Seven companies raised billion-dollar fundings in August, tied with a few months for the year鈥檚 second-highest count after July, when 13 companies did the same.

The largest startup funding deal in August went to 13-year-old which raised $5 billion at a $190 billion valuation.

Other companies across a broad range of industries raised billion-dollar-plus rounds, a testament to the strength of the technology sector impacting聽 a range of more traditional industries, including physical manufacturing, defense, aerospace and energy. They included defense tech startup ; , which performs custom AI fine-tuning for businesses; low-orbit satellite network ; nuclear energy company ; automated coding provider ; and home battery service .

Notable exits

On the IPO front, Hangzhou, China-based humanoid robotics company went public on Aug. 19 and soared 460% on its first day of trading on the .

The largest M&A news in August was 鈥檚 announcement that it aims to acquire open-source AI platform for $12.9 billion. Other notable acquisition news was Milan-based tech aggregator 鈥� plan to acquire 13-year-old database company for around $1.3 billion.

Big rounds are coming faster

Venture capital continues to concentrate rapidly among a small group of fast-growing companies. Two of August鈥檚 billion-dollar recipients illustrate the trend: Databricks added $56 billion to its valuation in just six months, while River AI raised both its seed and Series A rounds this year, amassing a staggering $1.1 billion in early-stage funding.

That accelerated cadence extended across August鈥檚 megadeals: Five of the seven billion-dollar funding recipients had last raised capital less than 12 months earlier, including three that closed their previous rounds earlier this year. The numbers underscore how quickly investors are doubling down on companies they believe can become the next generation of technology giants.

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Methodology

The data contained in this report comes directly from 小蓝视频色情网页版, and is based on reported data. Data is as of Sept. 2, 2026.

Note that data lags are most pronounced at the earliest stages of venture activity, with seed funding amounts increasing significantly after the end of a quarter/year.

Please note that all funding values are given in U.S. dollars unless otherwise noted. 小蓝视频色情网页版 converts foreign currencies to U.S. dollars at the prevailing spot rate from the date funding rounds, acquisitions, IPOs and other financial events are reported. Even if those events were added to 小蓝视频色情网页版 long after the event was announced, foreign currency transactions are converted at the historic spot price.

Glossary of funding terms

Seed and angel consists of seed, pre-seed and angel rounds. 小蓝视频色情网页版 also includes venture rounds of unknown series, equity crowdfunding and convertible notes at $3 million (USD or as-converted USD equivalent) or less.

Early-stage consists of Series A and Series B rounds, as well as other round types. 小蓝视频色情网页版 includes venture rounds of unknown series, corporate venture and other rounds above $3 million, and those less than or equal to $15 million.

Late-stage consists of Series C, Series D, Series E and later-lettered venture rounds following the 鈥淪eries [Letter]鈥� naming convention. Also included are venture rounds of unknown series, corporate venture and other rounds above $15 million. Corporate rounds are only included if a company has raised an equity funding at seed through a venture series funding round.

Technology growth is a private-equity round raised by a company that has previously raised a 鈥渧enture鈥� round. (So basically, any round from the previously defined stages.)

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But startup investors haven鈥檛 abandoned the sector, either, 小蓝视频色情网页版 data shows. Instead, they鈥檙e being more selective about their bets and putting more money into companies using AI and other technology to make construction, property operations and real estate transactions faster and less expensive.

That shift shows up in both the year鈥檚 largest funding rounds and biggest acquisitions 鈥� and, notably, much of the biggest funding activity is happening outside the U.S.

The broad trend: Even before the pandemic-fueled funding peaks, proptech startups received more than double the venture funding in 2019 than in more recent years. While investors haven鈥檛 given up on proptech, funding to startups in the space remains down as interest rates hover in the .

In case you forgot, during the COVID-19 pandemic, home buyers and owners had access to 15-year mortgage interest rates as low as 2.5%. Those historically low interest rates fueled investor interest in the space, especially in the U.S.

Today, venture investors are backing startups working in areas such as AI-driven construction, property operations, underwriting and transaction infrastructure with demonstrable ROI. At the same time, more generic real estate software and later-stage companies without exceptional growth face significant funding challenges, our data shows.

And interestingly, four of the five largest deals in 2026 to date took place outside the United States.

