being larger companies with over 50,000 suppliers. Besides those mentioned above, customers include General Bank of Canada, Live Oak Bank, Coastal Bank, Thread Bank, LiveView Technologies, Guardant Health, Rubrik, Alteryx and Bill. Coverbase is currently active in industries such as financial services, insurance, healthcare, pharmaceuticals and technology because they “demand high levels of compliance and security,” Chio noted. But the company is also expanding into telecommunications and critical infrastructure, and sees future opportunities in other highly regulated sectors such as energy and utilities, defense contracting, government, aerospace and aviation, medical devices and biotech, and payments and logistics. The company plans to use its new capital to expand into contract management and continuous security monitoring. It’s also planning to quadruple its sales force to meet “accelerating enterprise demand.” Presently, Coverbase has 12 employees. ‘A real and persistent pain point’ Walker Forehand, president and general partner at Canapi, said his firm was drawn to Coverbase because it believes the company is “solving a real and persistent pain point in enterprise procurement—particularly in highly regulated, security-conscious sectors.” “Vendor onboarding is typically slow, fragmented, and risk-prone,” he wrote via email. “Coverbase is flipping that dynamic by using AI to make procurement faster, more secure, and strategically valuable.” Forehand also believes that what sets Coverbase apart is its AI-native approach. “This gives enterprises a faster, smarter, and more secure way to adopt innovation without adding operational burden,” he said. “It’s not just about efficiency — it’s about turning procurement into a competitive advantage, which is a fundamentally different mindset from traditional solutions.” Related reading: Illustration: Dom Guzman
Unicorns Pick Up For The Second Month In A Row, Adding Close To $45B To The Board
A total of 20 companies joined The Crunchbase Unicorn Board in October, adding $44.5 billion in value. This was the highest valuation amount added to the unicorn board for a new cohort in the past three years. The number of new monthly entrants has picked up in recent months. The top 20 companies on the board have also been reshuffled and we’ve seen a marked increase in new decacorn-valued companies. Of the 20 companies that joined in October, 11 came from the U.S. China added three new unicorns and Sweden contributed two. Europe, the U.K., Germany and Ukraine each minted one new unicorn, as did India. Among the new entrants, New York-based open model developer Reflection and Austin-based residential battery operator Base Power each raised billion-dollar rounds that valued them as unicorns for the first time. The highest valued among the new unicorns were Reflection, which was valued at $8 billion, and San Francisco-based payments blockchain Tempo, valued at $5 billion. Exits A pair of companies from the unicorn board were acquired in October: Passwordless authentication company Stytch was acquired by Twilio, and Nexthink, an IT employee experience platform was acquired by Vista Equity Partners. In another October exit, data management tooling company dbt Labs merged with Fivetran in an all-stock deal. Three companies also went public: Silicon Valley-based travel and expense management company Navan, Shanghai-based e-commerce software platform Jushuitan Network Technology, and Beijing-based silicon wafer production company Eswin Materials. New unicorns Here are October’s 20 newly minted unicorns across multiple sections. AI led with four companies, transportation with three, and healthcare and financial services followed, each with two companies. AI Transportation Healthcare and biotech Financial services Web3 Energy Aerospace Professional services E-commerce Sales and marketing Defense tech Beauty Semiconductor Related Crunchbase unicorn lists: Related reading: Methodology The Crunchbase Unicorn Board is a curated list that includes private unicorn companies with post-money valuations of $1 billion or more and is based on Crunchbase data. New companies are added to the Unicorn Board as they reach the $1 billion valuation mark as part of a funding round. The unicorn board does not reflect internal company valuations — such as those set via a 409a process for employee stock options — as these differ from, and are more likely to be lower than, a priced funding round. We also do not adjust valuations based on investor writedowns, which change quarterly, as different investors will not value the same company consistently within the same quarter. Funding to unicorn companies includes all private financings to companies that are tagged as unicorns, as well as those that have since graduated to The Exited Unicorn Board. Exits analyzed here only include the first time a company exits. Please note that all funding values are given in U.S. dollars unless otherwise noted. Crunchbase converts foreign currencies to U.S. dollars at the prevailing spot rate from the date funding rounds, acquisitions, IPOs and other financial events are reported. Even if those events were added to Crunchbase long after the event was announced, foreign currency transactions are converted at the historic spot price. Illustration: Dom Guzman Clarification: This story has changed since its original publication to correct an error in the Exits section.
