To understand how institutional capital is navigating this gap, 蹤獲弝け News recently interviewed , a partner at . Ranum has spent nearly 15 years at the firm, where he focuses on B2B enterprise software, security and industrial infrastructure. Prior to joining Sapphire, he spent 12 years as a product management and strategy executive at .

His recent investments include core infrastructure plays such as and , as well as the industrial AI platform .

In this e-mail interview, Ranum breaks down how the definition of net revenue retention is shifting, why he believes 2026 will see a historic run of major tech IPOs, and where real enterprise demand is materializing on the factory floor.

This interview has been edited for clarity and brevity.

蹤獲弝け News: You���ve been at Sapphire for 15 years. Right now, public market software multiples are at decade lows as Wall Street worries about AI disruption, while private AI valuations are hitting record highs. As a growth investor caught in the middle, how are you valuing companies today? Are traditional growth metrics like net revenue retention still the gold standard, or has the math completely changed?泭

Ranum: The gap between public and private market signals right now is unlike anything I’ve seen. I think it creates a real opportunity for investors who can make sense of it. Public software multiples have come down hard, while private AI valuations are hitting record highs. Those two things can’t both be right indefinitely, but the fundamentals underneath are holding up. Gross margins, free cash flow, and NDR have actually improved. The market is broadly pricing in disruption risk, but the companies that are genuinely building enterprise value are still being built.

What that means for how I evaluate companies is that I’m spending more time on whether something is genuinely embedded in how enterprises work, not just whether the numbers look good today. NRR still matters. It tells you whether customers are finding real value. But it’s a lagging indicator. What tells me more is whether switching away from a product would meaningfully disrupt operations. If the answer is yes, that’s a more durable signal than any retention metric.

The current regulatory environment has essentially frozen large-scale tech M&A, and the IPO market is sluggish. If the traditional exit pathways are bottlenecked, how does that change the way you underwrite a Series B or C bet? Do companies just have to stay private and build to massive scale longer than they used to?泭

Ranum: I���d push back a bit on the framing that M&A is frozen. Software M&A activity actually picked up meaningfully in 2025, with deal value rising 40% year over year to $334 billion across 678 transactions. We saw that in our own portfolio with over half a dozen acquisitions in the past six months. What���s changed is the pricing. The valuations are being reset, but the deals are getting done.

On IPOs, I believe 2026 is shaping up to be a historic year, with having gone public, having filed, and reportedly set to file soon. If they follow through, we’re looking at some of the largest IPOs ever over the next several months. That’s a remarkable moment. Below that tier, though, the picture is more nuanced. Companies that meet today’s higher bar will wait for more favorable conditions, likely into 2027 or beyond. That means you have to build accordingly, focusing on margin alongside revenue, so you have real optionality when the time comes. The secondary market also helps, giving companies and their investors more flexibility as they wait.

You used to love investing in what you called ���boring software,��� or tools that quietly automated mundane enterprise tasks. Today, every software company claims to be an AI company. In 2026, does traditional SaaS even exist as a viable investment category anymore, or is a software startup inherently unbackable if it isn���t AI-native from day one?

Ranum: I don���t think the narrative is AI vs. SaaS. Instead, it’s AI plus SaaS. The companies that are struggling aren’t struggling because they’re SaaS businesses. They’re struggling because investors are in a ���show me��� era, and they don’t have clear answers yet.

Show me the free cash flow. Show me the path to profitability. Show me how AI is actually helping you win. You can’t get a stock bump anymore just by claiming you’re integrating AI. The market wants evidence of monetization.

The way I think about it is whether a company is building something that fundamentally changes how work gets done, or just layering AI on top of a workflow that a human is still doing. We used to back systems of record and workflow companies where the human was doing all the work. Now we’re in a position where the system itself can come in and actually do some of those tasks. That’s a different category of value entirely, and it changes what we look for. The bar has moved, but the opportunity is very real for the companies that can clear it.

Your core thesis is that the LLM stack is fracturing into distinct, standalone billion-dollar layers, such as orchestration (LangChain) and identity (WorkOS). But we���re seeing a massive border war. Big model providers like OpenAI are building their own tools, and data giants like are buying up security tools. How do standalone startups protect their turf when giants encroach from both sides?

Ranum: Both fracturing and consolidation are happening simultaneously, and I think that’s actually the right way to think about it. The moat isn’t about being first in a category. It’s about becoming genuinely embedded in how enterprises work. The companies I’m most excited about are the ones capturing orchestrated workflows in which the enterprise’s actual processes run through the product. That makes them very hard to displace, regardless of what the giants are building around them.

Because of your background at SAP, you know how enterprise buyers think. Right now, CFOs are looking at massive AI pilot bills and demanding to see actual ROI. When a startup is pitching an enterprise on a software governance or security tool, how do they defend that line item to a cynical CFO before the enterprise has even fully figured out its core AI strategy?泭

Ranum: What we consistently hear from buyers is that trust has become what actually separates the market. Security, governance, compliance, and auditability aren’t nice-to-haves anymore. They’re what make an AI deployment defensible when the CFO or the board asks hard questions.

And cost predictability is right alongside that. We’re in an era of greater focus on ROI, and enterprises want to know what this will cost them at scale before they commit. The vendors that can answer that question clearly are winning deals over the ones that can’t.

It feels like Silicon Valley is obsessed with the glamour of humanoid robots right now. Meanwhile, Sapphire���s big bets in this space, like Tractian, focus on practical, unglamorous industrial AI and predictive maintenance. Are humanoid robots an expensive venture capital distraction right now? Where is the actual, contract-signing enterprise demand on the factory floor today?泭

Ranum: The near-term ROI story is in constrained, high-value industrial settings such as packing, picking, inspection, and maintenance. These environments have clear labor economics, manageable deployment risk, and real buying cycles. That’s where the contracts are getting signed today.

Our portfolio company Tractian is a good example of what that looks like in practice. Unplanned downtime costs the world’s 500 largest companies roughly 11% of their revenue annually, which is a massive, measurable problem.

Tractian addresses it directly by combining sensor hardware with AI that detects early warning signs of equipment failure. The value proposition is concrete before you sign the contract, and the platform gets smarter the longer you use it. That’s the kind of embedded, compounding value we look for.

The humanoid era will come, but the gradient approach beats the all-or-nothing bet for near-term value creation. Start with specific, well-defined tasks where the payoff is obvious and work from there. The market is ready for that today.

Heavy industry and manufacturing are notoriously slow to change. A startup can’t just plug a modern AI API into a 30-year-old machine on a factory floor. For founders trying to build in the industrial tech space, is the winning strategy to build entirely new autonomous hardware, or is the bigger venture opportunity in retrofitting the world’s existing infrastructure with smart software?泭

Ranum: I believe the winning strategy is smart software layered on top of existing infrastructure rather than replacing it. Factories aren’t going to rip out 30-year-old machines because a startup has a better alternative. That’s just not how it works. The opportunity is in making those machines intelligent.

That said, the hardware-plus-software combination really does matter. You can’t get the data without the sensors. But the durable value is in the software layer that keeps learning over time. That’s where I���m focused.

In pure software, a buggy AI agent might mean a broken spreadsheet or a weird email draft ��� annoying, but fixable. In robotics and industrial tech, a mistake means a factory line shutting down or a broken multimillion-dollar asset. From a venture perspective, how much harder is it to scale a robotics startup when the cost of product failure is so high in the physical world?泭

Ranum: I’d actually reframe the question. The cost of failure in physical environments is what makes the value proposition defensible. When the downside of getting it wrong is measurable, the upside of getting it right is equally concrete. You can walk into a sales conversation and show a customer exactly what prevention is worth before they sign anything. That’s a different conversation than selling software, where ROI takes quarters to show up.

