M&A is back.

Below, we break down what’s driving the surge in deals, then zoom in on Nvidia’s latest purchase.

Buyers on the prowl

Q1’25 has already seen 11 $1B+ deals for VC-backed companies worth a combined $54.5B — blowing past quarters out of the water.

More than half of that value comes from Google’s $33B purchase of Wiz, the biggest VC-backed M&A exit of all time.

It’s not just tech startups — consumer & retail brands are getting snapped up too, like Pepsi’s $2B acquisition of Poppi.

But tech is leading the charge.

M&A activity in the sector rebounded 5% in 2024 and we expect it to gain more steam this year thanks to several factors:

• • Less regulatory pressure: Big tech players like Google are betting on a friendlier dealmaking climate with Lina Khan out as head of the FTC.
  • AI boom: Incumbents are anxious to get their hands on AI assets and infrastructure (see ServiceNow’s acquisition of MoveWorks and SoftBank’s acquisition of Ampere). 
  • Cheaper prices: Tech M&A valuations keep falling, encouraging strategic and financial buyers to get off the sidelines.

Nvidia’s M&A playbook

Among the Mag 7, Nvidia stands out for its aggressive acquisition strategy.

All told, Nvidia has snapped up 7 AI startups since 2021, with 4 of these in just the last year.

Last week it bought Gretel — reports place the exit valuation north of $320M (Gretel’s last disclosed valuation) but less than $1B.

Per CB Insights’ ESP ranking, Gretel is a leader in the synthetic training data market. 

Source: CB Insights — ESP ranking of players in tabular and text-based synthetic training data

Synthetic data offers a potential solve to 3 issues in AI development:

• A diminishing pool of high-quality text data to train more advanced LLMs.
• The need to preserve privacy by using anonymized data, critical to AI adoption in industries like healthcare and finance.
• The absence of real-world data to train physical AI models on tasks like driving cars or piloting humanoid robots.

CBI customers can see our analysis of 50 synthetic data providers here. 

By acquiring Gretel, Nvidia positions itself at the forefront of the synthetic data market and strengthens its position in emerging areas like physical AI.

Nvidia sees the physical domain as the next evolution of AI, according to CB Insights’ earnings call transcripts.

Source: CB Insights — Nvidia Q4 FY 2025 earnings transcript

Back in June 2024, we wrote about how Nvidia is investing in and partnering with companies focused on industrial applications, like digital twins and robotics, which can rely on AI for simulation and training.

See where else the $3T company is targeting growth in our Nvidia strategy map.

Related research from CB Insights:

• Analyzing Nvidia’s growth strategy: How the chipmaker plans to usher in the next wave of AI
• State of Venture 2024 Report
• 7 tech M&A predictions for 2025
• Generative AI is accelerating the timeline for fully autonomous driving

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According to CB Insights headcount data, AI firms such as Glean, Scale, and OpenAI have increased their workforce in the country by as much as 67% over the last six months. The country’s domestic AI companies have also seen 32% headcount growth over the same period.

Notably, India is now OpenAI’s second-largest market — with a user base that tripled in the past year — while global tech giants like Microsoft and Nvidia have made substantial infrastructure investments in the country.

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The AI boom has set the stage for a wave of tech M&A this year.

After 2 consecutive years of decline, tech M&A deals were up in 2024, with some of the largest deals centering on AI. AI companies have also bucked the general downward trend in exit valuations, instead seeing nearly double the median acquisition price from 2023 to 2024.

Using CB Insights’ predictive signals, such as Mosaic and M&A Probability, we’ve identified 7 AI-related areas where we expect to see M&A activity this year, as well as high-potential acquisition targets for each.

See highlights below, and download the full report for the rationale behind each prediction, as well as M&A target shortlists.

Tech M&A prediction highlights

• Big tech players set their sights on humanoid robotic: As physical AI takes off thanks to the rise of LLMs, humanoid robotics is becoming big tech’s next battlefield. Among high-potential acquisition targets, 1x stands out for its dual focus on industrial and consumer humanoids (just in January, it acquired Kind Humanoid to accelerate household robot development). This makes it a prime target for Meta, which recently announced plans to enter the consumer humanoid market.
• Enterprise tech heavyweights compete for AI infrastructure dominance: We’re already seeing strong signals from cash-rich companies such as Cisco and IBM, which are future-proofing their business models with AI investments. Hardware-aware AI optimization players CentML and Nota AI — which help accelerate AI model deployment while reducing compute costs — appear in our AI infrastructure acquisition target list. These companies have already shown quantifiable efficiency improvements as well as validation from Nvidia as a partner or investor.

Source: CB Insights advanced search. Data is dynamic (as of 2/27/2025).

• Data center energy demands fuel interest in cooling tech: Companies offering immersion and liquid cooling solutions enjoyed a funding rebound last year, attracting a combined $120M in fresh funding. Hypertec and Submer are high-potential acquisition targets in this space.
• Professional services firms seek AI capabilities: GenAI is coming for knowledge jobs — and leading professional services firms are buying AI capabilities to get ahead of it. One area where we see high M&A potential for professional services firms is to cater to clients’ responsible AI needs, with potential acquisition targets such as Lasso Security and HydroX AI.
• Pharma companies target AI drug discovery startups: AI drug discovery M&A is surging, with 12 deals in the sector since 2023. That M&A deal volume reflects both a maturing technology and growing urgency among pharma players to bring AI tech in-house.
• SaaS giants fortify their offerings with AI agent acquisitions: While some believe AI agents signal the death of SaaS companies, we anticipate SaaS leaders will acquire AI agent companies to avoid disruption. We’re already starting to see this happen with ServiceNow acquiring Moveworks for close to $3B in March 2025.

Source: CB Insights — ServiceNow Acquisition Insights

• Coding AI agents drive next wave of AI agent consolidation: Explosive growth, soaring valuations, a fractured AI agent landscape, and rising doubts about revenue defensibility make the coding AI agents market ripe for consolidation. While some players like Cursor look too expensive for an acquisition, we’ve identified Warp, Vidoc, and Bito as likely targets with high Mosaic scores and higher-than-average M&A Probabilities.

What is Mosaic?

Mosaic is CB Insights’ proprietary metric that measures the overall health and growth potential of private companies using non-traditional signals. Mosaic is widely used as a target company and market screener to identify high-potential emerging tech companies, typically defined as those with a score of 510 or higher.

What is M&A Probability?

M&A Probability is CB Insights’ proprietary signal that measures a private company’s chance of an M&A exit within the next 2 years. It is used to quickly screen and triangulate companies based on exit likelihood.

Combining both Mosaic Score and M&A Probability makes it easy to shortlist acquisition targets.

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As AI agents dominate the conversation, customers are growing skeptical about whether they can live up to the hype.

In March, we’ve interviewed 40+ customers of AI agent products and are hearing of 3 primary pain points right now:

1. Reliability
2. Integration headaches
3. Lack of differentiation

1. Reliability

This is the #1 concern raised by organizations adopting AI agents, with nearly half of respondents citing reliability & security as a key issue in a survey we conducted in December. 

According to CBI’s latest buyer interviews, AI agent reliability varies dramatically across providers. Many customers report a gap between marketing and reality.

“Whatever was promised didn’t work as great as said,” one LangChain user told us about the company’s APIs. “We encountered cases where we were getting partially processed information, and the data we were trying to scrape was not exactly clean or was hallucinating.”

