While deals fell in Q3’25, billion-dollar rounds to AI infrastructure players continued to drive the funding surge. But the activity isn’t limited to the largest players: investors are cutting bigger checks across every stage, signaling both conviction in AI’s potential and the high costs of AI development.

Among emerging opportunities, AI agents are a key focus for VCs and enterprises alike, with agent markets leading deal and M&A activity in the quarter.

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

• AI deal activity softens, but massive rounds support continued funding boom
• Consolidation remains in full force in the AI market
• Tech market deals highlight AI agent applications, rise of GEO
• The talent premium: AI companies valued at up to ~$100M per employee

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

AI deal activity softens, but massive rounds support continued funding boom

Deals to private AI companies globally fell 22% quarter-over-quarter in Q3’25, but funding remained above $45B for the fourth consecutive quarter.

Taken together, these trends indicate how top-heavy the AI venture funding landscape has become. 

The average deal size in 2025 YTD is $49.3M — up 86% from 2024. In the last 4 quarters, mega-rounds ($100M+ deals) have accounted for 75%+ funding. The average since 2021 (up to Q3’24) is 53%. 

At the same time, check sizes are trending bigger at the median across every stage this year. For example, the median early-stage deal is $3.4M in 2025 YTD, up from $2.5M in 2024. 

Investors are funneling capital into fewer, larger bets on perceived AI winners, driven by the massive infrastructure costs and competitive dynamics of foundation model development.

In Q3’25, there were 6 $1B+ rounds alone. The top 3 deals went to LLM developers — Anthropic ($13B, Series F), OpenAI ($8.3B, PE), and Mistral AI ($1.5B, Series C) — reflecting the high cost of frontier model development. While OpenAI hit $12B in annualized revenue in July 2025, it’s projecting roughly $8B in cash burn this year per reports. 

Other infrastructure players like Nscale (AI data centers, $1.1B Series B) and Groq (AI inference processors, $750M, Series E) were also in the top 10. The raises are indicative of the growth and attention technologies enabling AI are receiving, with earnings call mentions of data centers hitting record levels in Q3’25 and AI training & inference chips on track for record equity deal & funding activity this year.

Consolidation remains in full force in the AI market

The AI market is a hotbed for M&A activity. 

Q3’25 marks the second highest quarter on record for AI startup M&A (172 deals), following Q2’25 (181 deals). The US continues to gain share, with startups based in the country accounting for 59% of total exits, the highest share since Q2’21. 

Three of the top 5 exits in the quarter were related to AI agents: 

• Workday acquired Sana Labs, a company focused on enterprise workflows, for $1.1B. 
• NiCE acquired customer support startup Cognigy for $955M. 
• Atlassian acquired The Browser Company, the maker of the Arc and Dia AI browsers, for $610M. 

The activity signals enterprise software incumbents are looking to buy their way into accelerating their AI roadmaps. Workday was the second most active acquirer in the quarter with 3 acquisitions (behind Salesforce, with 4 acquisitions). The HR & finance software company also picked up agent builder Flowise and AI-powered recruiting platform Paradox.

Meanwhile, Meta made its first publicly disclosed acquisitions since 2022, acquiring voice AI startups Play AI and WaveForms AI.

Other notable top exits include AI security companies Lakera (acquired by Check Point for $300M) and Prompt Security (acquired by SentinelOne for $250M-$300M). Generative AI is expanding attack surfaces, driving large cyber players to opt for M&A to more quickly integrate AI security features into existing offerings.

Both Lakera and Prompt Security were founded less than 5 years ago, far “younger” than the average time to exit of 9.7 years in the quarter, underscoring how rapidly AI security has become mission-critical.

Review the AI security startups that are ripe for acquisition next in this brief.

Tech market deals highlight AI agent applications, rise of GEO

Among the 1,500+ tech markets that CB Insights tracks, those in the chart below saw the greatest number of AI deals in Q3’25 (note: companies may appear in multiple markets).

Industrial humanoid robot developers and coding AI agents & copilots remained at the top, while LLM developers also climbed back up in the rankings from Q2’25.  

One notable rising market is generative engine optimization (GEO), which refers to tools that help brands optimize their visibility in AI search platforms like ChatGPT and Perplexity. This emerging category (the most nascent in the list based on CBI Commercial Maturity scores) addresses the shift toward shopping and discovery happening on top of LLM interfaces.

OpenAI’s September 2025 launch of in-platform shopping capabilities in ChatGPT underscores this trend, establishing AI platforms as new commerce channels requiring specialized optimization strategies.

Using CB Insights’ Mosaic score — which measures private company health and predicts likelihood of success — we analyzed more than 20 GEO companies, ranking them by 1-year Mosaic score growth to identify the fastest-rising vendors. 

See the GEO partners best positioned to help brands win in AI search here. 

The talent premium: AI companies valued at up to ~$100M per employee

AI companies with lean headcounts and breakthrough potential are attracting sky-high valuations.

Humanoid robotics developer Figure leads the pack in Q3’25 at $104.3M per employee on a $39B valuation, despite reporting no revenue last year (though projecting $9B by 2029). Cognition follows with $98.1M per employee, based on its $10.2B valuation. While the coding AI agent startup has $150M+ in ARR (following its acquisition of Windsurf), this indicates a lofty revenue multiple of ~68x. 

Others topping the quarter’s valuation-per-employee list span the AI model (Anthropic, Mistral AI, Decart, Harmonic), infrastructure (Baseten), and application layers (OpenEvidence,  Sierra, Irregular). 

Whether these valuations prove prescient or overextended will largely depend on whether these companies can deliver on ambitious revenue projections in the years ahead.

RELATED RESOURCES FROM CB INSIGHTS:

• State of Venture Q3’25 Report
• State of Fintech Q3’25 Report
• State of AI Q2’25 Report
• State of Digital Health Q2’25 Report
• State of Tech Exits H1’25

The post State of AI Q3’25 Report appeared first on CB Insights Research.

AI-powered coding has become one of the fastest-growing enterprise use cases for LLMs. Startups like Anysphere (maker of Cursor), Replit, and Lovable have crossed $100M in ARR in record time, with adoption surging across professionals and amateurs, small companies and enterprises alike.

Satya Nadella says that as much as 30% of Microsoft’s code is now written by AI. But this breakneck adoption is exposing new pain points for enterprises: volatile costs and emerging security risks as AI-generated code volumes surge.

As agentic AI transforms software development, tools that deliver shipping speed, cost certainty, and de-risk AI outputs will separate sustainable enterprise adoption from expensive experiments.

Using CB Insights’ predictive intelligence on private companies, we mapped 90+ companies offering AI-powered solutions across 8 software development markets to reveal how agentic AI is collapsing the development stack, identify which capabilities set winners apart from losers, and uncover which features matter most to enterprises.