From a scaling perspective, the key is discipline about where you deploy first.

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Smith spent eight years on active duty, serving as a technical product manager building satellites and software for national defense for the and . When he returned home from a seven-month deployment, Smith looked at the data surrounding community college transfers to four-year universities and realized how low the success rates were.

���You have a higher chance of success [of attaining a bachelor���s degree] by pursuing a military academy ��� than if you go to any community college ���,��� Smith said in an interview with 蹤獲弝け News. “That didn’t really make sense to me.���

He then entered graduate school and launched in 2019 while working on his MBA泭at .

Now, the Los Angeles-based startup tells 蹤獲弝け News exclusively that it has secured a $13.3 million Series A funding round to scale its AI-native platform, which automates the manual back-office workflows that can slow down university admissions.

led the financing, which included participation from , , , , , , and others.

The new capital brings EdVisorly���s total funding to about $22 million and marks a significant valuation step-up from its previous tranches, according to CEO Smith.

The startup���s funding comes amid an overall downturn in investment in the sector. Venture funding to education-related companies has in recent years come in at a fraction of the sums such startups raised during the pandemic peak, when investment topped out at nearly $20 billion in 2021. Through the first half of 2026, however, edtech and education-related startups have raised just under $1.8 billion globally, . That���s below the $2.5 billion raised in the first half of last year, but a notch higher than the $1.4 billion raised in the second half of 2025.

Automating the back office

EdVisorly aims to take the slow, manual paperwork out of the college admissions and transfer process.

The software doesn’t decide which students get accepted and does not serve as a gatekeeper, Smith emphasized. Instead, he said, it is designed to handle the tedious, behind-the-scenes administrative tasks that bog down university staff.

The driver behind EdVisorly���s recent growth is its proprietary platform, EddyAI. The tool works to automate repetitive back-office workflows in the admissions and enrollment process, including tasks such as reading student transcripts and recalculating GPAs based on a university���s specific criteria.

“We automate a lot of the backend processes,��� Smith said.

For the 10.5 million community college students in the U.S. trying to transfer to a four-year university, the process is usually a total guessing game. EdVisorly aims to bridge that gap.

Applicants are able to upload their transcripts into its platform to run an unofficial credit evaluation. The app automatically reads their classes and matches them against university requirements. Even before they ever speak to an admissions counselor, families can learn exactly how their credits stack up, what a degree will cost, and how many semesters a student would have remaining.

On the university side, registrars use the same technology to process official transfer credits and quickly build new credit-matching rules, bypassing a process that historically required a human to review every course.

“The technology actually reads the transcript, it takes that data from the transcript, and it compares it to the equivalencies that the school has,” Smith noted. “There’s kind of an infinite number of ��� transferable credits and courses that could exist across the United States.���

The startup���s next iteration will focus on organizing all of that data, ���so that there’s no mystery as to whether a student’s credits will transfer,��� Smith said.

Both sides of the market

EdVisorly counts more than 100 colleges, universities and higher education systems as customers. Its roster includes institutions such as the , the , and . It has helped over 250,000 students since its inception, per the company.

The startup sells directly to higher education institutions via a B2B subscription model. Smith uses a management framework from his military days to run deployments: People come first, clear policies come second, and technology sits at the bottom as a tool to support them.

“We believe in not the concept of replacement, but truly repurposed,” Smith said. “Technology can best be implemented when you have people who are willing to adopt, and they’re innovative, and they’re excited.”

Its Series A funding will go toward upgrading the platform’s core engineering infrastructure and adding more UX designers to polish the student-facing app. Currently, the company has nearly 50 employees.

, managing partner and founder at Breachway Capital, said he is most excited about EdVisorly���s ���breadth of impact.���

���This is not a solution that optimizes for one side of the market at the expense of another,��� he wrote via email. ���It drives real efficiency and tangible value for institutions while delivering a meaningfully better experience for students navigating one of the most important decisions of their lives. That is a truly unique value proposition.���

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Created in partnership with the 蹤獲弝け angel fund , the initiative gives Omnea employees who have completed five years of service a chance to pitch for $250,000 in seed funding to launch their own companies.

The initiative doesn’t just aim to discourage employees from hiding their entrepreneurial ambitions from leadership but actively supports them.

“Starting a business, you don���t want to speak to investors who you don���t know. You want to speak to people you know and trust, who want you to succeed and know what they���re talking about,” said , founder and CEO of Omnea, in an exclusive interview with 蹤獲弝け News explaining why the company decided to start the initiative. “And ideally, you want to get the advice of your colleagues. But it���s always taboo ��� telling your colleagues you want to go start something and quit your job. I don���t think it needs to be.”

Eligible employees present their concepts in a single 30-minute pitch meeting to Freeman and Firedrop founder , with final investment decisions delivered within 24 hours. Alongside capital, accepted founders receive dedicated office space, operational support and ongoing coaching from Omnea���s executive team.

How the funding works

To keep the process simple for first-time business owners, the fund avoids strict, rigid formulas. Omnea has set a rough guidance benchmark of $250,000 against a $10 million valuation ��� which would convert to a 2.5% equity stake ��� giving new founders a sensible baseline so they aren’t left guessing about early-stage pricing.

However, the program is built to be highly flexible. Founders can instead opt for an uncapped, discountless Simple Agreement For Future Equity () note. Under this structure, Omnea provides $250,000 upfront with a valuation to be determined, leaving the final equity percentage open until the startup raises its next major round of capital.

���The only reason I’ve set guidance is so people know roughly where to start,��� Freeman said. “For 2.5%, we’re not causing dilution issues, and then the rest is up to them.”

The initial $250,000 is intended as a ���first check��� or seed funding. It provides just enough runway for teams to build an initial product and establish a personal salary, removing the financial fear of how to pay bills the day they stop working at Omnea.

“The fund is set up to be able to write lots of checks, and they don���t all need to land,��� Freeman notes. ���I suspect Omnean founders will raise big seed rounds of a few million dollars. So the initial $250,000 is just to get them on the journey there.���

Eliminating the ���side hustle��� friction

The structural flexibility mirrors the program’s primary cultural goal: removing the awkwardness of the hidden corporate side hustle. Traditionally, employees with entrepreneurial ambitions secretly grind on side projects, creating an environment that is healthy for neither their current job nor their future business.

The Future Founders Fund replaces that secrecy with transparency, allowing employees to openly discuss their ideas with leadership, set a clean transition timeline, and map out their launch.

���Somebody wants to start a business, but they can’t tell their employer or their team, so they’re awkwardly trying to have side hustles, and it’s not good for their business or their job,��� Freeman said. ���That’s not a good outcome, and this will solve that, because they can talk about it, they can have a timeline, they can plan.���

Sourcing capital from a network of elite operators

Rather than drawing on traditional institutional capital, the fund is fueled by a specialized pool of more than 150 angel investors, tech founders and executives who have backed the project individually.

The advisory and investor network features prominent global tech executives, including former COO , former COO , CEO , and CTO .

���These people aren���t doing it for money ��� they���ve made their money. Many of them are billionaires already,” Freeman told 蹤獲弝け News. “They do it because they enjoy it and want to give back and help the younger generation.”

Omnea chose to partner with Firedrop to ensure the fund received dedicated, professional management, leveraging Invernizzi���s existing network and infrastructure designed to support founders at the earliest stages of ideation, before business concepts are even fully formed.