The numbers: So far in 2026, global real estate-related startups have pulled in about $8.7 billion in seed- through growth-stage financing, per 小蓝视频色情网页版 . That compares to $24 billion in 2019, the second-highest year on record after the 2021 venture funding spike. It also compares to $12.3 billion raised in 2025. It appears that with four months left in the year, proptech funding is on pace to roughly match or slightly exceed 2025 levels.

Deal count is also down fairly significantly, with 794 deals so far this year. For context, the space saw more than 2,400 deals in 2019. Last year, the sector notched 1,446 transactions. The lower deal count signals both potentially decreased investor interest in the space and larger round sizes.

Noteworthy deals

The three largest deals in the proptech space so far took place in Europe, and two of those top deals involved companies doing work with steel.

Stockholm-based , a green steel startup, landed the largest haul in a private equity deal led by , also of Sweden. In June, the 6-year-old company raised about $1.6 billion in a transaction that made Wallenberg its majority owner.

In August, of Madrid raised $695 million in a venture round led by another Madrid-based company, , for its own green steel plant. The 3-year-old startup raised the money at a $3.1 billion valuation.

And in January, Amsterdam-based , a cloud-native hospitality management system, closed a $300 million Series D funding round at a $2.5 billion valuation. London鈥檚 led the financing for the 14-year-old company.

The only U.S. company to crack the top five when it comes to the largest deals was San Francisco-based autonomous construction tech startup , which raised $270 million in a Series B funding round in February. The financing, co-led by and , brought Bedrock鈥檚 total funding to over $350 million and valued the company at $1.75 billion.

Montreal-based AI-powered digital mortgage startup rounds out the list with a $216 million Series E raised in June at a $1.47 billion valuation.

Exits

There have been some meaningful proptech exits in 2026, although the activity is much stronger in M&A than in IPOs.

The only known significant initial public offering in the space was conducted in January by Columbia, Missouri-based , a construction-equipment rental company with a jobsite technology platform. EquipmentShare raised about $747 million in primary proceeds by pricing 30.5 million shares at $24.50. Including shares sold by existing holders, the offering totaled approximately $859 million.

Real estate-related startup M&A, however, has been robust in 2026 so far, with several of the largest transactions involving brokerage consolidation. Overall, the broad acquisition trend is centered around incumbents buying data, workflow ownership and distribution so they can build credible AI products more quickly.

The largest deal in the proptech space was $3.6 billion cash purchase of , which operated an AI-powered equipment maintenance and asset management platform, announced in May. (MaintainX had seen its valuation jump to $2.5 billion in 2025 after a $150 million Series D raise.)

There were several other large acquisitions.

• In January, completed its acquisition of in an all-stock $1.6 billion transaction that made it 鈥渢he world鈥檚 largest brokerage,鈥� according to .
• Construction tech giant announced in July that it was acquiring , a provider of aerial and ground-based reality-capture software for construction and other industries, for $845 million in cash. In a smaller deal, Procore also picked up construction AI-agent platform .
• Commercial real estate giant in August completed its $800 million cash purchase of , a housing-market data and technology provider for the homebuilding industry.
• And also in August, officially completed its $880 million acquisition of , forming a new parent entity named the Real REMAX Group.

The AI effect

AI is starting to move from the testing stage into everyday use across real estate and construction, according to a from and titled 鈥淧roptech鈥檚 Impact on Real Estate Innovation and Transformation.鈥�

The report says companies are using it to cut costs, make better decisions, and handle routine work more efficiently. Meanwhile, proptech is expanding beyond property-management software into areas such as construction, energy, infrastructure and climate technology.

Overall, proptech funding remains far below its pandemic-era highs, but the types of companies attracting money are evolving. Investors and buyers tend to favor businesses that can show they save customers time or money, particularly in construction, building operations and real estate finance. As such, the proptech sector increasingly includes companies that look quite different from those funded in years past.

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So far this year, a record $20.3 billion in global seed- through growth-stage funding has gone to companies in space- and satellite-related sectors, per 小蓝视频色情网页版 data. That鈥檚 already by far the highest annual tally on record, and we鈥檝e still got four months left in 2026.

