AI continues to dominate, capturing 50% of venture investment. At the same time, investors are doubling down on hard tech — hardware-focused and capital-intensive technology — driven by surging energy demands from AI, advancements in robotics, and growing defense interest.

Below, we break down the top stories from this quarter’s report, including:

• Funding tops $90B for the third straight quarter, while deal count declines
• Hard tech claims 6 of the top 10 largest deals
• AI companies command funding premiums across sectors
• Regulatory shifts push big tech from M&A to minority investments
• CVC deals hit a 7-year low as the tariff threat looms

We also outline the categories shaping venture dealmaking for the rest of 2025 — including stablecoins, defense tech, quantum, and nuclear energy.

Let’s dive in.

Download the full report to access comprehensive data and charts on the evolving state of venture across sectors, geographies, and more.

Top stories in Q1’25

1. Funding tops $90B for the third straight quarter, while deal count declines

Venture funding reached $94.6B in Q2’25, marking the second-highest quarterly figure since Q2’22 and the third straight quarter to surpass $90B.

While funding dipped slightly from Q1’25, the decline reflects normalization after OpenAI’s $40B raise inflated numbers in Q1. In fact, Q2 remained elevated even as foundation model developers accounted for just 3% of total capital, down from 36% in Q1’25 and 29% in Q4’24. This shift signals a broadening of venture activity beyond foundation models into the broader AI ecosystem and adjacent hard tech sectors.

With this continued momentum, annual funding is projected to reach nearly $440B, a 53% increase from 2024, pointing to a sustained recovery in venture investment.

At the same time, deal volume continues to decline, reflecting greater investor selectivity. Q2 saw just 6,028 deals — the lowest quarterly total since Q4’16. This puts 2025 on pace for around 25,000 deals, or nearly half the volume seen in 2022, even as total funding approaches similar levels.

While investors are pulling back on the number of deals, they’re deploying more capital per investment: the median deal size hit a new high of $3.5M in 2025 YTD. Rising check sizes and falling deal count underscore a shift toward fewer, higher-conviction bets.

2. Hard tech claims 6 of the top 10 largest deals

Six of the 10 largest deals in Q2’25 went to hard tech companies, which are firms building capital-intensive physical products.

This surge is driven by macro forces such as onshoring initiatives, clean energy investment, and the rise of physical AI, which is enabling new capabilities across robotics, autonomy, and industrial systems.

Mega-rounds ($100M+ deals) spanned multiple sectors:

• Montera Infrastructure: $1.5B for hyperscale data center solutions
• Commonwealth Fusion Systems: $800M for commercial nuclear fusion
• Neuralink: $650M for brain-computer interfaces

Geopolitical tensions are also pushing capital toward defense, where startups are securing large rounds:

• World View: $2.6B for high-altitude aerospace platforms
• Anduril: $2.5B for autonomous defense systems and defense connectivity platforms
• Helsing: $693M for AI-powered defense applications

Across the board, defense tech startups are now commanding a median revenue multiple of 17.4x, edging out AI companies at 17.1x and all other major sectors. This signals high investor confidence and competition, driving premium valuations across the defense tech sector.

With investor appetite moving toward physical infrastructure and embodied AI, the rise of hard tech represents a shift likely to define the next chapter of venture investing.

3. AI companies command funding premiums across sectors

The venture market is experiencing a pronounced “AI premium,” with median deal size for AI companies reaching $4.6M in 2025 — over $1M more than the broader market. 

But the premium isn’t just financial. AI companies also score higher on CB Insights’ Mosaic Score (success probability) and Commercial Maturity (ability to compete and partner) across most sectors, signaling stronger fundamentals and market readiness in the eyes of investors.

AI companies in auto tech — with most focused on autonomous driving — are commanding the highest premium. Their median deal size is $20.6M higher than non-AI auto tech peers, and their average Mosaic score is 99 points greater. This quarter, the largest AI auto tech deal went to Applied Intuition, which raised a $600M Series F round at a $15B valuation.

Robotics and cybersecurity follow closely, with AI firms in those sectors securing median deal sizes $10.7M and $6.4M larger than their non-AI peers.

Team pedigree is further amplifying the premium. Thinking Machines Lab — founded by former OpenAI CTO Mira Murati alongside veterans from OpenAI, Google, Meta, and Mistral AI — raised a record-breaking $2B seed round at a $10B valuation, making it the most valuable seed-stage startup ever. 

The deal reflects an increasingly common “go big or go home” investing mentality, as investors make outsized bets on high-credibility AI teams.

4. Regulatory shifts push big tech from M&A to minority investments

Big tech M&A — which includes M&A from Alphabet, Amazon, Apple, Microsoft, Meta, and Nvidia — is entering a sustained downturn. Annual deal activity is projected to hit just 12 transactions in 2025, a steady decline from 66 deals in 2014. 

US regulatory tightening caused M&A activity to collapse from 30+ deals in 2022 to just 8 deals in 2023 — the steepest single-year decline on record.

Big tech companies are adapting by taking large minority stakes, allowing them to circumvent federal antitrust review while still gaining strategic influence and access to key technologies. For example, Meta invested $14.8B in Scale — the largest funding round of Q2’25 — for a 49% stake, as did Microsoft with its recent investments in OpenAI. 

In 2025 YTD, big tech is on pace for 14 corporate minority deals, an increase from levels before the regulatory shift.

Big tech’s shift reflects broader M&A weakness across the market. Global activity has fallen 34% from 3,103 deals in Q1’22 to 2,053 deals in Q2’25, driven by high interest rates that have made financing more expensive and economic uncertainty that has made companies more cautious about acquisitions.

However, acquisitions of AI companies is one area where M&A is increasing. Activity reached record levels in Q2’25 at 177 deals — over double the 5-year quarterly average of 84 deals. This surge reflects companies’ need to acquire AI capabilities quickly rather than build them internally, as AI becomes essential for staying competitive.

While falling interest rates will help smaller deals rebound and provide a modest tailwind to overall M&A activity, we do not expect deal volumes to approach peak years. Big tech and other large corporations will remain constrained by regulatory scrutiny.

We are likely entering a new era where strategic partnerships and minority investments replace traditional M&A as a growth mechanism for major corporations.

5. CVC deals hit a 7-year low as the tariff threat looms

Corporate venture capital dealmaking has reached its lowest point in over 7 years, as CVC-backed investment totaled just $17B across 742 deals, down 8% quarter-over-quarter and representing the weakest performance since Q1’18.

CVC activity has fallen dramatically from its Q1’22 peak due to broader market pressures, including high interest rates and economic uncertainty. Tariff concerns are likely adding further burden to an already weakened market.

Despite fewer deals, median CVC-backed deal sizes have reached their highest levels since 2021. This suggests that CVCs are concentrating capital on fewer, higher-conviction investments.

CVCs are also collaborating more frequently. Deals involving 3+ CVCs reached a record high of 32% in Q2’25, reflecting both strategic necessity and market conditions: larger funding rounds in capital-intensive sectors like AI and hard tech may require multiple corporate partners to provide sufficient capital. At the same time, competition for access to the hottest technologies drives CVCs to team up rather than risk being shut out.

Breakout sectors of 2025

Below, we analyze venture funding across tech sectors to identify where investor conviction and market momentum are strongest.

Stablecoin funding is on pace to shatter its previous record

Stablecoin startups are experiencing an explosive year-over-year funding surge as stablecoins achieve mainstream adoption. Funding is projected to reach $10.2B in 2025, representing more than 10x growth from 2024.

Growing regulatory frameworks worldwide — such as the pending passage of stablecoin legislation in the US with bipartisan support — provide needed certainty for institutional investment, setting the foundation for exponential growth.

Multiple startups are taking advantage of the momentum. While the largest funding rounds occurred during the first quarter — with $2B deals for Avalon Labs and Binance — notable rounds also occurred during Q2’25, including:

• Flowdesk: $100M for digital asset trading and liquidity services
• Conduit: $36M for its cross-border business transactions platform
• Niural: $31M for an AI-enabled stablecoin and fiat payroll platform

Major financial services companies are also increasingly involved. Mastercard, Visa, and established banks are now enabling stablecoin transactions and issuing their own digital currencies, bringing institutional credibility to the space. Meanwhile, stablecoin issuers Circle and Ripple applied for banking licenses on June 30 and July 2, respectively, demonstrating their intent to operate like mainstream financial institutions.

Stablecoins are evolving beyond simple stores of value into yield-bearing tools and liquidity products. Solutions like liquidity mining, lending services, and yield-bearing stablecoins are receiving substantial investor attention. Cross-border payments companies powered by stablecoins are also gaining traction as affordable and accessible USD alternatives in emerging markets.

As regulatory frameworks solidify and institutional adoption accelerates, stablecoin companies are positioned to capture significant market share in global payments and financial infrastructure markets.

Defense tech momentum continues

Within the first two quarters of 2025, defense tech funding has already reached a new annual record of $11.1B.

The funding breakout is driven by multiple forces, including geopolitical instability and technology advancements, notably in drones and other unmanned vehicles.

Concurrently, the US Department of Defense is pushing to diversify the defense ecosystem through public-private partnerships and startup support.

The defense investor landscape is also rapidly evolving, with the number of unique investors in the space expected to increase 34% in 2025 to 950 from 710 the year prior. Traditional defense funds like Shield Capital and In-Q-Tel are now joined by generalist VCs, bringing more capital to fund a new generation of startups.

We expect continued investor interest in defense tech, as NATO recently agreed to increase defense spending from 2% to 5% of GDP by 2035, adding over $400B annually in market expansion. The 1.5% earmarked for security infrastructure aligns with venture trends in AI, cybersecurity, robotics, and technologies developed for both military and civilian use cases.

Quantum tech reaches an all-time high, halfway through the year

Quantum tech is attracting significant investor interest, reaching record annual funding levels at $2.2B within the first two quarters of 2025 — an increase of 69% from 2024.

The surge follows major hardware breakthroughs from Google, IBM, and Microsoft, which may drive confidence in leading startups even though the technology still lacks practical applications that outperform classical systems. Industry leaders like Fujitsu and Quantinuum — a subsidiary of Honeywell — expect fault-tolerant quantum computers by 2030 at the earliest.

