It is difficult to define the extent to which a specific company should morph itself into an “AI native” company. Does this add value for everyone?

AI does not automatically increase exit value. In some cases, it can reduce differentiation, compress margins, complicate diligence and make a company more difficult to acquire. Like pricing, customer service or go-to-market strategy, AI requires a careful balancing act between speed and defensibility, innovation and complexity, short-term productivity and long-term strategic value.

Let鈥檚 jump into three ways AI strategy can impact exit value.

Build an AI architecture that acquirers can trust

Many startups are rapidly adding AI copilots, model integrations, orchestration layers, prompt libraries, vector databases and third-party AI tools across the organization. This may accelerate product development and help teams ship faster. However, from the perspective of an acquirer, it can also create a more complicated architecture.

During due diligence, buyers care about how AI is being used. Which models are embedded in the product? Which vendors are critical to delivery? Where does customer data flow? How are outputs monitored? What happens if pricing changes, APIs break or regulation shifts?

A startup may see AI adoption as innovation. A buyer may see it as integration complexity, vendor dependency, compliance exposure and security risk.

This is especially important for strategic acquirers that need to integrate the target into a larger platform. If AI makes the product easier to scale, automate, secure and maintain, it can support valuation. If it creates a fragile layer of external dependencies, unclear data flows and difficult-to-audit decision-making, it may reduce confidence and lower the price a buyer is willing to pay.

Invest in proprietary data

Even one year ago, adding AI functionality to a product could create excitement by itself. Today, many AI features are becoming easy to replicate. Summarization, search, chat interfaces, recommendations, content generation and workflow assistance are increasingly available through the same underlying models and infrastructure. This matters for exits.

A strategic acquirer rarely pays a premium simply because a startup integrated the latest model. They pay for what they cannot easily build themselves: proprietary datasets, unique customer workflows, strong distribution, deep vertical adoption or network effects that improve with scale.

Founders should therefore ask a simple question: Is our AI strategy creating a defensible asset, or are we just adding features that competitors can copy within weeks or months?

Revisit your buyer map as AI redraws strategic boundaries

Historically, many companies built their exit strategy around a familiar buyer map. A cybersecurity startup might sell to a larger cybersecurity vendor. A vertical SaaS company might sell to a competitor in the same industry. A workflow automation company might sell to a productivity platform. AI is changing those boundaries.

As AI expands what platforms can do, strategic buyers are moving into adjacent markets they previously ignored. An infrastructure company may acquire an identity platform because AI agents need secure access controls. An ERP vendor may acquire workflow automation because AI is moving closer to business process execution. A data platform may acquire a vertical application because domain-specific data is becoming more valuable.

This means CEOs should revisit their buyer map every six to 12 months. The most logical acquirer today may not be the same one that would have been logical even one year ago.

is a strategic adviser to tech companies, investors, CEOs and boards, specializing in strategy, growth and M&A. He is a guest contributor to 兔子先生传媒 News and a university lecturer on strategy, finance and entrepreneurship. Learn more at and connect with him on .

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Think of the most famous boxers you know, likely the heavyweights: Muhammad Ali, Joe Louis, Mike Tyson. In a clash between titans, the advantages seem easy to understand, since the bigger fighter looks like the stronger one.

But size alone is not a strategy. Sugar Ray Robinson was a middleweight, not a heavyweight, and in the 1950s, A.J. Liebling said he looked 鈥渕ore like a loose-limbed dancer than a boxer.鈥�

Robinson鈥檚 advantage was completeness: speed, footwork, intelligence and stamina. In a famous 1951 match against Jake LaMotta, Robinson schooled the reigning, heavier middleweight champion with a 13th-round TKO.

Completeness also applies to companies. The market tends to assume that big companies will capture the biggest gains from AI. But AI is tough to get right at any size.

Look at , valued at $6 billion in 2024. It made headlines claiming its -powered chatbot could handle millions of conversations and do the work of 700 customer service employees. Customers hated the rollout, and by 2025, Klarna was . Or , which watched its value after ChatGPT commoditized its main offerings.

If everyone can get AI wrong, who wins?

Enter the scrappy middleweight

Each year we speak with thousands of operators and founders, and one pattern is clear: The biggest long-term gains from AI will not flow to heavyweight incumbents or many AI-native startups but to scrappy middle-market technology companies, the middleweights.

The next phase of AI disruption will be challenging, but middleweights can gain serious ground.

One objection: Won鈥檛 hyperscaler companies go after certain verticals? If Copilot inside 365 or 1聽agents can run a workflow, how does a middleweight company survive? The answer depends on what constitutes durable advantage. Horizontal platforms are built for generalized work, not the messy, regulation-heavy, category-specific workflows of the real world. Middleweights can win by making their software the system of record that AI calls into instead of software that AI replaces.

The odds for making big, impactful gains with AI right now favor the middle market, where proven growth companies can use customer trust, domain expertise, capital structure and speed to transform their businesses, taking market share from slower incumbents. With three-quarters of AI鈥檚 economic gains now being captured by just per , entrepreneurs who stand still may already be losing the round.

What makes for a winning middleweight company?

The best middleweight technology companies share the five traits below, all working together as a system.

Disciplined self-assessment. Middleweights are designed to act quickly on honest feedback, and their boards help them test where AI generates value versus where it merely consumes engineering capacity and budget.

Seat-based pricing is one area for brutal assessment. When autonomous agents do the work, the revenue model should reflect outcomes, not users. In 2023, customer service platform made a bold switch, pricing its AI agent Fin at 99 cents per resolved conversation. That agent became the company鈥檚 core offering, and it recently . Outcome-based pricing might seem painful at first (and reorganize your GTM team and their incentives), but it anticipates an agentic future.

