Among the top 50 private companies globally, the US and China account for 86% of the list, while AI startups represent 40%. These companies are reshaping industries and, in some cases, surpassing their public market competitors in valuation. 

OpenAI is reportedly poised to hit a roughly $500B valuation — putting it closer to the ranks of big tech than any other startup. At the same time, the current top 50 companies’ combined valuation represents under half of Nvidia’s current market cap of $4.3T, underscoring the relative scale of public tech giants.

Using CB Insights data, we analyzed the top 50 most valuable private companies globally to identify where value creation is happening in private markets. Below are the key patterns emerging from the group.

Track all 1,200+ unicorns

Key takeaways

• The United States and China dominate the global unicorn landscape, representing 86% of the top 50 companies. The US leads with 35 companies (70%), while China contributes 8 companies (16%), showing how concentrated tech innovation and capital formation remains within these two tech regions. The remaining 6 countries — Australia, France, Germany, Singapore, Sweden and the UK — each have only 1-2 representing companies.
• AI companies represent 40% of the top 50, signaling the market’s confidence in AI as a primary driver of economic value. These companies range from the big names building foundation models like OpenAI and Anthropic to specialized players tackling applications like defense systems (Helsing, Anduril).
• Abundant private funding enables companies to delay going public while continuing to scale. Today, startups are going public an average of 16 years after being founded, 4 years later than just a decade ago. Databricks recently surpassed its public competitor Snowflake in valuation ($100B) at its recent $1B Series K round. Meanwhile, ByteDance ($300B valuation), generated more revenue than Meta in Q1’25 while staying private. With plenty of private capital available and employees able to sell shares on secondary markets, companies can grow much larger without going public.
• Secondary transactions are increasingly driving valuations, with 7 consecutive quarters of YoY growth in transaction activity among VC-backed companies. Recent secondary sales at companies like Canva (valued at $42B, up from $32B in 2024), Revolut (valued at $75B, up from $45B), and OpenAI’s upcoming $10.3B secondary sale at a rumored $500B valuation demonstrate this trend. As startups stay private for longer, secondary sales are providing both liquidity and fresh valuations. 

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Advancements in AI and quantum computing are driving this growth, helping these platforms reach high enough accuracy levels to replace physical trials and rival traditional lab methods, while operating at a fraction of the cost and time.

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OpenAI’s recent $1.1B purchase of A/B testing & experimentation platform Statsig at a 27.5x revenue multiple — one of its largest acquisitions to date — highlights this shift as model performance gains become increasingly expensive and incremental. Statsig’s CEO Vijaye Raji will also join OpenAI as CTO of Applications to accelerate development of its consumer and enterprise products.

The trend is taking shape across the AI landscape this year. In August, Databricks acquired feature store company Tecton to support its AI agent business, while DataDog snatched up A/B testing company Eppo to strengthen its application development suite in May.

Using CB Insights’ predictive signals, including Mosaic company health scores and M&A probability, we’ve identified the product testing and development platforms that tech giants could acquire next.

Companies sourced from CB Insights markets covering feature stores & management, product management, product analytics, and A/B testing & experimentation platforms. 

Key takeaways

• AI-focused companies with proven revenue are prime targets: Almost all of the companies in the interest zone enable AI product development. Mixpanel and Snowplow provide product analytics for AI applications, and LaunchDarkly offers AI product feature management. Both Mixpanel and LaunchDarkly have crossed $100M in revenue, indicating product-market validation and traction. 
• C-level teams feature tech and data expertise that potential acquirers can bring in-house: 3 out of the 4 executive teams in the interest zone come from large tech and SaaS companies. The CEO of LaunchDarkly is ex-Salesforce, AWS, and Microsoft, and the CEO of Productboard spent time at HP and GoodData. Like Statsig, tech talent at the executive level is a ripe target for tech companies seeking this expertise. Based on CBI Funding Insights, LaunchDarkly, and Productboard used their most recent funding rounds to go after new talent and grow their teams, indicating technical expertise across levels. 
• Partnerships with larger tech and professional services companies signal validation and reach: Established tech and AI companies have business relationships with nearly all of the companies in the interest zone. Databricks partnered with and invested in Snowplow for AI-driven user analytics, Salesforce is a customer of Productboard, and LaunchDarkly integrates with AWS to reduce data transfer costs. Similarly, Productboard partnered with consulting firms like Boston Consulting Group and Slalom to expand reach, and Mixpanel grew its international presence with Seven Peaks and Altudo. These companies have grown not only their own platform capabilities but also their market presence, networks that potential acquirers may want to leverage for reach. 

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Several factors are driving this recovery. Recent cuts to public cancer research funding are leaving startups to drive innovation, while AI advances are accelerating precision medicine’s transition from development to standard care. Precision medicine customizes treatments based on individual genetic, molecular, and biological factors rather than applying uniform therapies, unlocking market opportunities for tech-enabled diagnostics and treatment selection.

Companies that can harness these technologies are attracting attention from healthcare incumbents, both through investments and strategic partnerships. With cancer rates continuing to rise and treatment revenues growing substantially over the past decade, this momentum seems likely to continue.

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These 2 companies alone accounted for over 50% of the funds raised last month, with valuations in the hundreds of billions of dollars, demonstrating that private markets are increasingly capturing value creation in tech. 

These companies are also growing their own influence and reshaping the AI landscape. OpenAI’s alumni founded Periodic Labs and at least five other mega-round recipients in 2025. Databricks is making agentic AI a core part of its business through product development and its recent acquisition of Tecton, positioning itself as another SaaS player in the AI agent battleground. 

Using CB Insights’ Business Graph, our monthly Book of Scouting Reports offers an in-depth analysis of every private tech company that has raised a funding round of $100M or more. It spotlights where private capital is concentrating, startups gaining momentum, and which companies are becoming tomorrow’s disruptors.

Download the book to see all 20 scouting reports.