Massive investments are flowing towards various quantum applications in 2025 so far:

• PsiQuantum: $750M to build million-qubit photonic quantum computers by 2029
• QuEra Computing: $230M to accelerate fault-tolerant neutral-atom quantum systems
• Multiverse Computing: $215M to scale its quantum-inspired AI compression technology

Government support has also increased, with $1.8B in public funding announced globally in 2024. For example, Australia committed $620M to PsiQuantum, while DARPA committed up to $200M in joint funding to assess the feasibility of industrially useful quantum computers.

As quantum technologies move toward commercial viability, the combination of record private investment, substantial government backing, and technical progress positions the industry for significant growth once practical quantum advantage is achieved in commercial applications.

Corporate interest drives a surge in nuclear energy funding

Funding to nuclear energy companies is projected to reach an annual record by the end of 2025 at $5B. Massive energy requirements for AI data centers — with US data center power consumption projected to triple by 2030 — are driving corporate interest in clean baseload power.

Big tech companies are leading the charge, with investments since 2024 across both small modular reactors (SMRs) and fusion technologies:

• Amazon invested in X-energy with plans to develop over 5 GW of SMR projects by 2039; Amazon also backed Realta Fusion
• Google reached agreements with Kairos Power for up to 500 MW of nuclear power by 2030; Google Ventures also invested in Commonwealth Fusion Systems and TAE Technologies.
• Microsoft reached a deal with Constellation Energy to reopen the Three Mile Island nuclear plant, while committing to purchasing fusion electricity from Helion Energy by 2028

Corporate interest has also skyrocketed, with earnings call mentions hitting record levels as executives grapple with the major power requirements for AI infrastructure.

Current and previous presidential administrations have reduced regulatory red tape for nuclear development, streamlining approval processes. The bipartisan approach creates stable regulatory support for long-term investments and should accelerate sector growth in the coming years.

As AI adoption continues, nuclear provides the only scalable solution for clean baseload power that intermittent renewables cannot match for always-on AI computing infrastructure. The combination of massive corporate demand and supportive regulatory frameworks positions nuclear for explosive growth in the years ahead.

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RELATED RESOURCES FROM CB INSIGHTS:

• State of Venture Q1’25 Report
• The AI agent market map
• 7 tech M&A predictions for 2025
• State of AI Report: 6 trends shaping the landscape in 2025
• 15 tech trends to watch closely in 2025

The post State of Venture Q2’25 Report appeared first on CB Insights Research.

Tech giants have rocketed ahead with a parade of autonomous (and agentic, in the case of OpenAI’s Operator) shopping tools. At the same time, payment behemoths Visa, Mastercard, and PayPal are all developing solutions to enable agentic payments. 

To keep up, retail leaders are building out the foundational layer for autonomous commerce, integrating AI technology into everything from search and personalization to supply chain automation.

Still, none of them have launched an autonomous shopping agent that can make purchases on behalf of a customer. But retailers are actively investing in AI with the aim of maintaining control of the customer experience, first-party customer data, and pace of innovation in the longer term.

We examined the top 20 global retailers by market cap and ranked them based on their current AI capabilities to gauge their preparedness to evolve with the rapidly changing AI landscape. We used CB Insights data on investments, acquisitions, partnerships, patents, and earnings transcripts since January 2023 to determine the companies’ AI activity. 

Our analysis scores the retailers in 2 key areas:

• Execution: The execution score measures a retailer’s current ability to deliver AI-powered products and services to customers and shoppers, as well as to deploy AI internally across business and back-office functions. We based this score on CB Insights data, including business relationships, product launch media mentions, and earnings transcripts.
• Innovation: The innovation score assesses a retailer’s track record of investing in, acquiring, or developing novel AI capabilities to power its future AI adoption. We based this score on CB Insights data, including patents, acquisitions, and deal-making activity.

Below are the 20 biggest global retailers by market cap, ranked by their preparation to transform their businesses and shopping with AI:

Key takeaways

• Top-ranked firms like Amazon and Alibaba are investing heavily in a core AI infrastructure that creates a scalable advantage that the rest of their business will build on. While Amazon and Alibaba have launched AI shopping solutions, both retailers’ real advantages lie deeper. They’ve built AI infrastructure from custom AI chips (Amazon) to proprietary LLMs (both retailers), which power AI development across their businesses. Amazon has positioned itself significantly ahead in future-looking innovation through investments, with a particular focus on foundational AI models and warehouse and supply chain automation.
• Partnerships with AI leaders and big tech companies will be most retailers’ ticket to AI adoption. Below the top 5 AI-ready retailers in the ranking, partnerships with big tech players and others are powering AI adoption. These relationships suggest that external partnerships and infrastructure will be essential for most retailers to effectively integrate AI throughout their operations.
• While agentic commerce remains further on the horizon, retailers are investing in AI to streamline operations, especially in merchandising. Leading retailers are deploying genAI tools for personalization and efficiency across several merchandising functions, both online and in-store, including genAI search, specialized merchant tools, inventory forecasting, and delivery route optimization. But it’s perhaps most notable that none of the retailers on the list have tackled agentic commerce. For now, retailers have placed big tech leaders, such as OpenAI and Google, in the driver’s seat to control agentic commerce infrastructure and offer retailers crucial partnership opportunities as the technology evolves.

Top-ranked firms like Amazon and Alibaba are investing heavily in a core AI infrastructure that creates a scalable advantage that the rest of their business will build on. 

While both Amazon and Alibaba have launched AI shopping assistants and merchandising tools, both retailers’ real advantage is in the potential to build on their foundational AI stack. 

Over the past 2 years, both companies have introduced genAI platforms through their cloud computing divisions. AWS created Amazon Bedrock in 2023, which lets businesses build genAI applications using various AI models, including Amazon’s own Nova model. Amazon also makes its own AI chips. 

Meanwhile, Alibaba Cloud launched its Qwen model family in 2023, its Model Studio in 2024, and in 2025 made its AI models for video generation open source. The company also continues to expand vertical solutions, including an AI-powered weather forecasting model to enhance farm performance, as well as multiple medical diagnosis and virus detection tools. 

In addition to its internal development, Amazon has positioned itself significantly ahead in future innovation. Across all of its subsidiaries, the company has made 53 investments and 2 acquisitions of AI companies since 2023, nearly twice as many as Alibaba (28 investments and 0 acquisitions). The company has invested in a variety of AI models (including 3 investments in Anthropic), agents, and applications, from warehouse automation to AI for crop modification to voice AI gaming.  

CBI customers: Track all of Amazon’s investments since 2023 here

These foundational tools enable downstream business units — from logistics and supply chain to healthcare and agriculture — to accelerate their own AI initiatives without reinventing the wheel.

Amazon’s Bedrock and proprietary chips, for instance, give its retail and logistics operations priority access to high-performance infrastructure, accelerating their deployment of AI in warehousing and delivery.

By owning the full stack — hardware, models, platforms, and end-user applications — these companies ensure their AI gains compound across business units, from consumer interfaces to internal operations.

Notably, the second tier of AI-ready retailers — Walmart, JD.com, and Coupang — is also building AI toolkits through proprietary infrastructure. Walmart has developed its own retail-specific LLMs and has introduced assistants for its merchants, associates, and shoppers, as well as AI tools for virtual fitting and shelf condition tracking. JD.com and Coupang have been similarly active in internally developing merchandising tools, including digital human livestream hosts and generative AI translation. 

Partnerships with AI leaders and big tech companies will be most retailers’ ticket to AI adoption.

Below the top 5 AI-ready retailers in the ranking, partnerships with big tech players and others are powering longer-term AI adoption. 

Lowe’s, for instance, began its partnership with OpenAI by utilizing machine learning tools across internal functions, including pricing and supply chain management. In 2024, the retailer introduced a GPT called “Product Expert” on ChatGPT, which specializes in home improvement questions. Then, in 2025, it launched 2 AI assistants – one for associates and one for shoppers – in collaboration with OpenAI. 

The Home Depot, meanwhile, has similarly relied on its partnership with Google Cloud to power its AI advancements for a decade. The company uses Google AI to improve inventory management and supply chain efficiency and has integrated Google’s AI and machine learning capabilities into its customer search, as well as its employee and consumer apps. The newest generative AI feature on Home Depot’s website, Magic Apron, is an AI agent specializing in home improvement projects; the company used Google Cloud’s vertical agents solution to build it.  

Source: CB Insights – Home Depot business relationships

These relationships suggest that external partnerships and infrastructure will be essential for most retailers to effectively integrate AI throughout their operations. Within these relationships, it will be crucial for retailers to consider how their external partners are utilizing their data to train models that their competitors may also leverage, which could erode any competitive advantages gained from the partnership. 

While agentic commerce remains further on the horizon, retailers are investing in AI to streamline operations, especially in merchandising. 

Big tech and AI leaders are leading the charge for agentic commerce, while traditional retailers are lagging. 

Perplexity, OpenAI, and Google have all introduced agents that can autonomously compare prices, read reviews, and complete purchases on behalf of consumers. 

While Amazon and Alibaba have both released browser agents with the capability to shop and buy for consumers, neither they nor any other retailers on this list have launched or partnered on agentic shopping or payments solutions. However, as autonomous commerce rapidly evolves, major retailers will have to act fast to stay competitive. 

In the meantime, for retail leaders, AI solutions for merchandising are becoming table stakes. More personalized and effective genAI e-commerce search is the most common upgrade, deployed by retailers from Walmart and JD.com at the top of the ranking to Target and CVS at the bottom. GenAI is also powering online content generation across retailers.

Retailers’ merchant teams are also using specialized genAI tools and assistants to quickly translate data and trends to inform product decisions. AI solutions are also fueling demand and inventory forecasting for companies like Amazon, JD.com, and Coupang.

Notably, AI-powered tools are not limited to digital or online use. Several retailers have developed AI solutions to enhance the in-store shopping experience and make it more efficient. 7-Eleven is partnering with Sony Semiconductor Solutions to track customer activity in aisles using vision detection. Home Depot, Target, Walmart, and others have also deployed specialized genAI assistants and agents to provide store associates with quick answers to customer questions. 

CBI customers: See how retail execs are talking AI and shopping

Looking ahead

While access to capital and AI firepower is helping Amazon, OpenAI, and Google lead the way in AI’s retail deployment, retail “natives” may have some advantages in the long run. Walmart, Coupang, and other major competitors have easy access to transaction data, an understanding of the retail value chain, and a built-in supply chain and fulfillment structure. In addition, many of the retailers on this list also have stores, where roughly 82% of all US retail sales still occur, giving them a continued profit source as well as a broader view of how consumers shop.