While no employees have formally entered the program yet ��� as the 4.5-year-old startup approaches its first cohort of five-year veterans ��� four employees have already signaled their intent to leverage the program to launch future ventures. Two of these individuals have run businesses before, while two are first-time founders. According to Freeman, ���based on their profiles, they���d have no difficulty raising money anyway.���

An internal ecosystem of ���future founders���

The fund serves as an aggressive recruitment and talent-density strategy, signaling that the company takes its team’s long-term career arcs seriously.

���Personally, if I thought that I wanted to be a founder in the future, I would want to join a company that shows it is going to support me as a founder,��� Freeman said. “Showing that we will invest time, energy and money is a pretty strong signal that we���re serious about our people.泭 I think it also shows that we take a long-term view on things.”

Currently, roughly 15% of Omnea���s 200-person workforce across London and New York consists of former founders, including executives who previously built venture-backed startups like , and . The company’s rigorous talent screening process historically involved interviewing over 10,000 applicants to secure its first 50 hires. Freeman believes it doesn’t make sense to wait until the company grows to 1,000 employees to launch this initiative, as early-stage environments are precisely where great founders are built.

By openly incentivizing employees to eventually leave and build their own enterprises, Omnea is explicitly optimizing for high-autonomy, founder-type personalities.

���Future founders work harder, care more and think outside of the box,��� Freeman said. ���I think these founder-type folk have the mindset that they will do whatever is needed to get to a successful outcome. Normal people may quit when things get tough; founder-type people lean in. They are energized by solving hard problems. Many of them actually like chaos.”

This mentality manifests in Omneans catching flights on short notice to assist clients with key meetings and building deep, authentic relationships that cause stakeholders to maintain ties with the team even after leaving their respective companies. Internal operations are structured to mirror this entrepreneurial friction.

The organization maintains a flat meritocracy in which product managers pitch roadmaps to cross-functional internal teams, engineers set their own deadlines based on direct commercial context, and go-to-market teams operate as localized chief executives.

From the Tessian blueprint to the McKinsey Model

The inspiration for the program stems directly from Freeman’s personal experience as part of the founding team at email security company . When he left Tessian to go out alone, he found the transition significantly more complex and isolating than necessary. While Tessian���s founding team supported him as angel investors, the lack of formal structure meant he had to figure out the mechanics of quitting, fundraising and building pitch decks in an unstructured environment.

���The same was true for , co-founder of . And (), (), and (). All of these founders came out of Tessian,��� Freeman pointed out. ���We should have made it easier for people to found their own things. That���s why I���m doing it at Omnea.���

Freeman is entirely unconcerned about losing top talent to this pipeline, noting that if an individual is committed to entrepreneurship, they will inevitably leave anyway. The fund simply captures and backs that drive rather than fight it.

���If people are going to build a business, they���re going to build a business. My setting up this fund isn���t pushing them out by any stretch,��� he said. ���In fact, if you���ve done five years at Omnea, the reality is that you���re highly paid and have lots of equity.”

Ultimately, Freeman emphasizes that this initiative is absolutely not philanthropy, but rather a strategy designed to deliver exceptional financial returns by backing elite operators. He points to as an architectural parallel, noting how it invests heavily in its thriving alumni network.

“McKinsey has a similar view with their alumni. They invest heavily in them, and people are part of that McKinsey network for life,��� he said. ���They have some business objectives there, but actually, a more buoyant entrepreneurial ecosystem helps everyone. I���d be so proud if Omnea can fuel that.���

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First, San Diego-based announced its of a Palo Alto, California-based software startup focused on making it easier for developers to run AI models across different types of computer chips.

At the same time, reports emerged that chip startup is finalizing an $800 million funding round led by , valuing the company at $10 billion. Together, the two deals underscore a growing reality in tech: As hardware remains scarce and expensive, the software layers that connect these chips are becoming just as valuable as the silicon itself.

Watching these shifts unfold firsthand is , a managing partner at who led early investments and holds board seats at both Modular and SambaNova.

Munichiello brings a pragmatic operational background to tech investing, having served as a captain and paratrooper in the U.S. military before transitioning to the private sector. He later worked as an early executive at , helping scale the warehouse automation company through its $775 million acquisition by .

With a background in mathematics and computer science from and an MBA from , Munichiello has spent his venture career focused on core software infrastructure, developer tools and data systems, including early backing of companies such as , and .

In this interview, he discusses the mechanics behind the Qualcomm-Modular deal, the practical realities of managing hardware scarcity, and what the current wave of consolidation means for the future of independent startups.

This interview has been edited for clarity and brevity.

蹤獲弝け News: The acquisition of Modular by Qualcomm highlights a massive push to decouple AI software from hardware fragmentation. Does this signal that the ultimate value in the AI stack is permanently shifting away from proprietary hardware architectures and toward developer-friendly software layers that can run across any compute environment?

Munichiello: The types of hardware required for AI in the future are becoming heterogeneous. Originally, it looked like it was just GPUs from , and then also GPUs from and other players. But now, the direction hardware is going is toward “disaggregated inference,” which basically means splitting apart the different compute used for different parts of answering a question when engaging with a model.

It increasingly looks like there will be three types of chips used in disaggregated inference: an AI-specific chip, a CPU and a GPU.

For a player like Qualcomm, all three of those components are present, so they need a software layer that sits across them. Everywhere else, Nvidia included, they usually sell alongside CPUs and accelerators, and there hasn���t really been a software solution that works across all of those.

When did you first start investing in this wave of AI infrastructure and semiconductors?

Munichiello: We���ve been investing in AI since 2016, starting as early as a company called , which was first company, sold to , and became part of the Siri team. After that, we invested in , co-founded by , which was later sold to and became an important part of its stack. HPE actually went on to be the compute partner for and worked very closely with as well.

We also got excited about semiconductors early, long before this current wave, when we led the Series A for SambaNova. I first met that company when it was just three people and a slide deck. We led that round in December 2017 ��� after led the seed investment ��� and I���ve sat on the board since. That initial investment was $15 million at a $480 million valuation.

It seems like a lot of legacy chip giants and major cloud providers are aggressively buying up infrastructure startups. What does this consolidation mean for early-stage founders? Are we entering an era where standalone startups need to plan for an early acquisition, or is there still a path to an independent IPO?

Munichiello: There is definitely a path to an independent IPO. showed that trajectory beautifully, and I’m really happy for and that team. There is absolutely a trajectory to build big, standalone businesses because the demand for compute is completely off the charts. We can’t make semiconductors fast enough, nor can .

Everyone is trying to find extra capacity by making everything more efficient. Technology often emerges with a big boom in mass demand and high prices, and then we figure out how to make it cheaper. We are in that efficiency step right now. Demand for inference is everywhere, from medicine and law to coding, customer support and finance.

We are trying to squeeze every last bit of value out of chips. Squeezing that value comes from using multiple types of chips: using cheaper CPUs when we can, GPUs when we need them, and the most expensive chips only for the most complicated parts of the process.

We are also evaluating software across the stack to ensure every aspect of these queries is as efficient as possible. It���s not surprising that there are a lot of acquirers. The universe of buyers has expanded from just semiconductor companies buying other semiconductor companies to software companies, hyperscalers and model companies buying chip companies, too. Amazon has Trainium and Inferentia; has Maia; has the TPU, and every big tech company wants to be able to say it has a chip.

How does the rise of open-source models shift this dynamic?

Munichiello: The universe of potential buyers expands even larger when open-source models become prolific. In the Qualcomm announcement, they talked a lot about their enthusiasm for open source ��� not just keeping Modular open-source, but for models to be open-sourced. When that happens, instead of enterprise companies paying hundreds of millions of dollars to model providers to do inference, the companies themselves will own their models and run them on their own hardware.