Note: Our market map includes private AI startups operating in the software development space that have received funding in the last 2 years. Companies may operate in multiple markets. This market map is not exhaustive of companies operating in the space.

Explore these companies sorted by their Mosaic score here

Key takeaways

Agentic AI is collapsing the software development stack — orchestration will separate winners from losers

The future is agentic. End-to-end agents are not just speeding up coding, they are combining planning, implementation, testing, and deployment into unified workflows, pushing the market from many fragmented tools toward fewer intelligent platforms. Adopters are feeling the same way.

3 out of 4 companies offering solutions for the software development lifecycle (SDLC) now have semi- or fully autonomous agentic features, including multi-agent capabilities. Companies offering agentic features also average a Mosaic score of 653, a 100 points higher than the average for companies without agentic features, signaling stronger overall health and growth potential.

Agentic AI is expediting code creation and enhancing developer workflows better than traditional coding tools, but reliability issues and enterprise requirements, such as compliance, governance, security, and quality assurance, will continue to drive demand for specialized agents at each SDLC phase.

Winners will provide unified orchestration layers that integrate specialized, best-in-class agents for each software development phase with governance and human oversight built in.

To operationalize AI, enterprises should evaluate vendors on integration with existing development infrastructure, governance controls that enable oversight and auditability, and observability into agent performance and associated costs.

Inference cost management is emerging as a key feature as AI coding bills soar

Reasoning models have inflated output token volume by 20x, pushing vendors away from seat-based pricing toward usage-based and hybrid models.

In response, cost control solutions are emerging as critical infrastructure. Larridin tracks AI costs and measures tool effectiveness, Cline is building cost controls directly into its offerings, while Paid reached a $100M valuation, pioneering outcome-based billing.

Enterprises should prioritize vendors with built-in cost governance. Look for solutions that offer cost-monitoring dashboards, usage forecasting, and budget controls as token volumes surge.

As costs shift from predictable seats to variable usage, expand AI governance beyond engineering to include stakeholders across finance, operations, and marketing. Accessible vibe coding tools are democratizing software development, enabling these non-technical teams to become builders and making cross-functional cost oversight essential.

For AI adoption, enterprises should look beyond traditional billing models. Consider vendors like Paid, Alguna, or Orb which offer results-based, AI-native billing to align costs with business value rather than infrastructure consumption.

Enterprise selects agentic AI providers based on security, integration depth, ability to ship software faster, and value-based pricing

End-to-end agents demonstrate strong market validation with an average Mosaic score of 745, indicating robust health, reliability, and growth potential.

We analyzed buyer interviews from the highest-valued agentic AI providers to identify enterprise selection criteria:

• Security compliance for AI-generated code 
• Ease of use for non-technical users and the ability to ship end-to-end solutions faster, 
• Deep and seamless integration with existing systems
• Transparent pricing justified by measurable value

As enterprises scale AI adoption, prioritizing vendors with native integrations and robust AI code security is key to reducing implementation friction. Check our AI security market to identify vendors leading in security and derisking AI.

Beyond immediate selection criteria, specialized coding small language models (SLMs) are an emerging trend to watch. Relace.ai’s recent $23M funding signals SLMs’ ability to improve coding efficiency at lower costs, a key enabler for value-based pricing models.

The blended AI coding market is temporary — expect separate markets for consumer and enterprise

There is a democratization of AI developer tools. Non-technical and professional use cases are hard to distinguish and startups are currently willing to blend the 2. The total addressable market (TAM) is no longer one or the other as companies can sell into both.

While vibe and enterprise coding markets are currently intertwined, we expect two distinct markets to emerge, targeting individual vibe-coders and professional developers, respectively. For example, Replit recently switched from targeting professional developers to non-technical users and gained momentum as a result.

Companies that can successfully pivot or maintain dual product lines will be better positioned. This suggests looking for teams with the flexibility to adapt their positioning as the market matures.

With an enlarged TAM, this offers investors a chance to diversify portfolios across both enterprise-focused and vibe-code-oriented AI coding tools.

Early traction metrics can mask user composition risks. Investors should verify if startups are winning with their intended customer by checking out the vibe-coding startups with the highest Mosaic scores.

Market descriptions

Software design tools

The software design market provides tools that create blueprints, technical specifications, and user experience designs for applications before development begins. Key activities include high-level design (system architecture and UI/UX structure), low-level design (data models, database schemas, API specifications, and component interfaces), and interface design (wireframes, mockups, prototypes, and design systems). Some platforms use AI to accelerate diagram generation, architectural planning, and technical documentation. These tools enable teams to visualize and validate both system architecture and user experiences before writing code.

Code generation & assistance

This code generation and assistance market offers tools and platforms that use AI to generate code, provide autocomplete suggestions, explain code snippets, debug issues, and assist with writing functions or modules. Solutions are delivered as IDE integrations, native IDE features, standalone applications, or self-hosted platforms. They accelerate coding by reducing boilerplate, catching errors, and offering intelligent recommendations. Some platforms include partial agentic actions that automate routine coding tasks. While some companies extend into adjacent phases like testing or deployment, their core offering focuses on code creation and maintenance.

Code review

The code review market provides tools that facilitate the systematic examination of code by developers or peers to identify errors, bugs, vulnerabilities, and adherence to coding standards. These platforms automate code analysis, provide static analysis capabilities, and offer collaboration features for reviewing and discussing code changes. Solutions range from standalone review platforms to integrations within version control systems and CI/CD pipelines. They help teams enforce consistency and quality across codebases and catch issues before they reach production. Some vendors incorporate AI to suggest improvements, detect security flaws, or automatically flag non-compliant code patterns.

Test automation software

The test automation software market provides platforms and tools that automate software testing processes across web, mobile, API, and desktop applications. Solutions enable QA teams and developers to design, create, execute, and manage automated tests through scripted frameworks, low-code/no-code interfaces, and AI-powered test generation. AI capabilities include automatic test case generation, failure prediction, self-healing when applications change, and test suite optimization. These platforms integrate with CI/CD pipelines, version control systems, and development workflows to enable continuous testing throughout the software delivery lifecycle. Both traditional and AI-driven solutions reduce manual testing effort and increase test coverage through automated validation.

Release automation software

The release automation software market provides platforms that automate and orchestrate the deployment of software releases from development to production environments. These solutions manage release planning, deployment automation, environment coordination, and progressive delivery strategies including feature flags and rollback capabilities. Platforms enable teams to schedule releases, coordinate deployments across multiple environments, manage dependencies, and ensure controlled rollouts to production. Solutions integrate with CI/CD pipelines, version control systems, and monitoring tools to provide end-to-end visibility into the release process. The market serves software development and operations teams seeking to standardize release processes and reduce deployment risk.

