During his tenure as chair from 1987 to 2006, Greenspan was renowned for his cryptic utterances on the economy, leaving rate-watchers befuddled as to whether they presaged a likely cut or hike. His wife, veteran correspondent , famously that their marriage took time because “he claims he proposed three times before I was able to understand. He was so oblique. It was like his testimony.”

In spite of his long history of obfuscation, however, Greenspan is best known for a fairly unambiguous two-word phrase: “irrational exuberance.” He coined it in a 1996 to theÌę , a conservative-leaning think tank, titled “The Challenge of Central Banking in a Democratic Society.”

One of the speech’s core points was the notion that pricing logic in an industrial economy dominated by durable goods and materials is far simpler than for a modern economy increasingly dominated by software and services.

“What is the price of a unit of software or a legal opinion? How does one evaluate the price change of a cataract operation over a 10-year period when the nature of the procedure and its impact on the patient changes so radically?” he mused, before turning to that most famous insight.

That insight, if I am translating Greenspan-speak correctly, was linked to the question of how one can establish long-term confidence in valuations of assets tied to fast-changing technologies and business models, like software, where prior notions of unit economics no longer applied.

“How do we know when irrational exuberance has unduly escalated asset values, which then become subject to unexpected and prolonged contractions,” he wondered. It’s a conjecture that 30 years later still has no obvious answer.

Notably, Greenspan’s speech actually predated the most heated periods of the dot-com boom, bubble and implosion, which began in the late 1990s and culminated with the hitting its cyclical peak in early 2000. During and shortly after that period, money-losing e-commerce companies like online grocer and pet supply retailer famously went public at then sky-high valuations before abruptly shuttering. Internet infrastructure providers fared even worse, exemplified by networking equipment maker going from Canada’s most valuable company to penny stock in a couple years.

But while losers lost big, winners eventually eclipsed them. Dot-com-era megastars and , for instance, are now worth nearly $8 trillion combined.

That brings us to one of Greenspan’s other well-known analogies: the lottery ticket.

In Congressional testimony in early 1999, pressed for his thoughts on then fast-rising share prices of hot internet companies, the Fed chair the stock-buying frenzy to playing the lottery. He observed that people have long been willing to pay more for a lottery ticket than their chances of winning would justify, simply because they are drawn to the remote chance of a huge win.

”And undoubtedly some of these small companies, which have stock prices going through the roof, will succeed and they very well may justify even higher prices,” he said. ”The vast majority are almost sure to fail. That’s the way the markets work in this regard.”

Fast-forward to today, and one is easily drawn to apply Greenspan’s analogy to the current AI mania. Once again, we’re seeing unprecedented valuations attached to money-losing companies, many in still relatively nascent stages of development.

In other ways, however, this time it’s not a dot-com lottery ticket redo. For one thing, the companies in which a retail investor might be buying said ticket are by no means small. , at its current market cap, is the sixth-most valuable U.S. public company. It’s priced like a winner, not a wanna-be.

Same holds true for recent valuations for and , both of which have confidentially filed for public offerings likely to debut in coming months. Anthropic hit a $965 billion post-money valuation, while OpenAI’s was recently around $852 billion.

One wonders what Greenspan would say about these stratospheric asset price levels. I’d suspect there are better than lottery-ticket odds that it would be something cryptic.

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Photo: Dr. Alan Greenspan, former Chairman of the Board of Governors of the Federal Reserve, speaks at the Per Jacobsson Foundation Lecture, October 21, 2007, in Washington, DC. (Photo by International Monetary Fund Photograph/Stephen Jaffe used under the .)

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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 91Ÿ«Æ· . 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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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 “headless” 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’s 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’s 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’s 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 “Ds.” 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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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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Since its launch almost two decades ago in 2007, the firm — which had an initial fund of just $3 million —Ìę has invested in around 550 companies. With this latest fund, its assets under management have reached $1 billion.Ìę

91Ÿ«Æ· News spoke with , the firm’s managing partner who joined Seedcamp in 2010 and , who rejoined the firm in 2022 to head up the select fund and establish a New York presence.Ìę

Since fund 2, it has invested in 100 companies per fund. “What we’ve learned is that you need a community to support each other,” said Espinal. The tipping point for the firm was 70 companies where it became clear that founders were helping one another, becoming customers, and teams starting new companies.

“We realized early on that the best thing a founder can get is access to another founder who just went through that experience — not necessarily a founder who is successful 10 years down the road and is a great figurehead, but someone just a little bit ahead. That’s effectively our secret sauce,” said Espinal.Ìę

Seedcamp also invests in software and vertical AI, but is careful about what is compelling and unique. “We’re trying to monitor so we’re not one of eight bets in one area that’s been overinvested within the AI vertical space, and making sure that you’re not betting on number 100 in a space that’s hypercompetitive,” Espinal said.Ìę

Seedcamp plans to invest in 35 new companies per year, totaling 100 to 120 for the new fund. It invests up to $1.3 million in its initial check, and will lead roughly 70% of those deals with a 5% to 10% ownership target.Ìę

The firm reserves 40% for follow-on seed and Series A rounds. Its select fund will invest in portfolio companies from Series B onward.