Key takeaways from August’s mega-rounds include:

• Tech giants get bigger as late-stage private market funding becomes the new norm: OpenAI accounted for over half of all mega-round funding dollars this month with an oversubscribed $8.3B raise. Databricks received a Series K — 1 of only 16 in history —  of $1B. Continuing the trend of tech companies staying private for longer, Databricks and OpenAI show that an IPO isn’t the only path to growth, signaling a shift in the financing model for late-stage startups. This is also illustrated by a rise in secondary transactions and tender offers meant to provide liquidity for early employees and investors, with OpenAI is in talks for a secondary share sale of $500B. Databricks is using its latest funds to grow its product set, expanding into AI agent development and agentic databases. 
• Early-stage unicorns show investor confidence in specialized AI: August minted 3 new AI unicorns. All are early-stage, and all are developing specialized AI. Decart is building real-time generative AI like talk-to-video models and reached $3.1B valuation following a Sequoia-led Series B, a 6x valuation jump. Materials science AI company Periodic Labs, despite no live product, is now valued at $1B after its first and only raise, led by Andreessen Horowitz. Field AI secured a $2B valuation after a Series A from Bezos Expeditions for its robotics AI. These companies all have lower-end Commercial Maturity Scores of 2 or 3, indicating they are emerging or deploying solutions. Yet their significant valuations signal that high-profile investors remain comfortable placing bets on earlier-stage companies, and that specialized applications beyond LLMs have entered the AI boom. 
• Over 60% of AI mega-round recipients are generating 8-figure revenue and above: Two-thirds of the AI companies receiving mega-rounds in August have a 2025 projected revenue of $40M or above, a signal that AI continues to generate not just investor interest, but actual commercial success. With an average Commercial Maturity Score of 4 (scaling solutions) and Mosaic health score of 892 (vs. 802 average for all August mega-round recipients), these companies are producing revenue while still in growth mode. Some, like Framer, are expected to produce $100M in revenue by 2026. Others are achieving exceptional capital efficiency today: EliseAI is projected at $100M in 2025 revenue and $670K per employee, more than double the revenue-per-employee of Databricks.

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To create the 2025 list, we analyzed 10 years of CB Insights’ Business Graph data, evaluating 12,000+ venture firms on portfolio outcomes (unicorns and exits), share of rounds led, portfolio quality via Mosaic Score, capital efficiency, and entry discipline. Smart Money VC portfolios offer a front-row view of where the sharpest investors are placing their bets. Use the list as an early indicator to spot emerging markets and promising founders.

Which VC firms are on the Smart Money list?

Firms are presented in alphabetical order.

1. Accel
2. Andreessen Horowitz
3. Bain Capital Ventures
4. Battery Ventures
5. Bessemer Venture Partners
6. Felicis
7. First Round Capital
8. Founders Fund
9. General Catalyst
10. Google Ventures
11. Greylock Partners
12. Index Ventures
13. Institutional Venture Partners
14. Kleiner Perkins
15. Lightspeed Venture Partners
16. Meritech Capital Partners
17. New Enterprise Associates
18. Norwest Venture Partners
19. Notable Capital
20. Redpoint Ventures
21. Salesforce Ventures
22. Sapphire Ventures
23. Sequoia Capital
24. Spark Capital
25. Thrive Capital

CBI customers: See Smart Money’s latest bets

How Smart Money VCs are outperforming the market

Our 2025 edition of Smart Money VCs:

• 6.5x more likely than the average VC to back a future unicorn
• 2.2x more exits per firm, either through M&A or IPO
• 2.3x higher share of rounds led, shaping pricing and syndicates

Smart Money syndicates amplify signal. The top pairs share dozens of portfolio companies — Sequoia & Andreessen Horowitz (43), General Catalyst & Andreessen Horowitz (42), and Sequoia & Lightspeed (36). Most widely backed across the cohort: Chainguard, Figma, and Wiz (each with 7 Smart Money backers).

Smart Money firms have also been the dominant backers of the AI wave — they backed 52% of new AI unicorns in 2023, 73% in 2024, and 77% in 2025 YTD — and that exposure is translating into outlier outcomes.

Since 2015, Smart Money VCs have backed 80 companies that exited at $10B+ — roughly 100x the $100M median exit. The largest Smart Money exits include Uber ($75.5B, 2019), Coinbase ($65.3B, 2021), and Coupang ($56.6B, 2021).

Mosaic shows where they’re headed next. Smart Money portfolios skew to higher Mosaic Scores — CB Insights’ 0–1,000 predictive rating of private-company health. The average portfolio Mosaic is 628 — about 2.6x the VC norm.

And the edge is most visible at the very top of the distribution: more than 65% of companies in the top 1% of Mosaic Scores are backed by a Smart Money VC. Top firms by average portfolio Mosaic include Meritech (759), IVP (741), and Thrive Capital (688). Standout companies in 2025 include Zepto, Bilt, Glean, Rippling, and Anthropic.

Where Smart Money is deploying now

Smart Money is still leaning into AI — especially agentic applications.

Over the last 18 months, agent-related categories led by deal count: coding agents and copilots (28 deals), agent development platforms (24), enterprise workflow agents and copilots (20), and legal agents and copilots (17). Infrastructure remained active as well, with 17 deals into LLM developers. Top recent AI deals by Mosaic include Glean (enterprise AI agents), Augment Code (coding AI agents), and ElevenLabs (voice AI).

Our M&A probability model points to cybersecurity as the most likely near‑term exit pool among Smart Money portfolios, with companies like Tenex.ai ranking highest. Activity is accelerating — highlighted by Google’s $32B acquisition of Smart Money–backed Wiz in March 2025. For acquirers, targeting Smart Money portfolio or syndicate companies can streamline diligence and post‑deal integration.

Outside the US, cybersecurity is also drawing Smart Money. Since Jan’24, Accel (84 deals), General Catalyst (64), and Lightspeed (55) are the most active by ex‑US deal count; their portfolios include companies like Tines, Cato Networks, and Torq.

Methodology

What is the CB Insights Smart Money list?

The Smart Money list is an unranked collection of the top 25 venture capital firms worldwide. We analyzed 12,000+ venture investors with 10+ unique portfolio companies using 10 years of CB Insights’ Business Graph data (2015–2025) to surface the highest performers via our Smart Money Index.

What makes a VC “smart”?

​​Comparable lists in other asset classes rank firms based on investment performance, but returns data is hard to come by in the VC world, and rates of return can be easily manipulated.

Our methodology factors:

• Portfolio outcomes — unicorn count/share and exit count/share
• Deal leadership — share of rounds led
• Portfolio quality — average CB Insights Mosaic Score
• Capital efficiency — portfolio value created per dollar raised
• Entry discipline — median stage at first check

Inputs were normalized and combined into the Smart Money Index. The top 25 became the 2025 Smart Money cohort.