Agility. Enterprise companies are weighed down by technical debt and legacy infrastructure. Middleweights have enough scale and proprietary data but not so much organizational mass that every experiment needs 10 layers of approval. Their agility is as much cultural as structural.

These are ambitious, scaling companies growing 20% or more with strong unit economics, and a tech-first mindset runs through the entire business, not just the engineering org. Take the restaurant software , where early AI gains came from product leads ; those product teams then built a flywheel connecting new product features to external communications, with LLMs continuously editing and improving instructions for AI agents.

Workflow ownership. In the AI era, the strongest moat is owning a complex workflow. Middleweights have spent years gaining this position 鈥� integrating into customer systems, accumulating exception-level data, learning operational nuances that take a claims process from 95% accurate to 99.5%. (The last 4.5 points are the moat.)

An company, , doesn鈥檛 just apply AI to contracts; its moat is absorbing the decision workflow around each contract. As a contract moves through approvals, negotiations and redlines, the important part is learning from the history of why internal teams decided the way they did. Well-positioned companies will hold the institutional memory that AI agents need to query to do their jobs.

Technical capacity. Most large companies are stuck in AI pilot purgatory, and the market still underestimates how operationally demanding AI deployment is. Middleweights have something most AI-native startups lack: years of working with real customers. , another FTV company, started in 2007 as a service-heavy cybersecurity business that has learned deep detection logic from operating in more than 1,000 customer environments, including some of the largest global enterprises. As the company saw rapid automation from machine learning, then more sophisticated AI, it moved in-house SOC analysts into higher-value product development roles, allowing engineers with deep cyber expertise to drive key R&D.

A well-capitalized balance sheet. Companies with cleaner balance sheets can move faster, absorb experimentation costs, pursue selective M&A, and keep investing through periods of disruption. Large legacy software companies carrying heavy leverage, optimized for cost-cutting and growing at 5%-10%, can鈥檛 be light on their feet and will struggle to reallocate capital aggressively enough into AI R&D.

The imperative

Plenty of boxers can be complete for one season. Sugar Ray Robinson executed consistently in 200 professional fights, mastering the sweet science with a reliable system. That same high standard now applies to companies in the AI era.

The window for transformational gains with AI is not open indefinitely. Speed is a middleweight leader鈥檚 advantage. Do not wait to perfect your AI strategy; start executing.

If you don鈥檛 know where to start, pick key workflows, map them and ask whether AI makes them more defensible or more exposed. The answer may determine whether you give up a round or win the match.

is managing partner at , where he oversees the firm鈥檚 global strategy and investment decisions. He has been a growth equity investor at FTV for more than 20 years, leading investments in enterprise technology and services and financial technology and services. Bernstein has over 25 years of private equity experience. Prior to FTV, he was a partner at and its predecessors where he managed the business and financial services group. He began his private equity career with and started his professional career in the investment banking division of in New York.

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This is a weekly feature that runs down the week鈥檚 top 10 announced funding rounds in the U.S. Check out last week鈥檚 biggest funding deal roundup here.

Startup investors poured capital into a varied lineup of large rounds this week, targeting sectors including physical AI, biotech, cybersecurity, AI infrastructure and fintech. By far the largest financing of the week was a $1.7 billion round for founder 鈥檚 physical AI startup, , followed by sizable investments for 3D AI model developer and battery technology company .

1. , $1.7B, physical AI: Atoms, the physical AI startup founded by founder , raised $1.7 billion in a funding round led by . Kalanick touted the Los Angeles-based company鈥檚 vision as 鈥渁bout the coming industrial revolution where large industrial economic sectors get completely digitized.鈥�

2. , $400M, AI for 3D: Silicon Valley-based Meshy AI, a startup developing foundation models for AI-powered 3D generation, closed on $400 million in Series B funding at a $1.5 billion valuation. Lead backers include , and , per 兔子先生传媒 data.

3. , $300M, battery technology: Battery technology company Sila secured $300 million in a new round led by and . The Alameda, California, company will use the funding to expand its silicon anode plant in Moses Lake, Washington.

4. , $300M, inference technology: Etched, a co-designer of chips, racks, software and manufacturing methods for use in frontier models, picked up $300 million in Series C funding. led the round, which set a $10 billion pre-money valuation for the San Jose, California-based company.

5. , $180M, fintech: Augustus, a startup aimed at providing financial institutions around the world direct access to dollar accounts, secured $180 million in Series B funding. led the round, which set a $1 billion valuation for the San Francisco-based company.

6. , $160M, defense tech: Cathedral, a startup aimed at expanding U.S. military cyber capabilities, reportedly $160 million with backing from Sequoia Capital and Andreessen Horowitz. The Washington, D.C.-based startup was reportedly founded by a 鈥媡eam of former DOGE employees.

7. , $130M, biotech: Crystalys Therapeutics, a biotech developing therapies for people living with gout, closed an oversubscribed $130 million Series B round. led the financing for the San Diego-based company.

8. , $120M, healthcare software: San Francisco-based Candid Health, developer of a revenue cycle management platform for the healthcare industry, landed $120 million in Series D funding led by .

9. , $100M, cybersecurity: Glow, a Palo Alto, California-based AI-powered endpoint security startup, launched from stealth and announced it has raised $180 million to date, of which, per 兔子先生传媒, $100 million comes from its newest financing. Lead backers include Sequoia Capital, , , and .

10. , $75M, cybersecurity: Boston-based Neo Security, a startup working on an agentic software control platform for enterprises, picked up $100 million in a new round led by and Andreessen Horowitz.