Code documentation & knowledge management

The code documentation and knowledge management market provides platforms that help development teams create, maintain, and preserve technical knowledge throughout the software development lifecycle. Solutions automatically generate documentation from code, maintain technical wikis, create API references, and answer questions about codebases using AI. These platforms capture what code does and why decisions were made, preserving organizational knowledge as team composition changes. The market serves software development teams seeking to reduce onboarding time, and maintain up-to-date documentation.

Code modernization & technical debt management

Code modernization and technical debt management market encompasses solutions that help organizations refactor, upgrade, and maintain legacy codebases while systematically addressing accumulated technical debt. It includes tools and platforms that automatically analyze and assess legacy code quality, dependencies, and technical debt, migrate applications from legacy languages to modern frameworks and automate code translation, modernization, and cloud migration efforts.

End-to-end software development AI agents

The end-to-end software development AI agents market offers AI agents that function as virtual software developers and engineers, independently handling end-to-end and full-stack development tasks with zero to minimal human supervision. Agentic AI understands requirements, writes code, debug, test, and deploy – operating across the entire software development lifecycle (SDLC) autonomously.

The post The AI software development market map appeared first on CB Insights Research.

Rohit Verma, President & Chief Executive Officer of Crawford & Company, shares executive insights on insurance affordability.

A few months ago, I sat down to review my monthly expenses and was stunned. My auto insurance premium had climbed over 30%, and my home insurance has almost doubled since the start of the pandemic. These aren’t discretionary expenditures — they’re essential protections. Friends and colleagues have shared similar stories, some even seeing insurance costs exceed their monthly mortgage payments, a shift unimaginable just a few years ago. One friend recently asked me, “Where is this going? Are we heading toward a future where insurance becomes unaffordable for the average household?”

In recent years, insurance rates have increased considerably, largely due to severe property losses from wildfires, storms, and hurricanes. Social inflation is also straining the industry. These pressures have forced carriers to raise deductibles and prices; trends reflected in property loss ratios. Necessary to maintain industry stability, these actions have also burdened policyholders, challenging affordability and accessibility, and intensifying financial strain. Crawford adjusters witness how today’s risk environment challenges insurance’s core purpose: transferring risk from insured to insurer.

Thankfully, recent reinsurance renewals suggest relief may be on the horizon, barring major catastrophes. As these dynamics unfold, we expect pricing and deductibles to ease, leading to normalization in the next 12 to 18 months. However, current premiums aren’t sustainable, and the industry must act now to support customers through this period.

This short-term relief opens a critical window to implement more sustainable solutions, like enhanced technology adoption, resilient rebuild strategies, and stronger safeguards against legal exposure and fraud. Smarter technology use, resilient rebuilding, and proactive stances against damaging practices offer meaningful progress toward stability, though none are quick fixes.

Technology is a vital lever to alleviate cost pressures and improve the insurance experience as the market normalizes. Advanced solutions, such as agentic AI, predictive analytics, and AI-powered risk insights, are already transforming insurance. These technologies streamline workflows, enable faster, more accurate estimates, and help insurers proactively manage risks.

These innovations go beyond efficiency; they reshape the customer experience. Policyholders benefit from faster claims resolution, more transparent communication, and tailored risk management. Leveraging real-time data and automation, insurers deliver responsive, personalized service, restoring trust and confidence, even amid market pressures.

This report, developed with CB Insights and Crawford & Company, offers a data-driven roadmap for addressing affordability. By analyzing tech momentum and affordability across nine P&C lines, we pinpoint where technology most effectively reduces loss costs and benefits policyholders. Findings show targeted adoption of advanced tech, especially in cyber, homeowners, and auto, delivers the greatest impact.

I encourage all of us as industry stakeholders to use this insightful report to explore investments, partnerships, and solutions. Together, we can build a more resilient insurance ecosystem — keeping coverage accessible, affordable, and customer-centric for all.

Rohit Verma
President & Chief Executive Officer
Crawford & Company

Overview

Insurance coverage is becoming increasingly unaffordable for businesses and consumers, with loss costs rising rapidly due to factors like extreme weather, labor shortages, and supply chain disruptions.

To improve affordability, insurance companies must prioritize innovative ways to deploy technology across loss prevention efforts. While technology alone is not enough to improve affordability, it offers the most tangible opportunities to lower costs.

Below, we identify top tech-driven loss prevention opportunities to improve insurance affordability across nine P&C insurance lines of business. We rank these opportunities across two axes:

• Tech momentum assesses startup ecosystem strength and tech applicability across a line of business. We measure tech momentum using CB Insights’ datasets such as deal activity, company headcount, and our proprietary Commercial Maturity — which measures a private company’s ability to compete or partner — and Mosaic scores — which measure the overall health and growth potential of private companies.
• Affordability pressure assesses the impact of loss cost increases for policyholders across a line of business. We evaluate affordability pressure by surveying Crawford & Company’s global claims experts, coupled with CB Insights’ Public Company Financials data.

Key takeaways: Opportunities with the greatest potential impact

1. Cyber leads in startup momentum, offering insurers the richest landscape of tech innovation to improve affordability.
2. Homeowners’ insurance faces the greatest affordability pressure, making it the most urgent line for loss-prevention technologies despite limited ready-made solutions.
3. Commercial and personal auto face an innovation gap, with high affordability pressure but low startup momentum — requiring insurers to carefully vet and selectively scale emerging solutions.

1. Cyber leads in startup momentum, offering insurers the richest landscape of tech innovation to improve affordability.

Cyber insurance faces only moderate affordability pressure, primarily from data breaches and hacks, compared to other lines like auto and homeowners that are more exposed to medical costs, materials shortages, and natural catastrophes (NatCats). What makes cyber unique is that leading players such as Coalition (an insurtech unicorn with $300M+ in revenue in 2024) actively embrace technology to prevent and reduce losses at scale.

Because the line is inherently tech-centered, nearly every new entrant presents a potential opportunity to lower loss costs. Risk management practices within cyber have generally reduced claims severity, as noted by Greg Smith, President, Canada at Crawford & Company:

“A larger number of smaller cyber claims are reducing severity, as is a more disciplined focus on underwriting and loss control in this space. Loss ratios for cyber lines in Canada have improved significantly in recent years because of improved underwriting and risk management.”

Between July 2022 and June 2025, cyber-focused startups completed more than 2,500 deals of at least $100K in funding, more than any other line of business analyzed. With a median CB Insights’ Mosaic score (success probability) of 615 out of 1,000, cyber startups tie general liability for the highest score across the 9 lines of business.