What can I do with this collection?

Explore the Smart Money Expert Collection on the CB Insights platform to filter deals, build screens, and make faster decisions.

If you are a venture investor and want to submit data on your portfolio companies to allow us to better score you in the future, please reach out to researchanalyst@cbinsights.com.

RELATED RESOURCES FROM CB INSIGHTS:

• All 100 AI unicorns since ChatGPT launched
• State of Tech Exits H1’25
• The top angel investors in AI
• Are AI unicorns starting to move beyond hype?
• Book of Scouting Reports: Enterprise AI Agents

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The market is already worth more than $2B, and appetite for these tools continues to accelerate. New players are flooding in: IDE startups are launching their own agents, 12 brand-new coding AI agent companies have been founded since 2024, and every major cloud provider and LLM developer has been rolling out offerings.

So who’s leading today, and who’s gaining ground fastest?

Using CB Insights’ revenue data, we measured the current size of the market and estimated market shares for players in the space. Download our book of scouting reports for an in-depth analysis of every private player with disclosed revenue in the market.

If you are active in the coding AI agent & copilots market and want to submit your company’s revenue data, please reach out to researchanalyst@cbinsights.com. 

See all coding AI agent & copilot companies ranked by revenue

Key takeaways

• Coding AI agent & copilot is a highly concentrated market, with the top 3 players currently holding just over 70% of the market. GitHub (owned by Microsoft) leads with an estimated $800M in ARR generated from its AI-powered coding offerings, demonstrating the power of superior distribution in the agentic AI space. With close to 40% of players showing low commercial maturity scores (emerging or validating), we expect leaders to be challenged and risk losing market share unless they turn to M&A to maintain their position — and technological edge.
• Explosive growth creates a dynamic leaderboard, with companies reaching and surpassing $100M in ARR at record pace. For example, Anysphere was generating $500M in ARR by June this year, up from $100M as of December 2024, a level it reached just 12 months after launching its product. Similarly, Anthropic scaled its AI coding solution (Claude Code) from 0 to $400M in ARR in just 5 months. This is adding more pressure on leaders as it highlights the low barriers to scale in this market, with new entrants able to win material share very quickly.
• The pie keeps getting bigger, with companies projecting top-line growth of 12x on average this year. Lovable recently said it expects to reach $250M in ARR by year-end, up from $10M at the start of 2025, and projects $1B by mid-2026 — a 100x increase in just 18 months. However, higher costs and reluctance from enterprises to adopt usage-based pricing could slow growth in the space or require significantly more funding to stay in the race.

Market overview

The coding AI agents & copilots market consists of AI-powered solutions that help software developers write, fix, test, and maintain code. These tools offer features like intelligent code completion, natural language code generation, automated testing, code review, debugging assistance, and technical debt management. Many solutions integrate directly with popular IDEs and development environments, while others operate as standalone agents or chat interfaces. The market includes both general-purpose coding assistants and specialized tools for specific programming languages, frameworks, or development workflows.

We count close to 100 players in this market, with a mix of early-commercial-maturity pure players (~40%), recently minted unicorns such as Anysphere and Lovable, leading LLM developers, and most big tech companies.

They have raised a combined $2.1B in equity funding so far this year, already surpassing the $2B raised last year. Traction in the market is also reflected in its average Mosaic score (a measure of company health) of 633, well above the 370 average across all private companies.

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Combining CB Insights’ proprietary data and AI, scouting reports provide insight into each company’s:

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Companies that hit $100M+ ARR in months, like Anysphere (maker of Cursor) and Lovable, now face LLM inference costs growing up to 20x, forcing rate limits and price hikes, and putting reverse acqui-hires (hiring founders and licensing the tech) on the table as some founders seek exits.

Using CB Insights’ data on company momentum, exit probabilities, and customer sentiment, we analyzed how the coding AI market is adapting to this economic shock and what other AI agent companies (and their backers) can learn:

• Reasoning models spark vibe coding’s explosive growth
• Reasoning token shock pushes adoption of new pricing models
• Margin pressure drives consolidation of talent in the coding AI agents market
• Open models and usage-based pricing offer solutions to the market’s current challenges

Reasoning models spark vibe coding’s explosive growth

The coding AI agents and copilots market has been on a roll, generating an estimated $1.1B in revenue in 2024 and minting unicorns in as little as 6 months, which is 4x faster than the AI industry average.

Anthropic’s release of Claude 3.5 Sonnet in June 2024 has primarily driven this early momentum. This technology helped developers transition from autocomplete to partial delegation of coding tasks with a model that could reliably call tools and handle multi-file edits.

But it is the emergence of reasoning models, and specifically Anthropic’s Claude 3.7 Sonnet’s reasoning mode in February 2025, that made vibe coding possible — giving a high‑level goal and delegating multi‑step implementation to the AI. Developers could now set goals like “make this component responsive” or “add error handling throughout” and let the AI plan and execute the changes, sparking explosive growth in the space:

• Anysphere’s ARR grew 5x in 6 months, from $100M in December 2024 to $500M in June 2025.
• Replit’s ARR increased from $10M at the end of 2024 to $144M in July 2025.
• Lovable became one of the fastest-growing software startups, reaching $100M in ARR just 8 months after launching.

See all coding AI agent companies ranked by revenue

Reasoning token shock pushes adoption of new pricing models

As revenue surged on the back of reasoning, costs rose even faster.

Reasoning models inflate output‑token volume roughly 20x, according to Artificial Analysis. Because inference is billed per token — and output tokens are typically priced higher than input — that surge translates directly into higher compute cost. Anthropic’s May 2025 step‑ups on Sonnet 4 and Opus 4 (priced at roughly 5x prior models) added further pressure just as adoption was accelerating.

This is particularly impacting enterprise deals, which businesses often negotiate on an annual, per‑seat basis. That structure leaves vendors carrying the risk of uncapped compute costs while revenue stays fixed.

Using CB Insights Customer Sentiment data, we find most contracts fall between roughly $6K and $100K a year, with a median around $25K for a 50‑developer team. While margins once sat at 80%-90% on these contracts, compute costs from reasoning models can flip margins deeply negative.