How To Raise Capital When You Don’t Sound Like An Insider
By Nick Lahoika The first question investors asked me in my early months of pitching was, “Where are you from?” The accent gave me away every time. Following the failed 2020 revolution in Belarus, I moved my company, Vocal Image, to Estonia. I arrived in Estonia with no English, no network and no understanding of the Western startup world. I spent months studying the language, practicing daily to improve my pronunciation and confidence. Even with my very basic English, I started pitching immediately. I met an angel who decided to invest after just one pitch. Only half a year after our relocation, we closed our first round of $250,000. In today’s market, where early-stage capital is shrinking, your ability to communicate is as critical as your product. Forty-four percent of U.S. unicorn founders are immigrants, and many of them started as outsiders. You may not “look the part,” but that doesn’t have to stop you from raising money. It certainly didn’t stop me. From that experience, here are three lessons that I believe are highly valuable for any founder aiming to stand out. Position yourself as a problem solver, not a capital-raiserNick Lahoika Investors meet hundreds of founders each year. Most of them open with how much they’re raising, not why they exist. When I started framing myself as someone obsessed with solving a real communication problem, not someone asking for capital, everything changed. People invest in clarity and conviction. Instead of limiting myself to talking about market size or monetization, I illustrated the problem: how speech anxiety, accents and vocal tension limit people’s confidence globally. When your story is rooted in a genuine mission, your accent, location or background stops being a liability and becomes part of the proof. Use body language to communicate confidence How you carry yourself speaks louder than your words. Investors read it instantly. For example, if you lean back when challenged, it looks defensive. That’s why when I answer questions, I lean slightly forward, smile and nod. It signals that I’m engaged and listening instead of trying to protect myself. Confidence also shows up in stillness. When you know your material, you don’t need to over-gesture. Remember that the goal is not to perform, but to connect. Smile first, listen fully and never interrupt. These small actions create a sense of trust long before you start talking about numbers. The studies we relied on in product development show that voices with a lower pitch are perceived as 40% more confident and authoritative. Founders don’t need to fake that, but they can train it, the same way they can train their pitch deck. Use pitch competitions as leverage As I worked on my communication skills, pitch competitions became my springboard. They didn’t guarantee investment, but they built momentum. And in three first years, we won six: TechChill, Latitude 59, StartupFair, AWS AI Challenge, the European AI Startup Program by Meta, Hugging Face and ScaleWay. Those events brought us $700,000 and connections that led directly to our seed round. Beyond the funding, there’s enormous value in visibility. By participating in these competitions, you get feedback, credibility and stage time. All of that accelerates learning and helps you make your story resonate across languages, markets and personalities. When you don’t sound like an insider, raising capital is about clarity, control and presence. Investors may notice your accent in the first five seconds, but if you master those next five minutes, they’ll remember your idea, not where you came from. Founders are obsessed with anxiously trying to get in front of investors, but anxiety kills a sale. You’ve already heard the advice from a startup mentor: practice your pitch, find your own mentors, and get feedback on your ideas. In my experience, ideas and passion are key, but it’s your polished soft skills that actually let you show that passion to anybody. Nick Lahoika is the co-founder and CEO of Vocal Image, a soft skills AI coaching startup. The company has more than 4 million downloads and 50,000 subscribers worldwide. His journey is deeply personal; he was bullied for unclear diction at school, which inspired his mission to help people communicate better. After being forced to flee his home country following the 2020 revolution, Lahoika arrived in Estonia with minimal command of English and used his own app to train his voice, securing his first round of funding within just six months. The winner of the AWS AI Challenge and Meta x Hugging Face European AI Startup Program, Vocal Image recently raised a $3.6 million seed round led by Educapital (France) and scaled to more than $14 million ARR. Related reading: Illustration: Dom Guzman
In An Agentic Era, VC Is Buying A-Player C-Suite Execs At Any Cost — Not ‘Staffing Up’