This surge of new entrants reflects rising enterprise concerns around AI security, which has spiked in executive commentary since the release of ChatGPT in late 2022. For example, Knostic, founded in 2023 and recently doubling its headcount from 22 to 44, helps enterprises identify and mitigate large language model data leakage risks.

Looking ahead, as individuals and businesses increasingly adopt AI, advanced cyber risk detection and rapid response capabilities will become critical. Insurance companies should prioritize evaluating partnerships with cyber startups or building comparable in-house capabilities to pass affordability gains onto policyholders.

2. Homeowners’ insurance faces the greatest affordability pressure, making it the most urgent line for loss-prevention technologies despite limited ready-made solutions.

Homeowners’ insurance experienced the steepest estimated loss ratio increases among all business lines analyzed, coinciding with rate increases spurred by natural catastrophes (NatCats). Survey respondents also pointed to fraud, labor shortages, and materials shortages as amplifying factors.

Reducing loss costs within homeowners’ insurance will depend on consistent data availability for individual homes and surrounding communities, as noted by Tim Butler, Head of Contractor Connection & CRD, Australia at Crawford & Company:

“For quite a number of years, there has been consistent talk of preventative measures in the form of water pressure meters and standard home tech. Unfortunately, it appears adoption of this technology remains an uphill effort. There is, however, an increase in the use of data available, which appears to be creating more consistency around loss cost.”

While more than 1,900 startup deals touch the homeowners’ space, many of these products were not designed specifically for insurers, requiring carriers to actively identify and adapt external technologies for loss prevention.

For example, Atmosic is developing low-power Internet of Things (IoT) charging infrastructure that insurers could implement via homeowner-provided sensors. Homeowners could receive these sensors at the start of hurricane season, ensuring extended power to generate data on risks that could result in a costly claim.

Beyond sensors, insurance companies should also eye non-traditional data and risk engineering methods to improve affordability. For instance, Figure, a humanoid robotics company founded in 2022 and backed by Bezos Expeditions, Microsoft, NVIDIA, and OpenAI, is targeting household deployments. In the future, humanoid robots could proactively maintain homes, generate maintenance data, and provide insurers with differentiated insights to mitigate loss risks.

Looking ahead, affordability improvements in homeowners’ insurance will require a broad set of technologies that support both loss control and proactive risk management. NatCat events, in particular, will continue to stress the market, presenting increasingly pressing needs to reduce loss costs to the greatest extent possible.

3. Commercial and personal auto face an innovation gap, with high affordability pressure but low startup momentum — requiring insurers to carefully vet and selectively scale emerging solutions.

Commercial and personal auto rank lowest in tech momentum across all lines of business analyzed. Many of these startups are in the electric vehicle space, a sector now facing market headwinds with reduced executive attention and a pullback in dealmaking.

Despite weak innovation supply, both auto lines remain under high affordability pressure, second only to homeowners. Survey participants cited macro-economic factors such as rising materials costs and social inflation as primary contributors to worsening claim trends, with Steve Blakemore, Managing Director, U.S. Loss Adjusting at Crawford & Company, noting:

“Cost of materials and specialized repair processes to include aluminum bodies and e-vehicles have increased significantly beyond affordable deductibles.”

Technology opportunities for loss prevention exist, but they require disciplined evaluation — and need to extend beyond established telematics capabilities. For example, AtoB is a payments platform for the trucking industry with investors including Bloomberg Beta and Mastercard. The company’s revenue is projected to reach $100M by the end of 2025.

Coupled with existing data from telematics capabilities, insurance companies could utilize AtoB’s spending data to offer policyholders predictive notifications — for instance, guidance on routes to avoid areas with a higher likelihood of collisions.

Insurance companies should also identify opportunities related to autonomous vehicles, particularly after Waymo’s $5.6B Series C funding round in October 2024. Autonomous vehicle technology offers insurance companies potentially valuable data points that can inform loss prevention strategies as autonomous driving becomes more prevalent.

Looking ahead, improvements to auto insurance affordability through technology will require access to unique data sources that provide differentiated insights into driving risks. Carefully selected tech partnerships can provide insurance companies with access to this data, enabling them to offer policyholders proactive notifications that curb risky actions and prevent costly losses.

Line of business spotlights

Prioritize

Commercial property

Overview: Insurance coverage for business buildings, facilities, warehouses, and commercial real estate against property damage and operational risks.

Insurance Affordability Outlook placement:

• Affordability pressure ranking — 4th
• Tech momentum ranking — 5th

Data highlights:

• Pool of 800+ deals analyzed
  • Median deal size — $10.8M
  • Median current Mosaic score — 563 out of 1,000
  • Most frequently listed current Commercial Maturity — Scaling (level 4 out of 5)
• Survey respondents indicated that commercial property has significant exposure to weather-related natural catastrophes.
• Survey respondents also identified materials shortages and supply chain disruptions as highly relevant factors to commercial property.

Potential startup collaboration: Doss is an AI-enabled enterprise resource planning platform serving industries like construction, manufacturing, and supply chain. The company more than doubled its headcount between July 2024 and July 2025. An insurance company could pursue a partnership with Doss to gain data access to flag supply chain shortage risks that could otherwise lead to costly repairs and restorations.

Construction

Overview: Specialized insurance coverage for construction companies, contractors, and building projects, addressing construction-specific risks including accidents, defects, and site safety.

Insurance Affordability Outlook placement:

• Affordability pressure ranking — 5th
• Tech momentum ranking — 3rd

Data highlights:

• Pool of 1,100+ deals analyzed
  • Median deal size — $4.0M
  • Median current Mosaic score — 562 out of 1,000
  • Most frequently listed current Commercial Maturity Score — “Deploying” (level 3 out of 5)
• Construction has the third-highest tech momentum, indicating ample opportunity for innovation in the space.
• Survey participants indicated the following factors as highly relevant: weather-related natural catastrophes, labor shortages, materials shortages and supply chain disruptions.

Potential startup collaboration: AUAR builds mobile, robotics-powered micro-factories for home construction. The company partnered with industrial giant ABB in 2024 to expand operations in the United States. Given that micro-factories can reduce construction costs due to the potential need for less labor, materials, and transportation, insurance companies could offer AUAR-partnered construction companies less costly premiums.

Cyber

Overview: Insurance coverage against cybersecurity threats, data breaches, and digital risks affecting business operations and customer information.

Insurance Affordability Outlook placement:

• Affordability pressure ranking — 6th
• Tech momentum ranking — 1st

Data highlights:

• Pool of 2,500+ deals analyzed
  • Median deal size — $6.0M
  • Median current Mosaic score — 615 out of 1,000
  • Most frequently listed current Commercial Maturity Score — “Deploying” (level 3 out of 5)
• Cyber has the highest tech momentum, boosted above the others as the line is inherently tech-enabled. Cyber startups have the highest weighted-average score across deals analyzed in the report, and the largest deal count analyzed across the lines of business.