That said, agents’ entry into shopping has the power to turn the traditional customer journey on its head. We believe that companies already owning critical distribution and financial services infrastructure, in addition to AI innovation, are best positioned to own the customer relationship — putting big tech leaders like Apple, OpenAI, and Google in the driver’s seat. To stay competitive, retailers and other consumer-facing businesses must quickly build partnerships with big tech and other agent leaders or risk consumers excluding them from their buying decisions.

RELATED RESEARCH FROM CB INSIGHTS

• The future of the customer journey: AI agents take control of the buying process
• Voice AI’s sweet spot: Ordering fries with that
• Shopify’s next move: How the e-commerce giant is evolving into a full-stack platform for commerce and media
• The generative AI for e-commerce market map
• The automated warehouse market map

For information on reprint rights or other inquiries, please contact reprints@cbinsights.com.

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In response, pharma companies are investing heavily in AI to discover drugs faster and navigate industry challenges more effectively through AI implementation. Those that aren’t investing risk falling behind competitors.

We analyzed the activity of the top 50 global pharma companies by market cap, together with their subsidiaries. Using CB Insights data on investments, acquisitions, partnerships, and earnings transcripts, we examined each entity’s AI activity and then ranked them based on their preparedness to evolve with the rapidly changing AI landscape.

Please click to enlarge.

Key takeaways

• This year’s pharmaceutical AI readiness rankings show tight competition as supply chain concerns drive companies to build domestic AI-integrated facilities. Just 3.9 points separate second-place Merck KGaA (70.7) from fifth-place Roche (66.8), compared to an 11-point gap in 2023. These new domestic facilities will serve as testing grounds for large-scale AI deployment in the coming years, determining which companies gain lasting competitive advantages.
• The pharma AI leaders are playing a dual-pronged game, cracking the AI-readiness code through complementary strategies of capital deployment and strategic alliances. Lilly and Merck KGaA lead on investments (13 and 10, respectively, since August 2023), while Roche and Bayer dominate business relationships (22 and 21, respectively). External collaboration drives top rankings as breakthrough innovations increasingly emerge from partnerships rather than internal development alone.
• Oncology has established itself as the top priority for pharma AI partnerships. This field captures one-third of all partnerships, with top startups collaborating with major pharma across the entire care continuum, from tumor profiling (Caris Life Sciences) to patient monitoring (Huma).

Pharma AI arms race intensifies as supply chain fears drive domestic facility buildout

The pharmaceutical AI landscape was particularly competitive this year, with just 3.9 points separating second place from fifth place, compared to an 11-point difference in 2023’s list. This tight clustering signals AI readiness has evolved from a competitive advantage for select leaders into a strategic imperative across the entire industry. Strong execution scores throughout the industry drive this competition, stemming from three common internal AI initiatives among top performers: new AI-integrated facilities, internal LLMs, and drug discovery platforms. 

AI-integrated facilities

Supply chain disruptions from tariffs and geopolitical tensions have catalyzed massive investments in domestic facilities, with nearly every major pharma company committing billions to new US and EU manufacturing and research centers. The biggest pledges have come from J&J at $55B, Roche at $50B, and Eli Lilly at $27B.

Beyond just replacing capacity, these proposed facilities present the opportunity to execute comprehensive digital transformation strategies, incorporating automation, IoT sensors, and AI into their core workflows. Planned AI integration spans from predictive maintenance to operational optimization. These new facilities will serve as testing grounds for the large-scale deployment of AI and automation over the next several years, with companies that execute integrations most effectively gaining competitive advantages in efficiency and innovation.

Internal LLMs

Internal LLMs have become the second pillar of pharma AI deployment, with companies either developing proprietary systems or partnering with big tech to enable the automation of data querying and document processing. For example, Pfizer‘s Amazon-powered Vox platform demonstrates how companies are deploying these tools for internal researchers’ use, while companies like Merck & Co and Bayer have implemented comprehensive LLM systems across business units.

Drug discovery platforms

Drug discovery platforms constitute the third common deployment area, with virtually every top-10 company building internal AI systems to analyze data, predict drug-target interactions, and guide experimental design. Examples include Sanofi’s CodonBERT platform to aid in mRNA design and AbbVie’s ARCH platform for consolidating data and aiding in target discovery. 

These trends illustrate that AI readiness has shifted from preparedness for emerging technology to effective implementation and agility, enabling organizations to stay at the cutting edge. The scramble among major pharma companies reflects this new reality: it’s no longer about getting ready for AI but about not being left behind in its application.

External activity drives top rankings

All AI-readiness leaders invested strongly in internal initiatives; what differentiated those at the top was their external activity, both partnerships and deal-making. 

Eli Lilly, this year’s top performer, made the most dramatic leap in the rankings, jumping from #14 in 2023 to #1 this year. The company’s record-breaking GLP-1 profits powered increased investment activity, including substantial AI investments. With this financial windfall, Lilly doubled its total direct investment spending from 2022 ($0.7B) to 2024 ($1.5B), which translated directly into AI leadership. Lilly’s 13 AI investments this year outpaced every other pharmaceutical company.

Lilly’s investment strategy reveals 3 key areas where the company sees AI’s greatest potential in pharmaceuticals: drug discovery (Insilico Medicine), medical devices (RetiSpec), and regulatory solutions (Yseop). Drug discovery represents the most significant focus, accounting for half of Lilly’s AI investments. Lilly’s impressive track record in this area includes 2 portfolio companies that successfully IPO’d last year — BioAge and Alto Neuroscience — suggesting strong prospects for current investments like Insilico Medicine, which boasts a 29% IPO probability score compared to the platform average of just 1%.

Since this year’s AI readiness rankings hinged largely on external investment and partnership activity, one might expect the list to correlate with market cap. While this is generally true, 2 companies stand out as high performers with AI readiness scores that significantly exceed what their market cap would suggest: Merck KGaA and Bayer. Ranking second and third, respectively, these companies demonstrate that strategic focus can be as important as financial resources.

Both companies achieved their high rankings through significant external engagement strategies. While matching competitors on internal AI initiatives, Merck KGaA recorded the second-highest number of AI investments (10), trailing only Lilly’s 13. Bayer secured the second-highest number of business relationships with 21 AI partnerships, just behind Roche’s 22.

Strategic partnership approaches can elevate AI readiness regardless of market cap, as these examples illustrate. Breakthrough innovations often emerge from partnerships and acquisitions rather than purely internal development, with over 70% of new molecular entity revenues since 2018 coming from externally sourced products, demonstrating the importance of external collaboration.

Oncology dominates pharma AI partnerships

Oncology has emerged as the clear focus for pharma AI partnerships, capturing 1 in 3 pharma business relationships among the 50 companies analyzed — far more than any other therapeutic area. This concentration stems from both market dynamics and cancer’s data complexity. Cancer rates continue rising worldwide while the field shifts toward precision oncology, creating opportunities for pharmaceutical companies to apply AI across multiple aspects of cancer care. Furthermore, cancer drug revenues have increased 70% over the past decade, creating substantial commercial opportunity for AI-enabled drug development.

The AI startups with the highest Mosaic scores that partner with big pharma in oncology each tackle completely different pieces of the cancer puzzle. These partnerships span the entire care continuum, from liquid biopsy screening (Caris Life Sciences) and antibody therapeutics (BigHat Biosciences) to patient monitoring (Huma) and diagnostic pathology (Aignostics).

While these companies all work in oncology, they demonstrate how AI addresses fundamentally different challenges across cancer care. Cancer’s data-rich environment and biological complexity make it a natural testing ground for AI innovation, suggesting that oncology will continue to drive the most cutting-edge applications in pharmaceutical AI.

RELATED RESEARCH FROM CB INSIGHTS

• The AI in drug R&D market map
• 3 shifts that will define the future of medtech
• Inside Novo Nordisk’s $33B manufacturing push to strengthen its obesity portfolio and build a biologics powerhouse
• Here’s how the 100 most promising AI startups in 2025 compare by the numbers
• The humanoid robots market map

For information on reprint rights or other inquiries, please contact reprints@cbinsights.com.

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These significant cash infusions signal where investors are placing the biggest bets at a given time and which startups are being positioned to shape or disrupt markets.

To track trends in mega-rounds, our monthly Book of Scouting Reports offers an in-depth analysis of every private company that has raised a funding round of $100M or more. The scouting reports provide insight into each company’s funding history and latest round; headcount; opportunities & threats; commercial maturity; and business health.

Download the book to see all 46 scouting reports.

Key trends from June’s mega-rounds include:

• AI attracts the largest funding rounds, fueled by tech talent wars: Meta invested a massive $14.8B in Scale, whose CEO is also joining the tech giant. Thinking Machines Lab raised $2B in seed funding without a live product, with several former OpenAI executives having joined the company. These rounds show how quickly AI talent is moving around the industry — and the hefty price tags that this talent can command.
• Industrials command a third of mega-rounds in June, indicating a hardware renaissance: Industrial companies (including defense, aerospace, energy, and robotics) drove many of this month’s $100M+ deals, from Anduril‘s $2.5B round to Helsing‘s nearly $700M deal. While AI is central to many of the companies in this sector, almost all are developing physical hardware and infrastructure. 
• Quantum computing players get a boost from AI and defense applications: Two quantum computing companies raised mega-rounds in June ’25: Infleqtion, which develops quantum sensing for defense, and AI 100 winner Multiverse Computing, which provides quantum-enabled model compression to speed up AI processing. While not a substantial share of deals, these investments point to an increased demand for quantum capabilities across high-growth applications.
• Capital is going toward product and R&D: 37% of mega-round recipients are directing these funds toward product development and core technology advancement, including AI. For example, Observe intends to use the capital to expand its AI observability features, while Impulse Space is planning R&D for new vehicles for NASA and defense customers. 

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By combining AI with physical dexterity, humanoids can perform complex tasks once limited to people, without the expensive facility modifications that traditional automation requires.

While manufacturing and warehousing use cases lead in early adoption, humanoids are expanding into healthcare, retail, and hospitality sectors, signaling widespread potential in industries that need human-like movement and flexibility.