So you firmly believe that IPOs are not totally off the table for early-stage tech and hardware companies?

Munichiello: Not at all. Look at , which is highly hardware-intensive. I think we will see many IPOs here in the next six months. I know of at least 15 or 20 companies that are planning to go public, so it is going to be a very busy period.

In a market where valuations are multiplying rapidly based on technical metrics like chip throughput, how are you able as an investor to separate real, sustainable product-market traction from early hype?

Munichiello: There are a lot of AI companies getting valuations that are disconnected from the business outcomes they are driving. True traction comes down to quarter-over-quarter execution, hitting sales demands and actually fielding physical systems for customers.

A company becomes highly attractive to investors when it delivers a massive volume of technology into production environments ��� like data centers for major enterprise brands and devices we use every day.

That, combined with incoming demand from “Neo-Clouds” (new data centers built specifically for inference), shows real traction. These players are looking for any chips they can get their hands on, and the concept of disaggregated inference ��� combining three different chip types to lower the total cost of ownership ��� is highly compelling. It also alters the competitive landscape; it shows that the market isn’t just a runaway race for one dominant player, but an opportunity for CPU providers to catch up as well.

GV has a track record of backing foundational tech long before the generative AI hype cycle. How has your framework adapted now that AI infrastructure capital requirements have skyrocketed? When a startup needs hundreds of millions just to compete at the frontier, how do you maintain a focus on the team and relationship without getting bogged down by the sheer scale of capital?

Munichiello: It has always been complicated to start from scratch and build a meaningful, generational company. We are not in the business of momentum investing. We don’t invest in something just because we think it will be marked up by other investors over time. We look for fundamental technologies and consequential businesses that can stand on their own.

When we met Modular, it was just Tim and Chris with an idea, and we convinced them to take our $23 million investment. At the time, we were nervous about valuing the company at more than $80 million or $90 million, and it ended up getting valued at $155 million in that first round.

We took 15% of the company right off the bat in a round that felt way out over its skis for that moment in the world. But they hired an amazing team of compiler engineers, started growing and built in a space that became the most strategic in all of AI.

We value different companies based on their specific markets. Some are incredibly capital-intensive and require billions of dollars, meaning we can’t do it alone. As an investor, we must bring our network and a syndicate of other investors who can write hundreds of millions of dollars in checks.

Software companies can move a bit faster, make more mistakes and pivot. In hardware, if you tape out a chip and it doesn’t work, you are set back for years and have to raise significantly more money. It���s much more binary when it comes to the physical world. A hundred million dollars goes a lot further in software because you can always optimize your token usage or engineering to shift directions, which is incredibly hard to do in robotics or hardware.

This acquisition represents a massive return on your initial investment. What does this success say about your broader investment philosophy?

Munichiello: It���s a fantastic outcome ��� a 27x return on our initial investment and roughly 10x on our total dollars invested. But we aren’t a firm that just leads a Series A and then steps back. We look to write massive checks and co-lead later rounds, especially when things get difficult.

It is inevitable that every company will hit a wall at some point ��� whether due to macroeconomic factors, team dynamics or customer challenges. We call these “crucible moments,” and they are what make companies truly interesting. In an internal email I sent to our team, I talked about loving curveballs. We are used to things going sideways, and that’s when we really step up and help our companies. We like to find these incredibly hard problems, back exceptional people with the character and grit to survive those moments, and help them build standalone businesses.

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蹤獲弝け data tells us Black startup founders still receive only a tiny sliver of venture funding. What the numbers don���t tell us is why investors continue to overlook those entrepreneurs, and more importantly, how the industry can improve the odds for Black and other underrepresented business leaders.

To better understand what’s driving the persistent gap ��� and what it will take to close it ��� 蹤獲弝け News spoke with six venture capitalists who actively back Black founders about where they believe the ecosystem continues to fall short and how it can improve outcomes.

While they offered different perspectives, several themes emerged: Venture firms need to broaden the networks they rely on to source deals, founders continue to face structural barriers long before they pitch investors, and lasting progress will require changes from both investors and entrepreneurs.

Expand beyond familiar networks

Underrepresented founders face distinct pressures as the venture industry retreats to its traditional networks, according to partner , who said the pullback in funding to Black founders overlooks where investors can discover market-outperforming businesses.

���The bar for all founders has gotten higher in recent years, and I don’t necessarily think that’s a bad thing,��� she said, pointing out that the surplus of capital during the previous market peak meant startups that probably should not have been funded received investment anyway.

Still, the subsequent market correction has triggered a familiar defense mechanism among institutional investors, she said. ���When things get hard, it’s human nature to revert to what you know and what feels safe,��� Kidder said. ���Unfortunately, that means back to the same networks, and so there’s been extra pressure on underrepresented founders.���

Instead of viewing diversity through a philanthropic lens, Kidder argues that the current environment means venture investors need to look outside of their conventional circles to beat the market. ���Alpha is more likely to be found outside that comfort zone in founders who bring different perspectives and solutions to the table, especially in healthcare,��� she said.

To date, Boston-based Seae has backed nine Black startup founders. Kidder notes that those entrepreneurs, like with the rest of the founders in the firm���s portfolio, bring ���extraordinary grit, experience and passion to building sustainable solutions for the market.���

, partner at , agrees that venture firms’ existing networks tend to limit who gets funded and he argues that expanding those networks requires deliberate action. To that end, his firm several years ago launched Lobby: Elevate, an event designed to support underrepresented startup founders.

The entrepreneurs who attended the firm���s Founders of Color Summit demonstrate that ���what is lacking for Black founders is opportunity and investment, not talent,��� he said.

Hornik said more venture firms need to intentionally create opportunities to meet founders they would not otherwise encounter, whether through events or by bringing more investors into direct conversations with underrepresented entrepreneurs.

Simply agreeing that bias exists, he said, won’t change investment outcomes.

“I don’t think there is a single white VC I respect who has funded a large cohort of Black founders, myself included,” Hornik said. “I can certainly do better.”

Because venture investing is inherently subjective, Hornik argues investors must actively push back against the implicit bias that can shape sourcing and partnership discussions. The funding statistics for Black founders won���t change unless investors are ���intentional about the problem,��� he said.

That view is echoed by , co-founder and general partner of , who believes that the many public commitments made by firms to back more Black founders in the summer of 2020 following George Floyd���s death were ���performative and not permanent.���

The ���unironic and quick��� retreat in the years since has been actively harmful to Black founders, he said. Going forward, the industry needs to make truly structural changes to see long-term improvement.

���The warm intro network is the biggest filter in venture, and it���s viewed as an asset, not a structural problem,��� he said. ���If you’re inside, you get meetings. If you’re not, you don’t, no matter how strong the company is. Pattern matching gets the headlines, but it’s downstream of who walks through the door.���

Rhodes argues that many venture capital funds treat sourcing as a “passive intake.���

In contrast, funds that systematically expand their top-of-funnel reach beyond traditional networks tend to discover companies that competitors miss.

That���s not a diversity initiative, he noted, but a distinct ���information advantage.���

Break down barriers before the pitch

For , managing partner at , a quality often overlooked by investors is resourcefulness. Having built a startup himself before becoming an investor, Johnson said many Black founders learn to build high-quality businesses with far less capital than their peers.

���Black founders innovate at the same quality and scalability as others, with a fraction of the capital,��� he said.

Ironically, that same scrappiness often stymies Black founders during the pitch process, according to , founder and general partner at and the author of

O���Donnell argues that many of the biggest obstacles emerge well before founders ever walk into a pitch meeting, though they continue there.

Venture firms recruit heavily from elite universities where Black computer science students make up only a small share of the student body, he said, while the broader tech ecosystem in Silicon Valley can feel unwelcoming to many Black engineers.