For many customers, reliability is largely a function of how complex the data and use cases are. For instance, the LangChain customer saw ~80% accuracy for simpler tasks, but “for complex tasks, the accuracy dropped to around 50%.” 

Organizations are tackling the reliability issue with 1) human oversight; and 2) more extensive model training.

An Ema customer, for instance, first has a subject-matter expert review outputs, and once “more than 90% of the responses that we have tested are now accurate, we let it fly.”

A customer for CrewAI, which orchestrates teams of AI agents into “crews,” takes an even more involved approach:

The customer still needs to intervene with their own ML algorithms when CrewAI is unable to handle outliers or unconventional data structures. If CrewAI is able to tackle these cases in the future, “that would be a huge leap forward.” 

Source: CB Insights — AI agent market map featuring CrewAI and LangChain in the infrastructure category 

2. Integration headaches

Integration limitations rank as another top customer pain point.

For one, lack of interoperability poses long-term challenges, as this Cognigy customer notes:

An Artisan AI customer echoes this: “It was a bit of a gamble that we were signing up for a product where they didn’t have quite all the integrations that we wanted.”

Where customers see real value from these tools is when they can support seamless data flow, especially through their existing tech stack. This buyer went with Decagon because of its integrations:

3. Lack of differentiation

More than half of private capital flowing into the AI agent space has gone to horizontal applications — but these markets, like customer support and coding, are becoming highly saturated.

“There’s so many short-term moats, but in the long term there is no moat,” one customer observed. “Whatever you build will be rapidly reproduced.”

In a crowded market, specialization will determine success.

Hebbia, for instance, has tailored its solution to financial players. An exec at a PE firm framed this as a selling point when getting internal buy-in: “When I bring tools to the deal team that live and breathe diligence and deal execution, ensuring that it’s aligned to what they know and understand and [that it] speaks their language is incredibly important.”

While many horizontal AI agents are actively deploying or even scaling their solutions, vertical AI agents remain nascent, with half still in the first 2 levels of Commercial Maturity. 

They’ll gain more momentum this year as enterprises prioritize solutions that are highly tailored to the needs of individual industries. 

CB Insights customers can read our latest interviews with AI agents’ customers here.

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Small teams are getting big payouts.

That’s what the M&A data for 2025 says.

Using CB Insights M&A transaction and headcount data for Q1’25 so far, we found that tech companies acquired for $100M or more had just 100 employees at the median.

Our analysts dove into the tech M&A landscape in a live briefing on March 18 — download the recording here.

We zoomed in on the companies exiting with teams of 100 employees or under, and they’re typically:

• young (7 years old on average)
• bootstrapped (a majority have raised under $20M in equity funding)

See the top 10 by valuation-to-employee ratio below.

CBI customers can explore these 10 startups here.

The top exit by that metric went to Voyage AI, which offers embedding models and ranking tools to improve AI search and retrieval.

With just 19 employees and a price tag of $220M — up 2x since its funding round last September — Voyage AI’s sale to MongoDB equated to $11.6M per employee.

Bon voyage

For MongoDB, Voyage AI represents an opportunity to own more of the AI development process and build customer trust, specifically around output reliability.

Source: CB Insights — MongoDB Q4 FY 2025 earnings call

This is one of the main hurdles to broader AI adoption.

In a December 2024 survey we conducted on AI agents — the clear next evolution for enterprise genAI deployment — nearly half (47%) of respondents cited reliability & security as a top obstacle.

To address this concern, platforms that help businesses organize, maintain, and leverage their data effectively will become even more important.

Acquisition radar

MongoDB isn’t the only one acquiring AI startups to stitch together more unified AI development tools.

Data management giants Databricks and Snowflake have been on AI acquisition sprees, acquiring 5 AI startups a piece since 2023 — more than any other acquirers globally.

The question is: Who’s next?

We expect the next wave of AI M&A targets to be those building out AI agent infrastructure.

Drilling down further, there are 47 startups with teams of 100 employees or less.

The most likely M&A targets, ranked using CB Insights’ Exit Probability, are:

• Letta (agent memory)
• Coval (agent evaluation & observability)
• Fixie (voice AI)

Source: CB Insights — Platform search of AI agent infrastructure startups

Another top contender is Unstructured, in the data curation space — the company has received previous backing from the venture arms of both MongoDB and Databricks.

Customers can unlock the full list of AI agent infra targets here.

RELATED RESEARCH FROM CB INSIGHTS:

• Tech M&A Predictions for 2025
• Databricks wants to be a one-stop shop for AI. Here’s how it plans to get there
• Our 6 predictions for the insurance space in 2025
• State of Venture 2024 Report

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While AI copilots have already made inroads across industries, the next evolution — autonomous agents with greater decision-making scope — is arriving quickly. AI agent startups raised $3.8B in 2024 (nearly tripling 2023’s total), and every big tech player is already developing AI agents or offering the tooling for them.

Implications for enterprises will be far-reaching, from altering workforce composition (with new hybrid teams of humans and AI agents) to maximizing operational efficiency through full automation of routine tasks. 

Below we identify 170+ promising startups developing AI agent infrastructure and applications. 

CBI customers: Track 250+ AI agent companies

We selected companies for inclusion based on Mosaic health scores (500+) and/or funding recency (since 2022). We included private companies only and organized them according to their primary focus. This market map is not exhaustive of the space.

Want to be considered for future AI agent research? Brief our analysts to ensure we have the most up-to-date data on your company. 

Outlook on AI agents

Fully autonomous agents remain limited due to issues pertaining to reliability, reasoning, and access. Most agent applications today operate with “guardrails” — within a constrained architecture where, for example, the LLM-based system follows a decision tree to complete tasks. 

Agents featured on this map include some combination of the following components: 

• Reasoning: Foundation models that enable complex reasoning, language understanding, and decision-making. These models evaluate information and form the cognitive core of the agent.
• Memory: Systems that store, organize, and retrieve both short-term contextual information and long-term knowledge.
• Tool use: Integration capabilities that allow agents to interact with external applications, APIs, databases, the internet, and other software. 
• Planning: The agent’s architecture for breaking down complex tasks into more manageable steps, reflecting on performance, and adapting as necessary.  

We expect more startups to move up the scale of autonomy as AI capabilities advance. Improvements in reasoning and memory will enable more sophisticated decision-making, adaptability, and task execution.

For example, in September 2024, legal AI startup Harvey announced that OpenAI’s o1 reasoning model, supplemented with domain-specific knowledge and data, was enabling it to build legal agents. The company, which raised $300M at a $3B valuation in February 2025, has doubled its sales force in the last 6 months, indicating rising market demand.

While the above market map highlights the private landscape (with a focus on enterprise applications), tech giants and incumbents are also launching agents. We predict big tech and leading LLM developers will own general-purpose AI agents, but there are many opportunities for smaller, specialized players. 

Looking ahead, watch for new form factors outside of the copilot/chatbot interface that will push the boundaries of what an “agent” is. Early indications of this include “AI-native” workspaces — tools and platforms built from the ground up around AI capabilities, rather than layering AI features on top of a traditional product. For instance:

• Eve’s legal platform aims to automate aspects of the whole case lifecycle (from case intake to drafting). 
• Hebbia’s Matrix product builds spreadsheets that mine information from files (in rows) and deliver answers to questions (in columns), proactively discovering, organizing, and surfacing data.
• With its Dia product, The Browser Company is exploring web browsing interfaces that can summarize content, automate repetitive web tasks, and even anticipate next actions.