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The humanoid robots market map

Now, our Book of Scouting Reports offers in-depth analysis on every single one of the private companies featured in the market map.

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

• Funding history
• Headcount
• Key takeaways (including opportunities and threats)
• Commercial Maturity score
• Mosaic score

Download the book to see all 49 scouting reports.

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Now, we’re examining the critical metrics behind these winners, revealing potential acquisition targets, partnership opportunities, and emerging competitors before they reshape the market.

Below, we analyzed the 100 winners to understand how the cohort stacks up, the markets we’re seeing emerge, top investors in AI, and more.

Learn more about the AI 100 here

Some highlights from our analysis: 

• AI infrastructure shows a maturity gap despite massive funding. Despite the already enormous amount of capital raised in this category, AI infrastructure still has overall low Commercial Maturity Scores and sees a lot of early-stage activity with a specific focus on efficiency. These AI 100 winners are betting on next-generation solutions like specialized AI chips, novel computing architectures with reduced energy consumption and optimized inference, and infrastructure designed for multimodal workloads that current systems can’t efficiently handle. 
• Autonomous vehicles are accelerating beyond the hype cycle. The auto & mobility market ranks third by Mosaic score, with companies gaining significant commercial traction following Waymo‘s recent success in scaling its robotaxi operations. This momentum validates years of R&D investment and suggests we’re entering a new phase of AV deployment. Read more in our recent autonomous vehicle analysis.
• Multimodal AI is driving the biggest breakthroughs. Voice AI platform Cartesia leads the largest year-over-year Mosaic score jump (+321), alongside other companies pushing beyond text-only models toward integrated voice, vision, and reasoning capabilities. This shift represents the next evolution of AI, especially for embodied AI systems like humanoids, moving from single-modality tools toward systems that can understand and generate across multiple forms of media simultaneously. 

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The accelerator that spotted OpenAI, Airbnb, and Stripe before they became household names is now placing bets across 4 key agentic AI areas: software development guardrails that de-risk “vibe coding”, web-browsing agents, backend workflow automation, and vertical agents penetrating highly regulated industries. 

For strategy teams, this represents both a roadmap of where agentic AI is heading and a curated list of potential acquisition targets, partners, and competitive threats.

Using CB Insights, we mapped the 70+ agentic AI companies in the Y Combinator’s 2025 Spring batch across 18 different categories.

Please click to enlarge.

Note: Categories are not mutually exclusive.

Check out the 140+ startups in Y Combinator’s newest cohort

Key Takeaways  

De-risking AI software development

Software development and testing is the second-largest agentic AI category of this batch, with 11 companies. This reflects the fact that software development AI agents are still booming, with 2025 funding already outpacing 2024 by 3x. Yet this cohort goes beyond coding AI agents,  providing engineering support, QA, and guardrails to make vibe coding less risky. 

A key focus of these companies is to make vibe coding less risky. For example, over half of the startups in this category focus on testing and review. Operative deploys browser agents that can test coding agents. Docket and Propolis use web agents to QA code and products. Startup Cubic reduces code review bottlenecks, and Jazzberry debugs code, both of which are issues becoming more prominent with the rise of vibe coding. 

A handful of companies are developing solutions to support software engineers in vibe coding and automated code generation. Delty is an AI agent that helps with system design and architecture based on deep codebase understanding, and StarSling provides AI agents to augment DevOps. 

These new tools will accelerate the growth of more reliable AI software development, boosting existing leaders in the space such as Cursor who could look to acquire them.

Web-browsing agents gather steam beyond general-purpose use 

Y Combinator’s dominance in web-browsing agents – backing over 50% of the existing market — signals this emerging category’s potential to become critical infrastructure for agentic AI. LLM giants like OpenAI are already building their own browser agents, but this isn’t deterring startups from entering the space

The Spring 2025 batch reveals how these startups are differentiating themselves by targeting high-value, specific applications rather than building general-purpose agents. 

For example,  Kaizen provides browser agents that enable outdated, legacy systems to connect with websites without the need for an API. Operative and Propolis are pioneering the use of browsing agents for software testing and quality assurance, areas where automation has historically struggled.

Agents capable of accessing and browsing the web can access more data and information than what is typically available in a company’s systems. This helps provide more context to agentic systems, improving decision-making, and ultimately autonomy. 

Agents are coming for the backend

Today, most AI agents focus on frontend interactions and applications, with customer service and enterprise workflow being 2 of the most well-funded AI agent markets. This Y Combinator cohort signals how agents are moving to the backend.

Cactus, Combinely, and Hemut are building back-office systems in areas like accounting and reporting. Caucus and Cohesive developed agent-based CRMs that go beyond the traditional enterprise space to target small businesses and government. Odapt allows custom application development in areas like finance and marketing, built on top of existing tools and systems. Cleon and Auctor AI are automating system implementations. 

Currently, these companies focus on narrowly defined, specialized backend workflows. Expanding into more end-to-end workflows will require greater trust in agentic AI applications. 

This trust can be partially built through the ability to benchmark AI agent performance. Kashikoi, Janus, and The LLM Data Company – all part of this Spring cohort – are working on this today. 

AI agents keep making inroads in highly regulated industries

Once an obstacle for new AI applications, the most highly regulated industries have emerged as targets for agentic AI startups. 32% of verticalized AI agent companies are actively deploying solutions, and 23% and 22% are emerging and validating, respectively, suggesting an oncoming growth spurt. 

This impending growth is fully displayed with this batch of Y Combinator companies, particularly in healthcare and financial services, which represent 19% of the agentic AI companies in this year’s Spring cohort. 

Customer service and engagement are common areas of focus within these verticals, with companies like Eloquent AI (financial services), Trapeze (healthcare), and Kaelio (healthcare). Other startups are delving deeper into industry-specific workflows, like Chestnut Mortgage and Approval AI (lending and mortgage), and Bitboard (healthcare operations).

We expect the next generation of industry-focused AI agent companies to go beyond operational support and handle research autonomously. 

A handful of companies in this batch tackle research assistance today, like Bramante Biologics and SynthioLabs in healthcare and Scalar Field for investment research. These startups lay the foundation for a future in which AI agents can proactively source, digest, and deliver information to human users or automate their roles altogether.

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In response to these cost pressures, pharma companies are using AI to make R&D more efficient. These capabilities enable organizations to quickly identify and evaluate promising drug candidates, influencing the selection of therapeutic approaches that advance to development.

AI could potentially cut years off the discovery process and compress clinical trial times by up to 30%. This would accelerate the delivery of new treatments to patients, unlock novel treatment approaches, and enable more personalized medicine. Companies that effectively leverage these AI capabilities will gain crucial advantages in speed, precision, and breakthrough discoveries.

Our analysis maps 225 AI-driven drug R&D companies across 27 markets. Below the map, we break down several trends shaping the future of pharmaceutical innovation, as well as share the methodology we used to select and categorize companies.

Please click to enlarge.

Note: This market map is not intended to be exhaustive, and categories are not mutually exclusive. For more, see the detailed methodology at the bottom of this report.

Key takeaways

• • AI tools for clinical development are more commercially mature than the emerging field of preclinical applications. According to CB Insights’ Commercial Maturity scores, 37% of clinical development companies have reached the most mature commercial stages (4: Scaling or 5: Established) compared to just 7% of preclinical companies. Late-stage funding follows a similar pattern — 9% of clinical development funding since 2023 has gone to late-stage companies, compared to just 3% for preclinical tools.
  • AI funding in drug R&D rebounded in 2024, with discovery engines capturing the majority of investments amid consolidation in the clinical trial sector. In 2024, equity funding grew to $3.8B (up from $3B in 2023), with AI-derived biologics and small molecules attracting $1.1B and $1B, respectively. Early 2025 momentum suggests the sector is on track to match last year’s strong performance.
  • Patient recruitment platforms and clinical trial management systems demonstrate the strongest momentum. Examining market performance through average CB Insights Mosaic scores (a proprietary measure of private-company health and growth), EHR-based recruitment platforms lead (716 out of 1,000), while trial management systems show highest deal growth (+150% YoY). Look to these markets as high-growth areas to track within the AI drug R&D space.

Clinical development AI tools have achieved commercial maturity, while preclinical applications offer emerging investor opportunities

The adoption of AI in drug R&D is still emerging, and its pace varies across different sectors.

While early-stage funding dominates all sectors, preclinical development remains the most nascent, with 81% of funding since 2023 directed to early-stage deals and only 3% to late-stage deals. 

Clinical development shows greater maturity — still led by early-stage deals, but with a more established cohort of companies in later stages (70% early-stage, 9% late-stage funding).

CB Insights’ Commercial Maturity metrics further highlight this disparity. 

In preclinical development, 45% of companies are in the earliest commercial stages (1: Emerging and 2: Validating) compared to 32% in discovery and just 15% in clinical development. 

Conversely, only 7% of preclinical companies have reached the most mature stages (4: Scaling or 5: Established) vs. 11% in discovery and a substantial 37% in clinical development.

The disparities in maturity stem from each sector’s unique characteristics:

• Clinical development AI solutions often build upon existing healthcare technology infrastructure, facilitating faster adoption. 
• The use of AI in discovery carries higher investment risks as companies develop unproven molecules from scratch. 
• Preclinical development, positioned mid-pipeline, offers more specialized solutions and faces stricter regulatory scrutiny, explaining its slower advancement despite growing momentum.

For investors, this creates a clear distinction: Clinical development companies provide stronger near-term return potential, while the emerging preclinical space offers better opportunities to establish early market advantages.

Explore all 700+ AI in drug R&D companies

Drug R&D AI funding recovers as discovery engines lead investments amid clinical trial sector consolidation

After declining YoY between 2021 and 2023, equity funding across AI in drug R&D rebounded in 2024, growing from $3B to $3.8B and significantly surpassing pre-pandemic levels ($2.7B in 2019). The momentum continues in 2025, which, after Q1, is on pace to match 2024’s performance, bolstered by Isomorphic Labs‘ $600M Series A round in March 2025.

Among markets, discovery engines led funding in 2024, with AI-derived biologics securing $1.6B in equity funding and AI-derived small molecules attracting $1B. This aligns with these companies’ higher funding requirements for developing therapeutics and conducting clinical trials. It also demonstrates investors’ strategic bets on AI’s potential to slash drug discovery timelines — with discovery engines serving as the primary vehicles to prove this capability.