���Silicon Valley itself is alienating,��� O���Donnell said. ���The Bay Area has no meaningful Black community, the interview panels are all-white, the lunchroom is all-white, and the neighborhoods are all-white. Qualified Black engineers rationally choose to work somewhere they won’t be isolated.���

���Not wanting to be the only Black person in the room isn’t a failure of ambition,��� he added. ���It’s a reasonable response to a visible signal about what the environment will be like.���

That disparity continues into the fundraising process itself, according to O’Donnell, who argues that underrepresented founders often ask for less capital and make more conservative projections because they’ve spent their careers facing greater scrutiny and are often expected to justify every dollar.

Venture investors, however, are by their very nature looking for founders who pitch ambitious, risky, fund-returning visions.

As one example, O’Donnell recalled a Black urban mobility startup founder whose pitch to VCs became caught between describing the large company he hoped to build and the modest business he had already created on the path to profitability.

The founder was ���pitching the way someone pitches when they’ve been taught that financial responsibility matters, but he was pitching in front of people who don’t care about financial responsibility at all,��� he said. ���They care about whether ��� if the risk was ramped up high enough ��� this could return a fund.”

Change the funding playbook

Many investors and Black founders who spoke with 蹤獲弝け News came to a similar conclusion: Improving venture outcomes for underrepresented founders will require changes on both sides of the table, with investors broadening who they meet and founders building businesses that make it increasingly difficult to overlook them.

For venture firms, that starts with intentionally expanding how deals are sourced, rather than relying on warm introductions and longstanding networks.

, general partner at , argues that the responsibility for changing outcomes rests primarily with the institutions that control the vast majority of venture capital.

���Venture firms led by white people and ‘model minorities’ should be asked the hard questions,��� said Robinson, whose early-stage venture fund is designed to back Black entrepreneurs. ���Their track records should be examined. Their implicit and explicit bias should be called out and they should have to answer for it.���

Robinson believes firms need to do more than wait for investment-ready companies to appear. Instead, she said, they should actively expand their sourcing pipelines and create programs that help founders reach the stage where they’re ready to raise institutional capital.

For founders, her advice is pragmatic. Entrepreneurs should spend less time chasing investors and more time building businesses customers want, she said.

“Black founders need to relentlessly pursue sales and customers as they have been indoctrinated to pursue investors,” she said, arguing that strong commercial traction gives investors “less of a choice but to invest” once the metrics become undeniable.

Rhodes, the general partner at Fictive Ventures, also offered a reminder that venture capital is only one potential financing path. Before pursuing that path to funding, startup founders should first determine whether their business actually fits the venture capital model and the growth expectations that come with it, he said.

The venture model is built around risk-taking, he noted, but there���s a double standard for white and non-white entrepreneurs: ���A Black founder’s first failure gets treated as confirmation,��� he said. ���A white founder’s first failure gets treated as experience.���

Still, if a Black founder is determined that venture capital is the right financing source, he or she should recognize that investors are buying a stake in the future outcome of the business.

That means personal backgrounds, stories and community impacts only matter to investors in so much as they serve to predict a financial return. ���Nobody is investing in you just because you’re Black,��� Rhodes said.

In fact, he believes that investors who frame their investment decisions around founder identity are typically the first to ���disappear in a downturn.���

Instead, Rhodes advises founders to focus on finding and building for the investors who ���truly understand��� the business and are committed to helping build it over the long term.

That���s a view echoed by Kidder. ���Focus on the build, get creative to show early proof points ��� build and leverage relationships where you’ve built trust and delivered results to seek out investors who believe in you and what you’re building,��� she said. ���And, don’t let the stats dissuade you from the dream. Trust your gut and focus on delivering sustainable results.���

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led the financing, which included participation from , and existing backers. The raise brings the company���s total funding to more than $76 million since its 2018 inception and values it at $127 million post-money. That���s more than double the valuation of its previous funding round ��� a $25 million Series A that closed in December 2023.

“Healthcare has spent decades generating enormous amounts of patient data without a reliable way to make that information usable,��� said xCures CEO in an exclusive interview with 蹤獲弝け News. ���We���re changing that.���

Venture investment in healthcare and biotech companies that have an AI bent has been on an upward trajectory in recent years. As of June 22, investors have put an estimated $8.5 billion into seed- to growth-stage funding for companies in AI-powered health tech categories, according to 蹤獲弝け data. In 2025, funding to the sector across all stages $15.8 billion. This year���s total is already nearly as much as the $8.6 billion raised in the category in all of 2024.

Pivoting to solve a problem

Founded in 2018 as a spinout from by , xCures initially launched to provide decision-support tools for patients with advanced cancer. At its inception, the company focused on patients with Stage 3 or Stage 4 recalcitrant cancer diagnoses, where standard care options were exhausted.

While working with thousands of patients across the country in a direct-to-consumer setting to build its initial model, the company encountered a systemic bottleneck.

���What we learned in the process was that the decision-making was hard,” Newton said. “These are complicated things, but doable. But the even harder thing was to get our hands on the data and information about the patient that we needed in order to give them the advice in the first place.���

At the time, patient records were arriving at the company in boxes and over fax machines. This logistical hurdle prompted xCures to pivot to build the underlying infrastructure needed to connect directly to national healthcare interoperability networks. Today, xCures hooks into these electronic exchanges on behalf of its customers, shifting its primary focus to structuring what Newton described as the industry’s ���dirty data.���

���The data in those medical records is incredibly dirty, so it’s duplicative. There are pictures of things, scans of things. There are errors that are caused because it’s all human entry,��� Newton explained. “There���s lots of narrative information, and we turn it all into something that basically is clinical intelligence or the clinical clarity an organization needs to make its next decisions.���

Creating a ���clinical clarity engine���

Patient information remains scattered across thousands of labs, hospitals, imaging centers and electronic medical records, often arriving as unstructured documents that are difficult to use in clinical workflows. This is where xCure can provide a differentiated experience, according to Newton.

���They’re [competitors] really in the transport business ��� moving data from Point A to Point B,��� he noted. “We think of our product as the executor’s clinical clarity engine. We’re in the business of taking that transported data and making it into something that’s actually instantly useful, versus just moving it from one space to another.���

The xCures Clinical Clarity Engine, he said, solves this by integrating capabilities to generate decision-ready checklists from automated patient histories, backed by evidence-grade data. Newton estimates that the engine is three to five years ahead of anyone else in the market. To date, xCures has processed more than 300 million medical records sourced from more than 550,000 healthcare locations nationwide, supporting clinical decisions for millions of patients across the U.S., per the company.

To manage this volume without incurring the extreme processing costs associated with running massive, unstructured files through generic models, xCures utilizes a variety of AI, combining its own home-built machine learning models with commercial frontier models from existing vendors. The company manages these tools through a proprietary governance framework.

���We really see it as the harness for ��� the process for applying AI, and how we make sure that the tasks that we’re asking the AI to do are appropriate and well-governed, and that the rules of engagement are really clearly defined,” Newton said.

High growth and enterprise adoption

This technological approach has driven impressive traction. Operating on a usage-based SaaS model with committed caps, xCures grew from roughly $3 million to $10 million in annualized recurring revenue in 2025, according to Newton, and it���s on track to break $20 million in 2026.

While xCures achieved cash-flow breakeven last year, the company has intentionally entered a capital-burn phase to build its team for its 2027 business pipeline, he added.