Category overview

AI agent infrastructure

This segment covers companies building agent-specific infrastructure. (We excluded general genAI infrastructure markets like foundation models and vector databases from the map.)

Development tools

A diverse ecosystem of tools has emerged to support agents’ development. These range from memory frameworks like Letta that enable persistent, retrievable memory across interactions; to tools that allow agents to take action via integration (e.g., Composio), authentication (e.g., Anon), and browser automation (e.g., Browserbase).

Another set of companies is giving agents more utility across payments (which includes companies developing crypto wallets for agents as well as virtual cards) and voice (development platforms and tools for testing AI voice applications as well as speech models).

Meanwhile, demand for simplified, comprehensive deployment options is driving the rise of AI agent development platforms — the most crowded infrastructure market on our map. 

LLM developers including Cohere (with its North AI workspace) and Mistral have launched their own agent development frameworks, while Amazon, Microsoft, Google, and Nvidia all offer AI agent development tooling. With many enterprises favoring established vendors due to lower risk, big tech companies have significant advantages here.

Trust & performance

Concerns around reliability and security have helped establish a market for agent evaluation & observability tools. Early-stage companies are targeting applications such as automated testing (e.g., Haize Labs) and performance tracking (e.g., Langfuse). 

Multi-agent systems, where specialized sub-agents work together to complete tasks, also show promise in improving accuracy. Insight Partners-backed CrewAI’s multi-agent orchestration platform is reportedly already used by 40% of the Fortune 500. 

Vendors are also tackling reliability concerns directly. Based on our briefings with 20+ AI agent startups in Q1’25, companies are using 5 primary methods to build user trust: 

1. Transparency
2. Human oversight
3. Technical safeguards
4. Security & compliance
5. Continuous improvement 

Horizontal applications & job functions

Horizontal AI agent startups make up nearly half of the map and overall landscape. 

This segment primarily features startups targeting enterprises, with industry-agnostic applications across job functions like HR/recruiting, marketing, and security operations. Companies in the productivity & personal assistants market, including OpenAI with its Operator agent, are targeting consumers and employees directly.  

The AI agent markets with the most traction — based on companies’ median Mosaic health scores — are customer service and software development (which includes coding and code review & testing agents). These markets are also among the most crowded due to the value agents bring to well-defined workflows and testable environments. 

We see this reflected in adoption, particularly at the customer service layer: Among 64 organizations surveyed by CB Insights in December 2024, two-thirds indicated they are using or will be using AI agents in customer support in the next 12 months. 

Overall, horizontal AI agent applications are more commercially mature compared to the infrastructure and vertical segments, with over two-thirds of the market deploying or scaling their solutions based on CBI Commercial Maturity scores. 

Vertical (industry-specific) applications

We expect increasing verticalization as startups carve out niches by solving industry-specific customer problems, especially in areas with strict regulatory scrutiny and data sensitivity.

This category features companies catering to industries including: 

• Financial services & insurance: The most crowded vertical category on the map with 11 companies, startups here are targeting a variety of finserv workflows such as financial research (Boosted.ai and Wokelo), insurance sales & support (Alltius and Indemn), and wealth advisory prospecting & operations (Finny AI and Powder). 
• Healthcare: Solutions in this market aim to reduce the volume of manual tasks for healthcare professionals across use cases like clinical documentation, revenue cycle operations, call centers, and virtual triage. Solutions from companies like Thoughtful AI (revenue cycle operations) and Hippocratic AI (staffing marketplace) are targeting end-to-end healthcare workflows. 
• Industrials: These companies look to optimize processes and equipment — including control systems, robots, and other industrial machines — without relying on consistent human intervention. For example, Composabl launched an agent platform in May 2024 that uses LLMs to create skills and goals for agents that can control industrial equipment. Public companies like Palantir are also active in this space. Learn more in our industrial AI agents & copilots market map. 

RELATED RESEARCH

• What’s next for AI agents? 4 trends to watch in 2025
• The enterprise AI agents & copilots market map
• The Multi-Agent AI Outlook
• Future of the workforce: How AI agents will transform enterprise workflows

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These LLM-based systems mark an evolution beyond copilots: AI agents can accomplish complex tasks on a user’s behalf with minimal intervention, from sales prospecting to compliance decisioning. 

In the rapidly growing landscape for agent infrastructure and applications, over half of companies in the market have been founded since 2023. Meanwhile, funding to startups in the space nearly 3x’d in 2024.

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The 2024 Change Healthcare cyberattack demonstrates the far-reaching consequences of cybercrime in healthcare. This attack compromised the protected health information of at least 100M people and cost parent company UnitedHealth around $3B.

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Imagine: your personal AI agent notifies you that a hair dryer you’ve been eyeing is now on sale. The product page highlights benefits tailored to your curly hair, while the agent confirms it will arrive before your upcoming trip.

With your approval, the agent handles the purchase through your secure wallet. Later, it proactively suggests complementary hair care products for the summer season.

This world of autonomous commerce isn’t as far off as it seems. Tech and e-commerce leaders — including OpenAI, Nvidia, Amazon, Walmart, Google, and Apple — are already building AI systems that are steps away from conducting transactions. 

AI agents will impact each stage of the customer journey, streamlining the path to purchase and fundamentally transforming how businesses build relationships with consumers and drive loyalty.

We use CB Insights data on early-stage fundraising, public companies, and industry partnerships to analyze how generative AI — especially AI agents — is transforming the customer journey.

In the 11-page report, we cover 3 predictions that emerged from our analysis: 

1. First-party transaction data will shape the future of AI-driven personalization. As personalization becomes more sophisticated at the awareness and consideration stages, companies with direct access to first-party data will have an edge.
2. Direct-to-agent (D2A) commerce will kill traditional loyalty. With AI agents handling browsing and shopping, traditional loyalty programs will lose effectiveness as agents optimize shopping across a select group of merchants.
3. A few AI agents will own the customer relationship. Companies like Amazon, Google, and Apple — with critical distribution and financial services infrastructure — are well-positioned in commerce.

RELATED RESEARCH FROM CB INSIGHTS

• How generative AI could supercharge retailers’ ad networks
• AI is revitalizing the “virtual try-on” space — this is what’s coming next, including interactive Google ads
• The enterprise AI agents & copilots market map
• 15 tech trends to watch closely in 2025
• Generative AI — e-commerce search
• AI-optimized e-commerce UX
• Generative AI — customer support operations

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Rather than full automation, the industry is embracing a more nuanced approach where technology augments human capabilities, creating hybrid workplaces where workers are upskilled to work alongside and manage robotic systems. 

Today’s modular and scalable automation solutions enable incremental modernization, allowing logistics providers to start small, prove ROI, and gradually expand their automated operations while maintaining market adaptability. 

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Companies are responding by developing solutions like fire surveillance drones to better monitor wildfires, as well as firefighting robots to minimize the severity when they occur. In fact, over 500 US fire departments have already deployed surveillance drones.

