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

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

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

Reasoning models spark vibe coding’s explosive growth

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

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

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

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

See all coding AI agent companies ranked by revenue

Reasoning token shock pushes adoption of new pricing models

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Open models cut costs, but enterprise requirements slow adoption

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

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

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

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

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

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

This dynamic won’t be limited to coding

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

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

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

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

RELATED RESOURCES FROM CB INSIGHTS:

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

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

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

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

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

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

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

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

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

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

Let’s dive in.

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

Top stories in Q2’25

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

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

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

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

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

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

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

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

CBI customers: Track the highest funding startups in Q2’25

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

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

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

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

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

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

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

3. AI companies command funding premiums across sectors

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

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

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

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

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

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

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

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

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

CBI customers: Track big tech M&A activity

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

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

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

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

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

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

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

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

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

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

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

Breakout sectors of 2025

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

Stablecoin funding is on pace to shatter its previous record

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

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

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

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

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

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

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

Defense tech momentum continues

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

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

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

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

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

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

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

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

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

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

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

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

Corporate interest drives a surge in nuclear energy funding

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

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

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

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

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

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

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RELATED RESOURCES FROM CB INSIGHTS:

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

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Initially known for data processing platforms in the defense and government sectors, Palantir has now embedded AI into its core products — AIP, Foundry, Gotham, and Apollo — to provide analytics and intelligence solutions across industries including automotive, defense, healthcare, manufacturing, and supply chain.

We analyzed Palantir’s investments, partnerships, and product moves since Q1’23 using CB Insights data to identify the 4 key areas driving its next chapter of growth — and the companies it’s aligning with to get there.

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

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

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

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

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

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

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

RELATED RESEARCH FROM CB INSIGHTS

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

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

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

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

Please click to enlarge.

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

Track Mosaic health scores for companies in these markets

Market descriptions

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

Prevention & preparedness: Climate & weather risk

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

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

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

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

Prevention & preparedness: Wildfire risk

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

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

Detection & monitoring

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

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

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

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

Firefighting

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

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

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

Damage assessment

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

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

RELATED RESOURCES FROM CB INSIGHTS:

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

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While AI copilots — which work alongside humans to speed up their workflows — currently comprise 90% of company activity, the tech will serve as a stepping stone to more autonomous solutions in the coming years. Eventually, AI agents could manage entire industrial processes, shifting human roles from operational tasks to strategic oversight.

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Our 2025 Tech Trends report provides a concrete roadmap for corporate leaders to navigate some of the most important technology shifts in the year ahead.

We include specific recommendations for action so that business leaders can get ahead of the next wave of value creation.

Here is a selection of key findings from the report:

• AI agents are given money to spend: AI agents’ utility is limited until they can make transactions seamlessly. A small group of tech players is building new infrastructure to make that happen.
• The future data center arrives: With data center power usage expected to more than double by 2026, big tech companies are morphing into energy innovators to support AI workloads. There’s a huge opportunity in improving data centers’ energy efficiency.
• Investment floodgates open for RNA therapeutics: RNA therapeutics developers are pioneering new ways to treat traditionally “undruggable” diseases, with a growing focus on neurodegenerative disorders like Alzheimer’s and Huntington’s diseases.
• AI M&A fuels the next wave of corporate strategy: AI’s share of corporate tech M&A has doubled since 2020. Tech incumbents like Nvidia, Salesforce, and Snowflake, as well as consultancies like Accenture, are rapidly acquiring AI startups to tap into enterprise demand. 
• Disease management enters a new phase with AI: AI is improving care delivery across 3 key areas of disease management: precise symptom evaluation; testing/screening for earlier disease detection (including before symptoms even appear); and finding at-risk individuals in datasets of entire patient populations. 
• Retail’s personalization imperative: Generative AI is unlocking 1:1 experiences across commerce touchpoints, with leaders like Target seeing a corresponding 3x boost in conversation rates. Personalization will become omnipresent in retailers’ offerings.
• And much more

Methodology

Our analysis relies on a wide range of CB Insights datasets, including financing and acquisition data, valuations, founding team and key people data, earnings transcripts, and more. We also leverage CB Insights’ proprietary scoring algorithms to measure business health (Mosaic) and maturity (Commercial Maturity), as well as the likelihood of acquisition (M&A Probability score). Throughout the report, we provide CB Insights customers with jumping-off points to dig deeper into the data behind the report.

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This spending is creating opportunities for growth across the AI data center value chain.

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B2B payments tools reach cross-functionally across companies, making it not only more essential, but also more complex, to efficiently integrate these tools into workflows.

To help strategy teams prioritize B2B payments tech markets in their planning decisions, we plotted markets across 2 dimensions:

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

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

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Nvidia has used its experience developing graphics processing units (GPUs) — which were originally developed for applications like gaming but turned out to be rather good at AI tasks — to become a dominant force in AI infrastructure and applications. 