The startup���s enterprise customer base consists of 25 clients, including lab diagnostic companies such as , and . Large hospital networks use the tool to ���instantly��� generate patient histories for operating room scheduling, screen for comorbidities and estimate operative times ahead of surgeries. The engine is also used by telehealth providers lacking robust Electronic Health Record architectures, as well as by Medicare Advantage plans seeking to automate population risk stratification, prior authorizations, medical-necessity documentation and administrative appeals.

Solving healthcare’s most expensive grunt work

Ultimately, Newton believes that reducing the immense administrative drag built into the American healthcare system is crucial.

“Companies like xCures really reduce the administrative burden and represent the fastest path to realizing value in healthcare for everybody who’s involved in it,” Newton said. “This idea that we can use AI not to do things that doctors should do, but just to make it all better, easier, faster, cheaper and better for everybody involved … there’s just a lot of, like, grunt work that you should do that’s really expensive, and so that’s probably the most immediate opportunity.”

, partner at Innovius, wrote via email that his firm backed xCures because it was impressed with its ability to ���locate, extract, and normalize messy data across thousands of incompatible sources.��� By applying real clinical context to surface exactly what matters, the investor noted that Mika Newton and his team are successfully “building the foundational AI data layer that will power the entire healthcare industry.”

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The company is a marketing analytics platform that acts as an independent referee of sorts to track which digital ads actually drive mobile app downloads and in-app purchases. It helps companies measure their return on ad spend while claiming to protect user privacy and block ad fraud.

While AppsFlyer CEO and co-founder declined to comment on specific deal details, he did confirm to Axios that , , and each took a minority stake in the San Francisco-based startup.

AppsFlyer���s most recent raise before this was in 2020. With the latest round, the company has now raised $1.3 billion in known funding since its 2011 inception, per .

Previous backers include , 1, , and .

���They believe what we believe: that attribution and measurement must be independent, unbiased and trusted,��� Kaniel was quoted as saying of AppsFlyer���s newest investors. ���As AI takes over more of how advertising gets bought and optimized, the signals feeding those systems become the most consequential infrastructure in the industry.���

He added that the company is eyeing the public markets, calling the financing ���a step on that path.”

So far in 2026, companies in sales, marketing and CRM categories have pulled in around $4.1 billion globally in seed- through growth-stage funding, per 蹤獲弝け . That puts the space on track to come in roughly flat with or a bit up from the prior three years ��� when annual funding hovering around the $8 billion mark ��� though still far below boom-era levels, when sales and marketing investment topped $20 billion. Notably, many of the startups funded in recent quarters have been AI-focused, with many of them offering agentic tools and automation in areas such as sales, marketing and customer experience management.

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Globally, robotics startups have so far raised $18.8 billion in 2026, compared to $15 billion in the full year of 2025. The figure also handily surpasses the $14.1 billion raised in the peak venture funding year of 2021, and we still have more than six months of fundraising left.

The impressive rise in funding reflects a marked shift in perception among venture investors about the robotics sector, which was traditionally considered an expensive, asset-heavy hardware gamble. In particular, investors appear to be drawn to startups working on embodied AI, or artificial intelligence with a physical body that interacts with the real world in real time.

Noteworthy recent rounds

The surge in funding is driven by a number of robotics-focused startups raising considerable capital from investors this year. Also, interestingly, two of the five largest raises in 2026 to date have been by Austin-based companies.

Topping the list of largest deals in 2026 so far is Austin-based , a defense tech startup focused on autonomous sea vessels. In March, the 4-year-old company raised $1.75 billion in Series D funding, bringing its total funding to around $2.6 billion. led the round, which set Saronic���s valuation at $9.25 billion ��� more than double its Series C level in 2025.

Earlier this month, Germany���s , a developer of AI infrastructure for robots to learn, collaborate and operate across real-world environments, said it secured up to $1.4 billion in Series C funding. led that raise.

In January, , a robotics company building an ���omni-bodied��� brain to operate any robot for any task, announced that it had raised $1.4 billion, tripling its valuation to over $14 billion. That financing came just over seven months after Skild raised at a $4.5 billion valuation. led the startup���s latest round, which included participation from , ���s venture capital arm.

On June 15, Beijing-based , which creates water robots and intelligent unmanned equipment, raised $1 billion in a massive Series A round led by .

And in February, AI-powered robotics company raised $520 million in an extension of its $415 million Series A raise in February 2025, bringing the total round to over $935 million. Existing backers , , and joined new investors, including and manufacturing giant in participating in the extension.

Interestingly, spinout has already raised two rounds in 2026. In March, the Palo Alto, California-based startup closed on a $500 million Series A round, co-led by and . Then in May, it raised another $400 million in a financing led by . The company is developing an AI-enabled industrial robotics platform focused on automating industrial and manufacturing tasks at scale.

Exits

While mergers and acquisitions have been relatively robust with several strategic buyouts, the robotics IPO landscape is a bit quieter, particularly in the U.S.

In China, however, a number of robotics companies have recently gone public. The of , targeting a $3 billion to $7 billion valuation, was considered a milestone for the industry. In March, the company filed for an to list on the , and its IPO was widely expected to spur other startups in the space to pursue their own public-market debuts.

, a startup based in China���s Shandong province that makes lightweight industrial robots, in May listed on the , raising about $86 million. And it did not disappoint. Robotphoenix closed its first full day of trading at HK$53.75 ($6.86 U.S.), up nearly 80%, though shares have dipped to the HK$37 range more recently.

On the M&A front, a number of Big Tech and automotive giants have been aggressively acquiring embodied AI and humanoid talent to anchor their physical automation strategies.

In February, AI-powered supply chain provider acquired , an Austin-based maker of autonomous forklifts and lift trucks.

Skild AI in April that it had picked up the robotics arm of in an effort to deploy its technology to warehouses.

And in May, tech giant entered the humanoid robotics field directly by acquiring San Diego-based . The team was absorbed into Meta’s Superintelligence Labs unit to accelerate training of its foundational physical AI model.

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His investment mandate primarily focuses on early-stage technology companies from seed to Series B that align with or impact the global telecommunications, network infrastructure and enterprise software sectors.

Under his leadership, AT&T Ventures targets investments in software, hardware and infrastructure sectors where AT&T’s network scale and internal engineering resources provide a distinct commercial or technical diligence advantage. Portfolio companies include enterprise and deep-tech firms such as , , , , and .

Prior to his current 12-year stint directing AT&T Ventures, Taneja spent more than two decades working across corporate development, venture lending and investment banking. He previously managed M&A and strategic investment activities for during ownership.

Taneja also served as a director at , where he focused on growth-capital debt and equity investments in mid- to late-stage technology businesses, as well as holding corporate finance and investment banking roles at and .

In an email interview with 蹤獲弝け News, Taneja shares why he believes that while AI has drastically lowered the barrier to building software, it has also shifted the definition of seed-stage technical risk.

The new dynamics, in his view, gives AT&T Ventures an opportunity to differentiate itself by offering immediate, real-world technical validation and network integration rather than just capital.

The interview has been edited for brevity and clarity.

蹤獲弝け News: If startups are building fully functioning apps by the seed round using AI, what does that mean for the traditional definition of technical risk? Is tech risk dead at seed, or has it just evolved into something else?

Vikram Taneja: The old definition of technical risk was ���can they build it?��� Although not entirely absent at the seed stage, I���d say it is becoming less relevant given the dramatically lower barrier to building software with AI tools.

But what replaced it is actually harder to answer: ���Is the tech defensible?��� Not just ���does it work?��� but ���does it compound?���

Data moats, proprietary training sets, network effects built into the architecture ��� that’s the new measure of durability.

In prior cycles, technical complexity alone created some natural protection. As a result, the technical risk conversation has shifted to focus on how a company defends itself over the next three to four years, especially as frontier labs move down the stack into application layers and start targeting entire verticals.

