According to CB Insights headcount data, AI firms such as Glean, Scale, and OpenAI have increased their workforce in the country by as much as 67% over the last six months. The country’s domestic AI companies have also seen 32% headcount growth over the same period.

Notably, India is now OpenAI’s second-largest market — with a user base that tripled in the past year — while global tech giants like Microsoft and Nvidia have made substantial infrastructure investments in the country.

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The AI boom has set the stage for a wave of tech M&A this year.

After 2 consecutive years of decline, tech M&A deals were up in 2024, with some of the largest deals centering on AI. AI companies have also bucked the general downward trend in exit valuations, instead seeing nearly double the median acquisition price from 2023 to 2024.

Using CB Insights’ predictive signals, such as Mosaic and M&A Probability, we’ve identified 7 AI-related areas where we expect to see M&A activity this year, as well as high-potential acquisition targets for each.

See highlights below, and download the full report for the rationale behind each prediction, as well as M&A target shortlists.

Tech M&A prediction highlights

• Big tech players set their sights on humanoid robotic: As physical AI takes off thanks to the rise of LLMs, humanoid robotics is becoming big tech’s next battlefield. Among high-potential acquisition targets, 1x stands out for its dual focus on industrial and consumer humanoids (just in January, it acquired Kind Humanoid to accelerate household robot development). This makes it a prime target for Meta, which recently announced plans to enter the consumer humanoid market.
• Enterprise tech heavyweights compete for AI infrastructure dominance: We’re already seeing strong signals from cash-rich companies such as Cisco and IBM, which are future-proofing their business models with AI investments. Hardware-aware AI optimization players CentML and Nota AI — which help accelerate AI model deployment while reducing compute costs — appear in our AI infrastructure acquisition target list. These companies have already shown quantifiable efficiency improvements as well as validation from Nvidia as a partner or investor.

Source: CB Insights advanced search. Data is dynamic (as of 2/27/2025).

• Data center energy demands fuel interest in cooling tech: Companies offering immersion and liquid cooling solutions enjoyed a funding rebound last year, attracting a combined $120M in fresh funding. Hypertec and Submer are high-potential acquisition targets in this space.
• Professional services firms seek AI capabilities: GenAI is coming for knowledge jobs — and leading professional services firms are buying AI capabilities to get ahead of it. One area where we see high M&A potential for professional services firms is to cater to clients’ responsible AI needs, with potential acquisition targets such as Lasso Security and HydroX AI.
• Pharma companies target AI drug discovery startups: AI drug discovery M&A is surging, with 12 deals in the sector since 2023. That M&A deal volume reflects both a maturing technology and growing urgency among pharma players to bring AI tech in-house.
• SaaS giants fortify their offerings with AI agent acquisitions: While some believe AI agents signal the death of SaaS companies, we anticipate SaaS leaders will acquire AI agent companies to avoid disruption. We’re already starting to see this happen with ServiceNow acquiring Moveworks for close to $3B in March 2025.

Source: CB Insights — ServiceNow Acquisition Insights

• Coding AI agents drive next wave of AI agent consolidation: Explosive growth, soaring valuations, a fractured AI agent landscape, and rising doubts about revenue defensibility make the coding AI agents market ripe for consolidation. While some players like Cursor look too expensive for an acquisition, we’ve identified Warp, Vidoc, and Bito as likely targets with high Mosaic scores and higher-than-average M&A Probabilities.

What is Mosaic?

Mosaic is CB Insights’ proprietary metric that measures the overall health and growth potential of private companies using non-traditional signals. Mosaic is widely used as a target company and market screener to identify high-potential emerging tech companies, typically defined as those with a score of 510 or higher.

What is M&A Probability?

M&A Probability is CB Insights’ proprietary signal that measures a private company’s chance of an M&A exit within the next 2 years. It is used to quickly screen and triangulate companies based on exit likelihood.

Combining both Mosaic Score and M&A Probability makes it easy to shortlist acquisition targets.

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As AI agents dominate the conversation, customers are growing skeptical about whether they can live up to the hype.

In March, we’ve interviewed 40+ customers of AI agent products and are hearing of 3 primary pain points right now:

1. Reliability
2. Integration headaches
3. Lack of differentiation

1. Reliability

This is the #1 concern raised by organizations adopting AI agents, with nearly half of respondents citing reliability & security as a key issue in a survey we conducted in December. 

According to CBI’s latest buyer interviews, AI agent reliability varies dramatically across providers. Many customers report a gap between marketing and reality.

“Whatever was promised didn’t work as great as said,” one LangChain user told us about the company’s APIs. “We encountered cases where we were getting partially processed information, and the data we were trying to scrape was not exactly clean or was hallucinating.”

For many customers, reliability is largely a function of how complex the data and use cases are. For instance, the LangChain customer saw ~80% accuracy for simpler tasks, but “for complex tasks, the accuracy dropped to around 50%.” 

Organizations are tackling the reliability issue with 1) human oversight; and 2) more extensive model training.

An Ema customer, for instance, first has a subject-matter expert review outputs, and once “more than 90% of the responses that we have tested are now accurate, we let it fly.”