Methodology

We tracked the largest announced rounds in the 兔子先生传媒 database that were raised by U.S.-based companies for the period of July 18-24. Although most announced rounds are represented in the database, there could be a small time lag as some rounds are reported late in the week.

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Looking broader into the entire market, I can see how this makes a lot of sense. Companies are becoming increasingly uneasy about where critical infrastructure and sensitive data live. AI fraud is getting better. Voice cloning is becoming more convincing. Vibe coding is allowing less experienced developers to build faster, but not always more securely. Quantum computing is still over the horizon, but serious companies are already thinking about what it may mean for encryption and long-term data protection.

For the past decade, cloud migration was treated as the obvious strategy. It gave companies speed, scale and lower upfront costs. Startups could launch without buying servers. Enterprises could modernize without rebuilding their own infrastructure.

That logic still holds. But we are witnessing an interesting shift where progress is happening so fast, security cannot keep up, thus creating an uneasy feeling causing decision-makers to revert back to older, and perhaps safer perceived strategies.

Here are three trends that I believe are pushing on-prem back into the limelight.

AI fraud is changing the security conversation

In many cases, cloud providers are more secure than what a company could build internally. The issue is that thanks to AI, attackers are becoming more sophisticated, and quick. AI makes phishing more polished, fake invoices more believable, and voice impersonation harder to detect. A call that sounds like the CFO or CEO asking for a payment approval is no longer far-fetched.

That changes how companies think about exposure. The attack surface is not only servers. It is identity systems, SaaS tools, APIs, employee workflows, permissions, contractors and support portals.

For sensitive systems such as communications, payments, identity and customer data, control becomes more valuable. On-prem does not guarantee security. But it can reduce dependency on outside platforms and give companies clearer ownership over the systems they cannot afford to compromise.

Enterprise AI may favor private infrastructure

Cloud AI APIs are excellent for testing. A company can launch a pilot quickly without buying GPUs, managing models, or hiring a large infrastructure team.

But enterprise AI is moving into production. That changes both the economics and the risk.

The most useful enterprise AI applications require proprietary data: contracts, source code, customer records, financial reports, support tickets, security logs, medical files and internal communications. This is the data that gives AI business value. It is also the data companies are most careful with.

For these use cases, on-prem or private AI infrastructure becomes more attractive. The model can run closer to the data. Access can be controlled more tightly. Retention, compliance and audit requirements become easier to manage.

There is also a cost angle. Token pricing is convenient in a pilot, but expensive at scale. When thousands of employees or customers use AI every day, paying per query can become a serious recurring cost. For stable, high-volume workloads, owning or controlling the infrastructure may be cheaper than renting every interaction forever.

Quantum risk is making long-term data protection more strategic

Quantum computing is not breaking enterprise encryption today. But the risk is already part of serious security planning.

The concern is that the minute quantum becomes commercial, all encrypted data sitting in the cloud will be transparent. No existing encryption will hold against a quantum computer. That matters most for companies holding long-life sensitive data: banks, healthcare providers, telecom companies, governments, defense-related organizations and infrastructure providers.

Regardless of whether or not on-prem is the best solution for all this, it is perceived as such. Hence, I believe it will drive higher demand for the legacy on-prem strategy. This early shift is also an opportunity, but that鈥檚 for another article.

is a strategic adviser to tech companies, investors, CEOs and boards, specializing in strategy, growth and M&A. He is a guest contributor to 兔子先生传媒 News and a university lecturer on strategy, finance and entrepreneurship. Learn more at and connect with him on .听

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In 2026, conversations about Black founders and venture capital have focused on access to funding. But as AI reshapes startup economics, the bigger challenge is no longer simply getting a first check, it’s raising enough capital at the seed stage to successfully reach Series A.

AI has fundamentally lowered the cost of building software companies. Founders can launch products faster, automate operations and accomplish with five employees what once required teams of 30. Yet while AI has reduced the cost of building a startup, it has not reduced the cost of scaling one. Companies still need resources to acquire customers, hire experienced talent, invest in go-to-market strategies, and generate the revenue and growth metrics institutional investors expect before leading a Series A round.

For Black founders, who continue to receive a disproportionately small share of venture capital, the inability to secure fully funded seed rounds has become one of the greatest barriers to building venture-scale companies.

AI is making seed capital more valuable, not less

One of the biggest misconceptions about AI is that startups simply need less money. In reality, AI has shifted when capital matters most. Because startups can now build products more efficiently, investors are increasingly rewarding founders who demonstrate real traction instead of polished ideas. Seed funding is no longer financing an experiment, it is financing proof.

That means founders need enough capital to move beyond building a product and toward building a business. Today’s Series A investors are looking for recurring revenue, customer retention, capital efficiency and repeatable growth. Those milestones require time, execution and sufficient capital.

The startups that reach them are increasingly those that raised enough capital early to stay focused on customers instead of constantly fundraising.

The numbers tell a stark story

The challenge is particularly acute for Black entrepreneurs. According to 兔子先生传媒 data, U.S. startups with a Black founder or co-founder received just $942 million in venture funding in 2025, only 0.32% of all venture capital invested in the nation. That represents one of the lowest funding shares in years and a dramatic decline from 2021, when Black founders raised $5.2 billion during the post-George Floyd investment surge.

While 2026 has shown encouraging signs, with Black-founded startups raising approximately $643 million by late May, the strongest quarter since mid-2022, the improvement was driven largely by a handful of unusually large financings, including a $350 million AI round. Across the broader ecosystem, Black founders remain significantly underrepresented in venture funding.

The issue isn’t simply that too little capital is available. It’s that many Black founders raise partial seed rounds that leave them without enough operating flexibility to achieve the milestones required for institutional Series A financing.

