Correctly calling a market peak is a notoriously tricky endeavor.
Case in point: When tech stocks and startup funding hit their last cyclical peak four years ago, few knew it was the optimal time to cease new deals and cash in liquidatable holdings.
This time around, quite a few market watchers are wondering if the tech stock and AI boom has reached bubble territory. And, as we explored in Friday’s column, there are plenty of similarities between current conditions and the 2021 peak.
Even so, by other measures we’re also in starkly different territory. The current boom is far more concentrated in AI and a handful of hot companies. The exit environment is also much quieter. And of course, the macro conditions don’t resemble 2021, which had the combined economic effects of the COVID pandemic and historically low interest rates.
Below, we look at four of the top reasons why this time is different.
Four years ago, funding to most venture-backed sectors was sharply on the rise. That’s not the case this time around. While AI megarounds accumulate, funding to startups in myriad other sectors continues to languish.
Biotech is on track to capture the smallest percentage of U.S. venture investment on record this year. Cleantech investment looks poised to hit a multiyear low. And consumer products startups also remain out of vogue, alongside quite a few other sectors that once attracted big venture checks.
The emergence of AI haves and non-AI have-nots means that if we do see a correction, it could be limited in scope. Sectors that haven’t seen a boom by definition won’t see a post-boom crash. (Though further declines are possible.)
The new offering market was on fire in 2020 and 2021, with traditional IPOs, direct listings and SPAC mergers all flooding exchanges with new ticker symbols to track.
In recent quarters, by contrast, the IPO market has been alive, but not especially lively. We’ve seen a few large offerings, with CoreWeave, Figma and Circle among the standouts.
But overall, numbers are way down.
In 2021, there were hundreds of U.S. seed or venture-backed companies that debuted on NYSE or Nasdaq, per Crunchbase data. This year, there have been less than 50.
Meanwhile, the most prominent unicorns of the AI era, like OpenAI and Anthropic, remain private companies with no buzz about an imminent IPO. As such, they don’t see the day-to-day fluctuations typical of public companies. Any drop in valuation, if it happens, could play out slowly and quietly.
That brings us to our next point: In addition to spreading their largesse across fewer sectors, startup investors are also backing fewer companies.
This year, the percentage of startup funding going to megarounds of $100 million or more reached an all-time high in the U.S. and came close to a record global level. A single deal, OpenAI’s $40 billion March financing, accounted for roughly a quarter of U.S. megaround funding.
At the same time, fewer startup financings are getting done. This past quarter, for instance, reported deal count hit the lowest level in years, even as investment rose.
The last peak occurred amid an unusual financial backdrop, with economies beginning to emerge from the depths of the COVID pandemic and ultra-low interest rates contributing to investors shouldering more risk in pursuit of returns.
This time around, the macro environment is in a far different place, with “a “low fire, low hire” U.S. job market, AI disrupting or poised to disrupt a wide array of industries and occupations, a weaker dollar and a long list of other unusual drivers.
What both periods share in common, however, is the inexorable climb of big tech valuations, which brings us to our final thought.
While the argument that this time it’s different is a familiar one, the usual plot lines do tend to repeat themselves. Valuations overshoot, and they come down. And then the cycle repeats.
We may not have reached the top of the current cycle. But it’s certainly looking a lot closer to peak than trough.
Illustration: Dom Guzman
Want to keep track of the largest startup funding deals in 2025 with our curated list of $100 million-plus venture deals to U.S.-based companies? Check out The Crunchbase Megadeals Board.
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 rounds here.
This was another active week for large startup financings. AI data center developer Crusoe Energy Systems led with $1.38 billion in fresh financing, and several other megarounds were AI-focused startups. Other standouts hailed from a diverse array of sectors, including battery recycling, biotech and even fire suppression.
1. Crusoe Energy Systems, $1.38B, AI data centers: Crusoe Energy Systems, a developer of AI data centers and infrastructure, raised $1.38 billion in a financing led by Valor Equity Partners and Mubadala Capital. The deal sets a $10 billion+ valuation for the Denver-based company.
2. Avride, $375M, autonomous vehicles: Avride, a developer of technology to power autonomous vehicles and delivery robots, announced that it secured commitments of up to $375 million backed by Uber and Nebius Group. The 8-year-old, Austin, Texas-based company said it plans to launch its first robotaxi service on Uber’s platform in Dallas this year.
3. Redwood Materials, $350M, battery recycling: Battery recycling company Redwood Materials closed a $350 million Series E round led by Eclipse Ventures with participation from new investors including Nvidia’s NVentures. Founded in 2017, the Carson City, Nevada-based company has raised over $2 billion in known equity funding to date.