Enveda stands out here, having raised a $130M Series C in November 2024, followed by an additional $20M investment from Sanofi in February 2025 — a strong endorsement of its platform, which combines machine learning, metabolomics, and robotics to identify novel compounds from medicinal plants. The company’s recent collaboration with Microsoft Azure (May 2024) further positions it to scale its generative AI capabilities.

Beyond discovery engines, quantum computing platforms had an exceptional 2024, raising $376M, while decentralized clinical trial platforms followed, securing $129M. Huma led the latter group with an $80M Series D round in July 2024, while the market simultaneously underwent a wave of consolidation, with 5 acquisitions in 2024 alone, doubling all exits since 2020. 

However, among these acquisitions, only Aparito (purchased by Eli Lilly in July 2024) leverages AI in its offerings through its Atom5 platform, which enables comprehensive remote data collection and AI-powered data analysis.

Analyze AI in drug R&D deals

Patient recruitment and quantum computing lead commercial momentum in AI-driven R&D

According to CB Insights’ Mosaic scores, the highest-momentum AI markets across phases of drug R&D are: 

• Discovery: Quantum computing
• Preclinical development: Drug development strategy & compliance platforms
• Clinical development: EHR-based patient recruitment platforms

Among these high-potential sectors, several companies are making significant advances. 

SandboxAQ (Mosaic score: 843) leads in the quantum computing space; its 2023 release of the AQBioSim technology stack combines AI and quantum algorithms to predict molecular behavior and accelerate drug discovery. This expansion into biotech applications attracted Sanofi, resulting in a partnership in October 2024. 

In the regulatory domain, Weave (Mosaic score: 575) has positioned itself as an early mover in AI-driven regulatory automation for life sciences. Its AutoIND platform, launched in 2024, claims to reduce IND application timelines by up to 70%. 

Among these top 10 markets, trial recruitment optimization tools and clinical trial management systems showed the most growth in deals from 2023 to 2024. This illustrates increasing investor confidence in technologies that address critical bottlenecks in clinical trial efficiency and challenges related to patient enrollment.

In these markets, the companies with the highest Mosaic scores demonstrate rapid advancement and growing investment appeal:

• In the clinical trial management space, Lindus Health (Mosaic score: 874) secured a $55M Series B round in January 2025 and established a partnership with the Clinical Data Interchange Standards Consortium (CDISC) in February 2025. This collaboration with CDISC — a nonprofit that sets standards mandatory for FDA submissions — focuses on automating data standardization using Lindus Health’s AI platform for trial protocol generation and analysis.
• Paradigm Health (Mosaic score: 822) leads in trial recruitment optimization with its AI-driven platform for patient recruitment and trial management. Its deployment across 400 research sites and 1,000 healthcare provider locations in 3 countries helped it secure a $203M Series A in January 2023. In November 2024, Japan’s National Cancer Center selected Paradigm for its nationwide clinical trial network to advance precision medicine initiatives, expanding the company’s footprint in the Asian oncology research market.

These market signals suggest AI’s most immediate and transformative impact on drug development will come not from scientific breakthroughs alone, but from technologies that systematically eliminate the operational inefficiencies that have historically extended development timelines and inflated costs.

Dive into all drug R&D tech markets

Methodology

To identify players for this market map, we reviewed AI companies in drug R&D markets and included startups with a Mosaic score of 400+ that have raised funds within the last 5 years. For markets where these criteria identified more than 20 companies (AI-derived small molecule drugs, AI-derived biological drugs, and molecular design platforms), we selected those that had raised at least $20M in funding. If further reduction was needed, only companies in the top 20 Mosaic scores are shown. 

Categories on the market map align with our recent 3-part series on AI in drug R&D:

• Discovery encompasses workflows from project inception through lead selection, where discovery platforms are companies whose products are AI software systems, while discovery engines are companies whose products are therapeutics discovered using proprietary AI systems. 
• Pre-clinical development covers lead development to the first regulatory filing (an Investigational New Drug (IND) application in the United States)
• Clinical development spans from the start of clinical trials through commercialization. 

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AI is already fueling revenue growth for these cloud giants. 

First, AI workloads require more computing resources than traditional workloads, thus increasing per-customer spending. Second, AI companies (with their own computing needs) are proliferating rapidly, now capturing 20% of all venture deals globally. Together, these trends create both enormous revenue opportunities and unprecedented infrastructure challenges.

At the same time, new competitors are emerging to serve this insatiable demand. The OpenAI-led Stargate Project, with its planned $500B investment, threatens to reshuffle the cards in the cloud computing space that AWS has led for over a decade in terms of market share.

In response, cloud providers are spending tens of billions to capture their share of AI computing spend.

In the 19-page report, we cover 3 strategic pillars that emerged from our analysis:

• Cloud providers are investing heavily in compute infrastructure to meet explosive AI demand. Amazon, Alphabet, and Microsoft are planning a combined $250B+ in capex spend, primarily for AI data centers, in 2025 in addition to vertically integrating into energy production with 6 nuclear partnerships and creating custom AI chips to control costs and gain competitive advantages.
• Strategic partnerships and ecosystem development are key to cloud dominance, as providers lock in strategic partnerships with leading model developers (such as Microsoft’s $13B investment in OpenAI), develop proprietary foundation models, and build out accelerator programs to seed AI ecosystems. For example, Amazon expanded its genAI-focused accelerator from 21 to 80 startups between 2023 and 2024 while more than tripling the value of the cloud credits offered.
• Cloud providers are expanding their AI service portfolios into agentic AI and security to drive adoption and consumption. Alphabet recently made its largest acquisition ever to expand into the cloud security space, spending $32B to buy Wiz, while all 3 players are racing to expand their agentic AI offerings, including developer tools, dedicated marketplaces, and customizable agents.

Additional resources:

• Building the agent economy: How cloud leaders are shaping AI’s next frontier
• The AI data center value chain: 12 high-momentum technologies powering the future of AI
• AI is making big tech even bigger — here’s how the trillion-dollar tech giants are deepening their moat and fueling future growth
• Data centers are reshaping nuclear development, driving billions in new power infrastructure investment

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Its Lattice operating system serves as a digital backbone for autonomous capabilities across land, sea, air, and space domains. Meanwhile, its Arsenal factory platform aims to modernize the defense manufacturing playbook by producing different autonomous systems on demand, potentially slashing production cycles from years to months while reducing costs.

Together, these strategies bring a startup mentality to defense and have helped Anduril reach a potential $28B valuation and $1B in revenue in 2024 — clear signals of traction among investors, government agencies, and global defense firms.

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GenAI adoption is changing how insurance companies operate, incumbents are meeting new competitors, and startups face a more selective dealmaking environment focused on profitability.

Industry leaders echo this reality: commentary from the InsurTech NY 2025 Spring Conference emphasized an increasingly fast-paced — and fast-changing — insurance industry.

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Now, our Book of Scouting Reports offers in-depth analysis on every single one of the AI 100 winners, from infrastructure to horizontal to vertical applications.

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

• Funding history
• Headcount
• Key takeaways (including opportunities and threats)
• Commercial Maturity score
• Mosaic score

Plus, the analysts behind this year’s AI 100 provide their perspective on every one of the winners.

Download the book to see all 100 scouting reports.

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After generative AI transformed content creation with large language models and AI agents enabled autonomous decision-making with predictive systems across enterprises and industrial applications, humanoid robots represent the next frontier as the embodiment of physical AI.

The humanoid market secured a record $1.2B in funding in 2024 and is projected to reach $2.3B in 2025, according to CB Insights data.

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Drawing on CB Insights’ Business Graph, which links data across private investments, business relationships, and public company disclosures, we surface key signals on how each cloud player is positioning itself in the agentic AI space and what their next move could be.

While all 3 providers are investing heavily in infrastructure to support agentic AI, they are taking distinct paths to monetization and market control — from proprietary models and low-code build tools to strategic partnerships and go-to-market accelerators.

Understanding these differences will prove critical in evaluating cloud alignment, competitive positioning, and agent-enabled product strategy.

Key takeaways

• Amazon positions itself as a neutral infrastructure layer for the agentic ecosystem, betting on in-house chips and seeding its ecosystem with 16 total investments in agent startups. These investments, which primarily take the form of cloud credits rather than equity, form a low-risk, high-volume strategy to lowering the barriers to building on AWS. Amazon’s approach combines enterprise enablement with strategic consumer-facing investments, signaling potential integration with its broader ecosystem.
• Google’s agentic offering centers around its Gemini foundational models, creating an open marketplace for partner-built agents leveraging its technological leadership — supported by 46 total partnerships and its Agent2Agent protocol. This partner-centric approach means Google can quickly populate its marketplace with specialized agents while maintaining Gemini as the differentiating foundation technology.
• Microsoft emphasizes a pre-built suite of agentic solutions to drive enterprise adoption, embedding Copilot agents throughout its productivity ecosystem. The company recently achieved 15M GitHub Copilot users, up 4x YoY, and recorded 1M custom agents created on its SharePoint and Copilot Studio platforms. Microsoft’s expansive enterprise client base gives it an in-built audience for new agent products.

Amazon positions itself as the neutral infrastructure layer for the agentic ecosystem

Amazon is approaching the agentic AI landscape as a pragmatic infrastructure provider, with a strategic bias toward enabling partners rather than competing with its own agent suite. This partner-first approach comes at a critical time, as Amazon has been playing catch-up in the agentic race — although recent moves, including forming a dedicated agentic AI group in March 2025 and unveiling its Nova foundational models in December 2024, signal a growing focus.

Amazon is betting on its in-house chips, Trainium and Inferentia2, to attract agentic AI workflows, as these chips can help reduce the cost and energy consumption associated with AI model training and inference. Amazon has already formed several partnerships with agentic AI startups such as Poolside and NinjaTech for them to train and run their AI agents on its in-house chips.

This approach — of providing robust infrastructure and letting specialized partners build solutions on top — is also reflected in its agent development tool offerings. While developers can build agents using Amazon Bedrock Agents, Amazon (unlike Google or Microsoft) doesn’t directly emphasize low-code/no-code solutions, instead enabling partners like SnapLogic to build such tools on its platform. 