Potential startup collaboration: Lakera is a security platform for genAI applications. The startup has a CB Insights’ Mosaic score among the top 2% of companies globally, and is one of the world’s most-promising AI startups as a 2025 AI 100 winner. Insurance companies could partner with Lakera to offer the company’s tech to support policyholders’ AI agents, identifying and protecting against potential data breach attempts from malicious prompts.

Homeowners

Overview: Personal insurance coverage protects residential properties and personal belongings against property damage, natural disasters, and household risks.

Insurance Affordability Outlook placement:

• Affordability pressure ranking — 1st
• Tech momentum ranking — 7th

Data highlights:

• Pool of 1,900+ deals analyzed
  • Median deal size — $4.2M
  • Median current Mosaic score — 562 out of 1,000
  • Most frequently listed current Commercial Maturity Score — “Deploying” (level 3 out of 5)
• Homeowners’ insurance leads in affordability pressure, with analysis zeroing in on claims severity and loss ratio change (the greatest across lines analyzed) as key pain points.
• Survey respondents indicated that homeowners’ insurance has significant exposure to weather-related natural catastrophes.

Potential startup collaboration: Honey Homes is an on-demand maintenance service currently serving homeowners in California, Illinois, and Texas. Insurance companies could evaluate partnerships with Honey Homes to gain access to trending service requests in localized areas, like upticks in window replacements across older homes. Insurance companies could then derive data-driven signals from those requests to inform preventive action for potentially costly events, such as sending plywood and sandbags to homeowners in a projected hurricane path.

Vet

Commercial auto

Overview: Insurance coverage for vehicles used in business operations, including fleet management, trucking operations, and commercial transportation risks.

Insurance Affordability Outlook placement:

• Affordability pressure ranking — 3rd
• Tech momentum ranking — 9th

Data highlights:

• Pool of 500+ deals analyzed
  • Median deal size — $7.0M
  • Median current Mosaic score — 613 out of 1,000
  • Most frequently listed current Commercial Maturity Score — “Deploying” (level 3 out of 5)
• Commercial auto had the lowest tech momentum ranking due to below-average company scores and deal count.
• Materials shortages, medical costs, and social inflation are key factors in commercial auto claims.

Potential startup collaboration: Outpost offers a network of managed freight terminals, featuring a gate management platform that reviews truck data like license plates and registration numbers using computer vision technology. The company doubled its financial capacity in September 2025 to $1B. Insurance companies could pursue a partnership conversation with Outpost to gain access to gate data for risk modeling purposes.

General liability

Overview: Broad business insurance covering third-party claims for bodily injury, property damage, and operational risks arising from normal business activities.

Insurance Affordability Outlook placement:

• Affordability pressure ranking — 7th
• Tech momentum ranking — 6th

Data highlights:

• Pool of 200+ deals analyzed
  • Median deal size — $6.6M
  • Median current Mosaic score — 615 out of 1,000
  • Most frequently listed current Commercial Maturity Score — “Deploying” (level 3 out of 5)
• Survey responses indicated that general liability is experiencing increased claims severity. Social inflation and increased medical costs are key factors relevant to general liability claims.

Potential startup collaboration: Relay provides communication and location-tracking devices for frontline workers across industries, like entertainment, healthcare, and hospitality. The startup’s headcount has grown rapidly in recent years, with a projected revenue of $100M by 2027. Insurance companies could evaluate offering Relay’s product to business customers, like concert venues and restaurant operators. The tech deployment would support employee responses to potential claims-triggering risks, such as wet floors that could lead to slip and fall incidents.

Inland and ocean marine

Overview: Specialized coverage for goods in transit, commercial equipment, and property that moves between locations or operates in maritime environments.

Insurance Affordability Outlook placement:

• Affordability pressure ranking — 9th
• Tech momentum ranking — 2nd

Data highlights:

• Pool of 900+ deals analyzed
  • Median deal size — $5.3M
  • Median current Mosaic score — 607 out of 1,000
  • Most frequently listed current Commercial Maturity Score — “Deploying” (level 3 out of 5)
• Inland and ocean marine has the second-highest tech momentum, indicating strong opportunities to improve insurance affordability — despite the lowest pressure to improve affordability across all lines of business assessed.
• Survey respondents indicated the following factors as relevant: fraud, labor shortages, materials shortages and supply chain disruptions, and weather-related natural catastrophes.

Potential startup collaboration: Altana AI is a supply chain intelligence platform backed by Google Ventures and Salesforce Ventures, and — as a CB Insights 2024 Insurtech 50 winner — one of the world’s most-promising insurtech startups. The company offers products for business interruption risk and supply chain network planning. Insurance companies could explore Altana AI’s platform to gain visibility into potential supply chain risks and reroute shipments that otherwise face heightened risk for damage or loss.

Personal auto

Overview: Individual insurance coverage for personal vehicles, protecting against accidents, vehicle damage, and liability arising from personal driving.

Insurance Affordability Outlook placement:

• Affordability pressure ranking — 2nd
• Tech momentum ranking — 8th

Data highlights:

• Pool of 900+ deals analyzed
  • Median deal size — $7.3M
  • Median current Mosaic score — 606 out of 1,000
  • Most frequently listed current Commercial Maturity Score — “Deploying” (level 3 out of 5)
• Personal auto has the second-highest affordability pressure among lines of business analyzed, primarily due to loss ratio change estimates.
• Startups relevant to personal auto ranked the lowest in momentum due to a low weighted average score.
• Survey respondents indicated challenges around claims severity.

Potential startup collaboration: NoTraffic is a traffic management company that offers IoT devices to collect data at intersections and an AI platform to support traffic optimization decisions. NoTraffic is a NVIDIA partner and previously participated in the NVIDIA Inception Program. Insurance companies could evaluate partnerships with NoTraffic to support deployments that could reduce traffic accidents and lower claims costs.

Workers’ compensation

Overview: Mandatory insurance providing medical benefits and wage replacement for employees injured on the job, covering workplace accidents and occupational hazards.

Insurance Affordability Outlook placement:

• Affordability pressure ranking — 8th
• Tech momentum ranking — 4th

Data highlights:

• Pool of 100+ deals analyzed
  • Median deal size — $5.0M
  • Median current Mosaic score — 581 out of 1,000
  • Most frequently listed current Commercial Maturity Score — “Deploying” (level 3 out of 5)
• Survey participants generally viewed medical cost increases and social inflation as relevant to workers’ compensation insurance.