4. Uniphore, $260M, agentic AI: Uniphore, developer of an AI platform for businesses to deploy agentic AI, closed on $260 million in a Series F round that included backing from Nvidia, AMD, Snowflake Ventures and Databricks Ventures. The round sets a $2.5 billion valuation for the Palo Alto, California-based company.
5. Sesame, $250M, voice AI and smart glasses: San Francisco-based Sesame, a developer of conversational AI technology and smart glasses, picked up $250 million in a Series B round led by Sequoia Capital. The startup is headed by former Oculus CEO and co-founder Brendan Iribe.
6. OpenEvidence, $200M, AI for medicine: OpenEvidence, developer of an AI tool for medical professionals that has been nicknamed the “ChatGPT for doctors” reportedly raised $200 million in a GV-led round at a $6 billion valuation. Three months earlier, OpenEvidence pulled in $210 million at a $3.5 billion valuation.
7. Electra Therapeutics, $183M, biotech: Electra Therapeutics, a developer of therapies against novel targets for diseases in immunology and cancer, secured $183 million in a Series C round. Nextech Invest and EQT Life Sciences led the financing for the South San Francisco, California-based company.
8. LangChain, $125M, AI agents: LangChain, developer of a platform for engineering AI agents, picked up $125 million in fresh funding at a $1.25 billion valuation. IVP led the financing for the 3-year-old, San Francisco-based company.
9. ShopMy, $70M, brand marketing: New York-based ShopMy, a platform that connects brands and influencers, landed $70 million in a funding round led by Avenir. The financing sets a $1.5 billion valuation for the 5-year-old company.
10. Seneca, $60M, fire suppression: Seneca, a startup developing a fire suppression system that includes autonomous drones that help spot and put out fires, launched publicly with $60 million in initial funding. Caffeinated Capital and Convective Capital led the financing for the San Francisco-based company.
We tracked the largest announced rounds in the Crunchbase database that were raised by U.S.-based companies for the period of Oct. 18-24. Although most announced rounds are represented in the database, there could be a small time lag as some rounds are reported late in the week.
Illustration: Dom Guzman
Nearly four years ago, the market hit a cyclical peak under conditions that in many ways look quite similar to what we’re seeing today.
Sky-high public tech valuations. Booming startup investment. Sharply rising valuations. And, a few cracks emerging on the new offering front.
Sure, there are quite a few differences in the investment environment, which we’ll explore in a follow-on piece. For this first installment, however, we are focusing on the commonalities, with an eye to the four highlighted above.
First, both then and now, tech stocks hit unprecedented highs.
In mid-November 2021, the tech-heavy Nasdaq Composite index hit an all-time peak above 16,000. Gains stemmed largely from sharply rising tech share prices.
Today, the Nasdaq is hovering not far below a new all-time high of over 23,000. The five most valuable tech companies have a collective market cap of more than $16 trillion. Other hot companies, like AMD, Palantir Technologies and Broadcom have soared to record heights this year.
While private startups don’t see day-to-day valuation gyrations like publicly traded companies, their investors do take cues from public markets. When public-market bullishness subsides, private up rounds tend to diminish as well.
In late 2021, just like today, venture investment was going strong.
Last time, admittedly, it was much stronger. Global startup funding shattered all records in 2021, with more than $640 billion invested. That was nearly double year-earlier levels. Funding surged to a broad swathe of startup sectors, with fintech in particular leading the gains.
For the first three quarters of this year, by contrast, global investment totaled a more modest $303 billion. However, that’s still on track for the highest tally in years. The core driver is, of course, voracious investor appetite for AI leaders, evidenced by OpenAI’s record-setting $40 billion financing in March.
The pace of unicorn creation is also picking up, which brings us to our next similarity.
At the last market peak, valuations for hot startups soared, driven in large part by heated competition among startup investors to get into pre-IPO rounds.
This time around, we’re also seeing sought-after startups raising follow-on rounds in quick succession, commonly at sharply escalated valuations. Per Crunchbase data, dozens of companies have scaled from Series A to Series C within just a couple of years, including several that took less than 12 months.
We’re also seeing prominent unicorns raising follow-on rounds at a rapid pace this year. Standouts include generative AI giants as well as hot startups in vertical AI, cybersecurity and defense tech.
During the 2021 market peak, even when the overall investment climate was buzzier than ever, we did see some worrisome developments and areas of declining valuations.
For that period, one of the earlier indicators was share-price deterioration for many of the initial companies to go public via SPAC. By late 2021, it had become clear that there were numerous “truly terrible performers” among the cohort, including well-known names such as WeWork, Metromile and Buzzfeed.