The strain showed up quickly. Cursor tightened rate limits and introduced overage charges despite crossing $500M in ARR, prompting backlash and refunds. Anthropic throttled Claude Code after individual users exceeded $10K in monthly compute on $200 plans.

Vendors are shifting to pass‑through and usage‑based pricing to align revenue with compute cost. Companies employing usage‑based approaches show stronger momentum in our Mosaic data (median Momentum Mosaic of 683 vs. 671 for the broader market), but enterprise buyers are pushing back on variable bills and month‑to‑month swings.

Expect coding AI agent vendors to adapt pricing and GTM: moving to seat‑plus‑usage hybrids, stricter per‑seat compute guardrails, and model tiering that reserves reasoning for high‑impact work. ARR growth will moderate as flat‑fee expansion gives way to usage‑aligned pricing.

Margin pressure drives consolidation of talent in the coding AI agents market

Reasoning-driven margin compression is forcing consolidation in a category that has seen dozens of new entrants over the past 12 months.

Traditional acquisitions aren’t off the table, but acqui‑hires and reverse acqui‑hires have become the most active exit structures recently — albeit with trade‑offs.

OpenAI and Anthropic have logged 3 acqui‑hires since early 2025. Across AI, recent moves (e.g., Microsoft–Inflection AI, Amazon–Adept, and Meta–Scale) signal a tilt to talent‑plus‑license amid potential antitrust scrutiny. In coding AI agents, Windsurf’s failed sale and Google’s follow‑on reverse acqui-hire underscore the pattern of buyers taking teams and leaving products behind.

In these deals, acquirers hire the team and license the tech, leaving customer contracts and infrastructure — and the associated compute liabilities — outside the transaction. What they’re buying isn’t raw model IP; they’re buying proven operators with successful track records.

CB Insights’ exit probability analysis points to the next likely targets: companies with high Momentum Mosaic scores but lower probabilities of traditional exits.

The likely cause: private‑market valuations have outrun what strategics or public investors will pay given reasoning‑driven margin pressure, product overlap, and antitrust scrutiny — making full‑company M&A or near‑term IPOs harder to underwrite.

Seven stand out as potential targets: Sourcegraph, Augment Code, JetBrains, Qodo, Lovable, Cognition, and Harness.

Expect more reverse acqui-hire deals over the next few quarters as big tech continues to push for talent while coding AI agent companies struggle under margin pressures.

Open models and usage-based pricing offer solutions to the market’s current challenges

Against that backdrop, two levers dominate today: open models and usage‑aligned pricing. Here’s how each is playing out — and where it falls short.

Open models cut costs, but enterprise requirements slow adoption

Moonshot AI’s Kimi K2, Alibaba’s Qwen-Coder, and Z.ai’s GLM-4.5 approach Claude on coding tasks at a fraction of the cost, and OpenAI’s gpt‑oss goes a step further by offering a model that can run on consumer hardware.

Yet users need to access these models either through self-hosting or a third party. For enterprises, that means fresh security reviews, stringent uptime service level agreements (SLAs), multi-hour agent-run testing, and new infrastructure to manage.

The result is slower adoption, especially for six‑figure contracts that expect Claude‑level reliability.

Usage-based pricing fixes vendor margins, but most enterprises resist variable bills

Buyers tell us that token-metered pricing is difficult to budget, and expectations around costs for these tools are already set. CFOs want to anchor budgets and avoid month-to-month swings tied to release cycles, while usage-based pricing is the exact opposite.

In the near term, expect a shift from per‑message metering to effort‑based task pricing: agents quote a fixed rate for a defined outcome (e.g., “add error handling across this service” or “convert this component to TypeScript”), bundling planning, tool calls, and verification into a single charge with a visible pre‑estimate. Tasks are tiered (S/M/L) with caps on reasoning usage and admin‑approved overages, giving CFOs predictable bills while keeping compute under control.

This dynamic won’t be limited to coding

Other agent categories with surging usage are likely to rework pricing and contracts as reasoning costs mount.

Customer service is already operating on usage/outcome models. For example, in May 2025, Salesforce’s Agentforce shifted prices from $2 per conversation to a hybrid-usage Flex Credits system, tying credits to necessary actions for an outcome. Zendesk did a similar shift in pricing strategy in November 2024. Yet reasoning‑heavy workloads still create margin risk when the compute to achieve a resolution outstrips the value captured.

Beyond customer service, expect similar recalibrations across legal, healthcare, and sales agents. Outcome‑ or usage‑based models don’t fully eliminate compute risk. Explosive top‑line growth can mask deteriorating unit economics as reasoning workloads scale, and recent mega‑rounds may not be enough to foot the bill. Many players will reprice, add stricter usage guardrails, or raise additional capital to stay in the game.

If you are a coding AI agent startup and want to submit your company’s revenue data, please reach out to researchanalyst@cbinsights.com.

RELATED RESOURCES FROM CB INSIGHTS:

• All 100 AI unicorns since ChatGPT launched
• State of Tech Exits H1’25
• The top angel investors in AI
• Are AI unicorns starting to move beyond hype?
• Book of Scouting Reports: Enterprise AI Agents

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The endgame is fully orchestrated construction sites where AI coordinates everything from material delivery and site preparation to assembly and inspections. While implementation challenges persist — such as the need for sufficiently advanced AI systems, integration with legacy software, and inconsistent connectivity at remote sites — progress toward this vision is moving forward.

Recent surveys show 92% of construction professionals report improved decision-making capabilities with reality capture technology. Similarly, leading firms like Skanska have developed autonomous AI agents that deliver safety guidance in real-time, while Turner collaborated with Versatile to use its AI-powered crane attachment for improved crane utilization, material handling, and production rates.

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Equity funding to drone developers has reached a record $5.5B already this year. More broadly, the drone market is projected to grow from $73.1B in 2024 to $163.6B by 2030, driven by defense spending, more permissive regulations, and AI advances that enable autonomous navigation, real-time object detection, and mission planning without human intervention.

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The tech’s potential has reshaped the innovation landscape  — with new AI unicorns now outnumbering non-AI unicorn births.

Below, we analyzed the 100 unicorns to understand how the cohort stacks up, top exits, leading investors, and more.