Potential startup collaboration: Protex AI offers a computer vision product that gives customers visibility into workplace risks (like speeding forklifts in warehouse environments) to prompt intervention by environment, health, and safety teams. DHL and Marks & Spencer are among Protex AI’s customers. Insurance companies could seek a partnership with Protex AI to offer the platform to policyholders facing elevated risks for workers’ compensation claims, like manufacturers and warehouse operators, due to workplace injuries.

Monitor

No lines of business fall within this category.

Methodology

The insurance affordability outlook provides an informational visual framework for insurance leaders to identify opportunities to improve insurance affordability for policyholders.

The opportunities encompass potential tech deployments for loss prevention and response across 9 lines of business: commercial auto, commercial property, construction, cyber, general liability, homeowners, inland and ocean marine, personal auto, and workers’ compensation.

The resulting visual plots the lines of business relative to one another across 3 categories:

1. Monitor — Lines of business with lower signals to warrant investment in tech to support insurance affordability. Insurance leaders should track developments in this space for future consideration.
2. Vet — Lines of business with moderate signals to warrant investment in tech to support insurance affordability. Insurance leaders should evaluate potential partnerships and tech deployment, pursuing the most-promising opportunities as innovation activities.
3. Prioritize — Lines of business with strong signals to warrant investment in tech to support insurance affordability. Insurance leaders should prioritize partnerships and tech deployments to operationalize.

Calculations across 2 axes — tech momentum and affordability pressure — guide plotting for each line of business.

Tech momentum

Tech momentum assesses startup ecosystem strength and tech applicability across the 9 lines of business.

This report leverages CB Insights’ AI-enabled deal search to identify 8,900+ venture-backed equity deals of at least $100K across the lines of business between July 1, 2022, and June 30, 2025. Deals were identified based on keywords specific to the lines of business, and some deals were excluded from the analysis. Deals analyzed in this report are not mutually exclusive, although the aggregate total constitutes approximately 9% of venture dealmaking between Q3’22 and Q2’25.

We generate a score for each deal that reflects the company’s momentum within the marketplace. The score uses CB Insights’ data, such as deal activity, company headcount, and proprietary Commercial Maturity and Mosaic scores. We include startups from across the venture landscape, although we assign greater weight to insurtechs given their direct relevance to the insurance market. Weighted average calculation guides the final ranking of the scores and the total number of deals analyzed across each line of business.

Affordability pressure

Affordability pressure evaluates the approximate impact of loss costs on insurance affordability for policyholders across the 9 lines of business.

We leverage 2 different data sources to measure affordability pressure for each opportunity:

• Survey of claims experts — 64 Crawford & Company employees provided directional insights across 160 line of business-specific responses on global loss trends.
• Loss ratio changes among publicly-traded insurers — Loss ratio changes between 2022 and 2024 across a sample of 15 companies: AIG, Allstate, American Financial Group, Arch Capital Group, AXIS Capital, Chubb, Everest Group, GEICO, Mercury General Corporation, Old Republic Insurance Group, Progressive, Selective Insurance, The Cincinnati Insurance Companies, Travelers, and W.R. Berkley. The data points are approximate based on the business mix of each of the 15 companies, and mindful of loss ratio dynamics in recent years due to the potential (or actual) impact of rate and price increases.

The final ranking is guided by the survey outputs and loss ratio change outputs, supported by ChatCBI reasoning leveraging data from across CB Insights’ Business Graph.

Additional notes

CB Insights has provided the information contained in this report for informational purposes only and does not constitute an endorsement or recommendation by CB Insights. Reasonable efforts have been made to ensure the accuracy of the information, and CB Insights makes no representation or warranty, express or implied, as to its completeness or accuracy.

Crawford & Company has not vetted, nor does it endorse, any of the companies or technologies mentioned in this report. These references are illustrative in nature and should be viewed solely as examples rather than recommendations.

The insurance affordability outlook is not an investment analysis, and should not be used to guide financial- or investment-focused decisions, including those pertaining to any insurance company operating across the analyzed lines of business. In addition, the report leverages a non-actuarial analysis and should not be used to discern financial performance (including loss ratio performance) across any lines of business analyzed.

This report is global in scope, although the analysis largely centers on the United States. Regulations and market dynamics differ across geographies, and the report does not account for every nuance across the industry.

We selected the 15 companies for the loss ratio change analysis due to comparable data reporting practices of loss ratio performance on annual reports using CB Insights’ Public Company Financials data. The report uses loss ratios as underlying loss costs are often not reported in a standardized format on annual reports. Loss ratios include loss adjustment expenses and typically spans lines of business. The changes in loss ratios were used to derive signals for lines of business subject to more affordability pressure.

The insurance affordability outlook is not absolute. The insurance industry and broader tech landscapes are subject to constant change, so future developments have the potential to impact the findings presented in this report.

About

Crawford & Company

Crawford & Company® is a leading global provider of quality claims management and outsourcing solutions with an expansive network of experts serving clients in more than 70 countries. Our unique ability to combine innovation and expertise advances our purpose to restore lives, businesses and communities across the globe. For over 80 years, clients have trusted Crawford to care for their customers as a seamless extension of their brand, keeping the focus where it belongs—on people. More information is available at www.crawco.com.

Contact: info@us.crawco.com

CB Insights

Headquartered in New York City, CB Insights is the leading provider of AI for market intelligence. The company aggregates, validates, and analyzes hard-to-find private and public company data. Its powerful AI tells users what it all means to them personally. The world’s smartest companies rely on CB Insights to focus on the right markets, stay ahead of competitors, and identify the right targets for sales, partnership, or acquisition. Visit www.cbinsights.com for more information.

Contact: researchanalyst@cbinsights.com

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In September, private companies globally raised 1,400+ early-stage rounds (noting this total will rise as more deals are published retroactively). Over 25% of startups that raised rounds are building AI-enabled products and services.

Download the full report to access comprehensive CB Insights data on early-stage activity, including top investors & deals, valuation data, and our predictive signals. Below, we highlight notable trends to watch.

Emerging trends & categories to watch

Click the links to see underlying deal activity. Categories are not mutually exclusive. 