This time around, the new offerings market hasn’t been quite so active. But among those that did go public in recent months, performance has been decidedly mixed. Shares of Figma, one of the hottest IPOs in some time, are down more than 60% from the peak.
Online banking provider Chime and stablecoin platform Circle have shown similar declines.
At this point, these are still generously valued companies by many metrics. But it’s also worth noting the share price direction in recent months has been downward, not upward.
Looking ahead, one of the more reliable techniques to determine whether we are approaching peak or already past is to look for more cracks in the investment picture. Are GenAI hotshots struggling to secure financing at desired valuations? Is the IPO pipeline still sluggish? Are public tech stocks no longer cresting ever-higher heights?
Cracks can take some time to emerge, but inevitably, they do.
Illustration: Dom Guzman
AI startups are raising capital at record speed. According to Crunchbase data, AI-related companies have already raised $118 billion globally in 2025. And, so far, traction looks impressive. AI startups are posting stellar revenue growth, and even the $100 million ARR milestone is often achieved.
While this growth is breathtaking, some analysts are beginning to question its sustainability. They warn that AI spending may soon reach a peak and that unprofitable tech companies could be hit hardest when the cycle turns. If that happens, many investors in AI will find themselves in a difficult position.
Predicting a bubble is rarely productive, but preparing for volatility is. It would be wise for both founders and investors to ensure that portfolio companies have enough resilience to withstand a potential market shock.
The key lies in assessing the durability of ARR. In a major downturn, the “growth game” quickly becomes a survival game. History suggests that while a few companies may continue to grow more slowly, the majority will struggle or disappear.
The question, then, is how to tell the difference between sustainable and hype-driven ARR.
Several factors set true, sustainable revenue growth apart from hype.
The first is customer commitment. Sustainable revenue comes from multiyear contracts, repeat renewal cycles and budgeted spend within core IT or operating lines. When revenue depends on pilots, proofs of concept or amorphous “innovation” budgets, it can vanish when corporate priorities shift. A company that touts these short trials as ARR is really reporting momentum, not recurring income.
This is what investor Jamin Ball has called experimental recurring revenue.
Traditional software firms can thrive with monthly churn in the low single digits — think 5% to 7%. But many AI companies are seeing double that. This means they have to sprint just to stand still, constantly replacing users who move on to the next shiny tool.
Another differentiator? Integration and workflow depth. Durable ARR is embedded into the customer’s core workflows, data pipelines or multiple teams. Ripping it out would be costly and disruptive. Hype ARR, by contrast, lives on the surface — lightweight integrations, fast deployments and limited stakeholders. Without unique intellectual property or deep workflow integration, such products can be replaced with minimal friction.
And finally, real growth is defined by clear value-add. True ARR is backed by measurable ROI, well-defined outcomes and long-term customer roadmaps.
In contrast, hype ARR is driven by urgency (we need to show our shareholders our AI deployment ASAP), or undefined ROI. In those cases, customers don’t even know how to define success. They are testing, not committing.
It is important to put ARR traction in context. Investors and founders should focus on a broader set of indicators — conversion from pilots to long-term contracts, contract length and expansion, net revenue retention, and gross margin trajectory. These metrics reveal if growth is sustainable.
It would also be helpful to assess the product’s real impact: efficiency uplift (more code, content, or customer conversations per employee-hour), accuracy improvement (e.g. for detecting bad actors), and higher conversion rates, among others. These metrics should exceed client expectations and outperform alternative tools. That’s what signals genuine value creation and a higher chance for experimental revenue to turn into durable ARR.
After all, AI may be changing how fast companies can form and grow, but it hasn’t suspended the basic laws of business.
For founders, the message is simple: Celebrate ARR if you so wish, but pair it with proof of retention, profitability and defensibility. For investors, resist the urge to chase every eye-popping run rate. The real competitive edge in this next phase of AI is stability, not spectacle.
Alexander Lis is the chief investment officer at Social Discovery Ventures. With 10-plus years of experience across public markets, VC, PE and real estate, he has managed a public markets portfolio that outperformed benchmarks, led early investments in Sumsub, Teachmint and Byrd, and achieved 20%-plus IRR by investing in distressed real estate across the U.S.
Illustration: Dom Guzman
Editor’s note: This article is part of an ongoing series in which Crunchbase News interviews active investors in artificial intelligence. Read previous interviews with Foundation Capital, GV (formerly Google Ventures), Felicis, Battery Ventures, Bain Capital Ventures, Menlo Ventures, Scale Venture Partners, Costanoa, Citi Ventures, Sierra Ventures, Andrew Ng of AI Fund, and True Ventures, as well as highlights from more interviews done in 2023.
Fueled by AI, both Dell and its investment arm are on a hot streak this year.