Excitement extends beyond obvious markers like a behemoth IPO. The latest quarterly from venture investor declares that 鈥渢he space economy has entered a new era,鈥� and that 鈥渃apital is flowing at unprecedented scale,鈥� with scant indication of a near-term pullback.

It鈥檚 a global phenomenon as well, with the United States, China and Europe accounting for the overwhelming majority of funding. So far this year, U.S. startups pulled in around $12.7 billion, more than 60% of global space tech funding. Just over 20% of funding went to China-based companies, while Europe pulled in about 10%.

Top fundraisers

Funding looks robust, but, as usual, the larger rounds cluster at later stages.

This is true for 2026 fundraising leaders. The top-ranked investment recipient, , pulled in $5 billion in a May Series H. (Anduril is a diversified defense technology company rather than a pure-play space tech company, but it includes space and satellites among its focus areas.)

Shanghai-based , also referred to as SpaceSail, which is developing a low-Earth orbit satellite internet constellation to rival , was another prodigious fundraiser, pulling in a $1 billion round in August.

, a Torrance, California-based developer of large, high-powered satellites, also picked up a big round, securing $500 million in Series D funding in July.

For a broader view, below we put together a list of nine of this year鈥檚 largest space tech funding round recipients.

Exits rising

Needless to say, space tech investors aren鈥檛 just deploying capital 鈥� they鈥檙e also seeing eye-popping exit returns.

SpaceX set an initial valuation of nearly $1.8 trillion for its June IPO 鈥� the largest by far of any public offering to date 鈥� and raised over $80 billion in the process. Shares of the rocket developer, launch provider, Starlink operator and AI hyperscaler have fluctuated since then, but recently hovered near the initial offer price.

Of course, no other company operating in the space tech sector will come close to that. Leaving that aside, however, we did see some offerings and acquisitions that were significant by most other comps.

One example was , a private equity-backed space and defense tech company, which went public in January at a valuation of over $4 billion. Its stock has fallen sharply since then, however, indicating that a space tech focus alone is not enough to keep shares aloft.

More recently, , operator of a satellite constellation that sells signals intelligence to defense and government customers, went public in May. Its shares are also down some from their first-day closing price.

Startup M&A deals are also happening. York Space Systems announced this year that it is acquiring , a provider of satellite communications terminals, in a $355 million deal. It acquired two other venture-backed companies this year for undisclosed sums: , a developer of satellite propulsion systems, and , focused on solar energy for space.

Another recent market entrant, , also made a significant acquisition, picking up , a developer of lunar landers and rovers, for $300 million in June.

Risks and rewards

Of course, even the most sunnily optimistic startup investors don’t expect space tech valuations to always move up and to the right. It鈥檚 a notoriously risk-prone sector, and even the sector鈥檚 high-valuation market newcomer, SpaceX, has suffered its share of rocket failures and other high-profile disappointments.

That said, startup backers clearly believe space tech rewards outweigh the risks. We鈥檒l see in coming quarters if that still holds true.

Correction: The 2025 dollar amount in the chart was updated.

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The current focus on AI IPOs is largely centered on public market performance. Investors want to know whether these companies justify their valuations and how their shares will trade after listing.

But everybody is watching the wrong metric. The more consequential story begins after the bell rings, when limited partners receive distributions and decide where to deploy that capital next.

At sufficient scale, AI IPOs become a capital formation event for the broader venture ecosystem. If several of the largest AI companies reach the public markets over the next few years, those exits could reshape venture fundraising and further concentrate capital among the industry鈥檚 largest firms.

The real story begins after the IPO

The more meaningful process starts when investors receive distributions from successful exits. Pension funds, university endowments, sovereign wealth funds and family offices rarely leave that capital sitting idle for long. As portfolios are rebalanced, investment committees begin evaluating new commitments across private markets.

Venture has spent several years waiting for meaningful liquidity. Higher private valuations may improve paper returns, but they do not return capital to limited partners. Only successful exits complete that cycle.

鈥檚 $85.7 billion IPO illustrates both the potential and the limits of a single listing. One IPO alone is unlikely to transform venture fundraising. But a sustained wave of listings involving companies such as , , and could steadily return capital to investors and give limited partners fresh resources to recommit.

Liquidity drives the next fundraising cycle

The importance of the next AI IPOs lies less in their individual performance than in their combined effect on venture fundraising.