The real gap is between seed and Series A

Historically, venture capital rewarded bold ideas and rapid expansion. Today’s market rewards disciplined execution. Investors expect startups to demonstrate product-market fit, meaningful revenue growth, and efficient operations before committing Series A capital. That has made the journey between seed and Series A longer and more demanding.

Black founders who raise only enough money to survive often find themselves trapped in a cycle of continuous fundraising. Instead of focusing on customers, product development and hiring, they spend valuable months chasing additional capital just to extend their runway.

In an AI-driven market where product cycles move faster than ever, that lost time can determine whether a startup becomes a category leader or gets left behind.

Oversubscribed seed rounds are a competitive advantage

This is why oversubscribed seed rounds are taking on new importance for Black founders. Traditionally, oversubscription was viewed primarily as a signal of investor demand. Today, it is becoming a strategic advantage.

Additional capital gives Black founders flexibility to weather slower fundraising markets, invest aggressively when opportunities emerge, and continue executing without returning to investors every few months. It also allows founders to pursue growth intentionally rather than reactively.

Capital efficiency remains important, but efficiency is most valuable when paired with enough capital to execute.

The AI economy requires longer vision

The venture industry often celebrates AI for making entrepreneurship more accessible. In many ways, that’s true. The barriers to launching a company have never been lower. But lowering the cost of starting a company does not eliminate the capital required to build an enduring one.

Closing the Series A funding gap is therefore not simply about increasing investment in Black founders. It’s about ensuring founders have enough money to reach the milestones that unlock future institutional capital. That鈥檚 how you create more Black unicorns.

For Black founders, the conversation should no longer focus solely on access to capital. It should focus on whether they have enough capital to compete. In the AI economy, the Black-led companies that endure won’t simply be those that build the fastest, they will be the ones with the resources to keep building long enough to win.

and are the co-founders of (Black Ops VC), an early-stage venture capital firm. Norman is a managing partner at Black Ops VC. He is also the CEO of , an AI-powered market research platform used by industry giants such as and that鈥檚 designed for the media and entertainment spaces to gather audience feedback on video content, and a partner at , an accelerator that provides intense programming, resources and capital to overlooked founders.

Along with serving as general partner at Black Ops VC, Green is the founder and CEO of , an AI-powered CRM and inventory management platform specifically designed for art galleries, dealers, auction houses and collectors.听

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Docter also proved adept at securing funding for research and other projects, a skill that ultimately caught the attention of venture capital firms and led him into the industry 26 years ago.

His technical roots are reflective of Palo Alto, California-based Dell Technologies Capital鈥檚 broader team. Its investors have degrees in fields including electrical engineering, computer engineering, computer science and data science, and many have worked at both large technology companies and startups.

That experience shapes the firm鈥檚 affinity for deeply technical founders and its approach to early-stage investing. When evaluating seed and Series A companies, the team focuses heavily on the potential impact of a technology: what problem it solves, what it could disrupt, and how well it works, often before traditional financial metrics become the central consideration.

Since its 2012 inception, Dell Technologies Capital has invested $1.8 billion across the enterprise stack and saw six high-profile exits at the end of 2025 alone.

In this interview with 兔子先生传媒 News, Docter also discussed how AI is reshaping SaaS and why he doesn鈥檛 believe the business model is headed for extinction. He also shared why he thinks distribution may ultimately separate the winners from the losers among AI startups, and more.

The interview has been edited for clarity and brevity.

兔子先生传媒 News: When you evaluate companies, do they all have to tie into what Dell does?

Docter: Not necessarily. I usually describe it as Dell Technologies Capital having a unique network you don鈥檛 get at any other VC firm. I鈥檓 using my words carefully because I鈥檓 not saying we鈥檙e better. I鈥檓 just saying we鈥檙e unique.

That unique network is that we have access to network and his company network, which has become even more relevant in this AI world but has always been very much in the middle of technology.

We leverage that network in two ways. One is to get another perspective on what鈥檚 going on in the world and understand technology and how it鈥檚 being used. What do Fortune 500 companies want or need? What is asking for? We have that perspective.

If you look at the other side of the coin, those are also the areas where Dell Technologies Capital can best help our portfolio companies. We have this perspective and this network that are really valuable. We can use those to the benefit of our portfolio companies, and that defines our investment philosophy.

classically said, 鈥淚nvest in what you know.鈥� The way I look at it is that we鈥檙e trying to invest in what we know because of who we are, our technical background and our unique network. But if I turn that over, that鈥檚 also where we can help. Invest in what you know, but also in what you can help with.

For founders building deep tech, there鈥檚 a fear of being on the right track, but too early. Some companies have had to wait more than a decade before they really took off. As an investor, how do you evaluate a team that is clearly building technology with incredible potential but is years ahead of the adoption curve? How do you help them survive that stretch of time?

Docter: You asked two questions in one. One is: How do you identify the founders you think can be successful? The second is: How do you keep them alive long enough to get to the finish line?

The answer to the first question hasn鈥檛 changed from how we鈥檝e always thought about it and how venture capital always thinks about it. First and foremost, you鈥檙e really betting on the people. This is a people business. I know you hear that all the time, but you really are betting on the people and the founders.

It鈥檚 not purely about the technical capability of the founders. There鈥檚 definitely an EQ part of the equation, which I think our team is really good at. Our group is good at quickly getting an opinion on a founder and whether he or she is capable. Then we usually spend additional time trying to pressure-test our initial thesis on that founder鈥檚 ability to be agile 鈥� to understand when they鈥檙e wrong and change directions or to be willing to get input from somebody else who might be way less smart than they are but has a different approach or way of thinking about the problem that opens up new avenues.