Download the full Book of Scouting Reports for in-depth analysis on the 100 AI unicorns. Combining CB Insights’ proprietary data and AI, scouting reports provide insight into each company’s:

• Key takeaways (including opportunities and threats)
• Valuation
• Headcount
• Outlook (Mosaic score and Commercial Maturity) 

Key takeaways

• Nvidia is building its AI ecosystem through strategic investments: Nvidia has backed 24 of the 100 AI unicorns (nearly 1 in 4), more than any other investor as it secures demand for its chips. Big tech overall has invested in over one-third of these unicorns, with Google (15), Microsoft (7), and Amazon (3) also placing strategic bets on their future customers and partners.
• LLM developers and AI agents dominate the unicorn landscape: Large language model development leads with 12 unicorns, while AI agent development platforms account for 5. These foundational infrastructure categories provide core building blocks, while coding AI agents & copilots (8 unicorns) represent one of the most successful application layers built on top of this foundation. Note: categories are not mutually exclusive. 
• Robotics emerges as an AI frontier: Physical AI is gaining momentum with 3 robot foundation model developers (Skild AI, Physical Intelligence, World Labs) and 3 humanoid robot developers (Figure, Unitree Robotics, Zhiyuan Robot) among the robotics companies achieving unicorn status. This signals strong investor confidence in AI’s transition from digital to physical applications.

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M&A volume stayed flat in the first half of the year, and the IPO market remained muted, though there are early signs of a potential second-half recovery. In the meantime, capital continues to flow into private tech companies at record levels, including a surge in secondary transactions. This is giving private tech companies more runway (and more reason) to delay public listings.

New exit models are also gaining traction. From large minority stakes to reverse acqui-hires, big tech companies are finding ways to access talent and technology without triggering regulatory review. These deal structures are starting to reshape how value is created, captured, and distributed across the ecosystem — for founders, investors, and employees alike.

Taken together, these trends point to a broader shift: private markets are becoming the dominant venue for value creation and capture in tech. With that comes the need for better private market investing infrastructure, including real-time data and context, turning private company intelligence into a new competitive advantage. 

Below, we break down the top stories from this first half of the year and our projections for the rest of the year, including:

• AI and $100M+ deals drive tech M&A momentum
• Signs point to tech IPO market rebound in H2’25
• Private tech markets top $2T in equity funding
• Secondaries get bigger and pricier
• New exit models emerge amidst the AI talent war

Download the full report to access comprehensive CB Insights data and charts on the evolving state of tech exits, in partnership with EquityZen.

Top stories in H1’25

1. AI and $100M+ deals drive tech M&A momentum

Tech M&A activity has remained stubbornly flat since Q4’23, stagnating at just over 2,000 transactions per quarter. We project Q3’25 to follow the same trajectory, with 2,040 deals.

Despite flat volume, this year is shaping up to be a record year in terms of M&A deal value, driven by an increase in the number of $100M+ acquisitions. These large transactions represent 4.7% of deal share so far this year, up from 3.8% from all of 2024, and a level not seen since 2021.

AI has also emerged as a bright spot, as corporations race to grab AI tech & talent.

M&A activity in AI reached record levels in Q2’25 at 192 deals, pushing AI’s share of tech M&A to 7.5% so far this year — almost double its share in 2021. Private companies have notably led some of the largest AI acquisitions in the first half of 2025, with OpenAI acquiring Io for $6.5B and Databricks spending $1B to buy Neon. 

The AI race is also pushing big tech companies to rethink their M&A strategy, after years of muted activity. 

Meta scooped up voice AI startups PlayAI and WaveForms this summer — marking its first acquisition since 2022 — in a bid to win the race to build the future of human-machine interactions. The company is betting that voice will become the dominant interface for interacting with AI.

During the company’s Q3’25 earnings call, Apple’s CEO mentioned being open to larger M&A deals to help accelerate its roadmap. This marks a significant move away from Apple’s historical focus on smaller acquisitions.  

Dive into 7 AI-related areas where we expect to see M&A activity this year, as well as high-potential acquisition targets for each, in the free report.

2. Signs point to tech IPO market rebound in H2’25

The global tech IPO market has remained muted during the first half of 2025, with 122 tech companies going public, in line with the numbers from 2024. But recent activity signals things may be picking up.

Figma successfully went public last month, in an IPO often referred to as a test of the public market’s appetite for tech companies. The company was valued at just over $16B at IPO and now boasts a market cap of $39B (as of 8/20/2025).

A few weeks later, crypto exchange platform Bullish followed a similar path, raising $1.1B in at a $5.4B valuation. The company is now trading at a 60%+ premium to its IPO price.

These recent examples have pushed several tech companies (crypto in particular) to announce they had filed to go public, reviving hopes of a tech IPO market rebound. Based on current trends, we project 84 tech IPOs for Q3 ’25 — above the 2-year quarterly average of 72.

Find 40+ blockchain companies with a high probability of going public in the next 2 years

But any rebound is likely to be modest, with private tech companies expected to remain private for longer and the role of IPOs potentially shifting to becoming a clearinghouse rather than a capital-raising mechanism, as predicted by Jared Carmel, Managing Partner, Manhattan Venture Partners:

“We’re witnessing a fundamental shift in how tech companies approach public markets. The average age at IPO has increased dramatically, from under 4 years in 2000 to 12 years in 2015 and nearly 16 years today. I expect this trend to accelerate, with companies staying private for 20+ years becoming the new norm.

The aggressive IPO pops we’ve historically seen are fundamentally unfair to founders and long-term investors who actually built these companies. Over the next several years, you’re going to see VCs, private equity, and sovereign wealth funds step in to extract maximum value before these companies ever go public. When they eventually do an IPO, they’ll go public at fair market value without the pop — essentially using public markets as a clearinghouse rather than a capital-raising mechanism.

This shift is already playing out in the data. We’re seeing record levels of private funding, exceeding $2 trillion in cumulative investment, and explosive growth in secondary transactions. The real value creation and liquidity will increasingly occur in private markets, rather than public ones. With companies staying private for two decades, secondary liquidity becomes absolutely critical — employees, early investors, and founders can’t wait 20 years for an exit.”

3. Private tech markets top $2T in equity funding

Private tech companies are staying private longer and now have more capital than ever to do so. 

Over $2T in cumulative equity funding has poured into private tech markets to date, with 90% raised in just the last decade. That funding has enabled companies to keep scaling before tapping the public markets. Today, startups are going public an average of 16 years after being founded, 4 years later than just a decade ago.