To help companies and governments understand the current wildfire tech landscape, we mapped 130 companies across 15 markets. We then organized tech markets by the wildfire lifecycle: 

• Prevention & preparedness: Solutions in this category help forecast extreme weather events — including wildfires — and assess their damage potential. We break this category down into: 1) broader climate & weather risk; and 2) wildfire risk, which includes solutions specifically designed for wildfires.
• Detection & monitoring: These solutions use cameras, sensors, and analytics platforms to detect outbreaks early and track their progression to aid firefighting strategies.
• Firefighting: These technologies — such as drones and robots — support the suppression of wildfires or help create firebreaks to limit their spread.
• Damage assessment: This includes solutions to evaluate the destruction caused by wildfires after they occur.

Please click to enlarge.

To identify players for this market map, we included startups with a Mosaic score of 400 or greater and leading corporations developing wildfire tech. Categories are not mutually exclusive and are not intended to be exhaustive.

Track Mosaic health scores for companies in these markets

Market descriptions

Click the market links below for info on the leading companies, funding, and more.

Prevention & preparedness: Climate & weather risk

Climate & weather financial risk modeling focuses on quantifying the financial impacts of climate change and severe weather events, helping businesses forecast and mitigate monetary losses. Leading companies like Bloomberg and Morningstar serve many industries, from agriculture to insurance to government.

Geospatial analytics analyzes and interprets geographic data (e.g., satellite imagery, GIS) for various industries, providing spatial insights and risk assessments. Startups in this market have raised a combined $508M since 2023 — the most funding of any market in this map.

Weather risk intelligence emphasizes real-time weather monitoring and predictive modeling to reduce operational disruptions and manage day-to-day weather-related risks.

Climate risk intelligence provides deeper analysis of long-term climate change hazards, guiding strategic decision-making and resilience planning for businesses and governments.

Prevention & preparedness: Wildfire risk

Catastrophe modeling simulates large-scale natural disasters (e.g., hurricanes, earthquakes) to estimate potential losses, primarily for insurance and reinsurance purposes.

Wildfire risk intelligence zeroes in on wildfire hazards with analytics and forecasting tools, helping organizations anticipate fire spread and prioritize mitigation. This market has the highest average company Mosaic health score (662 out of 1,000) among wildfire-specific tech markets.

Detection & monitoring

Wildfire detection cameras use specialized imaging (thermal, infrared) to spot fire signatures early and relay alerts from fixed vantage points.

Wildfire detection sensors are ground-based devices that monitor environmental conditions (e.g., temperature, smoke) to detect potential fires in real time.

Fire surveillance drones provide aerial monitoring of wildfires using sensors like thermal imaging, enhancing situational awareness and firefighter safety. Companies in this market typically offer drones for a wider set of applications beyond wildfires. For example, Skydio, which has raised $400M since 2023, serves industries such as industrial inspection and defense, in addition to fire surveillance.

Wildfire detection & monitoring platforms integrate satellite/aerial data, IoT sensors, and AI in a software platform to track and predict wildfire behavior at scale. ICEYE and Pano AI rank as leading startups here, offering solutions for enterprises and governments through platforms that use advanced imaging systems and AI models to predict potential wildfire locations and facilitate real-time detection and monitoring.

Firefighting

Firefighting drones actively suppress fires by delivering water or fire-retardant agents, often equipped with thermal imaging to pinpoint hotspots. This is among the most nascent markets in the map, with 89% of deals since 2023 going to early-stage companies.

Firefighting robots are ground units equipped with sensors and suppression tools (e.g., water cannons), enabling safer and more efficient fire combat in hazardous areas.

Autonomous heavy equipment encompasses self-operating machinery (e.g., bulldozers, loaders) used in construction, mining, or creating firebreaks, reducing human risk.

Damage assessment

Drone inspection & damage assessment uses drones to capture high-resolution imagery of properties for quicker, more accurate insurance claims evaluations.

Aerial & satellite claims assessment leverages imagery from planes or satellites to evaluate property damage — often focused on large-scale or remote loss scenarios.

RELATED RESOURCES FROM CB INSIGHTS:

• Weather risk intelligence funding surges as extreme weather events pile up 
• Wildfires are causing insurers to leave markets. Can wildfire risk analytics reverse this trend? 
• The Aerospace & Space Tech expert collection
• The Insurtech expert collection
• The satellite & geospatial tech market map
• The AI in defense tech market map

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Open-source AI is drawing unprecedented attention from developers and enterprises, driven in part by DeepSeek’s recent model releases.

Cost pressures and demands to improve the performance of generative AI applications are driving enterprise interest in the ecosystem as organizations seek more flexible and cost-effective alternatives to proprietary solutions. 

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January was a busy month for the generative AI space, headlined by DeepSeek‘s R1 model launch — matching OpenAI’s o1 model capabilities at just 5-10% of the cost, while open-sourcing the technology.

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A key factor in the slowdown was a sharp drop in funding from mega-rounds ($100M+ deals), which dropped 47% year-over-year (YoY) in 2024. This coincided with high-profile bankruptcies of established climate tech startups like battery manufacturer Northvolt.

However, this turbulence wasn’t limited to the private markets — public players like Lilium and Arrival also filed for insolvency/bankruptcy over the period, highlighting the commercialization challenges facing capital-intensive industries like climate tech.

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

Key takeaways from the report include:

• Climate tech investment activity continues to contract. Global climate tech funding fell for the second year straight in 2024, dropping by 40% YoY, with mega-round funding falling by 47%. However, the space still saw notable mega-rounds. This included deals to players modernizing the power grid, drawing participation from tech giants racing to secure clean energy for computing infrastructure.
• Grid tech and nuclear are gaining momentum to meet AI’s energy needs. Within climate tech, markets targeting the grid and power generation show the strongest growth potential, according to CB Insights Mosaic startup health scores. This momentum is driven in part by the massive energy demands (and expected continued demand) of AI data centers.
• Electric vehicle technology sees record pullback in deals. After years of steady growth, electric vehicle (EV) tech deal activity plunged 61% YoY in 2024 — its steepest decline on record. This points to broader challenges in the sector, like lower consumer demand for EVs and increased capital costs for scaling manufacturing operations.
• Climate tech M&A exits decline once again. Climate tech M&A exits dropped by 25% YoY to hit 284, the lowest count since 2020. At the quarterly level, M&A exits steadily declined over the course of 2024, falling from 104 in Q1’24 to 39 in Q4’24. Growing skepticism around environmental, social, and governance (ESG) initiatives could be a contributing factor.

We dive into the trends below.

Climate tech investment activity continues to contract

Global climate tech funding dropped for a second consecutive year in 2024. It fell by 40% YoY, with mega-round funding falling by 47% over the same period.

The funding slowdown played out differently across the globe. US climate tech showed resilience YoY with relatively steady funding despite fewer deals. Meanwhile, other countries saw steep declines in climate tech dollars, with China experiencing the sharpest drop (-66% YoY).

Amid the overall funding decline, climate tech still saw several notable mega-rounds. This included deals in Q4’24 for companies modernizing the power grid:

• Crusoe secured $600M at a $2.8B valuation to support its efforts to use waste natural gas to power large-scale data centers
• X-energy received $500M as it works to build small modular reactors (SMRs) capable of generating more than 5 gigawatts of electricity by 2039
• Form Energy secured $405M to accelerate production of its iron-air batteries capable of 100-hour energy storage

Notably, some of these deals drew participation from big tech companies racing to secure clean energy for computing infrastructure. For example, Amazon (via the Climate Pledge Fund) invested in X-energy’s nuclear development, and Nvidia invested in Crusoe’s sustainable computing infrastructure, reflecting big tech’s interest in solutions that can help meet rising AI data center demands.