I think that鈥檚 qualitative. It鈥檚 EQ more than IQ, but a lot of times that determines success. I don鈥檛 think this AI era has changed that. That鈥檚 consistently true.

The answer to the second question is even harder. How do you know if you鈥檙e betting on a deep-tech company and you know going in that this is a five-, seven-, 10-, 15-, or 20-year problem? It鈥檚 really, really hard to sustain that company.

You have to do a bunch of things smartly. You have to make sure you don鈥檛 overspend, because overspending can really kill a startup. You also have to have really good co-investor partners.

We feel like we are part of a venture capital ecosystem, and we always strive to partner and play nicely with others. As Michael says, 鈥淧lay nice but win.鈥� We always try to play nice but win.

It takes a village for these things to work, so it鈥檚 important to have the right constituents and partners around the table who can continue to fund the company for years and years. The timeline is absolutely compressed, so I think it is getting harder for that to happen.

The classic venture playbook often considers first-mover advantage to be everything. But the 鈥渟leeping giants鈥� thesis suggests the second wave 鈥� the companies with the foundational architecture in place when a catalyst like generative AI hits 鈥� may be the ones that win. Is being a first mover still the same advantage it used to be?

Docter: I think it can cut both ways. One of the things we talk about is whether a company is doing category creation 鈥� which means it鈥檚 creating a brand-new category of business or software product that doesn鈥檛 exist today and is going to be huge 鈥� or category disruption, meaning there鈥檚 already a very large category that exists and I鈥檓 going to disrupt it with my technology. I鈥檓 doing something much better, faster, cheaper or stronger.

It鈥檚 important to have a sense of whether a company is doing category disruption or category creation. If you鈥檙e doing category creation, being first means you have to educate everybody. It鈥檚 a heavy lift. It鈥檚 a daunting amount of work, capital and effort that goes into explaining something that doesn鈥檛 currently exist and why it鈥檚 going to be needed in the future.

A lot of times, first-mover advantage isn鈥檛 an advantage there. Category creation is often where the second, third or fourth company hasn鈥檛 had to spend all the effort. They can piggyback off the heavy lifting the first mover had to do.

But in cases of category disruption, I think there鈥檚 value in first-mover advantage. You鈥檙e disrupting a big, existing, multibillion-dollar category and doing something in a new or better way. Being first there is very beneficial.

There鈥檚 a lot of talk about AI agents replacing SaaS models. Do you feel that panic is overhyped? If so, why?

Docter: AI is disruptive to the SaaS world, without a doubt. It鈥檚 disruptive because it will change how software is built and consumed. Maybe even more importantly, it鈥檚 going to change how it鈥檚 priced. The per-seat pricing model is probably outdated and going to die. It鈥檚 going to be priced based on consumption or outcomes.

Everything is disrupted, but I fundamentally don鈥檛 believe all SaaS companies are going to die because of this. I believe the SaaS companies with smart, effective management will look at what AI can do for their businesses, which most already are. They鈥檙e going to adopt it, embrace it, and transform their companies using it. The ones that do will come out the other side as successful companies. They鈥檙e not going to go away.

How they charge and price might be different, but they鈥檙e still going to be the category winner or category leader. Remember that they have some fundamental advantages they can leverage.

One is brand. When I say a big SaaS name, you and I both know it. Pretty much everybody knows 1, and .

They can leverage their brands.

They also have incumbency, meaning they currently have the business. They have customers they鈥檝e sold to for years and years and have long-standing relationships with. If 鈥� and it鈥檚 a big if 鈥� they understand how to embrace the AI transformation that鈥檚 going on and leverage it, there can and will be winners.

There will be winners for sure, or people who come out okay. Without a doubt, there will also be SaaS companies that don鈥檛 make the turn. But is that any different from any other technological or industrial revolution? It鈥檚 always the case that there are a few with good leadership and management who are nimble and agile, even at scale, and they are successful. Others aren鈥檛.

As early-stage founders shift from pay-per-user to pay-per-outcome or other new models, how should they think about their go-to-market strategies and still seem attractive to investors?

Docter: One of the biggest questions we ask early-stage AI founders is: 鈥淲hat is your distribution strategy?鈥� That basically means: How are you going to go to market or get distribution for your product?

Today, that is a harder problem. In terms of differentiating yourself as a startup, I would say its importance has grown.

There will be many people with very good or disruptive technology. The winners are almost certainly going to be the people who figure out distribution first, best or fastest.

If I tie that back to the SaaS question, it鈥檚 clear that some SaaS companies won’t be able to transform themselves organically. They鈥檙e going to need to undergo an inorganic transformation, meaning they鈥檒l have to buy or acquire something that can help their company transform.

If you think about what I just said about early-stage AI startup founders, they need distribution. How do you get distribution? By partnering with an incumbent that has a brand in the space you鈥檙e trying to sell into, sell adjacent to or disrupt.

I think there is a recipe here for SaaS companies to be in acquisition mode for the next six, 12, 18, or 24 months to help transform their companies and make the curve. The incumbent can acquire technology that would take too long to build, and the startup gets distribution that would be much harder for it to build.

Dell Technologies Capital had incredible exit momentum late last year 鈥� including massive liquidity events like , and 鈥� right in the middle of a broader venture liquidity drought. What did you see in those specific businesses or the macro environment that allowed DTC to return capital so effectively when everyone else was stuck?

Docter: I鈥檇 love to say we saw it all coming, but the reality is we can鈥檛 time the market. It just doesn鈥檛 work that way. But we feel lucky that things are lining up the way they have. Netskope, Rivos, SingleStore, and recently, and .

We just try to stay really focused on backing great founders with deeply technical ideas. We鈥檙e investing early and know that sometimes it can take years for the market to fully catch up to what鈥檚 being built. You can see that pretty clearly across the outcomes you asked about. Netskope and SingleStore were at it for more than a decade, building products and businesses until the market met them.