The PC maker has seen demand for its server products surge with $20 billion in AI server shipments projected for fiscal 2026. At the same time, its investment arm, Dell Technologies Capital (DTC), has notched five exits — an IPO and four acquisitions — since June, an especially notable track record in a venture industry that has been challenged in recent years by a liquidity crunch.
On the heels of that success, we recently spoke with Dell Technologies Capital managing director Daniel Docter and partner Elana Lian.
DTC was founded more than a decade ago and operates as a full-stack investor, backing everything from silicon to applications.
“One big part of our network is the Dell relationship, which is the leader in GPU servers,” said Docter. As a result, Dell is connected to all the major players in the AI space, he said.
One of its earliest AI investments was during the machine-learning era in 2014 in a company called Moogsoft. Dell went on to acquire the alert remediation company in 2023.
DTC’s investment thesis was that the advent of machine learning was going to disrupt the tech industry. At that time, data had expanded to such a degree that new tools were required by the market to analyze data and find patterns, which informed the firm’s early investments in AI.
The investment team at DTC is largely comprised of technical people, often “double E” degree engineers.
Docter has an electrical engineering background, worked at Hughes Research Labs, now HRL Laboratories, and transitioned from engineering to business development. He joined the venture industry 25 years ago and spent more than a decade at Intel. He joined DTC in 2016 through Dell’s EMC acquisition.
Lian worked in semiconductors for a decade, joined Intel Capital in 2010, and then joined Dell Technologies Capital in 2024.
Docter believes we are in the fifth generation of AI, which becomes more powerful with every iteration.
“We’re seeing that AI is almost a data problem,” said Lian. “For AI to get better and better, there’s an uncapped ceiling where there’s high-quality data coming in.”
The team is meeting startups focused on training, inference, reasoning and continuous learning along with safety requirements. Data is core to these advancements.
Even the definition of data is changing. “It used to be a word, then it was a context, then it was a task or a rationale or a path. Then it’s reasoning,” said Docter. “Who knows what’s next?”
As AI improves, there is demand for frontier data and for specialized data in fields such as philosophy, physics, chemistry and business. Humans are in the loop as these capabilities expand, said Docter, which has informed some of the firm’s investments.
DTC is a financial investor, assessing a potential company on whether it is a good investment, rather than backing businesses based on Dell’s strategic goals.
Startup revenue is exceeding what was previously possible, Docter said: “I’ve been doing this for 25 years. I’ve never seen companies that have this type of revenue growth.”
The best deals are always hotly contested, he noted.
The question to ask when it comes to revenue, Docter said, is: “Is that an innovation CTO office budget? Or is that a VP of engineering budget?”
When assessing a potential portfolio investment the team asks: “Is revenue durable? Is there value in using this?”
The pace of investment also seems unprecedented. “We’ll meet with a company on a Tuesday for the first time and sometimes by Thursday, they have a term sheet that they’ve already signed,” he said.
The firm does not have a dedicated fund size, which is an advantage as it can be flexible in the size of the check as well as the stage to make a commitment and how it invests over time.
DTC has invested $1.8 billion to date across 165 companies. It likes to invest early, at seed or Series A, with check sizes running from $2 million to $12 million, and leads or co-leads 80% of new deals. The firm makes around 15 to 16 new investments per year.
Once DTC has invested, it looks at how the firm can help portfolio companies sell to potential customers across Dell’s deal partner network.
This year, DTC has posted five exits, including Netskope’s IPO and four acquisitions: Rivos by Meta, SingleStore by Vector Capital, TheLoops by Industrial & Financial Systems and Regrello by Salesforce 1.
DTC is investing a little more actively than it has in the past, but remains disciplined, Docter said. The investment team is focused on complex enterprise use cases and challenges, following the Warren Buffett rule, which is to invest in what you know.
The firm invests at the silicon level because you “can be incredibly disruptive to the ecosystem,” said Docter.
The DTC portfolio companies we discussed include the following in areas ranging from silicon to applications.
Infrastructure and hardware layer:
Applications:
A major area of interest for Lian is advancements in voice AI, the day-to-day human interaction with a machine.
It’s hard to imagine that the transformer architecture is the last and final architecture, said Docter. The firm has made investments in companies creating different architectures in Cartesia, a leader in state-space model, which has a longer context window building a new reasoning model with a different architecture, initially focused on voice AI. DTC has also invested in Israeli-based AA-I Technologies, which is working on a new type of reasoning model architecture.
“Right now, the opportunity of AI is this big, but this ball keeps on exploding,” said Lian. “The contact area is getting bigger and bigger. And that’s the same for the data.”
Illustration: Dom Guzman
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