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

Which VC firms are on the Smart Money list?

Firms are presented in alphabetical order.

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

CBI customers: See Smart Money’s latest bets

How Smart Money VCs are outperforming the market

Our 2025 edition of Smart Money VCs:

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

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

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

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

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

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

Where Smart Money is deploying now

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

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

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

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

Methodology

What is the CB Insights Smart Money list?

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

What makes a VC “smart”?

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

Our methodology factors:

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

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

What can I do with this collection?

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

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

RELATED RESOURCES FROM CB INSIGHTS:

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

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

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

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

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

See all coding AI agent & copilot companies ranked by revenue

Key takeaways

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

Market overview

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

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

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

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

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

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

Track these investors’ deals in real time

Key takeaways

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

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

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

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

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

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

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

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

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

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

Track 250+ companies building AI agent infrastructure

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

Please click to enlarge. 

Outlook & key takeaways

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

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

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

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

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

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

In less than a year:

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

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

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

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

Big tech pushes deeper into AI agent development

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

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

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

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

Autonomous agents drive the need for an oversight layer

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

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

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

White space opportunities for the AI agent ecosystem

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

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

Source: CB Insights Deal Agent

Category overview

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

Foundation models & infrastructure

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

• LLM developers
• Model deployment & serving 

Agent frameworks & development platforms

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

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

Tool integration

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

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

Context 

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

• LLM data preparation platforms
• Vector databases 
• Memory management

Orchestration 

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

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

Oversight

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

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

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

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

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

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

Download the book to see all 50 scouting reports.

Key takeaways from July’s mega-rounds include: 

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

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Market leaders are transitioning from AI pilots to production systems, leveraging robots that autonomously navigate complex environments and digital twins that optimize all aspects of the industrial value chain.

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Many of the world’s most influential financial services firms — like Allianz, J.P. Morgan, and Mastercard — have taken concrete action to adopt genAI technology.

The genAI adoption efforts have shaped 2 years’ worth of corporate strategy, unveiling key priorities — from the rise of agentic commerce to customer service copilots — across the competitive landscape.

Using CB Insights data, we identified and analyzed 100 real-world applications of genAI from 69 companies across banking, insurance, and payments.

Download the book to explore all 100 applications, and read on for 5 key takeaways and a breakdown of our methodology.

Key takeaways

1. Cross-functional platforms are now table stakes.

24% of applications center on deploying general-use genAI platforms to employees.

Prominent firms like BBVA have established enterprise-wide genAI capabilities across their organizations (typically via enterprise-wide deployments of platforms like Microsoft Copilot or ChatGPT). Early adopters — like Klarna, which shared in May 2024 that 87% of its employees are using OpenAI technology — now have over a year of genAI operational experience at scale, which can guide the development of more complex applications in the future.

Looking forward, financial services firms without a plan to provide genAI access to employees risk competitive disadvantage. Over the past 2 years, simply providing genAI capabilities to employees has shifted from cutting-edge innovation to standard operations.

2. Microsoft and OpenAI permeate the adoption landscape.

33% of applications analyzed disclose involvement from either Microsoft or OpenAI.

Microsoft and OpenAI (in which Microsoft has significantly invested) overwhelmingly permeate the landscape of genAI applications analyzed. Many of these applications anchor on foundational capabilities, from which organizations can build more complex applications and agents. Anthropic, Amazon Web Services, and Google Cloud follow a similar deployment pattern across multiple companies in the sector.

Looking forward, financial services firms should prepare for increasingly blurred “build, buy, or partner” decisions. The prevalence of genAI model developers (like OpenAI and Anthropic) and big tech partners (like Microsoft and Google) provide financial services executives with more flexibility to customize their tech solutions than what has traditionally been the case with many point-solution providers.

3. Emerging genAI vendors face a fierce competitive landscape.

Median Mosaic Scores among genAI startups analyzed are in the top 3% globally.

The 100 analyzed genAI applications include engagement from 25 startups as tech vendors, ranging from pre-seed companies like Twin — which offers an agent for invoice collection — to late-stage giants like Anthropic. These startups have a median CB Insights Mosaic Score — which measures the overall health and growth potential of private companies — of 732 out of 1,000, as of July 30, 2025.

