However, unlike September’s concentrated focus on raw compute and model training, October’s AI infrastructure deals spread beyond models to encompass the full stack, from semiconductors to open-source databases.

This funding distribution presents a clear pattern: investors are backing companies that help businesses use AI, not just build it. This includes agent infrastructure, workflow automation, and verticalized AI tools for legal, healthcare, and industrial sectors.

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

Download the book to see all 48 scouting reports.

The post Beyond foundation models: October mega-rounds span the full AI infrastructure stack appeared first on CB Insights Research.

Space cybersecurity is now a necessary defense, protecting satellites, ground stations, and mission operations from cyberattacks throughout their lifecycles. The threat is intensifying as large satellite constellations expand the attack surface and quantum computing shows potential to break current encryption standards.

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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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Training LLMs at scale is capital-intensive, and large funding rounds create a moat that shields model developers from new entrants.

Yet raw compute is a bottleneck, prompting bets on next-generation inference architecture — specialized chips and model fine-tuning — to bypass spiraling AI costs.

This investment pattern shows that scaling models alone is no longer sufficient. In the next phase of AI growth, performance, efficiency, and accessibility will determine which models and platforms emerge as winners. 

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

Download the book to see all 50 scouting reports.

KEY TAKEAWAYS

• Companies helping control AI costs are gaining momentum: 7 AI inference/performance optimization companies received mega-round funding in September, with over 70% already generating revenue. Baseten achieved 10x revenue growth in 12 months, Rebellions projects $68M for 2025, and Invisible generated $134M in 2024. Their client rosters include LLM and genAI developers like Cohere, Writer, and OpenEvidence), signaling strong demand and proven value.
• Cryptocurrency companies zero in on regulation and banking partnerships, boosting credibility and reach: 3 cryptocurrency companies received mega-round funding this month, and all either facilitate payments or offer digital currencies. 2 boast major banking relationships that validate institutional trust — Kraken partnered with PayPal, Circle, and Mastercard, and Fnality‘s Series C saw participation from 8 major banks, including Barclays, Bank of America, and Citibank. Fnality also received regulatory approval from the Bank of England, and Kraken acquired regulatory licenses in the United States and Europe, signaling that these companies are clearing compliance hurdles that have historically limited crypto’s reach. 
• Investors continue to place big bets on specialized, purpose-built AI ahead of commercial validation: Building on August’s momentum, early-stage bets are flowing to specialized AI companies built for specific industries and domains like robotics, manufacturing, materials science, healthcare, and pharmaceuticals. This month, 60% of verticalized AI mega-rounds occurred at Series A, and over 80% of these companies have Commercial Maturity Scores of 2, meaning their solutions are still being validated. Only robotics developer Auterion has live clients, while Lila Sciences and CuspAI have a single commercial partnership each. The rest — Periodic Labs, DYNA, and UltraGreen — feature strong founding teams or novel tech, suggesting investors are prioritizing talent and product over traction. As falling training costs fuel more vertical AI startups, team quality and product potential will remain critical funding criteria.

Want to submit your company’s funding data? Please reach out to researchanalyst@cbinsights.com.

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GenAI and agentic AI are expanding attack surfaces and fueling a booming AI security market. While companies are actively hiring for AI and security talent, as revealed by CB Insights’ Hiring Insights Agent, they have also opted for M&A as a faster way to seize the opportunity and integrate AI security features into existing offerings.

Most of these acquisitions are early-stage end-to-end platforms that combine offensive capabilities (red teaming, vulnerability management) with defensive features (incident response, alerts). Over half of the targets were Series A or earlier, 75% raised under $60M, and only one was generating revenue.

This points to a future where AI security becomes a table-stakes feature embedded in broader cyber offerings, rather than a standalone platform — driving large players to continue their acquisition spree.

Using CB Insights’ predictive signals, including Mosaic company health scores and M&A probability, we’ve identified the AI security startups that are ripe for acquisition next. 