Rivos was a little different. The founders had a strong point of view that a shift in computing was coming fast as AI workloads started to put real pressure on data center infrastructure. They were right and got to a significant exit in just under five years.

We really try not to over-rotate on timing and instead stay consistent in who we back and how we invest.

You鈥檝e talked about looking at startup traction to see whether revenue comes from an “innovation pilot budget” or a “core engineering production budget.” For a startup trying to raise its Series A or B right now, what evidence do they need to show you to prove their AI revenue is sticky and not just experimental hype?

Docter: The biggest question we are asking ourselves today when we talk about making any Series A or B investment is 鈥淚s their revenue durable?鈥� Everyone knows about the complete shift away from the SaaS seat-pricing model.

But what we鈥檙e also seeing is a huge shift away from recurring revenue to something I鈥檓 calling聽 鈥渞e-occuring鈥� revenue. I know that鈥檚 not really a word. What I mean by 鈥渞e-occuring鈥� is that, instead of showing multiyear contracts, a lot of revenue is uncontracted, meaning customers are not signing up for annual or multiyear deals. But they are signing up for projects, sometimes very large projects.

My suggestion to startups looking to raise substantial rounds is to show how customers engage and keep coming back for more. The ability to say 鈥渨e got our first deal with in October, and they did a second deal with us in January, and we already did our third deal in March鈥� is very powerful.

Given DTC鈥檚 unique position, how do you advise founders to leverage a corporate venture capital relationship differently than a traditional institutional VC, especially when navigating a rapidly shifting market like this one?

Docter: The answer really is that the investor type is irrelevant. The one thing founders should universally do with every investor on their cap table is ask for more help. 鈥淵ou don鈥檛 get what you don鈥檛 ask for.鈥� I know that鈥檚 an old saying, but it absolutely holds true.

So many founders, especially first-time founders, are reticent about asking for help or advice. Don鈥檛 be. Play to your investors’ strengths and ask them for the help they can deliver. Whether it鈥檚 management advice, introductions to decision makers at Fortune 500 companies, or access to channel sales. Ask!

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But then you ask the only question that really matters: 鈥淲hy are the numbers improving?鈥�

That is where the actual strategic discussion begins.

Was growth improving because the company found a repeatable sales motion, or because it offered large discounts? Was retention strong because the product became deeply embedded in customer workflows, or because renewals had not yet come under pressure? Was gross margin structurally strong, or were infrastructure costs simply being pushed into the future?

Metrics and KPIs are useful. They give us a snapshot of the business. But they do not shine a light on strategy. They are the result of strategy 鈥� or sometimes the result of a lack of it.

Here are three areas where founders and boards should look deeper into unit economics and the strategies behind them.

LTV/CAC: Look at the quality of acquisition

LTV/CAC is one of the most important SaaS metrics. A strong ratio usually suggests the company can acquire customers efficiently and retain them profitably. But two companies can both report a 4x LTV/CAC ratio and still be very different businesses.

One may reach that ratio because it has strong positioning, low acquisition costs through partner programs, viral marketing, high retention through workflow integrations, and expansion revenue from additional products or services. Another may reach the same reported ratio because it charges higher upfront prices, assumes a longer customer lifetime, or has not yet seen churn show up in the data. On paper, both look efficient. In practice, one may have a healthy acquisition engine while the other may be relying on assumptions that still need to be proven.

When reviewing LTV/CAC, boards should ask:

• Is the company clearly positioned?
• Is it focused on the right customer segment?
• Are customers coming from scalable channels or expensive paid acquisition?
• Is pricing strong enough to justify the sales effort?
• Do we have cross-sell and upsell opportunities baked into the offering?
• Is the payback period reasonable?

A weak LTV/CAC ratio is not always a sales problem. Sometimes it is a positioning problem, a pricing problem or a market-selection problem.

GRR and NRR: Understand why customers stay

GRR and NRR are critical because they show whether customer revenue stays and expands. But they do not explain why customers stay or expand. Strong dollar retention usually comes from becoming embedded in the customer鈥檚 workflow.

The product delivers fast time-to-value, integrates with important systems, becomes part of a daily process, and becomes difficult to replace.

That is when expansion becomes easier. More seats, more usage, more modules, more geographies, more products. This is why setting a board goal to 鈥渋ncrease NRR鈥� is not enough. The real discussion should be around onboarding, integrations, product depth, customer success, pricing tiers and expansion paths.

Dollar retention improves when the product becomes more valuable, more embedded and more scalable within each customer.

Rule of 40 and Rule of 4: Check the quality of growth

ARR growth matters, but the board should ask what kind of growth it is. The Rule of 40 shows whether the company is balancing growth and profitability.

But a better number can come from real efficiency, or from cutting too deeply into product, customer success and future growth. The Rule of 4 adds a simple durability check: ARR growth divided by annual customer churn should be above four. If it is low, growth may be hiding a leaking bucket.

So the board should ask two questions:

Are we becoming more efficient, or simply underinvesting?

Are we growing on top of a loyal customer base, or replacing customers we should have kept?

Let鈥檚 use these metrics to dive deeper into the core long-term strategy.

is a strategic adviser to tech companies, investors, CEOs and boards, specializing in strategy, growth and M&A. He is a guest contributor to 兔子先生传媒 News and a university lecturer on strategy, finance and entrepreneurship. Learn more at and connect with him on .

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This is a weekly feature that runs down the week鈥檚 top 10 announced funding rounds in the U.S. Check out last week鈥檚 biggest funding deal roundup here.