“Building is so much easier and faster now,” Howe said. “Signals of product-market fit are there earlier. The founder DNA is still the same, but the ability to see it in action earlier is there with the AI lift.”

New York presence

Howe, who heads up the New York office, noted that European companies are heading to the U.S. earlier. “Historically, maybe we’d see a company raise a round and stay in Europe, dominate their local market, raise a few more rounds, and then come to the U.S.” she said. “Now we’re seeing them come right from the get-go.”

From fund 3, its 2014 vintage fund, the firm’s return is 13x distributions to paid-in capital, with Revolut, UiPath and seed investments from that fund.

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• Ìę

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

This week was not an exceptionally busy one for large funding deals, though we saw sizable rounds in a lively mix of sectors ranging from AI to fintech to quantum computing and cybersecurity. The biggest raise was for AI world-model developer, which secured a $310 million Series B. Venture investors also put money into AI infrastructure and AI models for biotech.

1. , $310M, artificial intelligence: Menlo Park, California-based Odyssey raised $310 million at a $1.45 billion valuation in a Series B round led by . Other investors included ,,,, and . Odyssey develops AI world models that create multimodal simulations of real-world environments. The startup has now raised $337 million in funding to date, .

2. , $140M, fintech: New York-based Chronograph secured a $140 million private equity round led by . The company provides portfolio monitoring, reporting and diligence software for private capital investors, an increasingly important market as private assets continue to grow. The new raise, which it describes as growth capital, brings its total funding to date to $160 million, according to .

3. (tied) , $100M, AI infrastructure: Boulder, Colorado-based Hydra Host raised a massive $100 million Series A led by . A of other investors joined, including ,, , and . The company operates a bare-metal GPU platform that connects customers to distributed AI computing infrastructure. With the latest investment, it has raised just under $119 million to date.

3. (tied) , $100M, cybersecurity: Startups that promise to protect companies in the AI era are also raising massive sums right out of the gate. This week, Santa Clara, California-based Ent.AI emerged from stealth and said it has raised $100 million in seed funding led by. Other investors included,, 1,, and. The company, founded by former executives and members of the Security Copilot team, offers an AI-powered workspace security platform that it says can analyze user and AI-agent behavior in real time to proactively prevent cyber threats.

3. (tied) , $100M, cybersecurity, defense: Arlington, Virginia-based Twenty Technologies secured a $100 million Series B at a $1 billion valuation. The round was led by, with participation from, and. The company develops AI-enabled cyber warfare systems for the U.S. military and intelligence community, helping automate and accelerate offensive cyber operations at scale. Founded by former cyber operators and defense technologists, Twenty Technologies has now raised $138 million to date,. It’s part of a growing wave of venture-backed startups building software for military and national security purposes.

3. (tied) , $100M, quantum computing: Berkeley, California-based Atom Computing raised a $100 million Series C led by that brings its total private investment to date to just over $191 million, . and also backed its latest round. Along with the venture money, Atom also received a $100 million Letter of Intent from the under the CHIPS and Science Act that gives the startup additional public backing in exchange for a minority government stake. The company develops neutral-atom quantum computers, one of several competing architectures seeking to commercialize quantum computing. It is one of several quantum startups to receive sizable funding deals this year, following a record-breaking venture investment year for the sector in 2025.

7. , $65M, biotechnology: Watertown, Massachusetts-based Triveni Bio raised a $65 million Series C co-led by and. Additional participation came from. The company develops antibody-based therapeutics for immunological and inflammatory diseases. It has now raised $272 million total from investors, .

8. (tied) , $52M, semiconductor infrastructure: Menlo Park, California-based AttoTude secured a $52 million Series C led by. Other investors included ,,,, 2, and. The startup develops high-speed interconnect technology for AI and hyperscale data centers and has raised $142 million to date, according to . It comes amid robust funding for semiconductor startups this year.

8. (tied) , $52M, digital media: Beverly Hills, California-based Richard Roths Media raised a $52 million venture round led by . The company says it delivered AI-driven marketing and advertising services for “high trust” industries such as banking, law and healthcare. The investment appears to be its first outside capital, per 91Ÿ«Æ·.

10. (tied) , $50M, artificial intelligence: San Francisco-based Bland AI raised a $50 million Series C led by . The of other investors includes , , founder , and others. The company develops AI-powered voice agents that automate inbound and outbound phone conversations for enterprises, a category that has seen growing adoption as businesses look to replace traditional call-center workflows. It has raised $106 million to date, according to .

10. (tied) , $50M, fintech: Brooklyn-based Interchecks secured a $50 million Series C led by,, and. The company operates a payments platform that allows businesses to manage deposits and payouts through a single API, reflecting continued investor interest in infrastructure that simplifies financial operations. It has now raised just under $79 million to date.