See the top AI security startups ranked by M&A probability here

Key takeaways

• Securing the data pipeline will be a new frontier in AI security. Companies with the highest M&A probability in this group focus on securing AI data pipelines, including TrustLogix (with a 56% chance of being acquired in the next 2 years) and Protecto (with a 64% chance). Both are early-stage and have not received funding past Seed. Their combination of strong Mosaic (more than 1.5X the overall average of 370) and low funding positions these companies as low-cost acquisition targets that could add value in industries with highly sensitive data.
• Partnerships with large tech firms signal product validation, with 5 out of the 8 interest zone companies having partnered with large tech or cyber firms. 2 companies partnered with Snowflake for data privacy, 2 partner with Amazon, 1 integrates with OpenAI ChatGPT, and 1 has an ex-Google CISO on board. This market is nascent, with all companies either validating or deploying solutions and having raised at most Series A funding. In a market that’s so early-stage, partnerships are a proxy for market validation to potential acquirers.
• C-suite tech expertise is a talent acquisition opportunity: 50% of founding teams  come from large tech companies. The CEO of HydroX AI spent time at Meta, HP, and LinkedIn, the C-suite of Protecto comes from Microsoft, Apple, and Oracle, and the founding team at Lasso Security comes from SAP and Check Point. This expertise at the highest levels makes these companies potential targets for acquirers looking to bring AI talent in-house. As Check Point CEO Nadav Zafrir noted in a recent earnings call, “in this AI world, it’s all about talent and the critical mass of talent that you can bring into the organization.” 

Want to submit your company’s profile data? Please reach out to researchanalyst@cbinsights.com.

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

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

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

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

Key takeaways

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

Want to submit your company’s profile data? Please reach out to researchanalyst@cbinsights.com.

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

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

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

Download the book to see all 20 scouting reports.

Key takeaways from August’s mega-rounds include:

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

Want to submit your company’s funding data? Please reach out to researchanalyst@cbinsights.com.

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

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

Download the book to see all 46 scouting reports.

Key trends from June’s mega-rounds include:

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

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So far this year, 53 companies have reached billion-dollar valuations, putting 2025 on pace to exceed the 80 unicorns minted in all of 2024.

Artificial intelligence is the key driver behind this surge, with AI startups accounting for over half of all new unicorns in 2025 so far. These AI-native unicorns are also breaking the mold, reaching $1B+ valuations on faster timelines, hitting the milestone in 6 years versus the typical 7.

Here’s what today’s unicorn landscape signals about the future of tech:

• 1 in 5 new unicorns are AI agents, with AI taking over the unicorn landscape, representing 53% of all new billion-dollar companies in 2025 so far. Among the newest unicorns, 12 are building AI agents, including Hippocratic AI (healthcare), Cyberhaven (data security), and Parloa (customer support). 
• Newer unicorns generate 83% more revenue per employee than older ones, with $814K per employee on average, compared to the $446K average across all unicorns. This reflects automation-first approaches and leaner operations that avoid the operational bloat older unicorns accumulated during their growth phases. For example, among unicorns born in 2025, the company with the highest revenue per employee is soft drink company Olipop ($1.2M/employee), followed by AI sales agent unicorn Clay ($1M/employee).
• Consumer and fintech companies are most primed to exit, boasting the highest M&A probability scores among the top Mosaic-scoring companies. While payments company PPRO tops the list with a 53% probability of getting acquired in the next 2 years, consumer & retail companies dominate the middle tier with ID.me (41%), Cart.com (33%), and Vestiaire Collective (31%), suggesting acquirers see solutions like identity verification, e-commerce infrastructure, and marketplace platforms as prime M&A targets.

CBI customers: Dive into data on every unicorn

Market map of billion-dollar startups

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

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

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

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

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

Download the book to see all 49 scouting reports.

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

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

Learn more about the AI 100 here

Some highlights from our analysis: 

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

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

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

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

Please click to enlarge.

Note: Categories are not mutually exclusive.

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

Key Takeaways  

De-risking AI software development

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

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

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

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

Web-browsing agents gather steam beyond general-purpose use 

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

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

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

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

Agents are coming for the backend

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

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

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

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

AI agents keep making inroads in highly regulated industries

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

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

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

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

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

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

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

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

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

Download the book to see all 100 scouting reports.

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