U.S. startups announced sizable funding rounds at a steady clip during a truncated holiday week, with energy and AI leading the way.

Houston-based energy startup secured the biggest round, a $1.75 billion strategic financing, followed by , a developer of infrastructure for companies running open source AI models, and , a provider of compliance tools for enterprises.

Other big rounds were for companies focused on therapeutics, homebuilding, and even lacrosse.

1. , $1.75B, energy: Houston-based Joulent, a provider of energy infrastructure focused on the demands of artificial intelligence and other compute-intensive industries, raised $1.75 billion in a strategic investment backed by through its arm.

2. , $800M, AI infrastructure: Together AI, developer of an infrastructure layer for companies running open source AI models, secured $800 million in Series C financing. led the round, which set an $8.3 billion post-money valuation for the San Francisco-based startup.

3. , $180M, compliance: LeapXpert, a provider of tools for tracking enterprise communications for compliance needs, closed on $180 million in growth financing. led the financing for the New York-based company.

4. , $135M, AI software development: Redwood City, California-based 8090 Solutions, developer of a platform for building enterprise software with coordinated AI agents under human-led oversight,聽 picked up $135 million in a round led by 1. The company, founded in 2024, counts prominent startup investor as co-founder and CEO.

5. , $126M, biotech: Boston-based Beeline Medicines, a startup focused on precision therapies for autoimmune and inflammatory diseases, secured $126 million in Series A extension funding backed by , and . The financing follows a previously disclosed $300 million Series A.

6. (tied) , $100 million, professional sports: The Premier Lacrosse League, a men’s professional lacrosse league in North America, closed a $100 million Series E financing round led by and . New York-based PLL said the deal represents the largest capital raise in the history of professional lacrosse.

6. (tied) , $100M, video-based AI: Twelve Labs, a San Francisco-based startup developing AI systems trained on video archives, raised $100 million in a Series B round co-led by and .

8. , $95M, AI for homebuilding: Higharc, a developer of AI-enabled tools for designing homes and managing workflows around homebuilding, picked up $95 million in Series C funding. led the financing for the Durham, North Carolina-based company.

9. , $85M, biotech: Cambridge, Massachusetts-based Flare Therapeutics, a startup targeting transcription factors to develop treatments for cancer and other ailments, raised $85 million in Series C funding led by and .

10. , $65M, AI privacy: Venice, developer of a platform enabling private, surveillance-free access to a wide array of AI models, secured $65 million in Series A funding led by . The round set a $1 billion valuation for the 2-year-old Sheridan, Wyoming-based startup.

Methodology

We tracked the largest announced rounds in the 兔子先生传媒 database that were raised by U.S.-based companies for the period of June 27-July 2. Although most announced rounds are represented in the database, there could be a small time lag as some rounds are reported late in the week.

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This is a weekly feature that runs down the week鈥檚 top 10 announced funding rounds in the U.S. Check out last week鈥檚 biggest funding deal roundup here.

This week, most of the largest U.S. startup funding rounds centered around the sector one would suspect: artificial intelligence. This was true for the week鈥檚 largest venture financing, a $1.5 billion Series F for AI inference technology provider , as well as a majority of rounds in the Top 10. Beyond that, the next-biggest area for startup funding was biotech.

1. , $1.5B, AI inference technology: Baseten, a provider of systems software to run AI applications workloads, raised $1.5 billion in Series F funding, its fourth fundraise in 18 months. , , , and co-led the round, which set a $13 billion valuation for the San Francisco-based company.

2. , $1B, digital marketing: AppsFlyer, a San Francisco-based provider of data analytics with digital marketing as a core use case, reportedly secured more than $1 billion in a Series E funding round at a post-money valuation of $2.7 billion. Backers reportedly include , , and .

3. , $650M, AI inference technology: San Francisco-based Groq closed on $650 million in new funding led by and that it says will be used to scale its AI inference cloud technology and infrastructure. The investment comes just over six months after an acquihire-type transaction in which hired away its founder and key team members and licensed its technology.

4. , $330M, ophthalmic therapies: Ollin Biosciences, a developer of therapies for vision-threatening diseases, picked up $330 million in Series B funding. and led the financing for the Austin-based company.

5. , $320M, foundational AI: General Intuition, developer of a foundational AI model based on gameplay, secured $320 million in Series A funding at a $2.3 billion valuation. led the financing for the New York-based company, while backers including and participated.

6. , $250M, government software: Peregrine Technologies, provider of a platform used by public safety agencies and other government entities, secured $250 million in Series D financing. , , , , and led the financing, which set a $6.8 billion valuation for the San Francisco-based company.

7. (tied) , $200M, risk intelligence: Palo Alto, California-based Quantifind, developer of a risk intelligence platform for financial crime detection and national security operations, closed on $200 million in growth financing led by .

7. (tied) , $200M, foundational AI: San Francisco-based Mirendil, a frontier lab building systems that excel at AI R&D, says it raised a seed round of $200 million led by and . The startup also counts as a backer.

9. (tied) , $190M, AI infrastructure: AI networking infrastructure startup Upscale AI raised $190 million in Series A extension funding, bringing total financing to $500 million. led the round, which set a $2 billion valuation for the Santa Clara, California-based company.

9. (tied) , $190M, biotech: San Francisco-based Osanni Bio, a therapeutics platform focused on ophthalmic therapies and other treatments, secured $190 million in Series B funding led by .

Large non-US deals:

The week also brought some large European rounds:

, $569M, defense tech: Berlin-based defense tech startup Stark reportedly raised $569 million in a financing led by and .

, $546M, insurance: Paris-based health insurance startup Alan secured $460 million in new investment in primary and secondary equity led by .