By Pukar C. Hamal Venture capital has become a mechanism for extracting executives from trillion-dollar companies and paying them whatever it takes to build in an AI-native world. We’re not funding companies anymore — we’re buying access to the few hundred people who’ve built AI systems inside Google, OpenAI and Meta. Safe Superintelligence was founded by Ilya Sutskever (ex-OpenAI chief scientist), Daniel Gross (ex-Apple AI lead), and Daniel Levy (former researcher at OpenAI). The company operates with roughly 20 employees. So far, it’s raised $3 billion at a $32 billion valuation, without a product or any revenue. What they do have is three executives from trillion-dollar companies who understand how to build superintelligence. That alone commands $1 billion in capital per team member. This has become the playbook. Pukar C. Hamal Microsoft agreed to pay Inflection AI $650 million to use its models and hire DeepMind co-founder Mustafa Suleyman as CEO of Microsoft AI, along with most of Inflection’s 70-person team. Meta CEO Mark Zuckerberg reportedly offered Andrew Tulloch up to $1.5 billion over six years. Google struck a $2.4 billion nonexclusive licensing deal with Windsurf and hired its CEO, co-founder and select R&D staff. Meanwhile, OpenAI CEO Sam Altman has publicly stated that Meta offered top OpenAI talent $100 million signing bonuses. The traditional venture formula is inverting. It used to be simple: raise $20 million, spend 95% on growth and headcount, allocate 5% to executive comp. Now the majority of capital flows toward recruiting a handful of executives who understand how to operate in an AI-native environment. But most founders can’t compete in $20 million bidding wars. And so there’s an asymmetric play, and it’s 10x cheaper. Why executive judgment is the new scarce resource In an agentic era, AI systems write code, process data, handle customer service and automate operations. The scarcest resource has become the judgment of executives who know how to orchestrate these systems effectively. Think about what this means in practice. A decade ago, $100 million might have hired 200 engineers. Today, that same capital might fund five FAANG executives at $10 million each, with the remaining $50 million allocated to compute, AI tooling and a skeleton crew of 20 to 30 people overseeing autonomous agents. Executives from frontier AI companies command massive premiums because they possess knowledge that doesn’t exist elsewhere. They navigate what’s possible with current AI capabilities, understand the economics of model training and inference costs, and can anticipate regulatory frameworks before they’re codified. What this means for capital formation This shift creates a new power dynamic. Founders who can attract marquee executives unlock fundraising rounds that would be impossible based on traction or revenue alone. VCs evaluate deals increasingly on “who’s building this” rather than traditional metrics like customer acquisition cost or gross margins. The clearest signal: Voyage AI, with 19 employees, was acquired by MongoDB for $220 million — $11.6 million per employee. In an agentic world, team size has become irrelevant. How to compete with the asymmetric playbook Most founders reading this can’t offer $10 million to $20 million equity packages to marquee executives. But there’s a counterintuitive strategy emerging: Instead of competing directly for executives who’ve built AI systems at frontier labs, target the operators who’ve integrated them at scale inside Fortune 500s. A chief technology officer who deployed LLMs across 50,000 employees at JPMorganChase or Walmart understands enterprise AI adoption patterns that most OpenAI researchers don’t. Here’s the asymmetric approach we’re seeing work: 1. Hire the “translator” executives, not the “builder” executives. A former VP of engineering from Databricks who integrated AI into enterprise workflows is more valuable for a B2B AI startup than a research scientist from DeepMind. They’re 10x cheaper and often more relevant to your actual go-to-market challenges. 2. Offer board seats, not just equity. The most compelling pitch to executives earning $800,000 at FAANG companies isn’t just equity — it’s offering: (a) a board seat they’d never get at a big company; (b) meaningful ownership in a high-growth company; and (c) the chance to compress 10 years of career advancement into two to three years. The value proposition isn’t “get rich” — it’s autonomy, impact and an accelerated path to becoming a recognized operator in AI. 3. Build technical credibility through advisory networks, not executive hires. Instead of hiring one $5 million executive, allocate $500,000 across 10 advisers from Google, Meta and Microsoft who can provide technical validation during enterprise sales cycles. 4. Target executives in “golden handcuff” situations. The best candidates aren’t those getting $100 million offers — they’re the overlooked VPs at trillion-dollar companies who’ve built AI systems but are stuck behind org politics. They have the expertise, they’re ready to leave, and they’ll join for $2 million or $3 million equity packages if you can articulate a clear path to relevance. The companies winning without massive war chests aren’t trying to out-recruit Anthropic for research talent. They’re targeting enterprise operators who understand how AI systems actually get deployed at scale — and building credibility networks instead of expensive org charts. The real tradeoff We’re witnessing the formation of a technical aristocracy. A few thousand individuals now command compensation packages previously reserved for successful founders, as