The company has also been investing heavily in agentic AI startups, with 16 unique investments since 2023 — more than both Google and Microsoft combined. However, 12 of these were made through non-equity accelerator programs that provide cloud credits and technical enablement rather than capital. 

This low-risk, high-volume approach lowers the barriers to building on AWS while seeding future clients at minimal cost. It also embeds AWS infrastructure into early-stage agent development, capturing mindshare before competitors can gain traction.Amazon’s investments reveal a strategic interest in consumer-facing AI applications that complement its existing business. Three of its four equity investments are in consumer-focused companies — Please and NinjaTech (personal AI agents) and Cartesia (voice AI) — aligning with Amazon’s consumer strategy and the recent launch of Nova Act, its web-browsing agentic AI targeting developers. 

These investments suggest AWS is taking a dual strategy: framing itself as an enterprise infrastructure provider for partners while developing consumer-facing capabilities that could enhance Amazon’s broader ecosystem, including potentially a revamped Alexa.

This could lead Amazon to make an acquisition that accelerates monetization of consumer-facing agents. Acquiring a startup developing agent payment infrastructure, for instance, would support efforts to enable autonomous transactions.

See 4 agent payment infrastructure startups Amazon could acquire

Google’s agentic offering centers around its Gemini foundational models

Google has positioned itself as the central platform provider in the agentic AI landscape, building a comprehensive ecosystem centered around its proprietary Gemini foundation models. Unlike Amazon’s infrastructure-focused approach or Microsoft’s enterprise application strategy, Google is creating an open marketplace for partner-built agents that leverage its technological leadership.

To boost adoption of its Gemini models for agentic AI, Google unveiled its AI Agent Space last year, a dedicated marketplace exclusively for partners’ agents. This is complemented by Google’s agent interoperability initiative, the Agent2Agent (A2A) protocol, which enables AI agents to communicate effectively regardless of their underlying frameworks or vendors. As a sign of traction for A2A, Microsoft recently announced it would adopt the protocol, in addition to the 50+ supporting partners Google already unveiled early April this year.

Google leads in agent-related partnerships with 46 collaborations — 2x as many as Microsoft and Amazon. Almost half of these are with agentic AI startups, including AI coding agents like Cursor, Augment Code, and Replit. This partner-centric approach means Google can quickly populate its marketplace with specialized agents while maintaining Gemini as the differentiating foundation technology.

Source: CB Insights — Google’s business relationships. Note: includes business relationships for Google Cloud.

Enterprise partnerships also demonstrate Google’s strategy in action. Its recent Salesforce collaboration will empower Salesforce customers to build Agentforce agents using Gemini, while Deloitte has launched over 100 ready-to-deploy AI agents powered by Google’s models. According to Google Cloud, more than 60% of generative AI startups are now building on its platform.

Google has also been partnering with leading venture capital firms and accelerators, like Sequoia, Lightspeed, and Y Combinator, to promote the use of its technology (such as TPUs and Gemini models) to fast-growing startups that are building with AI. 

Google’s development toolkits — Vertex AI Agent Builder, Agent Designer in Agentspace, and Agent Development Kit — offer solutions for both technical developers and non-technical users, reflecting Google’s goal of becoming the complete platform for agent creators and consumers alike. 

Rather than building a comprehensive first-party agent suite, Google is embedding itself into the tech stack of emerging agentic players, making Gemini the platform of choice for agent innovation. 

To maintain its edge in safe scaling and cross-agent coordination, Google may look to acquire companies focused on monitoring, governance, and lifecycle tooling, such as Galileo — a leader in AI evaluation backed by Databricks and ServiceNow.

See 20+ AI observability & evaluation startups Google could acquire

Microsoft emphasizes a pre-built suite of agentic solutions to drive enterprise adoption

Microsoft’s offerings center around a comprehensive suite of pre-built agents deeply integrated into its productivity ecosystem. While Amazon focuses on infrastructure and Google on promoting its foundational models, Microsoft aims to deliver immediate business value through turnkey solutions.

The company leads the market in pre-built agent offerings, with its Copilot suite including Analyst, Researcher, Security, and Dynamics 365 autonomous agents — all powered by its exclusive access to OpenAI’s models. 

This strategy has driven strong adoption: Microsoft’s Q3 FY’25 earnings call revealed that GitHub Copilot’s developer base has surpassed 15M users (up 4x YoY), while 1M custom agents were created during that quarter through Copilot Studio and SharePoint.

Source: CB Insights — Microsoft Earnings Insights

Microsoft’s development tools (Copilot Studio, Azure AI Agent Service) cater to both technical and non-technical users, but the company’s primary advantage comes from embedding agentic capabilities throughout its productivity ecosystem. The November 2024 launch of Magentic-One, a multi-agent system for enterprise deployment, further enhances Microsoft’s position in business workflows.

Unlike Amazon’s broad ecosystem-seeding or Google’s push to embed Gemini models into any agentic workflow, Microsoft concentrates on initiatives that complement its in-house agentic tools. Its partnership with Moveworks exemplifies this strategy, allowing employees to access Moveworks’ specialized agents directly within Microsoft 365 Copilot and Teams.

Microsoft’s approach demonstrates the power of integration over technological differentiation in driving enterprise adoption. By leveraging its existing relationships and software suite, Microsoft has established dominance in high-value business workflows where agentic AI delivers immediate productivity gains — and where competitors must overcome Microsoft’s entrenched position.

To round out its Copilot suite and reinforce its workflow ownership strategy, Microsoft may seek acquisitions in sectors where it lacks native agent offerings, like recruiting, healthcare administration, or logistics.

Identify which vertical AI agent startups Microsoft could acquire

Related research on AI agents and big tech:

• Enterprise AI agents & copilots: Our growth projections for the $5B+ market
• The AI agent market map
• AI is making big tech even bigger — here’s how the trillion-dollar tech giants are deepening their moat and fueling future growth

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We highlight three pivotal trends from the event — backed by examples and CB Insights data — that capture the sector’s shifting dynamics in 2025.

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While deal volumes remained mostly steady, funding increased 51% to $66.6B, with the majority of this going to infrastructure companies like OpenAI. 

Meanwhile, vertical AI is gaining momentum, with healthcare unicorns dominating Q1’s new unicorn cohort — a sign of investor confidence in AI’s increasing specialization.

Download the full report to access comprehensive data and charts on the evolving state of AI. 

Key takeaways from the report include:

• AI funding grows 51% QoQ to hit $66.6B — a new quarterly record — as industry incumbents secure mega-rounds. This boost was largely driven by a handful of major infrastructure players like OpenAI ($40B round), Anthropic ($3.5B Series E), and Safe Superintelligence ($2B Series B). Even without OpenAI’s massive round, Q1’25 would still represent the second-highest funding quarter ever (following Q4’24). Deal count decreased only slightly, falling 7% QoQ to 1,134. 
• Healthcare dominates the new AI unicorn pool. More than half of the 11 AI companies that reached $1B+ valuations in Q1 are developing healthcare solutions. These healthcare AI unicorns made up 30% of all new unicorns across VC and include Hippocratic AI (healthcare models and agents) and Insilico Medicine (AI drug discovery).
• AI agent companies lead M&A activity amid increasing consolidation. The 3 largest of 85 AI acquisitions in Q1’25 went to companies offering enterprise AI agent technology. The markets these companies occupy — like agent development platforms and multi-agent systems — boast among the highest average Mosaic scores across industries, reflecting strong company health and signaling the potential for more exits. 

We dive into the trends below.

AI funding reaches record $66.6B in Q1’25 as industry incumbents secure mega-rounds

AI funding grew 51% to $66.6B across 1,134 deals in Q1’25. This quarter’s funding total represents nearly two-thirds of all AI investment in 2024 ($101.5B), suggesting full-year 2025 funding will blow previous years’ tallies out of the water. 

The surge was fueled by mega-rounds concentrated among a few infrastructure giants, most notably OpenAI’s massive $40B VC round, along with Anthropic’s $3.5B Series E and Safe Superintelligence’s $2B Series B. Even without OpenAI’s landmark funding round, Q1 would be AI’s second-strongest funding quarter ever.

While total funding surged, the relatively stable deal count suggests larger deal sizes — especially to already established market leaders — rather than simply more companies receiving investment. In fact, in 2025 YTD, the median deal size of $5M represents a 4-year high. 

CBI customers: Track 17K+ AI companies

Healthcare dominates the new AI unicorn pool

While infrastructure companies received the lion’s share of funding, healthcare AI players led in new unicorn creation. 

Healthcare companies claimed the majority of new AI unicorns in Q1’25, with 6 out of 11 total AI companies reaching the $1B+ milestone. Even when looking at the venture landscape beyond AI, healthcare AI players drove nearly 1 in 3 new unicorn births in Q1. 

While healthcare AI unicorns are developing diverse applications across the care continuum, half of these newly minted unicorns apply AI to support provider workflows. These include:

• Hippocratic AI (patient follow-up)
• Abridge (clinical documentation)
• OpenEvidence (healthcare decision-making)

This trend highlights both growing demand for clinician-support tools and strong investor conviction in AI’s ability to deliver returns in the healthcare industry.

See every current AI unicorn

AI agent companies lead M&A activity amid increasing consolidation

Agentic solutions led the top AI exits in Q1’25, securing the 3 largest deals among 85 acquisitions — establishing agents as the primary focus of industry consolidation. 

These acquisitions align with the high Mosaic scores (which measure company health and growth potential on a 0-1,000 scale) across AI agent markets. Top agent categories all score well above the average of 370 across industries: autonomous agents & digital coworkers (721), AI agent development platforms (715), and multi-agent systems & orchestration (705).

The blockbuster exits of companies like Moveworks, Weights & Biases, and OfferFit show that enterprise buyers are increasingly seeking to build comprehensive agent solutions to gain a competitive edge. 

See rising markets in the CBI Market Leaderboard

MORE AI RESEARCH FROM CB INSIGHTS

• The AI agent market map
• AI 100: The most promising artificial intelligence startups of 2025
• Enterprise AI agents & copilots: Our growth projections for the $5B+ market 
• What’s next for AI agents? 4 trends to watch in 2025

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These LLM-powered applications can execute complex tasks autonomously (agents) or alongside a human (copilots) and have come for just about every horizontal job function.