As capital flows back to limited partners, investment committees gain both the liquidity and the flexibility to make new commitments. How those commitments are distributed will shape the industry鈥檚 next phase.

Recent fundraising trends suggest capital is likely to remain concentrated. According to the , the 10 largest U.S. venture funds captured nearly one-third of all capital raised in 2025, while first-time fund formation in more than a decade. If a new wave of liquidity reaches the market, established managers with proven track records are likely to receive the largest share.

offers a useful illustration. The firm recently raised over $15 billion across five funds, an amount equivalent to more than 18% of all U.S. venture capital dollars raised during 2025. Stronger distributions could leave the industry鈥檚 largest firms in an even better position to raise successor funds.

Capital will not flow evenly

Limited partners typically increase commitments to managers with established track records before expanding relationships with emerging firms. Successful exits reinforce confidence in those managers, making them the natural destination for a disproportionate share of new allocations.

The effects extend beyond fundraising. A $15 billion fund approaches ownership, pricing and portfolio support differently from a $500 million fund. Large funds need meaningful ownership and outcomes capable of returning multibillion-dollar vehicles. They can lead larger rounds, pay higher prices, defend ownership through multiple financings, and support companies for longer.

This is not a liquidity flywheel. It is a concentration flywheel. Successful investments generate distributions. Those distributions help the industry鈥檚 largest firms raise larger successor funds, reinforcing their competitive advantages. Over time, liquidity strengthens fundraising, and fundraising strengthens market position. The market may become larger without becoming broader.

Founders will feel the effects. Large investment platforms can finance companies for longer and compete more aggressively for ownership in the relatively small number of businesses capable of producing returns at their scale. The result could be a more pronounced barbell market: a limited group of companies attracts enormous amounts of capital, while businesses outside the dominant sectors face a more constrained financing environment.

Pay attention to LP liquidity, not just IPO pricing

Public investors will remember this AI IPO cycle by its opening prices. Venture investors may remember it for something else entirely.

It may be the moment capital began concentrating around a handful of firms at a speed the industry has never experienced.

The IPOs themselves will make headlines. The redistribution of power inside venture capital will shape the next decade.

is a general partner at , a VC firm investing in early-stage startups in AI, cybersecurity and digital health, and helping them expand into the U.S. market.

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All told, fintech startups raised $28.6 billion globally in the first half of 2026, a 22.7% increase from the first half of 2025, but down 17.3% compared to the $34.6 billion raised in the second half of last year. (It鈥檚 important to note that H2 2025 marked the strongest six-month funding period for fintech startups since the second half of 2022.)

Fintech funding in the first half of 2026 also topped the sector鈥檚 investment totals in 2020 and the pre-pandemic year of 2019, though they remain lower than the peak funding year of 2021 as well as 2018.

Historically, the United States has led the globe when it comes to fintech funding, and the first half of this year was no exception. More than 52% 鈥� $15 billion 鈥� of the global fintech funding in H1 flowed into companies based in the U.S. The United Kingdom was the second-largest recipient of capital, with companies there raising a collective $2.7 billion. India came in third, with a total of $1.9 billion raised, 小蓝视频色情网页版 data shows.

Deal count drops

Even as dollar volume climbed, deal flow into venture-backed fintech startups fell fairly significantly in H1 2026, 小蓝视频色情网页版 data shows. The first half of the year saw 1,605 funding deals announced in the sector, a 25.7% decline from the more than 2,161 completed in H1 2025 and down 40% from H1 2024.

Where investors are placing their bets

Active fintech investors who spoke with 小蓝视频色情网页版 News said they see a split market emerging.

In general, the startup investment market has been cleaved into two extremes, with funding either pouring into brand-new companies or concentrating into a tiny handful of larger, established giants, according to , a partner at (Google Ventures).

The fintech sector is following the same pattern, Sakach told 小蓝视频色情网页版 News via email, but its biggest players are using their size in an unusual way. 鈥�2026 marks the definitive ‘lab-i-fication’ of the modern corporation,” she noted, with some fintech platforms using their scale and steady profits to fund experimental new divisions.