Review the full deal dataset

AI agents

Similar to last month, companies targeting AI agent applications raised over 50 deals (out of 1,485). Key trends to note include: 

• “Smart Money” is all in on AI agents: The top 25 VCs identified by CB Insights backed 13 AI agent startups in September. This represents nearly 20% of all of the early-stage activity from these VCs in the month. Focuses include security (Akto, Fabrix Security, Terra Security) and governance, risk, and compliance (Geordie, Zania), indicating enterprise adoption and risk management are key investment priorities. 
• Customer service is one of the most established use cases but is still seeing early-stage traction: AI agents handling customer service, support tickets, and user interactions represent one of the largest early-stage agent categories in September (8+ deals). Support operations have clear unit economics, high volume repetitive tasks, and direct cost savings compared to human agents, driving continued activity here. Top companies to watch based on Mosaic scores include Doo (Mosaic: 747) and Rauda AI (Mosaic: 687). 
• Emerging voice AI sector: Voice and phone agents are attracting dedicated investment (6 deals, 11% of agent activity) as investors bet on solutions that can tackle the unique technical challenges of voice interactions (i.e., real-time latency requirements, natural speech processing, emotional intelligence, etc.). Confido Health and Prosper, for example, are focused on healthcare applications. Meanwhile, Vida and Vaani Research are building infrastructure to develop voice AI/phone agents. Review the voice AI development platforms market to compare 30+ vendors in the space.

Robotics

Companies building robots, and the systems that power them, raised over 70 deals in the month. 

Within robotics, defense & security applications led early-stage activity (17 deals, 24% of total robotics activity), reflecting geopolitical tensions driving investment in autonomous defense systems and surveillance.

Other notable traction is in foundation models and operating systems for robots, as investors bet on horizontal platforms (4 deals, 6%):

• The leading September round in this category went to DYNA, which raised a $120M Series A. The company is ranked as a “challenger” in our robot foundation model developers market. 
• Another highlight is InOrbit, which focuses on robot orchestration and was founded by Florian Pestoni and Julian Cerutti, who have experience at a range of large tech companies, including Google, Atlassian, Meta, and IBM. The founders’ strong pedigree is reflected in the company’s CB Insights Management strength score, which ranked the highest of all the companies raising early-stage deals in September.

Management strength score

CB Insights’ Management strength scores (out of 1,000) the founding and management team’s prior achievements and likelihood of achieving future success, like a high-value exit. 

Especially at the earliest stages of the startup lifecycle, the strength of the management team serves as a key signal of potential. 

AI for scientific discovery & materials development 

Three of the largest early-stage rounds of the quarter went to companies looking to accelerate scientific discovery and materials development with AI: 

• Periodic Labs ($300M Series A at $1B valuation)
• Lila Sciences ($235M Series A at $1.23B valuation)
• CuspAI ($100M Series A at $520M valuation) 

Both Periodic Labs and Lila Sciences are also building “autonomous labs” — with AI designing, conducting and iterating on experiments. All 3 companies are operating at Commercial Maturity level 2/5 (Validating), indicating they’re still testing and refining their products.

Methodology

This report includes equity early-stage financings (convertible note, angel, pre-seed, seed, Series A) to private companies in August 2025. We excluded companies that are later-stage that raised an angel round or convertible note in the month. Categorization based on company descriptions.

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OpenAI‘s September 2025 rollout of in-platform checkout via Shopify and Stripe enables shoppers to search, decide, and buy entirely within ChatGPT. Adobe reports that traffic from generative AI platforms to US e-commerce sites surged 4,700% year-over-year in July 2025, and is accelerating every month.

To compete, brands must establish a strong presence in AI search — or risk losing share in the emerging agentic shopping journey. Generative engine optimization (GEO) is the solution: startups in this space are building tools to track, measure, and optimize brand visibility across ChatGPT, Claude, Google AI Overviews, and other AI-generated answers, creating a new SEO arms race.

Using CB Insights’ Mosaic score — which measures private company health and predicts likelihood of success — we analyzed more than 20 GEO companies, ranking them by 1-year Mosaic score growth to identify the fastest-rising vendors. By examining their capabilities and growth signals, this analysis highlights the GEO partners best positioned to help brands win in AI search.

Explore the generative engine optimization (GEO) market

• Native GEO solutions have the first-mover advantage. Of the 8 GEO companies with the most momentum — each posting more than the market’s average 14% Mosaic growth in the past year — 7 were founded between 2023-2025. All but 2 companies with Mosaic scores of 600+ also launched in the same window. These “native” GEO companies were built specifically for LLM visibility rather than retrofitting or bolting onto SEO tools, giving them advantages in data collection, multi-model monitoring, and content recommendations. 
• Four capabilities have emerged as table stakes, and vendors without this full stack will fall behind. Analysis of the CB Insights market scorecard for companies with the top Mosaic scores (600+) highlights a consistent feature set: multi-platform monitoring across ChatGPT, Perplexity, Google AI Overviews, and emerging engines; competitive benchmarking that tracks share of voice against rivals; sentiment analysis measuring how brands are portrayed; and actionable insights on content gaps and adjustments. Vendors that deliver this complete capability set are the ones that will lead the market. Buyers should prioritize these features when evaluating potential partners.
• Automated content generation is becoming a differentiator. While most GEO tools diagnose visibility, leaders are building autonomous content engines that create LLM-optimized content. Profound offers AI content workflows using preset templates to write AI-friendly material from aggregated citations. Surfer‘s Content Editor suggests LLM-ready content modifications. Scrunch constructs parallel “AI-friendly” website versions optimized for machine consumption alongside human-facing sites. As GEO evolves, the platforms that can both track and directly shape how LLMs read the web will dictate the rules of agentic discovery. Brands’ demands for the most efficient solutions will force pure-play monitoring tools to build or acquire content capabilities.

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Every big tech company and a rapidly growing private market landscape are building agent offerings targeting enterprise use cases and industries from financial services to manufacturing.

For enterprises across sectors, one question is becoming unavoidable: Which AI agent strategies will separate market leaders from those left behind?

Enterprises are under pressure to build and implement agents as these LLM-based systems change how companies operate, hire, and scale.

Across 9 reports, discover where startup innovation is pointing, promising partnership and acquisition targets, and key trends to watch based on CB Insights predictive intelligence. Download the report for free.

This 68-page report covers: 

Foreword from Manlio Carrelli, CEO of CB Insights

Outlook on AI agents

6 AI agent predictions looking into 2026

The AI agent ecosystem — Who are the startups, infrastructure providers, and emerging revenue leaders to watch?

• The AI agent market map
• The AI agent tech stack
• The AI agent revenue race

AI agents make inroads across enterprise workflows — How are agents reshaping coding, customer service, and backend operations at scale?

• Y Combinator’s 2025 Spring batch reveals the future of agentic AI
• Building the agent economy: How cloud leaders are shaping AI’s next frontier
• The summer of vibe coding is over — How reasoning models broke the economics of AI code generation

Industry applications gain momentum — Where are vertical-specific agents gaining adoption and delivering measurable ROI?