Similarly, the distribution question shows up much earlier. ���How can you get this to market?��� is increasingly asked at the seed stage rather than later in the cycle.

We���re also seeing increased competition for investors to secure larger stakes at seed that they would have previously pursued at the A round. This is driving investors to be more thorough at the seed stage, and founders have to be prepared to meet higher expectations across the board.

When anyone can use AI tools to spin up a working app in a weekend, product execution happens fast, but moats can be incredibly shallow. At the seed stage, how are you separating a truly defensible platform from a beautifully executed wrapper?

Taneja: In early 2025, we saw a wave of AI wrapper companies built on top of frontier models like ‘s GPT, ���s Claude or LLaMA, and a lot of capital flowed into them. What���s changed is that frontier LLMs have now clearly started to take more of a platform approach ��� moving into the application layers and beginning to pick off the low-hanging fruit.

This is why defensibility becomes critical in AI investing. No platforms are totally defensible, but on some level, you have to ask that question now at the seed stage.

We���re looking for platforms using proprietary data that can���t be replicated by AI, companies that have embedded deep domain expertise ��� areas where general-purpose AI still lacks industry context ��� into their workflows, or highly specialized ecosystems or niche markets that provide another layer of insulation in categories that are too targeted for frontier labs to pursue directly.

Are you seeing a change in the actual headcount or makeup of seed teams? If AI handles the heavy lifting of the initial code, are these founders spending their seed capital on engineers, or are they shifting resources immediately to distribution and go-to-market?

Taneja: There is still an engineering focus in the early stage, as there should be, but we are increasingly seeing product, sales, or partnership roles becoming sought after earlier than in the past. And the reason is, as you stated, that it���s easier to build a working prototype, or even a production-ready application, so the focus very quickly turns to establishing trials with customers or exploring distribution paths to dial in the product features.

For strategic investors like AT&T Ventures, where we often do proof-of-concepts with potential portfolio companies, this is very exciting. We get a chance to work with companies earlier in their formation, can get real technical validation much earlier than otherwise, and can similarly try to find a path to collaborate more quickly.

AT&T Ventures has traditionally played heavily in the Seed to Series B space. If institutional VCs are rushing to seed to grab larger stakes because the tech is mature, how does that change the competitive landscape for CVCs? Are you finding yourself competing directly with traditional multistage funds earlier than before?

Taneja: The makeup of seed rounds has definitely changed. Multi-stage funds used to show up at Series A or B when there was enough traction to underwrite. Now they’re at seed because, as we discussed, the companies are mature enough, and they are trying to find winners earlier in the cycle. So yes, we’re in the same rooms as before.

But I’d push back on the idea that we’re competing directly.

A Tier 1 financial VC���s seed check and an AT&T Ventures seed check are different instruments. They are offering capital, brand, guidance and pattern recognition from backing hundreds of companies.

We’re offering something a financial VC structurally does not: our network teams working with your product in a production environment, oftentimes before we even write the check, for example. That’s free diligence running in both directions. We’re validating the company, but it’s also receiving a real-world signal from one of the world’s largest network operators.

For a seed-stage company that’s already solved the building problem and now needs distribution, that���s tangible value and complementary to what financial VC firms are providing. So that competitive pressure has actually sharpened our value proposition. It forces us to bring more than just capital to the table.

Historically, corporate partners want to see enterprise readiness, security compliance and scalability ��� things a seed startup rarely has. If a seed startup has a fully functioning product but is still a two-person team, can an enterprise like AT&T actually run a pilot with them, or does the corporate integration timeline become a bottleneck?

Taneja: It starts with strategic rationale. That has always been the entry point for us at AT&T Ventures, and that hasn���t changed. If that is in place, then it doesn���t always require full enterprise readiness to start a pilot. It can be a structured trial or a highly targeted engagement, depending on the company’s stage.

We have a number of ongoing proof of concepts with portfolio companies across areas such as AI-RAN, connected infrastructure and computer vision.

The key is clarity upfront ��� clarity on what the objective of the engagement is and how we measure success. Once that is clear, even early-stage companies can be integrated into a learning or testing environment without unnecessary delay. The goal is to make the AT&T relationship feel like an accelerant to further adoption.

If seed is the new Series A in terms of product maturity, are you seeing Series A pricing bleed into the seed round? How are you disciplined about valuations when the product looks like a Series A, but the company infrastructure is still very early?

Taneja: Seed pricing indeed looks different than maybe four or five years ago. We���re routinely seeing seed deals priced in the low- to mid-single-digit-million range at about $20 million to $25 million post-money. This is pretty much where Series A deals were a few years ago. But it���s not necessarily unjustified ��� the makeup and traction of seed-stage companies are much further along than predecessor vintages as we���ve discussed.

We stay disciplined by being explicit about what we’re actually underwriting. We’re not just underwriting the financial return on this round ��� we’re underwriting the strategic value of the relationship over a five- to 10-year horizon.

Does this company make AT&T’s network more intelligent? Does it open up a new customer segment? Does it validate a thesis we’re building around? Are there commercial opportunities beyond our initial thesis? When you frame it that way, it gives us a longer horizon to work with and provides multiple levers to pull.

And honestly, that’s where our engineering and product teams play a key role. They help us decipher whether the product that looks like a Series A is actually built like one, or whether it’s a great demo sitting on a foundation that hasn’t been stress-tested. That technical read bolsters our conviction when making investments.

A functional AI app at the seed stage still requires massive infrastructure. When you evaluate these early-stage companies, how much does their underlying architecture and how they handle data processing or edge computing factor into your decision?

Taneja: Architecture is a key part of our diligence process. The way we think about it really depends on the ultimate use case. Is it for internal use ��� i.e., a tool that AT&T will be working with in our environments ��� or is it something we���d be distributing or incorporating into some form of product offering?

If the former, all aspects of the architecture will be reviewed, and this is most likely to occur throughout trials and proof of concepts as we develop a technical understanding of the application or product. If it���s the latter, then we���re likely most interested in understanding how this product architecture scales over time and what it means from a cost, latency and infrastructure perspective. We love to see companies embracing edge-related technologies, but that doesn���t preclude us from working on applications that use traditional data processing methods.

You���ve spoken before about your interest in ���physical AI��� and robotics (like Apptronik). The software lifecycle is easily compressed by generative AI, but hardware and physical deployment take time. Does this ���seed is the new Series A��� trend apply to pure-play software strictly, or are you seeing AI accelerate physical tech and IoT at the early stage too?

Taneja: Physical AI is a sector we���ve been looking at quite a bit, particularly because inference and decisioning in autonomous systems, robotics and connected devices create a very different type of demand profile on networks.

The software layer is clearly accelerating ��� things like perception, control systems and decisioning are moving faster because of AI (the rounds show it!). That will ultimately help pave the way for the adoption of physical AI. However, the physical deployment cycle still takes time, so you don���t see quite the same level of time compression there.

What is interesting for us at AT&T is the intersection ��� how intelligence is moving closer to the edge and how that changes the way networks need to be architected to handle those workloads.

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Only around $942 million ��� or just 0.32% of total U.S. venture funding ��� went to startups with a Black founder or co-founder last year, per 蹤獲弝け . That���s one of the lowest shares in years, and down more than two-thirds from just three years prior.

This year has started off on a slightly rosier note, with $643 million raised by U.S.-based startups with a Black founder or co-founder as of May 20. The majority of that was raised in the first quarter, marking the most raised in a single quarter since Q2 2022, when $653 million was raised by Black founders or co-founders.