Within the overarching space, 2 markets are already generating over $1B each in annual revenue: enterprise workflow agents & copilots (those targeting a wide range of applications such as general productivity and research use cases) and coding agents & copilots.

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Despite this contraction, median deal size has climbed to $10M this year (up from $8.9M in full-year 2024), revealing that CVCs are making fewer but larger investments as economic uncertainty persists.

The quarter highlighted 2 other dominant forces reshaping the CVC landscape: US startups captured 70% of global CVC-backed funding — the 2nd straight quarter at 70%+ — while AI startups secured 7 of the top 10 CVC deals worldwide. This reflects an intensifying race among CVCs to secure competitive footholds in leading technologies before rivals gain the upper hand.

Download the full report to access comprehensive data and charts on the evolving state of CVC across sectors, geographies, and more.

Key takeaways from the report include:

• Deals continue their downward trend as investors remain selective. Global CVC deal volume fell 13% QoQ to 728 deals in Q1’25, reaching the lowest quarterly total since Q1’18. Mega-rounds ($100M+) accounted for 59% of the $18.7B in total funding, showing that CVCs are still making substantial bets in a more selective environment.
• US companies dominate CVC investment dollars. In Q1’25, US startups captured $13.1B (or 70%) of global funding from deals with CVC participation. Within the US, Silicon Valley maintained its leading position with $7.5B across 97 deals, underscoring its continued importance as a strategic hub for corporate investment.
• AI continues to command CVC attention and dollars. AI startups secured 7 of the 10 largest CVC-backed deals in Q1’25, with these deals representing 31% of all quarterly funding among CVC-backed deals. The biggest deal was Anthropic‘s massive $3.5B Series E round, backed by the venture arms of Cisco and Salesforce.
• Early-stage deal share holds steady at the highest level in over a decade. Early-stage investments made up 65% of all CVC deal activity in Q1’25, matching the high-water mark sustained annually since 2023. With the median early-stage deal size growing to $5.8M this year so far, CVCs are placing larger bets on nascent companies that have long-term growth potential.
• Salesforce Ventures leads with the strongest Q1 portfolio. Among CVCs with 5+ investments in Q1’25, Salesforce Ventures leads the way with the highest average Mosaic score, followed by Qualcomm Ventures. Salesforce Ventures’ Q1’25 investments include 2 of the largest rounds this quarter — Anthropic ($3.5B) and Together AI ($305M) — signaling the importance of AI in its growth strategy.

We dive into the trends below.

Deals continue their downward trend as investors remain selective

Global CVC deals fell 13% QoQ to 728, the lowest quarterly total since Q1’18. CVC-backed funding also declined 22% to $18.7B. 

Despite the pullback, $100M+ mega-rounds accounted for 59% of total funding, indicating that CVCs are still making large, strategic bets but in a more selective environment.

While the number of deals decreased, the median size of CVC-backed deals increased to $10M — up from $8.9M last year — as CVCs write larger checks for companies they believe will deliver long-term strategic value.

The shift toward fewer but larger deals reflects a broader flight to quality across venture capital. Notable mega-rounds in Q1’25 included Anthropic’s massive $3.5B Series E round, which represented nearly 19% of all Q1 CVC-backed funding globally and showcased the concentration of capital in market-leading companies.

US companies dominate CVC investment dollars

US companies captured 70% of global CVC-backed funding in Q1’25, securing $13.1B despite macroeconomic volatility. The US funding share represents the 2nd quarter in a row at 70% or above, up significantly from the historical norm of ~50% before 2023.

Silicon Valley maintained its position as the epicenter of CVC investment, attracting $7.5B across 97 deals — more than half the total US funding.

Several unicorn rounds powered the US’ strong funding quarter, including those from Anthropic, NinjaOne, Lambda, and Apptronik.

The capital concentration is striking given that US companies represented just 37% of global deal volume (269 of 728 deals). The substantial gap between deal share and funding share highlights a key regional difference in investment approach, with US deals ballooning in size. The median US deal reached $17M in Q1’25 — over 50% more than Europe, the next highest region, at $10.9M.

However, as corporate uncertainty grows due to shifting tariff policies, the US’ funding dominance will be a critical trend to monitor in the coming quarters.

Hear from CBI analysts on the global impact of tariffs

AI continues to command CVC attention and dollars

AI dominated the biggest CVC investments in Q1’25 — securing 7 of the top 10 deals — as CVCs place massive bets on startups with the potential to reshape industries.

CVCs are investing in AI companies across diverse areas. These range from general-purpose AI agents & copilots to hardware applications like Apptronik’s AI-powered industrial humanoid robots.

Other leading CVC-backed AI deals in Q1’25 include:

• Isomorphic Laboratories: $600M (backed by Google Ventures, NVentures) in a record-breaking round for AI-powered drug discovery
• Lambda: $480M (In-Q-Tel, NVentures) for AI computing infrastructure and cloud services
• Together AI: $305M (Prosperity7 Ventures, Salesforce Ventures, SE Ventures, SK Telecom Ventures) for building open-source foundation models and infrastructure
• Harvey: $300M (Google Ventures, OpenAI Startup Fund, REV Venture Partners) for specialized AI solutions for the legal industry
• Innovaccer: $275M (Danaher Ventures, Microsoft’s M12) for healthcare data integration and AI-powered analytics

For parent corporations, these investments go well beyond financial returns. They provide strategic access to technologies that could determine competitive advantage in the AI era.

Track the latest CVC-backed AI deals

Early-stage deals hold at the highest levels in over a decade

Early-stage investments remain at a record share of CVC activity, accounting for 65% of all deals in Q1’25 — matching the same level seen over the past 2 years and up 7 percentage points from where it was in 2021.

The strategic shift comes with increasing commitment levels, as the median early-stage deal size grew to $5.8M in Q1’25. Rather than spreading smaller amounts across many startups, CVCs are making substantial, focused bets on promising early-stage companies.

Regional strategies show notable differences: Asia leads with 39% of early-stage CVC deals, compared to 33% in the US and 21% in Europe. This suggests that corporate investors in Asia are particularly aggressive in securing access to emerging technologies at the earliest possible stage.

Across all markets, this pronounced shift toward early-stage investing reflects a fundamental change in CVC strategy: corporate investors are prioritizing gaining early access to innovation rather than supplying later-stage growth capital, positioning themselves to shape technological development from the beginning rather than joining after validation.

Salesforce Ventures leads with the strongest Q1 portfolio

Among CVCs with 5+ investments in Q1’25, Salesforce Ventures leads with the highest average Mosaic score for its Q1’25 bets (891 out of 1,000), followed by Qualcomm Ventures (840). Salesforce Ventures’ Q1’25 investments include 2 of the largest rounds this quarter: Anthropic ($3.5B) and Together AI ($305M).

Meanwhile, Google Ventures was the most active CVC in Q1’25 with 17 companies backed, followed by Japan-based investors Mitsubishi UFJ Capital and SMBC Venture Capital with 15 companies each. With 11 companies each, In-Q-Tel and Mizuho Capital rounded out the top five, highlighting the dominance of US- and Japan-based corporate investors.

Track top CVC-backed companies by Mosaic score

MORE VENTURE RESEARCH FROM CB INSIGHTS

• State of Venture Q1’25 Report
• State of Fintech Q1’25 Report
• State of Digital Health Q1’25 Report

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Given this momentum, the ecosystem is larger and more challenging to navigate than ever.

Our annual AI 100 list is designed to cut through this noise and highlight the next wave of AI winners, with a focus on early-stage players that are showing strength in terms of market traction, investor quality, and talent.

Leveraging CB Insights datasets such as deal activity, industry partnerships, team strength, investor strength, patent activity, and our proprietary Mosaic Scores, we selected 100 winners out of a cohort of 17K+ companies. We also analyzed CB Insights’ exclusive interviews with software buyers and dug into Analyst Briefings submitted directly to us by startups.

Below, we map out the winners, categorizing them based on their core offering. Key trends and category definitions follow. Customers can track activity of all of these companies in this watchlist. 

Please click to enlarge. Data as of 4/23/25.

CBI customers: Get data on the winners

Key takeaways on the AI 100

1. AI agents dominate the conversation. These applications, which automate tasks and processes for human users, are the next wave of genAI. Having made their way into virtually every horizontal and enterprise function, AI agents are also coming for infrastructure and verticalized applications. AI agents and supporting infrastructure make up 21% of this year’s companies, and the investors we spoke with consistently cited this space as a priority. 
2. ML security has become table stakes. The need to secure AI applications has grown in lockstep with the proliferation of genAI and agentic AI. 46% of strategy team leaders point to security as the primary barrier to genAI adoption, according to a recent CB Insights survey. Machine learning security companies are hardening AI algorithms and foundational models like LLMs, while also defending against increasingly sophisticated AI-powered attacks. 
3. AI observability and governance are critical gaps. Widespread use of AI is exposing the technology’s cracks — hallucinations, lack of orchestration, and output inaccuracies. It’s clear that AI ubiquity can’t exist without robust monitoring. Companies are rising to meet this need. Startups in this year’s list cover areas like observability and governance, while a small cohort also monitors AI agents to ensure reliability and compliance.   
4. The future is physical. Looking ahead, AI will evolve beyond software AI agents to a physical state. Advances in disparate areas of AI development — including robotics, multimodal image and voice models, edge computing, synthetic data, and spatial intelligence — provide the scaffolding for physical AI, which pairs AI software with hardware to take action in physical environments. Industrial humanoids represent an early manifestation of this, while future permutations could include fully autonomous defense drones, home companion robots, and more.
5. Vertical applications are exploding. In 2024, the horizontal companies in this AI 100 cohort received more funding than their vertical and infrastructural counterparts — $1.6B compared to $1.2B each for infrastructure and vertical. But in 2025 so far, the funding picture looks very different: Vertical winners lead the way with $1.1B in funding raised.

Category breakdown

AI INFRASTRUCTURE 

On the foundation model front, infrastructure newcomers are rapidly releasing models that rival industry leaders, signaling a maturing market where technical excellence and novel approaches increasingly compete with raw computing power. We identified winners across large language, edge, reasoning, small language, and multimodal models. 

Meanwhile, as AI applications — particularly agents — become more autonomous and widespread, the need for robust monitoring, governance, and cybersecurity solutions has grown in lockstep. 