wealth transfers from broad-based tech employment to an elite operator class. Venture capital has fundamentally transformed from a growth capital fund into a talent acquisition fund. The AI gold rush will eventually end, but the economic structure it’s creating is permanent. In the agentic era, you don’t raise capital to hire engineers — you raise it to hire executives who know how to orchestrate AI agents that do the actual work. The question isn’t whether the rules have changed. They have. The question is which version of the new game you’re playing: competing for $20 million executives from frontier AI labs, or building asymmetrically with $2 million enterprise operators and credibility networks. Both paths work. Only one is accessible to most founders. Pukar C. Hamal is founder and CEO of SecurityPal, which eliminates the security review bottleneck that stalls enterprise deals for companies such as OpenAI, Figma and MongoDB. Born in rural Nepal, he built a profitable company with a 24/7 security operations command center in Kathmandu, proving that world-class execution doesn’t require Silicon Valley overhead. He writes about capital formation and the economics of AI-era operations. Illustration: Dom Guzman
Why This OpenAI And CoreWeave Investor Thinks The AI Market Is ‘Dangerously Overheated’
Mark Klein, president and CEO of SuRo Capital, believes that the AI startup market is not just frothy, but “dangerously overheated.” Founded in 2011, San Francisco-based SuRo Capital Corp. is a publicly traded investment fund that invests in “high-growth” venture-backed private companies. Naturally, Klein is bullish on OpenAI, considering that SuRo Capital owns a position in the buzzy startup, now the most highly valued private company in the world. But in his view, much of the rest of the AI ecosystem — including the capital that is chasing it — is inflating market caps. Mark Klein, president and CEO of SuRo Capital Klein believes that the fact some second-tier AI and adjacent startup valuations are tripling in months, not years, “with little change in fundamentals,” will not bode well in the long term. It’s a velocity he’s never seen in his 30 years as a VC investor. Besides OpenAI, SuRo was an early investor in CoreWeave and owns positions in unicorns including Vast Data, Canva, Plaid and other companies. The firm has seen exits in the likes of Dropbox, Facebook (now Meta), Lyft, ServiceTitan, Spotify and X. But for now, when it comes to AI investing, Klein is playing it safe. He’s holding on to cash, waiting for prices to correct. Crunchbase News interviewed Klein via email to get his thoughts on the AI space and why he believes OpenAI “copycats and the capital chasing them” could be creating more problems than solving them. Would you say that we’re in an AI bubble? The current AI market is meaningfully different from a bubble. There is genuine progress and value being created in model capability and in the buildout of AI infrastructure. At the same time, it is possible that some companies, particularly in the application layer or adjacent software categories, are a bit ahead of current fundamentals, given the strong interest in the space. There seems to be some concern that some of these companies don’t have the revenue or product to back-up such high valuations. Do you believe that’s true? AI is not a monolith. Valuation dynamics differ depending on where a company sits in the stack. There are three categories: infrastructure, LLM/models and applications. It is important to differentiate between these areas. Infrastructure and certain vertical applications with clearer unit economics may appear more grounded. Some of the early application tools may appear more extended, with the potential to face consolidation or pricing pressure as the market develops. What do you mean by holding on to cash and waiting for prices to correct? Our strategy has been to stay disciplined. We are seeing tremendous deal flow, which gives us the ability to be selective and focus on the opportunities that best fit our investment framework. We look for market-leading companies that are building transformational products, but we balance that with long-term value creation for shareholders. That means being mindful of valuations and deploying capital when expected returns justify the risk. Which specific AI subsectors do you see as the most overhyped? When looking at the frontier model leaders, and specifically OpenAI, we see a company driving a very meaningful step change in one of the most transformative technology shifts. Their progress is influencing investment activity at a scale larger than historical national efforts such as the Apollo Space Program or the Interstate Highway buildout. Some of the companies with the most aggressive valuation moves, especially relative to underlying performance, appear in the layers built on top of these foundational models. Many companies are attracting investment at this layer, though it is reasonable to expect that not all will ultimately win or become category leaders. As with any technological cycle, we should expect real value creation alongside some companies that fall behind or are consolidated as the market matures. Related Crunchbase query: See also: Illustration: Dom Guzman