Grid tech and nuclear are gaining momentum to meet AI’s energy needs

Comparing median CB Insights Mosaic scores (a measure of private tech company health and growth potential on a 0–1,000 scale) for climate tech companies that raised equity funding in 2024 reveals the most promising markets in climate tech.

Grid tech and nuclear markets — covering technologies directly integrated into and operated by utilities to enhance power system reliability, flexibility, and clean energy integration — dominate the top 10 climate tech markets by median Mosaic score, highlighting their growth potential.

Surging energy demand from AI data centers is in part responsible for these markets’ momentum. For example, nuclear fusion and small modular reactors could provide continuous clean power generation, grid storage enables reliable renewable energy delivery, and virtual power plants help optimize massive power loads.

See 100+ high-potential climate tech companies

Electric vehicle technology sees record pullback in deals

Electric vehicle tech deals experienced their steepest decline on record in 2024, with deal count plunging 61% YoY to 243.

High-profile bankruptcies underscored the sector’s capital-intensive manufacturing challenges in 2024. Battery manufacturer Northvolt filed for bankruptcy a year after raising $1.2B, as it struggled to scale production efficiently. Electric van maker Arrival — which went public in 2021 at a $13B valuation — also filed for bankruptcy last year amid mounting production costs and the inability to raise funding.

Even the auto industry’s most prominent EV champions scaled back their electric ambitions throughout the year:

• GM delayed its Orion Assembly EV truck plant by 6 months and cut 2024 EV targets by 17%
• Toyota postponed US EV production to 2026
• Ford canceled plans to produce an all-electric three-row SUV, pivoting to a hybrid approach instead
• Volvo dropped its 2030 all-electric goal

Climate tech M&A exits decline once again

In 2024, climate tech M&A exits fell by 25% YoY to hit 284 — the lowest count since 2020.

At the quarterly level, M&A exits steadily declined over the course of 2024, falling from 104 in Q1’24 to 39 in Q4’24.

The decline in M&A activity coincided with key changes in market conditions, including the rise of economic headwinds, political uncertainty, and growing skepticism around environmental, social, and governance (ESG) initiatives.

For example, ESG tech markets collectively saw equity funding decline 54% YoY in 2024. On the corporate side, mentions of ESG in earnings calls have trended down since peaking in Q1’22.

As skepticism toward ESG initiatives grows, some companies appear to be placing lower priority on climate tech acquisitions that were previously considered strategic imperatives.

See recently funded climate tech companies with high M&A probabilities

MORE CLIMATE TECH RESEARCH FROM CB INSIGHTS

• Data centers are reshaping nuclear development, driving billions in new power infrastructure investment
• Why fleet management leaders are racing to acquire next-gen telematics capabilities — and which M&A targets could be next
• The AI data center value chain: 12 high-momentum technologies powering the future of AI
• The top 25 utility companies by AI readiness
• Big Tech in Energy: How Amazon, Google, Microsoft, & Nvidia are advancing the global energy transition
• Sodium-ion batteries are poised for a breakout moment — energy players should prepare

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However, global CVC deal count dropped to its lowest level since 2018 as CVCs become more selective.

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:

• CVC-backed funding grows, deal activity slows. Global CVC-backed funding increased 20% YoY to $65.9B, but deal count fell to 3,434, the lowest level since 2018. All major regions saw deal volume declines, with Europe dropping the most at 10% YoY.
• CVCs are all in on AI. AI startups captured 37% of CVC-backed funding and 21% of deals in 2024 — both record highs. Counter to the broader decline in deals, CVCs ratcheted up AI dealmaking by 13% YoY as they race to secure footholds in the space before competitors gain an insurmountable edge.
• The flight to quality continues. Among deals with CVC participation, the annual average deal size hit $27.3M in 2024, tied for the second highest ever. Amid fewer deals, CVCs are increasingly aggressive when they do decide to invest.
• Early-stage deals dominate. Early-stage rounds comprised 65% of 2024 CVC-backed deals, tied for the highest share in over a decade. Biotech startups made up half of the top 20 early-stage deals.
• CVC-backed funding plummets in Asia. In 2024, Asia’s CVC-backed funding dropped 34% YoY to $7B — the lowest level since 2016. China is leading the decline, with no quarter in 2024 exceeding $0.5B in funding. CVCs remain wary of investing in the country’s private sector.

We dive into the trends below.

CVC-backed funding grows, deal activity slows

Global CVC-backed funding reached $65.9B, a 20% YoY increase. The US was the main driver, increasing 39% YoY to $42.8B. Europe also saw CVC-backed funding grow 18% to $12.3B, while Asia declined 34% to $7B.

$100M+ mega-rounds also contributed to the rise, ticking up 21% YoY to 141 deals worth over $32B in funding.

Meanwhile, deal count continued its decline, as both annual (3,434 in 2024) and quarterly (806 in Q4’24) totals reached their lowest levels in 6 years.

Annual deal volume fell by at least 6% YoY across each major region — the US, Asia, and Europe — with Europe experiencing the largest decline at 10%.

However, Japan-based CVC deal volume remains near peak levels, suggesting a more resilient CVC culture compared to other nations. Two of the three most active CVCs in Q4’24 are based in Japan: Mitsubishi UFJ Capital (21 company investments) and SMBC Venture Capital (15).

Track recent activity from 1,000+ CVCs

CVCs are all in on AI

AI is driving CVC investment activity, much like the broader venture landscape. In 2024, AI startups captured 37% of CVC-backed funding and 21% of deals, both record highs.

In Q4’24, the biggest CVC-backed rounds went primarily to AI companies. These include:

• AI search startup Perplexity’s $500M Series C, backed by Nvidia’s NVentures 
• Coding AI copilot Poolside’s $500M Series B, backed by the venture arms for Citigroup, HSBC, Capital One, LG, eBay, and Nvidia
• AI computing processor developer Lightmatter’s $400M Series D, backed by GV

CVCs are also investing in the energy companies powering the AI boom, such as Intersect Power, which raised the largest round at $800M (backed by GV).

Expect the trend to continue into 2025, as emerging AI markets mature further, such as AI agents & copilots for enterprise and industrial use cases; AI solutions for e-commerce, finance, and defense; and the computing hardware necessary to power these technologies.

The flight to quality continues

In 2024, the annual average deal size with CVC participation reached $27.3M, a 34% YoY increase and tied for the second highest level on record, exceeded only by the low-interest-rate environment of 2021.​

Median deal size also increased, though only by 8% to $8.6M.

Even though the number of CVC-backed deals declined in 2024, the increase in average annual deal size reflects a focus on companies with strong growth prospects. CVCs are prioritizing quality and committing more funds to a select group of high-potential investments.

Early-stage deals dominate

Early-stage rounds (seed/angel and Series A) made up 65% of CVC-backed deals in 2024, tied for the highest recorded level in more than a decade.​

In Q4’24, biotech companies were the early-stage fundraising leaders, accounting for 10 of the 20 largest early-stage deals. Biotech players City Therapeutics, Axonis, and Trace Neuroscience all raised $100M+ Series A rounds, with City Therapeutics and Axonis notably receiving investment from the venture arms of Regeneron and Merck, respectively.