The consistently low numbers have led some Black founders to turn to investing in an effort to help level the playing field. 蹤獲弝け News talked with two such founders to hear more about their experiences in raising capital and what they���ve learned from investing.

Clarence Bethea

founded , an extended warranty startup, in 2014. He went on to raise nearly $30 million in venture capital before the startup was ultimately acquired by in 2024.

The process of raising capital for a St. Paul, Minnesota-based startup as a Black founder was arduous, he recalls, describing it as being especially ���very hard in the beginning.���

���I believe that raising money for anyone is very difficult. When you add in race, gender, and proximity, it becomes even more difficult,” he told 蹤獲弝け News in an email interview. ���… I often tell founders, raising that first million will be your hardest. Do I believe that race played a factor [in making it harder to raise capital]? Yes! Because it plays a factor in every part of my life.���

It didn���t take long for Bethea to come to a distinct realization: The system was never designed with everyone in mind.

���This system wasn���t built for me, and I knew that from day one,��� he reflects. Yet, rather than allowing that structural reality to become a barrier, he shifted his focus toward mastery.

���My focus quickly became about learning and understanding the game of venture capital,��� he said. ���I didn���t want the fact that it wasn���t for me to get in the way of being a part of it.”

Bethea later made the leap into venture capital itself. In 2023, he joined , one of Upsie���s backers, as an investor and entrepreneur-in-residence. The move, he said, was motivated partly ���by the people,��� and wanting to be in an environment where he was “encouraged to learn deeply about the industry and how to look at deals.”

But it was also driven by a deeper mission to alter the very dynamics he faced on the other side of the table.

���I wanted to be a voice for founders who either looked like me, weren���t in-network and didn���t match the normal ���pedigree��� of a founder,��� he said.

Stepping into the investor’s shoes provided Bethea with a dual perspective, he said, both validating his instincts as an entrepreneur and revealing new dimensions of the fundraising puzzle.

Becoming a VC ���confirmed some things that I knew were true as a founder, but it also opened my eyes to ways founders can improve their chances,��� Bethea said.

From his vantage point as an investor, he routinely witnessed what he described as the same avoidable mistakes being made by talented teams. That realization prompted him to move on from his role at True Ventures earlier this year and became the catalyst for his current venture, ������

Bethea describes the initiative as an ���always-on��� educational platform, course and live-programming series designed to give early-stage entrepreneurs clear, unfiltered insight into the real mechanics of company building and venture fundraising.

Built on ���lived experience,��� the platform equips founders with more than 75 high-level videos and 90 workbook pages in an effort to demystify how venture decisions are actually made, what makes a pitch fundable, and how to approach fundraising strategically. The impact is already tangible, according to Bethea, as it���s helped two founders raise millions so far using its frameworks.

Ultimately, his time in the venture capital trenches has left him looking toward the future with a striking amount of hope.

���I’m more optimistic than ever before,” he said, pointing to technological shifts as a potential massive equalizer for underrepresented builders.

���AI brings down the walls of building an MVP, talking to customers, and starting to gain traction,��� he said. ���That���s really exciting for founders who don’t fit the normal founder stereotype. But we have to get better at the game of venture.���

Cortney Woodruff

Over the years, has founded and raised venture capital for two startups: , an online platform that provides software services to personal trainers, and , an online learning platform that provides online courses taught by notable, Black innovators that was co-founded by actor .

Those experiences led him to conclude that while building a company is universally grueling, the playing field is far from level. Reflecting on his early days as an entrepreneur, he notes that “raising venture capital is hard for almost everyone, especially first-time founders,” given that investors must make highly risky decisions with limited information. Yet, he simultaneously observed a stark disparity in how different founders are evaluated.

���I often felt young minority founders were expected to arrive as finished products,” Woodruff told 蹤獲弝け News in an email interview. “There seemed to be less patience, less coaching, less developmental support. I watched founders receive years of benefit-of-the-doubt capital while learning on the job. Many minority founders are expected to prove everything upfront.���

This friction became undeniable during pitches for his first company, Trainersvalut. Despite walking into meetings with customers and real revenue traction, Woodruff recalls that he and his team often left ���feeling like we were still being evaluated as an idea rather than a business.���

He came to that determination after a number of confusing rejections. While founders would naturally assume they are competing on product, execution and traction, Woodruff eventually concluded that it���s usually more related to familiarity.

���Many investors are looking for patterns they���ve seen before,��� he said. ���If your background, network, school, or story doesn���t fit those patterns, you often have to produce significantly more evidence before receiving the same conviction.

���That realization changed how I viewed entrepreneurship and venture capital,��� Woodruff added.

Driven by a desire to learn more about how decisions were made from the other side of the table, Woodruff began angel investing. The move pulled back the curtain on the industry’s inner workings, confirming just how deeply venture capital relies on pattern recognition to signal success.

���What surprised me was how much venture capital is driven by pattern recognition,��� he said. ���Investors are trying to identify signals that increase the probability of success. The challenge is that those signals are often informed by prior successes, which can unintentionally narrow the range of founders and ideas that receive attention.���

Sitting in the investor’s chair also reframed his perspective on institutional bias. As a founder, it is easy to view every rejection as personal or discriminatory, but underwriting deals revealed to him just how difficult these choices are. Today, Woodruff views the industry’s shortcomings in diversity through a systemic lens rather than an individual one.

���The people who talk about bias often underestimate the role of networks, while the people who talk about networks often underestimate the role of bias,��� he said. “Most investors are not waking up trying to exclude people. However, they are often sourcing opportunities from familiar circles, relying on familiar signals, and backing founders who feel familiar to them. Over time, those patterns compound.���

This concentration of networks helps explain why venture capital continually underinvests in Black founders. Because VC is fundamentally relationship-driven ��� reliant on referrals, universities and existing investor circles ��� homogeneous networks naturally yield homogeneous deal flow.

���I don���t think the issue is simply that investors don���t want to fund Black founders,��� Woodruff said. ���I think many investors never encounter a sufficiently diverse set of founders in the first place.���

In his view, the resulting disparity isn’t always about who eventually gets a check, but who is given the grace to stumble and iterate. Throughout his years in the ecosystem, Woodruff said he has routinely watched founders with stronger traction receive less enthusiasm than those with stronger narratives.

���The difference is often not who gets funded eventually. The difference is who receives patience, coaching, introductions, and the opportunity to grow into the founder investors believe they can become,��� he said.

Now, Woodruff uses his position to bridge that gap, treating mentorship and network access as critical forms of capital. He focuses on guiding founders through an unfamiliar system, helping them avoid missteps, and opening doors to rooms they otherwise wouldn’t enter.

When looking toward the industry’s future, his outlook is balanced by both optimism and pragmatism. Woodruff is heartened that conversations around representation are more visible than ever and that technology has drastically lowered the barrier to entry for small teams building meaningful businesses. Yet, he recognizes that “systems change slowly. Networks evolve slowly. Institutions evolve slowly.”

Ultimately, he rejects the premise that venture capital can be fundamentally reengineered for fairness.

“I don���t think venture capital was designed to be equitable. It was designed to generate returns,” Woodruff said. Instead, he believes the real paradigm shift will come from diversifying the perspectives of those who write the checks.

“If every investment committee has similar backgrounds, similar networks, and similar reference points, they will naturally gravitate toward similar founders and similar ideas. I don���t believe the economics of venture capital need to change as much as the pattern recognition does,��� he said. ���The most successful investors in the future may be the ones who can recognize extraordinary opportunities in places others have been trained to overlook.���

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• Share Of Startup Funding For Black Founders Hits Multiyear Low
• Funding To Black Startup Founders Quadrupled In Past Year, But Remains Elusive

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