Because these companies have significant data and distribution advantages, they are becoming magnets for top-tier workers, according to Sakach. For instance, she said, is now competing directly with top AI research labs for engineering talent, while is using its dominant position to build out new products in enterprise billing and blockchain.

For early-stage startups inside the U.S., the focus is shifting away from copying legacy financial services toward creating entirely new categories.

Wealth management is seeing a massive surge, driven by an influx of assets from a younger generation demanding AI tools, Sakach pointed out.

Fintech startups are also targeting massive, hidden corporate headaches.

鈥淎 50% reduction in global chargebacks is a ~$60 billion opportunity when accounting for both the merchant and banking overhead,鈥� she said.

The biggest shift, however, is happening around artificial intelligence and financial services. 鈥淐oding was AI’s first killer use case; financial markets could be the second, given its extraordinarily broad corpus of data,鈥� said Sakach, pointing to new concepts such as automated hedge funds and prediction markets.

, partner at , said the firm鈥檚 investments into the fintech sector have surged this year, as areas such as money movement infrastructure, stablecoins and tracking of real-world assets on the blockchain draw attention.

鈥淲e’ve never been busier: The quality of founders, the size of the markets they’re going after, and the maturity of the technology being built has never been more impressive,鈥� he said.

Those trends showed up among fintech鈥檚 largest fundraisers last quarter, with companies such as New York-based , which is building an agentic decision platform for banks and insurers, and , an African payments infrastructure startup, clinching some of the period鈥檚 largest funding deals. Both raises took place in June, with Taktile raising a $110 million Series C funding round led by and Flutterwave landing a Series E round of an undisclosed amount that valued the company at $3.2 billion.

Risks and opportunities

Even with a wealth of new opportunities in the sector, investors are also wary of the risks introduced by AI and hype around businesses that don鈥檛 have a clear path toward growth or profitability.

Sakach was particularly skeptical of new stablecoin networks that lack a clear way to get users, personal credit card startups with tough profit margins, and traditional banking software.

The problem with selling software to legacy banks is that their slow buying cycles 鈥渆ffectively break the hypervelocity speed needed for AI-level product evolution,鈥� she said. Instead, Sakach believes that AI tools will likely succeed by embedding highly specialized engineering teams directly into specific business units.

The era of the generic digital bank or basic payment app is largely over, in Overdorff鈥檚 view: 鈥淲ithout a real wedge or distribution advantage, it’s hard to build a durable business there.”

The real value of AI right now is its ability to act as the central engine for financial products rather than just a side feature, Overdorff believes. Startups are using the technology to compress complex underwriting, fraud detection and advisory workflows 鈥渢hat used to take teams of analysts weeks into tasks that happen in minutes.鈥�

As a result, traditional industries such as tax and audit are being completely upended, he said.

Traditional financial institutions, which are usually the slowest to adopt new tech, are finally bringing AI into their core operations, though Overdorff cautioned 鈥渢hat shift is opening up as much risk as opportunity.鈥�

He also flagged the cybersecurity risks associated with the rapid adoption of new technologies and AI into the financial system. 鈥淭he compliance and governance layer becomes just as important as the AI itself,鈥� he wrote.

Mega-valuations keep top fintechs private

While the fintech IPO market was robust in 2025, it has been markedly quieter in the U.S. so far this year. Three fintech companies went public in the first half of 2026, and they were all foreign companies opting to list in New York: Brazil鈥檚 and and Japan鈥檚 . That鈥檚 the same number of finance-related startups that went public in the first half of 2025, when , and made their debuts.

Many of the fintech companies expected to list in 2026 have remained private, often at escalating valuations. That includes fintech giants such as Stripe, , Ramp, , and others that have opted for more private financing, secondary sales or simply waiting out the public markets.

For example, in February, payments infrastructure giant Stripe announced it had inked deals with investors to provide liquidity to current and former employees through a tender offer at a $159 billion valuation. That valuation represented an impressive 49% increase from the $106.7 billion Stripe was valued at in September, when it completed .

In early June, expense management startup Ramp announced a $750 million funding round at a $44 billion valuation, just a few months after raising $300 million at a $32 billion valuation.