Among all early-stage CVC-backed companies, the largest round went to Physical Intelligence, a startup focused on using AI to improve robots and other devices. Physical Intelligence raised a $400M Series A with investment from OpenAI Startup Fund.

Explore CVCs’ top early-stage company targets in Q4

CVC-backed funding plummets in Asia

Asia’s CVC-backed funding continued its downward trend in 2024, decreasing 34% YoY to $7B.

China was the main driver, with CVC-backed funding coming in at $0.5B or less every quarter in 2024.​ CVCs remain wary of investing in startups in the nation, which faces a variety of economic challenges, including a prolonged real estate slump, cautious consumer spending, strained government finances, and weakened private sector activity amid policy crackdowns.

In Japan, on the other hand, CVC activity remains robust. In 2024, funding with CVC participation ($1.7B) remained on par with the year prior, while deals (502) actually increased by 11%.

MORE VENTURE RESEARCH FROM CB INSIGHTS

• State of Venture 2024 Report
• State of Fintech 2024 Report
• State of Digital Health 2024 Report

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Venture funding surged past the $100B mark for the first time as AI infrastructure players pulled in billion-dollar investments. A wave of M&A deals and rapidly scaling AI unicorns further underscored the tech’s momentum.

Download the full report to access comprehensive data and charts on the evolving state of AI across exits, top investors, geographies, and more.

Key takeaways include: 

• Massive deals drive AI funding boom. AI funding hit a record $100.4B in 2024, with mega-rounds accounting for the largest share of funding we’ve tracked to date (69%) — reflecting the high costs of AI development. Quarterly funding surged to $43.8B in Q4’24, driven by billion-dollar investments in model and infrastructure players. At the same time, nearly 3 in 4 AI deals (74%) remain early-stage as investors look to get in on the ground floor of the AI opportunity. 
• Industry tech sectors lose ground in AI deals. Vertical tech areas like fintech, digital health, and retail tech are securing a smaller percentage of overall AI deals (declining from a collective 38% in 2019 to 24% in 2024). The data suggests that companies focused on infrastructure and horizontal AI applications are drawing greater investor interest amid generative AI’s rise.
• Outside of the US, Europe fields high-potential AI startup regions. While the US dominated AI funding (76%) and deals (49%) in 2024, countries in Europe show strong potential in AI development based on CB Insights Mosaic startup health scores. Israel leads with the highest median Mosaic score (700) among AI companies raising funding. 
• AI M&A activity maintains momentum. The AI acquisition wave remained strong in 2024, with 384 exits nearly matching 2023’s record of 397. Europe-based startups represented over a third of M&A activity, cementing a 4-year streak of rising acquisitions among the region’s startups. 
• AI startups race to $1B+ valuations despite early market maturity. The 32 new AI unicorns in 2024 represented nearly half of all new unicorns. However, AI unicorns haven’t built as robust of a commercial network as non-AI unicorns, per CB Insights Commercial Maturity scores, indicating their valuations are based more on potential than proven business models at this stage.
• Tech leaders embed themselves deeper in the AI ecosystem. Major tech companies and chipmakers led corporate VC activity in AI during Q4’24, with Google (GV), Nvidia (NVentures), Qualcomm (Qualcomm Ventures), and Microsoft (M12) being the most active investors. This reflects the strategic importance of securing access to promising startups while providing them with essential technical infrastructure.

We dive into the trends below.

For more on key shifts in the AI landscape in 2025, check out this report on the implications of DeepSeek’s rise.

Massive deals drive AI funding boom

Globally, private AI companies raised a record $100.4B in 2024. At the quarterly level, funding soared to a record $43.8B in Q4’24, or over 2.5x the prior quarter’s total. 

The funding increase is largely explained by a wave of massive deals: mega-rounds ($100M+ deals) accounted for 80% of Q4’24 dollars and 69% of AI funding in 2024 overall.

The year featured 13 $1B+ deals, the majority of which went to AI model and infrastructure players. OpenAI, xAI, and Anthropic raised 4 out of the 5 largest rounds in 2024 as they burned through cash to fund the development of frontier models. 

Overall, the concentration of funding in mega-rounds reflects the high costs of AI development across hardware, staffing, and energy needs — and widespread investor enthusiasm around the AI opportunity. 

But that opportunity isn’t limited to the largest players: nearly 3 in 4 AI deals (74%) were early-stage in 2024. The share of early-stage AI deals has trended upward since 2021 (67%) as investors look to ride the next major wave of value creation in tech.

CBI Customers: Track 15,000+ AI companies

Industry tech sectors lose ground in AI deals

Major tech sectors — fintech, digital health, and retail tech — are making up a smaller percentage of AI deals.

While the overall annual AI deal count has stayed steady above 4,000 since 2021, dealmaking in sectors like digital health and fintech has declined to multi-year lows. So, even as AI companies make up a greater share of the deals that do happen in these industries, the gains haven’t been enough to register in the broader AI landscape.

The data suggests that, amid generative AI’s ascendancy, AI companies targeting infrastructure and horizontal applications are drawing a greater share of deals. 

With billions of dollars flowing to the model/infra layer as well, investors appear to be betting that the economic benefits of the latest AI boom will accrue to the builders.  

Outside of the US, Europe fields high-potential AI startup regions

Although US-based companies captured 76% of AI funding in 2024, deal activity was more distributed across the globe. US AI startups accounted for 49% of deals, followed by Asia (23.2%) and Europe (22.9%). 

Comparing median CB Insights Mosaic scores (a measure of private tech company health and growth potential on a 0–1,000 scale) for AI companies that raised equity funding in 2024 highlights promising regional hubs. 

Explore 500+ high-potential AI startups

European countries dominate the top 10 countries by Mosaic score (outside of the US). Israel, which has a strong technical talent pool and established startup culture, leads the pack with a median Mosaic score of 700.

Overall activity on the continent is dominated by early-stage deals, which accounted for 81% of deals to Europe-based startups in 2024, a 7-year high.

The European Union indicated in November that scaling startups is a top priority, pointing to the importance of increased late-stage private investment in remaining competitive on the global stage.

AI M&A activity maintains momentum

The AI M&A wave is in full force, with 2024’s 384 exits nearly reaching the previous year’s record-high 397.

Acquisitions of Europe-based startups accounted for over a third of AI M&A activity in 2024. Among the global regions we track, Europe is the only one that has seen annual AI acquisitions climb for 4 consecutive years. Although the US did see a bigger uptick YoY (16%) in 2024, posting 188 deals. 

In Europe, UK-based AI startups led activity in 2024, with 32 M&A deals, followed by Germany (18), France (16), and Israel (12). 

Major US tech companies, including Nvidia, Advanced Micro Devices, and Salesforce, participated in some of the largest M&A deals of the year as they embedded AI across their offerings.

Search for recently funded AI companies with high M&A probabilities

AI startups race to $1B+ valuations despite early market maturity 

AI now dominates new unicorn creation. The 32 new AI unicorns in 2024 accounted for nearly half of all companies passing the $1B+ valuation threshold during the year. 

These AI startups are hitting unicorn status with much smaller teams and at much faster rates than non-AI startups: 203 vs. 414 employees at the median, and 2 years vs. 9 years at the median. 