Looking forward, financial services firms should prepare for increasingly capable tech vendors seeking to sell their genAI products. These vendors must exhibit a clear advantage over the alternative of building in-house solutions.

4. Customer-facing genAI will become increasingly prevalent.

16% of applications center on customer engagement & self-service capabilities.

Firms like ING, Wells Fargo, and Truist show that customer-facing genAI assistants are capable of powering millions of customer interactions. Customer-facing genAI deployment will accelerate as companies like Mastercard, Visa, and PayPal deploy applications centered on “agentic commerce,” where customers can autonomously shop and complete transactions with AI payments agents.

Looking forward, financial services firms need to develop a gameplan for how they will engage customers with agentic AI. The market opportunities for enterprise agents and copilots are growing, so customer-facing applications will quickly emerge.

5. Impact is now tangible, but success definitions remain elusive.

Only 30% of applications disclose quantitative tangible impact from deployment.

Most of the application sources analyzed lack disclosure of tangible impact (i.e., numbers, percentages, or figures to quantify effectiveness). Among the impact metrics that are available, the top-cited focus on operational considerations like call-handle times.

Looking forward, any financial services firm has the opportunity to define “what good genAI adoption looks like” across the sector. The lack of clear success definitions creates an opportunity for financial services firms to stand out among peers.

Methodology

We used CB Insights’ Business Graph — including data points like Dealmaking, Business Relationships, Earnings Transcripts, and Media Mentions — and third-party company releases to identify 100 real-world genAI applications across banking, insurance, and payments. These applications were disclosed between July 2023 and April 2025.

Then, using CB Insights’ Team of Agents, we analyzed these applications across 10 categories. Applications are detailed based on disclosure date, and are not exhaustive of a given company’s genAI initiatives. Applications and categorizations are not mutually exclusive or exhaustive of activity within their respective industries.

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Based on CB Insights Commercial Maturity data, 42% of these companies are already deploying or commercializing their solutions (deploying, scaling, established), with a few leaders already crossing $100M in ARR. This includes Anysphere’s Cursor ($500M in ARR) as well as Windsurf and Moveworks, both of which reached $100M ARR shortly before being acquired.

This commercial traction signals the rapid adoption of specific types of AI agents by enterprises and who the early market winners are. The companies generating the most revenue often target workflow-heavy sectors where AI delivers immediate ROI — primarily coding and enterprise workflows. 

We expect these categories to continue driving adoption (and revenue), and predict the enterprise AI agents & copilots space will generate close to $13B in annual revenue by the end of 2025, up from $5B in 2024. 

Using CB Insights revenue data, we identified the top 20 private startups offering AI agents as their primary offering and analyzed how they are rewriting the VC funding playbook (see below graphic).

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

See the top AI agent startups ranked by revenue here

Key takeaways

• Top revenue-generating AI agent startups are just under 5 years old on average, with 50% of them having been founded in the last 3 years. This signals how quickly these AI-native companies are scaling and monetizing their products with recent breakouts including Cursor ($500M revenue, founded 2022), Mercor ($100M, founded 2023), and Lovable ($100M, founded 2023).
• Customer service AI agents command the highest valuation premiums, with an average revenue multiple of 127x compared to 52x on average across all top 20 AI agents by revenue. This valuation gap signals that investors are betting on aggressive revenue acceleration in customer service AI, driven by the sector’s universal market applicability and the expectation that businesses will rapidly replace human support teams with AI agents. 
• Some AI agent startups are already as capital-efficient as big tech companies. Mercor ($4.5M revenue per employee) and Cursor ($3.2M per employee) already surpass the likes of Microsoft ($1.8M per employee, FY 2024) and Meta ($2.2M per employee, FY 2024), and rivaling Nvidia‘s efficiency levels ($3.6M per employee, FY 2025). 

However, as new entrants enter the AI agent market at a record pace — both startups and tech giants pivoting into AI agents — the question becomes whether these early revenue wins can translate into defensible market positions. 

We expect competitive moats to emerge through proprietary data advantages, deep vertical specialization, and the creation of switching costs through deep integration into customers’ critical business workflows. 

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Investors and big tech alike are betting that voice will be the dominant interface for interacting with AI, enabling a move away from traditional browser and mobile interfaces toward natural conversational interaction. 