Late-stage rounds have also reached new extremes, with Databricks joining the exclusive “Series K” club in July. Just 16 Series K rounds have ever been raised — half in the last 5 years — signaling the growing normalization of ultra-late-stage private fundraising.

Private market check sizes have also grown dramatically. The past 18 months alone account for the largest Seed, Series A, Series B, Series D, and Series E+ rounds on record. And more dry powder is on the way: a recent executive order in the US is opening the door for 401(k) retirement accounts to invest in private markets, potentially unlocking a new wave of capital for private tech companies.

As regulatory barriers fall and new investment vehicles emerge, private tech company investments will increasingly define institutional — and eventually retail — portfolios. 

But there’s a catch. 

Private companies operate in information shadows, beyond public view. Institutions will increasingly need real-time data and context on companies not subject to quarterly reporting, turning private company intelligence into a new competitive advantage. 

4. Secondaries get bigger and pricier

The last 7 quarters have seen YoY growth in secondary transaction activity among VC-backed companies, with no signs of slowing down. As tech companies stay private longer and valuations continue to climb, secondaries are playing a growing role in providing liquidity.

In August 2025, OpenAI reportedly launched a tender offer at a $500B valuation, a sharp jump from its last reported $300B. The offer gives current and former employees a chance to cash out while attracting new capital from institutional buyers. Canva followed a similar playbook, recently conducting a secondary sale at $42B. That’s $10B higher than its October 2024 valuation, which was also set during a prior secondary transaction.

These moves are helping long-time employees and early investors realize returns, while giving latecomers a shot at high-growth companies.

Investor demand is heating up too. According to EquityZen, average discounts in secondary markets have compressed to just 13% below last-round valuations — the lowest level observed between Q1’23 and Q2’25. That pricing shift reflects growing competition and perceived upside, even in companies potentially years from IPO.

While large players like SpaceX, Ripple, and OpenAI continue to dominate transaction volume, interest is expanding to smaller unicorns and breakout startups. In Q2’25, 7 of EquityZen’s top 10 secondary movers had Mosaic scores over 800 and valuations north of $1B, including names like Axiom Space, Brex, and Skild AI.

Explore 500+ private tech companies with strong secondary potential

As secondary markets mature, they’re reshaping liquidity expectations — and giving investors new ways to get exposure to private tech winners without waiting for the IPO window to reopen.

5. New exit models emerge amidst the AI talent war

The intensifying race for AI talent is driving a new wave of unconventional exits in the tech ecosystem, bypassing traditional M&A while still delivering strategic value to acquirers. 

Tight regulation has pushed big tech companies to shift away from full takeovers and toward deal structures that offer access to technology and, more importantly, talent, without triggering antitrust alarms.

Large minority stakes have emerged as one such mechanism. In Q2’25, Meta invested $14.8B for a 49% stake in Scale, marking the largest private funding round of the quarter. As part of the deal, Meta hired Scale’s CEO and founder, Alexandr Wang. At their current pace, big tech companies are on track to complete 14 corporate minority deals in 2025.

Reverse acqui-hires  — where acquirers buy the team (fully or partially) and license the technology — are also gaining momentum. These hybrid transactions often include lucrative licensing fees that serve as a partial liquidity event for investors.

Notable examples include:

• Google hiring key personnel from Windsurf to join its DeepMind division, including CEO Varun Mohan and co-founder Douglas Chen 
• Amazon hiring key members of Adept
• Microsoft bringing in employees from Inflection AI

These transactions let acquirers cherry-pick talent and assets without facing regulatory hurdles or needing to buy out entire cap tables.

But it’s not just big tech adapting;  major LLM developers are adopting similar tactics. OpenAI and Anthropic have collectively made 3 acqui-hires so far in 2025 — Context.ai, Crossing Minds, and Humanloop.

These nontraditional exits may complicate fundraising and hiring for AI startups, as investors and employees weigh the risk of being bypassed in partial team acquisitions. In response, both groups may begin negotiating protective terms to ensure they aren’t left behind.

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Among them, a few are set to win big, having placed early bets on Cursor, Cognition, and Sakana AI, and 200+ other AI companies.

Using CB Insights data, we analyzed which angel investors are building the most robust AI portfolios, and asked them to share their views on where AI is heading. Below is our ranking of the top 15 angel investors based on AI activity since January 2024, and key takeaways on the list.

Make sure we’re representing your full portfolio by reaching out to researchanalyst@cbinsights.com to set up a review of CB Insights’ coverage of your investments.

Track these investors’ deals in real time

Key takeaways

• Elad Gil tops the ranking with 36 AI deals since January 2024, ahead of Gokul Rajaram and Jeff Dean, each with 30 deals. Gil has scored heavy-hitters in the genAI space, such as AI search engine Perplexity, coding agent Cognition, and AI data platform Scale. 

• Nearly 90% of the top angels’ AI investments target the application layer. These companies build on top of foundation models to solve specific use cases, including browser agent Yutori, computer vision development tool Roboflow, and enterprise search platform Onyx AI, each backed by 3 or more top AI angel investors.

“Over the next 1-3 years, I expect the application layer to be very fruitful for AI startups. There are a tremendous number of spaces that were hitherto inaccessible for software but now are opened up thanks to AI.” — Gokul Rajaram

• 40% of companies backed by the top AI angels are founded by big tech veterans. This includes Meta’s 14-year product design leader Julie Zhou who founded the AI-powered analytics platform Sundial, and Nvidia’s 8-year engineering lead Ambuj Kumar, who launched AI security agent startup Simbian.

“AI is a relentless technology. Things are moving so fast and the models are getting better every day. Whenever you have a space that’s moving so quickly, the one constant you can bet on are the founders who are capable of navigating this change. My strategy is to simply find the founders building companies that are the right vessel to deliver the dramatic progress we’re seeing in model capabilities.” —Kulveer Taggar

• Angels are positioning early in multimodal AI’s next wave. With modalities like voice attracting $371M as of July 2025 funding and humanoid robotics projected to reach $2.3B by year-end, investors are betting on the technologies that will power the next phase of human-AI interaction. Our top angel investors have backed World Labs (world model developer), LiveKit (multimodal AI development platform), and Hamming (AI voice agent) since January 2024 as examples of this shift towards multimodal in action.