CBI Customers: Dive into data on every unicorn

These trends reflect the current AI hype — investors are placing big early bets on AI potential. Many of these unicorns are still proving out sustainable revenue models. We can see this clearly in CB Insights Commercial Maturity scores. More than half of the AI unicorns born in 2024 are at the validating/deploying stages of development, while non-AI new unicorns mostly had to get to at least the scaling stage before earning their unicorn status.

Tech leaders embed themselves deeper in the AI ecosystem

In Q4’24, the top corporate VCs in AI (by number of companies backed) were led by a string of notable names: Google (GV), Nvidia (NVentures), Qualcomm (Qualcomm Ventures), and Microsoft (M12). 

As enterprises rush to harness AI’s potential, big tech, chipmakers, and other enterprise tech players are building their exposure to promising companies along the AI value chain.

Meanwhile, startups are linking up with these players to not only secure funding for capital-intensive AI development but also access critical cloud infrastructure and chips.

MORE AI RESEARCH FROM CB INSIGHTS

• What DeepSeek’s model releases mean for the future of AI
• Here’s how leading strategy teams are successfully driving generative AI adoption in their organizations
• The foundation model divide: Mapping the future of open vs. closed AI development
• 15 tech trends to watch closely in 2025
• Inside the AI drug discovery arms race: Record M&A activity, a biologics funding spree, and more
• The industrial AI agents & copilots market map

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The AI company, which emerged from Liang Wenfeng’s hedge fund High-Flyer, released an open-source reasoning model (named R1) in January 2025 that rivals the performance of OpenAI’s o1 reasoning model.

DeepSeek says it trained its base model with limited chips and about $5.6M in computing power — a fraction of the $100M+ US rivals have spent training similar models — thanks to some clever techniques.  

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These attacks can inflict damages costing billions of dollars. Since 2017, every critical infrastructure cyberattack causing an estimated $1B+ in damages has affected the healthcare sector in some capacity, highlighting its particular vulnerability to digital threats.

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But only 32% of strategy leaders report active genAI deployments at their organizations.

To identify pain points and success stories for genAI adoption, we surveyed 50 senior strategy leaders working at companies across major industries.

Download the full report to understand how leading strategy teams navigate genAI adoption, their key challenges, and the tactics separating successful implementations from stalled initiatives.

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The divide between open-source and closed-source AI models is reshaping tech industry dynamics. 

Tech leaders have staked out clear positions: Meta and xAI are open-sourcing models like Llama 3.1 and Grok-1, while Google and OpenAI have largely walled off their systems. Investment flows are also split between both approaches. Since 2020, private open-source AI model developers have attracted $14.9B in venture funding, while closed-source developers have secured $37.5B — reflecting different bets on how AI innovation will unfold.

The core difference lies in access: closed-source approaches keep model details and weights proprietary, while open-source development makes these elements available so models can be more freely studied, run, and adapted.

Companies building generative AI applications must understand this evolving landscape as it has crucial implications for the infrastructure they adopt. Based on current trends, we expect:

1. Consolidation around frontier models: Closed-source models from players like OpenAI, Anthropic, and Google will dominate the market. Only tech giants like Meta, Nvidia, and Alibaba are likely to sustain the costs of developing open-source models that can compete on performance with proprietary ones. Frontier model training costs are growing 2.4x annually, driven by hardware, staffing, and energy needs, according to Epoch AI.
2. Revenue and investment gaps threaten open-source model developers’ viability: While burning cash, closed-source leaders like Anthropic and OpenAI lead the private market in funding, revenue, and commercial traction. Open-source developers face similar costs but struggle to generate revenue or attract capital investment ($14.9B vs. closed-source’s $37.5B since 2020). This suggests they will move to commercialize their closed models (e.g., Mistral AI) and/or pivot to smaller, specialized offerings (e.g., Aleph Alpha).   
3. Smaller models drive open-source adoption: Industry leaders, alongside a range of smaller players, are releasing smaller, specialized open-source models, as evidenced by Microsoft‘s Phi, Google’s Gemma, and Apple‘s OpenELM. This suggests a two-tier market for enterprises evaluating the landscape: closed-source frontier models for the most sophisticated applications and open-source smaller models for edge and specialized use cases.

Below, we use CB Insights data to map out the open-source and closed-source AI landscape. Our analysis focuses on foundation models — the powerful, general-purpose AI systems that form a critical infrastructure layer.

CB Insights customers can track every company mentioned in this analysis using this search. We used the Generative AI — large language model (LLM) developers and Generative AI — image generation market profiles to establish the private market landscape, focusing on companies that have received funding and are developing foundation models. 

Table of contents

Consolidation around frontier models

• Industry leaders are divided in their approaches
• Closed-source developers lead the private market in equity funding
• Performance gaps converge, with largest companies’ models topping leaderboard

Revenue and investment gaps threaten open-source model developers’ viability

• OpenAI dominates LLM adoption and revenue, followed by Anthropic
• Open-source’s path to revenue remains unclear
• Investors hedge their bets

Smaller models drive open-source adoption

• A wave of smaller foundation model players will move away from frontier model development
• Market bifurcation accelerates

Consolidation around frontier models

Industry leaders are divided in their approaches 

Many big tech companies — like Google and Apple — are releasing a combination of open and closed models, typically keeping their flagship models proprietary while releasing lighter-weight open models as an extension of their research efforts.

Meta and Nvidia, meanwhile, are also open-sourcing flagship models. 

Note: When developers “open-source” AI models, they do so on a spectrum, publicly disclosing some combination or element of the: model weights (the learned parameters of a neural network, crucial for the model’s performance and capabilities as they encapsulate the knowledge acquired during training), underlying source code, and original training data. Open-sourcing may also involve licensing the model for free commercial use.

Open-source proponents are preparing for an open-source future

Meta CEO Mark Zuckerberg wrote in July that “Meta is committed to open source AI,” with the belief that an open ecosystem will eventually become the standard. On earnings calls, Meta is the most active big tech company in terms of open-source mentions. 

At the same time, Zuckerberg acknowledged in April on Dwarkesh Patel’s podcast that the company will only continue open-sourcing “as long as it’s helping us.” 

In July 2024, Meta released the model weights for its latest Llama model family so developers can fine-tune the model (train it on custom data). However, the source code and model architecture remain unavailable, limiting full modification or analysis. Meanwhile, Nvidia released both the model weights and training code for its NVLM 1.0 family of large multimodal language models in September 2024. 

Closed-source proponents view revenue as crucial for top resources and talent

For example, Baidu CEO Robin Li said in an internal memo that open-source models “make little sense.” From a business perspective, he noted, “Being closed source allows us to make money, and only by making money can we attract computational resources and talent.”

Safety remains central to the debate

Critics of open-source AI models fear they will be misused by malicious actors to access harmful information (like how to build a bomb or write code for a cyber attack). They also raise national security concerns, with critics suggesting foreign actors’ ability to use open-source models to advance military applications (like weapons systems and intelligence tech) will undermine strategic advantages held by countries that currently lead in AI development. 

Closed models use techniques like Reinforcement Learning by Human Feedback (RLHF) during fine-tuning to limit the harmful content the model can produce. Open models, meanwhile, are more likely to be deployed without these safeguards. 