We’ve heard this in our conversations with AI investors, as well. Mozilla Ventures, a lead investor in Credo AI, views governance as a strategic imperative. Mohamed Nanabhay, Managing Partner, notes: 

 “…We think that the AI governance sector itself will take on a crucial role of creating value for enterprises, allowing companies that leverage governance to deploy AI faster through the reduction of risk with a greater competitive advantage as a result.”

Category definitions:

DATA

• Synthetic data: Artificially generated or altered information that mimics real-world data without privacy concerns. Aaru uses a multi-agent approach to create population simulations for predictive decision-making applications like consumer behavior and electoral modeling.  
• Data preparation & curation: Tools and platforms that clean, transform, label, and organize data to make it suitable for AI training and deployment, encompassing data cleaning and specialized data processing. Unstructured, for instance, helps organizations capture unstructured data from various documents and convert it into AI-friendly formats such as JSON to train LLMs.
• Vector databases: Solutions that provide enterprises with an easy way to store, search, and index unstructured data at a speed, scale, and efficiency that current relational (and non-relational) databases cannot offer. For example, Qdrant provides an open-source vector database that allows developers to build production-ready applications that use nearest neighbor search functionality.

DEVELOPMENT & TRAINING

• Foundation models: Pre-built AI algorithms and architectures that can be deployed, fine-tuned, or integrated into applications, spanning general-purpose foundation models and specialized domain-specific models. This category includes large language, edge, reasoning, small language, and multimodal AI models. For instance, Archetype AI‘s Newton model processes multimodal sensor data and natural language to provide insights and predictions about physical environments.
• Agent building & orchestration: This category covers AI agent development platforms for building, orchestrating, and monitoring agents. Companies like LangChain provide a framework for building context-aware reasoning applications with tools for debugging, testing, and monitoring app performance across the entire application lifecycle.
• Computer vision & spatial intelligence: Technology that enables AI systems to understand, interpret, and interact with physical spaces and 3D environments, including mapping, navigation, and spatial data processing capabilities. Notably, World Labs develops Large World Models (LWMs) that enable AI systems to perceive, generate, and interact with both virtual and real 3D environments using spatial intelligence.

OBSERVABILITY & EVALUATION

• AI observability platforms: These platforms monitor, measure, and assess AI model performance, reliability, and outputs, including tools for testing, benchmarking, and continuous improvement of AI systems. For instance, Arize’s platform allows teams to monitor, diagnose, and improve the performance of AI models and applications in production through tools based on open-source standards that integrate with existing AI infrastructure.
• Governance: Solutions that establish policies, processes, and controls for responsible AI development and deployment, covering risk management, compliance, ethical oversight, and transparency requirements. For example, Credo AI offers a platform that automates AI oversight, risk management, and regulatory compliance while providing AI auditing to ensure system integrity and fairness.
• Machine learning security (MLSec): Technologies that protect AI systems from vulnerabilities, attacks, and data breaches, including techniques for securing model training, inference, and data pipelines. Solutions developed by companies like Zama enable computation on encrypted data, allowing for privacy-preserving machine learning across industries that require data privacy and security.

ACCELERATED COMPUTING & HARDWARE

• Edge: Platforms that provide the infrastructure and models to operate AI on “edge” devices such as tablets, IoT, autonomous vehicles, or smartphones. For example, EdgeRunner AI constructs an ensemble of small, task-specific models that work together to solve complex problems locally on devices, ensuring data privacy and security for heavily regulated industries.
• Photonics: Solutions that use light (photons) instead of electrons for data processing, with the potential to significantly increase computing speeds. Companies in this category provide memory, interconnects, and system architecture. Xscape Photonics develops bandwidth-efficient photonics solutions to support AI/ML infrastructure. 
• Quantum: Companies providing novel techniques like model compression and hardware to support quantum commercialization. Multiverse Computing provides AI model compression technology to enable quantum AI workloads and processing.
• Chips: Traditional chips, in addition to chips to support new AI technologies. Etched develops chips designed specifically for transformer inference, capable of processing extensive data for applications such as real-time voice agents and content generation.

HORIZONTAL AI

This category includes industry-agnostic solutions across visual media, text, code, audio, and interfaces. These function-specific solutions address common business needs regardless of industry, offering specialized intelligence that complements both vertical applications and foundational infrastructure.

AI agents in particular are beginning to upend the way in which enterprises think about software. Decibel Partners, a lead investor in multi-agent platform Dropzone AI, sees a movement toward productizing agents as full systems. Jéssica Leão, a Partner at Decibel, articulates this vision further:

“…We’re going to see the software world change because, again, you’re selling agents almost as if you’re selling back-end software.”

Horizontal AI solutions are increasingly tailored to serve distinct business functions while remaining broadly deployable. Startups in this category are developing sophisticated AI systems that excel in capabilities like content generation, customer support, process automation, and software development — all of which can be applied across industries. 

Category definitions:

• Content generation: AI systems that create text, images, video, and other media forms — spanning automated content production and multimodal generation. For example, Moonvalley’s genAI video model helps filmmakers by enabling prompt adherence, motion generation, and physics simulation using cleaned, fully licensed data.
• Customer service: AI agents that autonomously handle customer service tasks or augment human agents. Sierra‘s platform, for instance, provides intelligent agents for customer support that engage in personalized interactions and integrate with existing call center technologies.
• Cybersecurity: AI-powered solutions that detect, prevent, and respond to digital threats, vulnerabilities, and attacks, covering network security, threat intelligence, and automated incident response. Companies like Binarly use AI to detect and remediate vulnerabilities in firmware and software supply chains.
• General-purpose humanoids: AI systems embedded in robotic bodies that mimic human capabilities, enabling physical interaction through perception and manipulation. For example, Figure develops autonomous humanoid robots that combine human-like dexterity with AI to perform a variety of tasks across industries like manufacturing, logistics, warehousing, and retail.
• Process automation: Intelligent systems that autonomously handle repetitive business workflows, increasing efficiency by eliminating manual tasks. Orby AI offers a platform that observes enterprise processes and generates executable automations — particularly for complex, data-heavy operations in industries like tech and finance. 
• Software development & coding: AI solutions that assist with software development, code generation, debugging, and programming tasks, including automated code completion tools. For instance, Poolside offers foundation models and APIs that can be fine-tuned using a company’s own codebase and documentation to support internal dev teams.
• Video security: Technologies that enable real-time analysis of video feeds, supporting faster detection and response to security threats. Coram AI develops cloud-based security camera systems with features like real-time AI alerts and natural language video search, allowing businesses to monitor properties remotely without extensive hardware replacements.

VERTICAL AI 

Vertical AI is on the rise, with this year’s vertical winners surpassing the other category winners to capture over $1B in combined funding in 2025 YTD. They span 10 industries that represent a convergence of high-value problems, rich data availability, and regulatory momentum.

Some of the VCs we spoke with see specialization as the way of the future. Lila Tretikov, Partner and Head of AI Strategy at New Enterprise Associates (a lead investor for Twelve Labs, World Labs, and Orby AI), told us:

“We believe that there is going to be specialization, even within the model layer. And there’s going to be innovation in this layer, especially as we look at verticalization for specific use cases.”

The most well-represented verticals on this year’s list are healthcare (8 companies) and life sciences (6 companies). The healthcare industry as a whole is seeing breakthrough applications across multiple AI modalities — from agentic AI systems that can augment clinical workflows, to advanced machine vision for medical imaging analysis, to AI-accelerated drug discovery platforms that dramatically reduce R&D timelines.

This year’s cohort also saw significant representation in gaming & virtual assets (5 companies), finance & insurance (4 winners), and aerospace & defense (4 winners). 

Category definitions:

• Aerospace & defense: AI solutions designed for aerospace engineering, aviation operations, military applications, and defense systems, including autonomous navigation and threat detection technologies. For instance, Quantum Systems creates eVTOL unmanned aerial systems that serve critical defense applications, most notably in Ukraine. 
• Auto & mobility: AI applications for autonomous vehicles, transportation optimization, fleet management, and mobility services. Companies like Wayve are developing AI systems that use LLMs to provide real-time natural language explanations of driving decisions, helping improve users’ confidence.
• Energy: Platforms that optimize energy production, distribution, and sustainability, including battery intelligence and AI assistance for electric grids. For example, Liminal leverages ultrasound and machine learning inspection solutions to improve battery cell quality, cost-effectiveness, and safety while enabling confident scaling of production. 
• Finance & insurance: AI solutions for financial services, banking, investment, and insurance sectors, covering payments, risk assessment, and portfolio monitoring. Skyfire’s financial stack enables AI agents to perform transactions without credit cards or bank accounts, allowing businesses to monetize their products, services, and data through AI agents.
• Gaming & virtual assets: AI technologies that enhance gaming experiences, virtual environments, digital asset management, and immersive entertainment, including content generation and NPC (non-player character) intelligence. Altera‘s platform creates digital human beings that can interact with users and perform tasks autonomously, bringing empathy and human-like traits to digital interactions.
• Healthcare: AI applications focused on clinical care delivery, medical operations, and patient management, including tools for clinical documentation automation, medical imaging analysis, decision support systems, remote patient monitoring, and healthcare supply chain optimization. In the dental field, Overjet provides an AI platform that enhances clinical care through radiographic analysis and optimizes claims processing for providers and payers.
• Life sciences: AI solutions for pharmaceutical research, drug discovery, protein engineering, biological data analysis, and therapeutic development, including platforms for multiomics analysis, antibody design, foundation models for biology, and scientific experiment automation. Lila Sciences has developed a platform that integrates AI with autonomous laboratories to design, conduct, observe, and redesign experiments for scientific discovery.
• Legal: AI tools for legal research, document analysis, contract management, compliance, and legal workflow automation, including case management, due diligence, and contract review. AI-powered tools like Eve help law firms streamline the full case lifecycle from intake to litigation by automating case intake, drafting legal documents, and managing discovery processes.
• Manufacturing: Technology that optimizes industrial processes like factory automation, using virtual development and simulation. PhysicsX applies machine learning to physics simulations that optimize design and engineering processes across industries including aerospace, medical devices, and electric vehicles.
• Supply chain: AI solutions that enhance logistics and supply chain operations, including warehouse management and route optimization & visibility. Dexory combines stock-scanning robots with a digital twin platform to provide real-time inventory and warehouse analytics for logistics and supply chain operations.

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