“The next generation of AI leaders will be cross-functional teams with deep vertical expertise. As foundational models continue to commoditize very fast, the edge will go to founders who work backwards from user pain points and harness emerging modalities like audio, robotics, world models.” —Mehdi Ghissassi

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Underpinning this momentum is an emerging tech stack. Infrastructure layers — from foundation models to oversight — are helping enterprises build, deploy, and manage AI agents more effectively.

Using the CB Insights Business Graph and proprietary signals, we mapped 135+ promising private companies building infrastructure for AI agents.  

Below the map, we outline the emerging markets and trends investors and strategy leaders should be watching.

Track 250+ companies building AI agent infrastructure

We selected companies for inclusion based on Mosaic health scores (500+) and funding recency (since 2023). Includes private companies only, organized according to their primary focus. Excludes general enterprise workflow automation platforms and non-pure-play LLM developers. This market map is not exhaustive of the space.

Please click to enlarge. 

Outlook & key takeaways

Private market momentum points to payments, voice, and security as key markets to watch

The AI agent tech stack is a high-momentum landscape, based on CB Insights Mosaic startup health scores. Private companies across the markets outlined below have an average Mosaic score of 768 — more than double the average of 370 for all private companies. They also have an average Commercial Maturity Score of 3, indicating widespread solution deployment.

A deeper dive into these scores, partnerships, and funding reveals 3 emerging markets to watch:

• Voice AI is the new battleground for the next wave of AI agents: With an average Mosaic score of 756 and nearly $400M in funding in 2025 so far, voice AI development platforms are building momentum. Big tech also recognizes voice as an essential AI building block — Meta’s first acquisitions since 2022 this year were PlayAI and WaveForms AI, both operating in audio and voice AI. 
• AI agent security startups see rapid momentum growth: AI agents create new attack surfaces and data breach risks, driving urgency for agent security startups. Companies in the market averaged a 56-point Mosaic score growth over 12 months, with Zenity, WitnessAI, and TrojAI each gaining 100+ points. The companies with the highest jumps in Mosaic score are partnering with larger tech firms and cybersecurity leaders. Public and established companies have also entered the conversation, with identity leader Okta and cybersecurity giant Palo Alto Networks both building agent security into their platforms.  
• AI agent payments startups get backing from incumbents: Agent payments infrastructure is one of the more nascent markets in this tech stack, with an average Commercial Maturity of 2.4 (validating) and an average Mosaic score of 697. The barrier to entry in payments is high, requiring complex technical and regulatory infrastructure. In an indication of the tech’s potential, established card and payment networks are investing and partnering with startups in the market: Coinbase backed Skyfire and Catena, Visa invested in Payman, and American Express participated in Nekuda’s recent seed round. Others like Crossmint and pre-funding PayOS have partnered with Visa and Mastercard.

Major LLM providers and tech incumbents all try to own a piece of the open standards pie

The growth of AI agents and development platforms has created a need to facilitate communication between agents and access to context. LLM developers and major tech companies are competing to own these standards. 

In less than a year:

• Anthropic launched Model Context Protocol (MCP), standardizing how AI agents connect to external tools and data sources 
• Google created the Agent-to-Agent (A2A) Protocol that allows agents to collaborate with each other, regardless of underlying framework 
• IBM introduced Agent Communication Protocol, which enables inter-agent communication across technologies and systems within a local environment 

These protocols have quickly become table stakes across the AI agent value chain. Professional services firms like Accenture, McKinsey, Deloitte, and KPMG contributed to Google’s A2A, and big tech companies like Microsoft and AWS support MCP. Meanwhile, startups in the tool libraries & integrations platform market like Speakeasy and Stainless are helping companies build MCP-compatible interfaces for their APIs (known as MCP servers), enabling AI agents to interact with their services.

MCP for the win: Make your AI smarter with our data and tools

Any MCP-compatible AI agent can tap into CB Insights’ datasets and tools – including ChatCBI – without a single line of code. Install our server into your environment to get started. Learn more here.

Big tech pushes deeper into AI agent development

While the above market map highlights the private landscape, tech giants and incumbents are also active across the AI agent infrastructure landscape. The top 3 global cloud providers — Amazon, Microsoft, and Google — are expanding their AI agent offerings across development tooling, hosting, orchestration, and more. 

Dive into the full report on how cloud leaders are shaping AI’s next frontier here. 

With many enterprises favoring established vendors, big tech companies have significant advantages in AI agent development. Similarly, enterprise software incumbents like Salesforce (Agentforce) and ServiceNow (AI Agent Marketplace) have launched agent platforms and marketplaces targeting their installed bases. 

Yet startups across the stack are carving out defensible positions by solving specific technical challenges and pushing the boundaries of what agents can do across areas like multi-agent orchestration (CrewAI) and enterprise data preparation (LlamaIndex). In the crowded AI agent development market, end-to-end platforms like WRITER and Dust are differentiating with vertical-specific implementations and promising speedy deployments. 

Autonomous agents drive the need for an oversight layer

AI agent reliability remains a major challenge in the landscape. Agents that fail, hallucinate, or behave unpredictably create immediate business risk. 

This is driving activity across observability, evaluation, and governance applications. The market has already seen 2 acquisitions in 2025 YTD. Early-stage activity highlights emerging technical needs, such as voice agent testing, with both Cekura ($2.4M seed) and Coval ($3.3M seed) focusing on evaluating and monitoring voice AI agents via simulated conversations. 

Securing agents is a growing priority across the stack. Based on one-year funding activity, the AI agent security & risk management market is the fastest-growing cybersecurity segment we track as agents proliferate across enterprise environments. 

White space opportunities for the AI agent ecosystem

As the AI agent tech stack matures, we predict the following areas will attract increasing innovation based on early-stage activity and recent product launches: 

• AI agent marketplaces: Distribution is a competitive advantage, with all major cloud providers launching dedicated AI agent marketplaces, including AWS in July 2025. Companies like Olas and Agent.ai are looking to differentiate through specialized agent discovery and customization. 
• AI agent monetization: Monetization emerges as an untapped opportunity, with companies like Paid giving visibility into AI agent costs and profit opportunities, and AGI Open Network tokenizes AI agents as tradable assets on blockchain networks.
• Cost management: At the end of the AI agent value chain, cost monitoring & productivity measurement will become more important as agents operate autonomously. For example, a16z-backed Larridin aims to give organizations visibility into AI spend and tool effectiveness. Other companies like coding AI agent Cline are building cost control solutions directly into their platforms to manage AI inference expenses. 