On the other hand, open-source AI proponents argue, as highlighted in Mozilla’s Joint Statement on AI Safety and Openness with 1,800+ signatories, that increasing access to foundation models will ultimately make them safer, thanks to increased transparency, scrutiny, and knowledge sharing. 

Closed-source developers lead the private market in equity funding

The private market is also split, with closed developers leading in equity funding. 

While both Mistral AI and xAI are proponents of open-source, both of their flagship models are currently closed. 

The cost to develop frontier models — taking into account hardware, staffing, and energy consumption costs — is growing 2.4x per year. This is driving the fundraising race. 

Performance gaps converge, with largest companies’ models topping leaderboard

Leading open-source models, like Meta’s largest Llama model, are making their way onto the MMLU leaderboard — a test that evaluates a language model’s knowledge and reasoning skills. The expanded version, MMLU-Pro, includes more challenging questions to assess advanced reasoning capabilities in AI models.

At the same time, proprietary models continue to outpace open-source ones by several months in terms of release dates. 

The leaderboard itself is dominated by the largest companies in both big tech and the private market, indicating market consolidation at the frontier level. 

At this stage, a16z partner Marc Andreessen has posited we could be approaching a “race to the bottom” — a future point where there are no moats for foundation models, and open-source performance is on par with closed-source. This has come into focus in recent months as frontier labs like OpenAI and Google have focused on smaller model development and other products (like agents) as performance gains slow and as release dates for the largest models (such as a potential GPT-5) get pushed back.

Below we look at how revenue and adoption gaps in the private market also point to increasing consolidation.

Revenue and investment gaps threaten open-source model developers’ viability in the private market

OpenAI dominates LLM adoption and revenue, followed by Anthropic

As LLM developers burn through cash, the focus has shifted to customer adoption — and revenue. 

Based on CB Insights business relationship data, OpenAI is far ahead of its peers in terms of its disclosed partnerships and client relationships. 

This business relationship analysis is limited to publicly disclosed partnership, client, and licensing agreements for pure-play model developers to highlight adoption trends. Relationships are not exhaustive and are directionally representative of trends across model developers’ partner and client relationships.

In terms of revenue, OpenAI leads, with projections of $3.7B in annual revenues for 2024 and $11.6B for 2025. However, it’s also been burning cash: the company projected midway through the year that it would lose $5B in 2024.

Open-source’s path to revenue remains unclear

While revenues for open-source model developers are not publicly available in most cases, reports suggest revenue generation is more limited — especially given the competition from Meta’s Llama.

The embattled Stability AI reportedly generated $8M in 2022 and less than $5M in the first quarter of 2024 (while losing over $30M). In June 2024, it secured an $80M funding deal that included the forgiveness of $100M in debts owed to cloud providers and other suppliers. 

Meanwhile, Mistral AI has an unclear path to revenue, per The Information reporting — it sells access to its API, and under 10% of its users pay for Mistral’s larger commercial models through partners. Most of its smaller, open-source models are free. 

Source: CB Insights — Mistral funding insight

Following the traditional approach to monetizing open-source businesses — building paid support offerings or tools (plugins, security, migration, apps on top) around the open-source core — some model developers are now building more enterprise capabilities into their platforms. 

For example, Databricks offers security and other paid support services around its open-source LLM, DBRX. Similarly, Aleph Alpha launched in August 2024 a “sovereign AI” platform designed to help corporations and governments deploy LLMs (not necessarily its own) with added control and transparency features to serve the European market. 

Investors hedge their bets

Most leading investors in private foundation model developers have backed companies developing both closed and open models.

Corporate investors figure heavily — Nvidia, Alibaba, and Microsoft, for example, have offered computing power and funds for development. These investments are aimed at feeding their core business focuses, such as AI chips and cloud computing. AWS, Azure, and Google Cloud all host both open and closed models.

Venture investors are taking sides:

• Coatue, the leading VC by unique companies backed, has called open source “the heartbeat of AI.” It’s taking a complementary approach: “We see open-source models as firmly having a place alongside proprietary ones.”
• a16z’s founders are proponents of open-source models, arguing that their transparency and accessibility will help ensure that AI is developed securely and ethically. In 2024, the two largest a16z-backed AI deals went to open-source LLM developers xAI and Mistral AI.
• Meanwhile, Founders Fund partner John Luttig has argued that the future of foundation models is closed-source. Khosla Ventures’ Vinod Khosla (a backer of OpenAI) also argues in favor of closed-source AI for safety reasons. 

The investor split reflects uncertainty over which ecosystem will dominate and where the greatest value creation will occur. The relative difference in funding totals ($14.9B in equity funding to open-source model developers vs. $37.5B to closed-source), as well as the data available on revenue, suggests that a closed approach for private developers appears poised to win out, especially given the most performant open models at this point are from big tech leaders.

Smaller models drive open-source adoption

A wave of smaller foundation model players will move away from frontier model development 

The conditions of a) high compute costs, b) limited moats, and c) competition from big tech have created a market ripe for a shake-up.

We’re seeing a wave of smaller foundation model players:

• Collapse into big tech: Adept, Inflection, and Character.AI have all essentially been “acqui-hired” by big tech companies, with founders and large portions of teams joining the acquirers. These deals reflect the high costs of model development, with licensing payments often directed to investors. 
• Paywall frontier models: Some open-source AI developers now sell access to premium models while keeping basic versions free — similar to strategies used by big tech. For example, Mistral AI’s flagship model Mistral Large is built for commercial use (not open-source) and is available on Azure in partnership with Microsoft.
• Focus on smaller, open-source models: Developers like Germany-based Aleph Alpha and Israel-based AI21 Labs have shifted in 2024 from competing on general-purpose LLMs to building lighter-weight, optimized models and related AI tools. These models are open-source, with paid services layered on top.

Market bifurcation accelerates

Based on these trends, the AI model market is splitting into two tiers:

• Frontier models are largely dominated by closed-source offerings from well-funded players (OpenAI, Anthropic, Google), which can sustain growing compute costs. Meta’s Llama remains the most notable open-source alternative.
• Smaller models, optimized for specific use cases or edge deployment, are supported by a growing open-source ecosystem. These small language models (SLMs) have fewer parameters than LLMs, making them cheaper to train and easier to run.

Industry leaders are releasing smaller, open-source models to advance research efforts and to promote edge applications: Google with Gemma, Microsoft with Phi, and Apple with OpenELM. 

For example, Microsoft highlighted in a recent earnings call: 

“We have also built the world’s most popular SLMs, which offer performance comparable to larger models but are small enough to run on a laptop or mobile device. Anker, Ashley, AT&T, EY, and Thomson Reuters, for example, are all already exploring how to use our SLM Phi for their applications.” — Satya Nadella, CEO of Microsoft, Q2’24 Earnings Call  

Meanwhile, of the 11 private SLM development platforms we identified, roughly half are already in the process of deploying their products.

Smaller, open models are also gaining traction in sectors like financial services and healthcare, where keeping sensitive data on-premises can be a need.

For example, a VP of machine learning at a health insurance company needed a solution for training healthcare models and looked to Hugging Face’s open-source library. In our May 2024 conversation, the buyer highlighted the opportunity of SLMs for their use case:

For now, it’s clear a hybrid approach is winning with enterprises: they will look to closed-source frontier models for the most sophisticated applications and open-source smaller models for edge and specialized use cases.

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