The H2 outlook

The trend of capital concentration seen in the first half of the year will continue into H2, Overdorff predicted, with 鈥渕ega-rounds for a small set of category leaders, and a tougher fundraising environment for everyone else.鈥�

And while AI adoption will continue to deepen rather than flatten out, the industry will also be watching the stock market closely. The conversation around IPOs is heating up for mature fintech companies, though Overdorff notes that 鈥渢he timing may hinge on how other high-profile tech IPOs perform this year.鈥�

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To understand how institutional capital is navigating this gap, 小蓝视频色情网页版 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.

小蓝视频色情网页版 News: You鈥檝e 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?聽

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?聽

Ranum: I鈥檇 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鈥檚 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 鈥渂oring software,鈥� 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鈥檛 AI-native from day one?

Ranum: I don鈥檛 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 鈥渟how me鈥� 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鈥檙e 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?聽

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鈥檚 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?聽

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?聽

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鈥檓 focused.

In pure software, a buggy AI agent might mean a broken spreadsheet or a weird email draft 鈥� 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?聽

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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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.

小蓝视频色情网页版 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鈥檛 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鈥檝e 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 鈥� after led the seed investment 鈥� and I鈥檝e 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鈥檚 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 鈥� 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 鈥� 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 鈥� combining three different chip types to lower the total cost of ownership 鈥� 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鈥檚 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鈥檚 a fantastic outcome 鈥� 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 鈥� 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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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鈥檚 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 .

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

He added that the company is eyeing the public markets, calling the financing 鈥渁 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 小蓝视频色情网页版 . That puts the space on track to come in roughly flat with or a bit up from the prior three years 鈥� when annual funding hovering around the $8 billion mark 鈥� 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鈥檚 valuation at $9.25 billion 鈥� more than double its Series C level in 2025.

Earlier this month, Germany鈥檚 , 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 鈥渙mni-bodied鈥� 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鈥檚 latest round, which included participation from , 鈥檚 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鈥檚 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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So far in 2026, U.S. companies have pulled in nearly 80% of global seed- through growth-stage financing, per 小蓝视频色情网页版 data. That鈥檚 a sharp divergence from the years leading up to the AI boom, when American companies typically secured less than half of all investment.

Gap for AI is even more pronounced

The U.S. share of artificial intelligence-related investment is even greater.

So far this year, nearly 88% of AI-related startup funding, or $319 billion, went to U.S.-headquartered companies, per 小蓝视频色情网页版 data. Of that, most went to just two recipients, and .

Since both Anthropic and OpenAI are on track for public market debuts later this year, it鈥檚 possible next year鈥檚 comps will be less lopsided, as they won鈥檛 be raising any more giant late-stage financings. We鈥檒l see.

Large venture hubs outperform small and mid-sized ones

Although no other country comes close to the U.S. for startup funding, a few of the larger technology investment hubs are seeing year-over-year gains.

Funding to China鈥檚 startups, in particular, is on the rise after several sluggish years. So far in 2026, startups have raised over $33 billion, per 小蓝视频色情网页版 data, already surpassing the total for all of 2025.

The United Kingdom is also looking up. U.K.-based startups have pulled in $16.5 billion so far this year, compared to $19.5 billion in all of 2025. AI and fintech are the country鈥檚 leading sectors for investment.

Other mid-sized venture markets are seeing funding levels this year that are on track to be flat or moderately higher year over year, per 小蓝视频色情网页版 data. In Europe, this includes France, Spain and Germany.

In Asia, India, Japan and South Korea are also neither way up nor way down. Canada and Australia, meanwhile, aren鈥檛 in a slump but also aren鈥檛 seeing any major AI-focused funding raised this year.

Maybe it鈥檚 a US bubble?

Now that more than three-fourths of startup funding is going to U.S. companies, it seems timely to note that the country is home to only a little over 4% of the global population.

On the tech startup front, it鈥檚 undoubtedly an impressive 4%. The U.S. has an unrivaled track record for building leading technology companies, along with the capital and talent to keep on doing so.

That said, certain trends do warrant some serious bubble consideration. The anomalously high concentration of startup funding into American companies is one of them.

Surely many of the countries in which the remaining 96% of people on Earth dwell possess entrepreneurial talent, infrastructure and economic might that could support more than just a measly 12% share of AI startup funding. If one was a betting type, it鈥檚 hard not to argue that the odds for that look pretty good.

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