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

For information on reprint rights or other inquiries, please contact reprints@cbinsights.com.

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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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Prefer to listen in? Check out our discussion of the report here: 

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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Funding to the space set a new record in 2023 — bucking the broader decrease in venture funding for the second year in a row.  

While still a nascent technology, startups are pursuing a broad range of potential use cases for quantum computing (which process information in a fundamentally different way to typical computers) across numerous industries — from training AI models more efficiently to simulating otherwise hard-to-model chemical reactions to quickly solving complex logistics problems, among many others.

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In addition to these larger factors at play, procurement teams must work cross-functionally to ensure other parts of the business have the tools and supplies they need. This requires that they maintain relationships with suppliers, ensure legal compliance, and analyze data across their organization to evaluate how current vendors are supporting workflows. 

Tech companies are helping procurement teams more efficiently source and compare new vendors, maintain supplier relationships, assess renewals, and more. In the market map below, we identify 82 tech vendors addressing critical aspects of procurement across 9 categories. 

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Wiz landed a $10B valuation in February just 3 years after its founding. Orca was last valued at $1.8B in 2021. 

Both are part of a group of cybersecurity startups — like Aqua Security and Lacework — that have grown rapidly with the cloud computing wave. 

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Microsoft is betting that generative AI could tip the competitive scales in its favor for decades to come.

The tech giant has poured billions into ChatGPT maker OpenAI to become one of its largest stakeholders, while also providing computing infrastructure to power the startup’s resource-hungry operations.

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Get up to speed with our explainer video — and read on for the full report.

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The company’s portfolio includes some of the most iconic and successful companies in the world, like SolarWinds, Blue Coat, Stamps.com, and SailPoint.

Thoma Bravo has participated in 27 transactions since 2022. The firm also invested in 5 mega-round ($100M+) deals and took multiple companies private at billion-dollar valuations during that time frame.

Using CB Insights data, we mapped how Thoma Bravo spread its investments and acquisitions since 2022 across categories like cybersecurity, financial services, enterprise software, and more.

CB Insights clients can dig deeper into all 27 deals listed above using this CB Insights platform search.

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Fintech was Sequoia Capital’s top investment category in 2022, representing nearly a quarter of the firm’s deals.

Using CB Insights data, we mapped how Sequoia Capital spread its 2022 fintech investments across categories like capital markets, personal finance, real estate, and more.

CB Insights clients can dig deeper into all 25 deals listed above as well as explore Sequoia Capital’s bets beyond fintech using this CB Insights platform search.

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New technology is not only helping companies navigate the aftermath of these shifts, it’s changing the way they hire, manage, and provide for their employees. Investors are taking notice — funding to the HR tech space more than tripled from $5.1B in 2020 to $18.7B in 2021.

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Download the buyer’s guide to see our overview of this software category, including the key considerations, buyer thought processes, and the questions you should be asking. Questions like:

• What is the vendor’s global reach?
• What level of customization does the vendor support?
• What is the vendor’s global reach?

“The critical thing is to find a partner that has the global reach that matches not just your global reach, but your ambition for future global reach.” — SVP of People & Culture, global apparel & footwear group

Buyers interviewed for this report include:

• Head of FP&A, technology company with $1B+ valuation
• Director of Talent Intelligence, software company with $1B+ market cap
• COO, fintech with $100M+ funding

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Download our scorecard to quickly compare the below vendors. To read the interview transcripts for each solution, reserve your Yardstiq subscription here.

• Deel
• Oyster
• Papaya Global
• Remote
• Velocity Global

Other platforms for distributed teams mentioned by buyers include:

• ADP Streamline
• Atlas (fka Elements Global Services)
• Globalization Partners
• Gusto

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