10. (tied) , $50M, artificial intelligence, biotechnology: Menlo Park, California-based Radical Numerics emerged from stealth and said it has raised a $50 million seed round led by, with participation from , and . The startup is developing AI models designed to simulate and predict biological systems, with the goal of accelerating drug discovery and advancing precision medicine.

Large non-US deals:

• The largest startup deal outside of the U.S. this week was very large indeed, and also very unusual. , the Chinese AI chatbot startup that briefly roiled public AI-related stocks in early 2025, reportedly took its first outside financing, worth roughly $7.4 billion. The Series A deal, however, comes with a lot of atypical caveats, notably that investors in the deal didn’t actually receive a stake in DeepSeek, but rather in an LLC controlled by founder , per . Those investors also reportedly face a five-year lockup and receive no voting rights.

Methodology

We tracked the largest announced rounds in the 91Ÿ«Æ· database that were raised by U.S.-based companies for the period of June 13-18. 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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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 91Ÿ«Æ· 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.

91Ÿ«Æ· 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.

Related 91Ÿ«Æ· queries:

Related reading:

• In Charts: Seed Deals Keep Getting Bigger As Odds Of Reaching Series A Fall Dramatically
• Amid Record Robotics Funding, Apptronik Raises $520M Series A Extension To Boost Production Of Humanoid Robot Apollo

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Six months later, a new tech came along that shook the market. While only a few customers left and the financials remained strong, the stock went down, fast.

This is the danger boards must confront. Disruption rarely announces itself during a crisis. It often appears when the business still looks strong.

For boards, AI, and eventually quantum computing, and other technologies, should not be treated as another technology trend. These technologies can reshape pricing, customer expectations, cybersecurity, product development, talent needs and the company’s business model itself.

Under this fast pace, evolving tech world, company boards should consider the following three points.

Measure the cost of inaction, not just the cost of adoption

Most boards ask: “How much will this AI initiative cost?” That’s the easy question.

The harder question is: “What will it cost if we are late?”

If a competitor uses AI to reduce costs, accelerate delivery, improve personalization or launch faster products, the cost of delay may be far greater than the investment required. The company may lose pricing power, customer loyalty and market relevance before the damage fully appears in the financials. Every major technology discussion should include a “cost of inaction” analysis.

What happens if the company is 12, 18 or 24 months behind? Which margins come under pressure? Which customers become vulnerable? What market image will I have that will impact future clients? Which parts of the product become commoditized?

Challenge the business while it still looks successful

Boards often become more aggressive only when performance weakens. By then, options are limited. The real test is whether the board can challenge management when revenue is growing, customers are renewing and the strategy still appears to work. Success may blur your vision as to what can go wrong.

Boards should regularly ask: Which part of our business would be most vulnerable if AI (or the next big tech change) made it cheaper, faster or easier to deliver? Which revenue stream depends on friction? Which product feature could become free? Which customer process could be automated by someone else?

These questions may feel uncomfortable when things are going well. That is precisely when they matter most.

Build the company that would disrupt your current company

Instead of asking only how to defend the current model, boards should ask management to design the competitor they would fear most.

What would that competitor do differently? How would it price? What teams would it build? What technologies would it use? Which costs would it eliminate? Would it bypass traditional distribution channels?

This exercise forces the company to think offensively. It pushes management to consider bold changes before they become urgent.

For AI, the impact is already visible across software, services, analytics, support, marketing and operations. For quantum, the timeline may be longer, but the strategic implications could be significant in cybersecurity, finance, pharma, logistics and materials science.

Boards do not need to chase every trend. But when technology changes how work is done at the core, when it changes cost structures, speed of development, brand reputation and distribution channels, it becomes a board-level issue.

is a strategic adviser to tech companies, investors, CEOs and boards, specializing in strategy, growth and M&A. He is a guest contributor to 91Ÿ«Æ· News and a university lecturer on strategy, finance and entrepreneurship. Learn more at and connect with him on .

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This tracker includes layoffs conducted by U.S.-based companies or those with a strong U.S. presence and is updated at least bi-weekly. We’ve included both startups and publicly traded, tech-heavy companies. We’ve also included companies based elsewhere that have a sizable team in the United States, such as , even when it’s unclear how much of the U.S. workforce has been affected by layoffs.

Layoff and workforce figures are best estimates based on reporting. We source the layoffs from media reports, our own reporting, social media posts and , a crowdsourced database of tech layoffs.

We recently updated our layoffs tracker to reflect the most recent round of layoffs each company has conducted. This allows us to quickly and more accurately track layoff trends, which is why you might notice some changes in our most recent numbers.

If an employee headcount cannot be confirmed to our standards, we note it as “unclear.”

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 91Ÿ«Æ· . 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. 91Ÿ«Æ· 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 91Ÿ«Æ· 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 91Ÿ«Æ· 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.”

Related 91Ÿ«Æ· query:

Related reading:

• 91Ÿ«Æ· Data: Venture Dollars For Black Startup Founders Stay Scarce Despite AI Funding Boom
• 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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