• 3 markets fueling the shift to agentic commerce
• The industrial AI agents & copilots market map
• 100 real-world applications of genAI across financial services and insurance

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We recently presented CB Insights’ data on how AI is elevating customer engagement across insurance at an InsurTech NY-hosted executive roundtable.

Here are the key takeaways:

1. It’s early days for AI-enabled customer engagement, so insurance companies have a rare opportunity to define what good looks like.
2. AI-enabled customer engagement will soon be table stakes, accelerating as partner ecosystems emerge and mature.
3. Agentic commerce offers insurance companies opportunities to capture new sales channels.

Download the 40+ slides to reveal insights like:

• Agentic commerce offers a clear opportunity to shape the future of embedded insurance, signaled by the emergence of partnerships with companies like eBay and Priceline.
• A new tech stack now powers agentic operations across industries, and insurance is no exception. AI agent oversight offers an opportunity to support compliance activities.
• AIG’s actions to develop its “agentic ecosystem” signal an imperative for business growth.

CB Insights customers can download the full presentation using the sidebar.

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For professional services firms, this momentum poses an existential question: What happens to the traditional consulting model when clients have direct access to AI-powered expertise? 

The industry is already pivoting. McKinsey has deployed around 12,000 AI agents internally to support consultants and enable leaner project teams. Meanwhile, Accenture announced in June that it would merge five units into a new “reinvention services” business line aimed at helping clients overhaul their operations with AI.

Across the sector, leading strategy and tech consulting firms have collectively pursued more than 100 AI agent-related partnerships, investments, and acquisitions since 2023, per CB Insights data.

Note: Graphic features publicly disclosed or otherwise confirmed relationships announced since 1/1/2023, focusing on AI agents & related infrastructure. It is not intended to be exhaustive. Click to enlarge.

The stakes are clear: with momentum building but enterprise pilots often stalling, the firms that move beyond advice to building and orchestrating will shape the future of the AI economy.

Drawing on CB Insights predictive intelligence and interviews with senior AI and data executives, this report offers a forward-looking roadmap for how professional services firms can capture value over the next two years and beyond. Our analysis points to four key strategic opportunities: 

1. Orchestrate the AI agent tech stack: Guide clients through implementation, integration, scaling, and governance of AI agents across fragmented infrastructure, from model selection to trust and performance management.
2. Activate proprietary data for intelligent agents: Turn firm and client data into fuel for more capable, context-aware agents — differentiating through domain-specific IP, data infrastructure, and strategic partnerships.
3. Turn services into scalable AI products: Evolve from custom consulting engagements to platform-based delivery, industry-specific solutions, and new pricing models.
4. Build the human-AI workforce: Redesign internal talent models to work alongside AI agents while shaping offerings to help enterprises adopt and manage AI, from agents to robots. 

Download the full report to discover leading firms’ strategic moves, where startup innovation is pointing, and promising partnership and acquisition targets — based on CB Insights predictive intelligence on the world’s companies and markets.

Table of contents

• Orchestrate the AI agent tech stack
  • Core consulting workflows see AI agent disruption
  • Firms are already leaning in to build the stack
  • An AI ecosystem model is emerging
  • Private market momentum points to key orchestration opportunities
  • Agent reliability failures drive demand for governance layer
• Activate proprietary data for intelligent agents
  • Leading firms build out the data layer for agentic AI
  • Firms create data moats around core practice areas
• Turn services into scalable AI products
  • Platform strategies replace project-based delivery
  • Strategic startup investments reinvent the consultant’s toolkit
  • Shifting service delivery drives pricing model experimentation
• Build the human-AI workforce
  • Professional services firms face a workforce restructuring
  • AI-native startups preview the future efficiency of enterprise workforces
  • Managing AI becomes a new competitive moat

How we built this report: Over the last 9 months, we conducted in-depth interviews with senior leaders at leading professional services firms to understand the impact of AI agents on the future of the industry. To map out where the landscape is headed, we used CB Insights’ Market Intelligence Agents to interpret signals from across CB Insights data, including the following datasets and primary research: 

• Industry partnerships, deal activity, earnings transcripts, and proprietary scores for company momentum
• Surveys with enterprise leaders and briefings with AI agent startups

Get full access with your free CB Insights trial: https://www.cbinsights.com/trial-signup/ 

Scope of professional services: This report focuses primarily on strategy and technology consulting firms (including the Big Four). While the industry extends to legal, engineering, and specialized advisory services, our analysis centers on firms that directly compete with or complement AI agent capabilities in enterprise transformation and technology implementation.

Note: Named firms did not review the data contained in this report. 

In upcoming research on the future of professional services, we’ll dive deeper into competition in the “service-as-software” market and firms’ evolving talent models.

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Since 2022, the share of legal tech equity deals going to AI-focused companies has climbed from 25% to 63% in 2025 YTD. So far this year, over 95% of equity funding going to legal tech startups has gone to AI-focused companies.

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Since we published our first agent revenue ranking in July, the sector has continued to gain significant momentum: Harvey reached the $100M revenue threshold, category leaders like Sana Labs are exiting (Workday’s $1.1B acquisition), and startups like Sierra are raising mega-rounds ($350M Series D at a $10B valuation).

Using CB Insights revenue data, we identified the top private startups generating $10M+ in revenue that offer AI agents as their primary offering and analyzed their revenue performance across categories (see the graphic below). 

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

See the top AI agent startups ranked by revenue here

Key takeaways 

• Coding AI agents are racing ahead in commercialization, with 6 software development agents making the top rankings, including market leaders like Anysphere’s Cursor ($500M in ARR) and Replit ($150M). These startups have demonstrated that they’re the most capital-efficient category, averaging $1.4M revenue per employee (compared to $594K per employee across all top agent categories). We recently identified the leaders of coding AI by market share — read more here.
• Customer service AI agents command the highest valuation premiums, averaging 219x revenue multiples (compared to 80x across all top revenue-generating AI agents). This valuation gap reflects investor confidence in the sector’s applicability and the expectation that businesses will rapidly replace human support teams with AI agents.
• These revenue leaders average just 3.8 years old. Despite their youth, CB Insights’ Commercial Maturity data shows the majority are already deploying or scaling their products, demonstrating the industry’s compressed timelines from startup to commercial success.

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

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

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

Reasoning models spark vibe coding’s explosive growth

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

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

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

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

See all coding AI agent companies ranked by revenue

Reasoning token shock pushes adoption of new pricing models

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Open models cut costs, but enterprise requirements slow adoption

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

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

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

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

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

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

This dynamic won’t be limited to coding

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

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

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

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

RELATED RESOURCES FROM CB INSIGHTS:

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

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

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

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

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

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

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

Key takeaways

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

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

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

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

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

Download the book to see all 50 scouting reports.

Key takeaways from July’s mega-rounds include: 

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

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