But a host of established companies and startups are investing heavily to capture a share of the growing AI computing market. This includes building new types of chips specifically for training and running AI models, evolving traditional processors like CPUs and GPUs to better support AI workloads, and exploring novel information processing technologies such as quantum and neuromorphic computing. 

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In the US, checks and cash have already declined from representing half of all B2B payments in 2019 to a projection of less than a third (32%) in 2024, according to eMarketer. In emerging markets, paper’s share of B2B transactions is likely higher, but coming down thanks to the rise of digital commerce and digital operating systems for merchants.

Companies offering B2B payments solutions are digitizing and automating formerly manual processes like creating invoices, processing payments, and analyzing payments data. Many are building platforms that consolidate the steps in the B2B payments value chain in one place, enabling faster and more transparent payments and financing between businesses. 

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In 2024 so far, a16z has backed more than 20 AI startups working within disruptive categories. For example, this year, it has invested in several AI-driven copilots and agents designed to automate key workflows in big industries like healthcare and finance. It has also turned its attention to multimedia generation startups expediting the creation of a wide variety of content, from images to videos to audio.

While championing AI’s advancement, the firm also acknowledges associated risks — its founders are proponents of open-source models, arguing that their transparency and accessibility will help ensure that AI is developed in a secure and ethical way. So far this year, the two largest a16z-backed AI deals have gone to open-source large language model (LLM) developers xAI and Mistral AI.

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Humans will still play a crucial role — but instead of assembling parts or operating machinery, they will maintain robots and keep them running.

In these future factories, robots coordinate in unison, completing work automatically — without breaks, every hour of the day — to get products out the door. 

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An empowered digital workforce would reshape industries as we know them. The implications would be enormous, changing how companies hire and scale, as well as what they can achieve with a small headcount. 

That future isn’t too far off. 

The idea of autonomous AI agents — LLM-powered bots that can independently reason and execute tasks — caught on like wildfire in 2023, marking an important evolution beyond chatbots and copilots. 

OpenAI CEO Sam Altman has described agents as “AI’s killer function” as recently as May 2024.  

While much of the tech remains limited in its ability to execute tasks reliably, use cases are gaining traction in horizontal enterprise applications like customer support, sales, and engineering.

We mined CB Insights startup, financing, business model, and buyer interview data to map the evolving landscape and analyze its future. 

In the 28-page report, we cover: 

• The state of AI agents: Investment is surging to companies in the space, but limitations — most notably, agent reliability — remain. 
• Leading horizontal applications and impacts: The landscape of VC-backed agent startups is dominated by a focus on horizontal applications — across sales, customer support, and other enterprise and general productivity workflows.
• Emerging industry applications and opportunities: While few agentic companies focus on single industries, companies are emerging to target workflows across financial services, industrials, and more. 

Download the full report to get all of the data and analysis.

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In the last 2 years, it has backed more than 20 AI startups — mostly via its OpenAI Startup Fund (which has LPs including Microsoft and other OpenAI investors) — many of which are building on OpenAI’s own infrastructure. The company itself also doles out occasional grants and has acquired 3 startups in the last year.

OpenAI’s recent investments point to potential growth opportunities for the firm, especially as it faces pressure to earn its eye-watering $80B valuation and stay ahead of competitors in the fast-moving generative AI market. 

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While Nvidia initially developed its graphics processing units (GPUs) for gaming, these chips turned out to be ideal for powering AI tasks. Now, the company is focusing its efforts on providing the computing hardware — notably its A100 and H100 GPUs — and the software infrastructure required for developing generative AI applications.

Amid the generative AI rush, Nvidia has grown rapidly. In fact, it recently surpassed Microsoft and Apple to become the world’s most valuable company. To bolster its leadership position and keep ahead of AI computing competitors like AMD and Intel, Nvidia has forged relationships with companies across the AI landscape.

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The industry is built around encoding information in quantum entities called “qubits” — which have a probability of being either 1 or 0, as opposed to traditional bits that can only be one or the other — to open up new computational approaches and ways of handling data that are not possible on conventional devices.

For example, quantum computing could allow entirely new algorithms that are much more efficient at some key tasks — like complex optimization problems, sorting through large datasets, and running simulations. Though the tech is not yet mature, companies are already working to apply quantum computing to commercial applications like drug discovery, finance, materials discovery, and logistics. Investors are excited — startups in the space saw equity funding surge to $1.3B in 2023, a rare bright spot for venture last year. As quantum computers become more capable in the coming years, expect commercial interest to rise quickly.

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The company has faced multiple high-profile incidents over the past 6 years — including crashes involving its planes and a fuselage blowout. This series of events has resulted in a decline in consumer trust, aggravated airlines, and lost sales to rival Airbus.

Amid these challenges, Boeing has shaken up its corporate leadership and moved to improve its manufacturing quality. For instance, the company is exploring a reintegration with Spirit AeroSystems to help address production flaws.

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