Recent technological advancements have made this vision increasingly viable, with voice capabilities now delivering near-instantaneous responses with sub-300ms latency that matches human conversational flow. This speed breakthrough is critical to unlocking voice AI’s full potential, as Chris McCann at Race Capital, a backer of PlayAI, explains:

“Voice is how people naturally communicate – but most voice AI systems still sound robotic or have high latency in their responses. We believed fast, expressive voice tech would be critical to making AI feel human and useful in the enterprise, especially for IVR, customer support, and sales.”

With voice becoming an increasingly fundamental modality for the AI-powered future and big tech competing to win the AI device race, owning the building blocks that shape human-AI communication is becoming mission-critical. Expect a wave of acquisitions as companies scramble to secure voice AI capabilities.

Using CB Insights’ Mosaic score which measures company health, we identified the top M&A targets in the voice AI space and what makes them such compelling targets (see below graphic).

See the full list of voice AI startups ranked by Mosaic score

• Voice synthesis platform ElevenLabs tops the market with a Mosaic score of 955, making it an attractive acquisition target. Proprietary voice generation technology is becoming as valuable as foundational AI models, positioning the highest-quality voice synthesis as core infrastructure rather than a feature add-on.
• Enterprise-focused Cresta delivers immediate ROI, with some customers reporting 50% cost reductions in contact centers, and positioning it perfectly for companies looking to leverage voice AI to immediately impact enterprise productivity.
• Ultra-low latency startups like Cartesia have an edge, as their ability to deliver sub-100ms capabilities positions them as essential for truly conversational AI experiences that matches human conversation patterns. 

Investors also see companies owning the full-stack as a having key technological advantage compared to those relying on third-party components. This was part of the rationale for investing into PlayAI according to Chris McCann of Race Capital:

“Most voice AI startups rely on open source or other third-party components. PlayAI built the full stack in-house—their own TTS engine, real-time streaming, and sub-100ms latency. That gave them full control and a clear technical edge, which let them power real-time agents for support, sales, and IVR across several Fortune 500s.”

As the AI arms race continues, acquisitions will continue to be focused on talent, tech, and infrastructure rather than existing revenues. Companies that secure advanced voice AI capabilities now will dominate the next phase of AI adoption – whether they integrate into their existing offerings or cash-in on selling the tooling back to others.

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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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Insurance leaders are looking to emerging AI capabilities to reshape how they work. For instance, on AIG’s August 2024 earnings call, chairman and CEO Peter Zaffino highlighted an objective to use AI to “redesign and refine the end-to-end underwriting workflow.”

The industry’s opportunity in generative AI centers on increasing decision-making capabilities and operational speed, from improving quote-to-bind ratio to offering real-time guidance for employees. Two forces drive the opportunity: tech advances across the broader genAI ecosystem; and a wealth of unstructured data within the insurance industry.

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The space is already seeing a steady stream of M&A activity, as established players seek to quickly acquire novel capabilities and fill in gaps in their AI talent.

But amid the frenzy, which specific genAI markets are most primed for exits — and what should corporate strategy and M&A teams be doing about it?

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While the tech has been in development for years, it was the launch of OpenAI’s ChatGPT that suddenly brought generative AI — artificial intelligence that generates new content (text, code, images, audio, etc.) — to the masses.

Corporate attention to generative AI has since skyrocketed, with big tech and incumbents scrambling to harness its potential to improve productivity, scale automation efforts, and accelerate digital transformation. Many fear that failing to adapt will mean being outcompeted by the companies that do.

Meanwhile, hundreds of generative AI startups have emerged, followed by billions of dollars in investment. Investors are keen to ride the wave as startups take aim at disrupting entire industries.

In this report, we use CB Insights datasets — including tech company financings, valuations, revenues, business relationships, public earnings calls, and customer perspectives — to help you understand what’s going on in the market and the players and trends to watch.

The 122-page report covers:

• The generative AI boom a decade in the making
• The current genAI landscape and the players competing in each market
• The latest moves from big tech firms like Microsoft, Google, Nvidia, Meta, and Apple
• The race to dominate genAI infrastructure, plus the latest on closed vs. open-source development
• GenAI opportunities for healthcare, financial services, and retail
• The 50 most promising generative AI startups to watch
• The emerging trends that will shape the future of generative AI

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