Source: CB Insights Deal Agent

Category overview

Click into each market to view the full description and market players on the CB Insights platform. 

Foundation models & infrastructure

Large language models (LLMs) form the cognitive core of AI agents. This layer also covers the compute, hosting, and inference systems required to serve models at scale. 

• LLM developers
• Model deployment & serving 

Agent frameworks & development platforms

Companies in this layer provide the software frameworks, SDKs, and low-code environments used to design, build, and deploy AI agents across different modalities and use cases.

• AI agent development platforms (including developer frameworks & platforms and low-code platforms)
• Voice
• Payments infrastructure  

Tool integration

AI agents leverage “tools” to interact with external systems and perform real-world actions, such as browsing the web. This includes Model Context Protocol (MCP) implementations that standardize how agents connect to data sources and tools.

• Browser infrastructure 
• AI web search APIs & scrapers 
• Tool libraries & integration platforms

Context 

This layer supplies agents with structured data, embeddings, and memory systems so they can retain, retrieve, and apply relevant information over time.

• LLM data preparation platforms
• Vector databases 
• Memory management

Orchestration 

This is the coordination layer that manages complex workflows involving multiple AI agents or models. 

• Multi-agent systems & orchestration 
• LLM benchmarking & model routing

Oversight

Companies here target authentication, security, monitoring, and governance functions that ensure agent actions remain safe, compliant, and aligned with intended outcomes.

• Authentication platforms
• Security & risk management platforms 
• Observability, evaluation, & governance 

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Capital is flooding the landscape, with 1 in 2 venture dollars going to AI startups. 

These trends reflect AI hype. Investors are placing big early bets on AI potential, with many companies still proving out sustainable revenue models. For example, Thinking Machines Lab — co-founded by former OpenAI CTO Mira Murati — raised a record-breaking $2B seed round at a $12B valuation in June 2025 with no product yet launched. 

But beyond the hype, CB Insights Commercial Maturity scores highlight a shift toward increasing maturity among the AI companies hitting unicorn status. We highlight the trend below. 

Dive into the data — including revenue — on AI unicorns

Commercial Maturity: Measures a private company’s current ability to compete for customers or serve as a partner based on 30+ signals. Ranging from 1 to 5, it provides a clear, quantitative measurement for where a company lies in its development and growth process.

2024: Racing ahead of revenue

More than half of the AI unicorns born in 2024 were at the validating/deploying stages of development. Meanwhile, non-AI new unicorns mostly had to get to at least the scaling stage before earning their unicorn status.

2025: The maturity shift

In 2025 so far, the gap with non-AI unicorns is closing. Both new AI and non-AI unicorns are mostly in the scaling stage. 

This indicates that investors are increasingly looking to proven business models over pure potential, and that companies themselves are gaining traction with customers as the genAI space matures. 

For example, among the AI startups surpassing the $1B+ valuation mark in 2025 are: 

• Sales automation platform Clay (CM: 4), which is projecting $100M in revenue this year
• Workflow automation platform Tines (CM: 5), which has 200 business relationships on the CBI platform
• AI scribe Abridge (CM: 4), which has $117M in contracted ARR

If 2024 was the year of AI hype with investors placing big early bets, 2025 marks a shift toward increasing maturity as AI unicorns prove out sustainable revenue models.

Compare AI startups by Commercial Maturity

Want to be considered for future AI research? Connect with us at researchanalyst@cbinsights.com to ensure we have the most up-to-date data on your company. 

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Half of US federal agencies already report high levels of AI adoption, with these systems projected to handle most routine government functions within the next decade. Similar adoption patterns are emerging across municipal governments and international government bodies, particularly in Europe and the Asia-Pacific region.

Generative AI has already transformed procurement and fleet management through automated contract analysis and vehicle optimization, with major partnerships formed between government agencies and providers like Microsoft, Palantir, and specialized govtech firms.

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AI companies drove the surge, accounting for half of all mega-rounds. Many are building foundation models tailored to complex real-world use cases like robotics and healthcare.

Using CB Insights’ Business Graph, our monthly Book of Scouting Reports offers an in-depth analysis of every private tech company that has raised a funding round of $100M or more, to spotlight where capital is concentrating, which startups are gaining momentum, and who’s shaping the next wave of market disruption.

Download the book to see all 50 scouting reports.

Key takeaways from July’s mega-rounds include: 

• Clinical AI moves from development to scaling, with both Aidoc (a clinical AI foundation model developer) and Ambience (an AI medical scribe) having raised mega-rounds last month to build upon their early success and scale across more health systems. Last month also saw OpenEvidence and Tala Health raise $100M+ rounds to bring agentic AI solutions to clinicians, with the latter joining the fast-growing AI unicorn list. 
• Investors keep betting big on the next wave of the AI boom, physical AI. Recent commercial breakthroughs in the autonomous vehicle space and heightened interest in the humanoid space are driving capital toward physical AI infrastructure. This includes robotics foundation models (Genesis AI, TARS), and hardware platforms for embodied AI model training (Galaxea AI). China-based Meituan led both the $100M Series A extension in Galaxea AI and the $125M Seed round in TARS, as it doubles down on physical AI investments.
• AI newcomers are openly taking on tech giants. Half of last month’s mega-rounds went to AI companies, which accounted for 7 of the 13 new unicorns minted during that time. Some of these companies are directly targeting incumbents such as Reka AI which positions itself as a lower-cost alternative to OpenAI or Anthropic, and Perplexity which targets Google‘s core search business with its new browser product. 
• Fintech is minting a new class of financial services challengers.  Fintech companies accounted for more mega-round deals than any other vertical in July, including 2 of the top 4 largest rounds. Ramp’s valuation jumped from $16B to $22.5B in mere weeks, while Bilt more than tripled in value, from $3.3B to $10.8B. Beyond fundraising, fintech leaders are pursuing aggressive expansion strategies. iCapital raised $820M last month to accelerate its acquisition strategy focused on seizing the private markets opportunity. 

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