Methodology

We tracked the largest announced rounds in the 兔子先生传媒 database that were raised by U.S.-based companies for the period of June 18-26. Although most announced rounds are represented in the database, there could be a small time lag as some rounds are reported late in the week.

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It used to be that if you invested in SaaS, you slept well at night. Returns were predictable because the business model was subscription-based and incredibly scalable: build a horizontal cloud-based platform to target as wide a market as possible, charge per seat and grow by expanding the user base.

1, and their peers returned billions to investors on that model. But now, due to AI, where AI agents are replacing humans as the user (through what the industry calls 鈥渉eadless鈥� models) and upending the per-seat model, the SaaS market has lost its predictability. January’s $300 billion single-session wipeout is a leading indicator that the old SaaS model has passed its peak.

Investors are retrenching and trying to predict what鈥檚 next as the three frontier AI companies vault into the public markets at multitrillion-dollar valuations. We would argue that these infrastructure platforms enable the next wave of software innovation: AI-native software that automates and enables the $2 trillion white-collar services market.

Generic, horizontal SaaS, as we know it, is a declining legacy model (like on-premise software before it), but investors still have reason to be optimistic about the software market. That鈥檚 because AI-native software is going after a much larger opportunity than SaaS ever claimed and the productivity gains and value creation opportunities are unprecedented. The target markets are vertical industry focused and highly specialized, priced differently and built on proprietary data moats that didn’t exist five years ago.

Death of per-seat pricing

SaaS has always been priced on a per-seat basis. That model evaporates the moment AI agents generate most of the usage. A company that once needed 100 CRM licenses for its sales operations team may soon need just 50.

Technology companies facing that reality have to choose a new path forward beyond connecting people鈥檚 workflow: perform and charge for the actual work done (usage) or based on outcomes (ROI). A legal AI platform charges per contract drafted, doing the work of a lawyer. Here the software charges for some fraction of the labor it replaces. A spend management AI-native software application can take a percentage of overages found or a chargeback software application could take a fee on the value of the chargebacks it successfully recovers.

The next era of AI-native software runs on automation and performing knowledge-worker actions, not connecting workers or workflows. These solutions reach beyond IT budgets to much larger labor budgets. The companies that adapt will build faster, deliver more value and command a premium for it.

Horizontal is a liability

Generic horizontal SaaS is the most vulnerable to this changing market. If an entire product is a wrapper around a workflow that an AI agent can now handle autonomously, the value proposition may be greatly reduced. Form builders, project management platforms, SMB-focused CRMs, off-the-shelf social schedulers: these categories are compressing fast and may not recover.

The defensible positions now belong to vertical niche specialists, companies that have built what we call the three 鈥淒s.鈥� Distribution through a recurring and longstanding customer base.

Domain expertise specialized to operate in regulated or complex industries. Proprietary data that drives decision-making and is closely held by customers and inaccessible to frontier models.

When your product is built around the specific workflows, terminology and compliance requirements of one industry, ending a vendor relationship is less about migrating data and more about rebuilding a complex web of experiences, corner cases and historical knowledge. Customers stay not because they’re trapped, but because the cost of retraining, reconfiguring and finding a vendor who understands their world is too high.

The more deeply a company understands the regulatory environment, the operational constraints, and the institutional logic of a specific industry and a specific customer, the harder it becomes to displace.

Legal contract repositories, insurance underwriting criteria, bank loan performance data; once embedded in a model and a workflow, these assets create high switching costs that dwarf anything a generic SaaS contract ever produced. You can export a Salesforce contact list. You cannot export your underwriting logic.

People are part of the product

The model that will define the next decade of B2B software deliberately combines software and services, what practitioners call Human-in-the-Loop, or HITL: pairing agentic intelligence with human judgment at the points in a workflow where it matters most.

Legal, healthcare, cybersecurity, construction, financial services, defense; these verticals are defined by high stakes, regulatory complexity and contextual judgment. Routine and repetitive tasks may be mostly automated, but some portion of decisions will always require human judgement because the cost of errors or omissions is prohibitive.

This solutions-centric customer relationship changes what a software company fundamentally is. When a vendor is embedded in how a client operates, handling onboarding, workflow design, optimization and quality control, it accumulates something pure SaaS rarely achieved: proprietary data, domain expertise and institutional trust. Every client engagement makes the product smarter and each deployment deepens the moat.

This is why the most durable software businesses of the next decade will be built inside verticals, not across them. The companies that understand this will stop treating services as a cost of implementation and start treating them as a compounding asset.

A bigger market than SaaS ever was

Even capturing a small fraction of what projects is a $6 trillion annual productivity opportunity from AI transformation dwarfs the traditional enterprise software market. AI-native vertical platforms no longer just compete for the technology budget, they also compete for the labor budget, the compliance budget and the risk budget. That’s a much bigger pie and a more strategic partnership conversation than any per-seat SaaS vendor ever got to have.

The winners won’t be companies that bolt AI onto existing SaaS products, or that add a services layer as an afterthought. They will be the firms with true subject matter expertise that happen to run on AI-native software. They will collapse the boundary between software and services entirely, building businesses whose value compounds with every customer relationship and every data asset they accumulate.

The AI-native software company is a fundamentally different kind of company than the SaaS era ever produced. And it’s worth considerably more.

is the founder, managing partner and chair of the investment committee at . He launched Lateral with a strategy to allocate first institutional growth capital to independent, owner-operated middle-market businesses underserved by typical buyout firms. Previously, he served as a managing director at , a venture capital and growth equity firm that has invested in more than 300 companies including , , , , and . De Silva also previously co-founded , a marketplace for construction equipment that was sold to for nearly $800 million. He received an MBA from , a master of philosophy from the , and an undergraduate degree from .

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