A customer for CrewAI, which orchestrates teams of AI agents into “crews,” takes an even more involved approach:

The customer still needs to intervene with their own ML algorithms when CrewAI is unable to handle outliers or unconventional data structures. If CrewAI is able to tackle these cases in the future, “that would be a huge leap forward.” 

Source: CB Insights — AI agent market map featuring CrewAI and LangChain in the infrastructure category 

2. Integration headaches

Integration limitations rank as another top customer pain point.

For one, lack of interoperability poses long-term challenges, as this Cognigy customer notes:

An Artisan AI customer echoes this: “It was a bit of a gamble that we were signing up for a product where they didn’t have quite all the integrations that we wanted.”

Where customers see real value from these tools is when they can support seamless data flow, especially through their existing tech stack. This buyer went with Decagon because of its integrations:

3. Lack of differentiation

More than half of private capital flowing into the AI agent space has gone to horizontal applications — but these markets, like customer support and coding, are becoming highly saturated.

“There’s so many short-term moats, but in the long term there is no moat,” one customer observed. “Whatever you build will be rapidly reproduced.”

In a crowded market, specialization will determine success.

Hebbia, for instance, has tailored its solution to financial players. An exec at a PE firm framed this as a selling point when getting internal buy-in: “When I bring tools to the deal team that live and breathe diligence and deal execution, ensuring that it’s aligned to what they know and understand and [that it] speaks their language is incredibly important.”

While many horizontal AI agents are actively deploying or even scaling their solutions, vertical AI agents remain nascent, with half still in the first 2 levels of Commercial Maturity. 

They’ll gain more momentum this year as enterprises prioritize solutions that are highly tailored to the needs of individual industries. 

CB Insights customers can read our latest interviews with AI agents’ customers here.

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Small teams are getting big payouts.

That’s what the M&A data for 2025 says.

Using CB Insights M&A transaction and headcount data for Q1’25 so far, we found that tech companies acquired for $100M or more had just 100 employees at the median.

Our analysts dove into the tech M&A landscape in a live briefing on March 18 — download the recording here.

We zoomed in on the companies exiting with teams of 100 employees or under, and they’re typically:

• young (7 years old on average)
• bootstrapped (a majority have raised under $20M in equity funding)

See the top 10 by valuation-to-employee ratio below.

CBI customers can explore these 10 startups here.

The top exit by that metric went to Voyage AI, which offers embedding models and ranking tools to improve AI search and retrieval.

With just 19 employees and a price tag of $220M — up 2x since its funding round last September — Voyage AI’s sale to MongoDB equated to $11.6M per employee.

Bon voyage

For MongoDB, Voyage AI represents an opportunity to own more of the AI development process and build customer trust, specifically around output reliability.

Source: CB Insights — MongoDB Q4 FY 2025 earnings call

This is one of the main hurdles to broader AI adoption.

In a December 2024 survey we conducted on AI agents — the clear next evolution for enterprise genAI deployment — nearly half (47%) of respondents cited reliability & security as a top obstacle.

To address this concern, platforms that help businesses organize, maintain, and leverage their data effectively will become even more important.

Acquisition radar

MongoDB isn’t the only one acquiring AI startups to stitch together more unified AI development tools.

Data management giants Databricks and Snowflake have been on AI acquisition sprees, acquiring 5 AI startups a piece since 2023 — more than any other acquirers globally.

The question is: Who’s next?

We expect the next wave of AI M&A targets to be those building out AI agent infrastructure.

Drilling down further, there are 47 startups with teams of 100 employees or less.

The most likely M&A targets, ranked using CB Insights’ Exit Probability, are:

• Letta (agent memory)
• Coval (agent evaluation & observability)
• Fixie (voice AI)

Source: CB Insights — Platform search of AI agent infrastructure startups

Another top contender is Unstructured, in the data curation space — the company has received previous backing from the venture arms of both MongoDB and Databricks.

Customers can unlock the full list of AI agent infra targets here.

RELATED RESEARCH FROM CB INSIGHTS:

• Tech M&A Predictions for 2025
• Databricks wants to be a one-stop shop for AI. Here’s how it plans to get there
• Our 6 predictions for the insurance space in 2025
• State of Venture 2024 Report

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Open-source AI is drawing unprecedented attention from developers and enterprises, driven in part by DeepSeek’s recent model releases.

Cost pressures and demands to improve the performance of generative AI applications are driving enterprise interest in the ecosystem as organizations seek more flexible and cost-effective alternatives to proprietary solutions. 

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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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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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To stay on top, OpenAI has raised billions of dollars in funding and ramped up its hiring into the thousands of employees.

It has also acquired 3 startups in the last year to add new enterprise tech capabilities and talent, as noted in the Acquisition Insights on OpenAI’s CB Insights profile. 

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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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Microsoft’s market cap is hovering at all-time highs as investors have gotten behind its embrace of the technology. Meanwhile, Nvidia joined the club after cruising past the $1T and $2T market cap benchmarks in less than a year on the back of demand for its high-end AI chips. 

Where big tech is putting its money — including investments and acquisitions — provides a window into each player’s strategy. The number of AI deals backed by the group increased 57% in 2023 compared to 2022. Notably, Meta and Apple didn’t invest in any AI startups in 2023, though Meta has been active in developing its own open-source AI models and Apple acquired an AI video compression startup last year.

Below, we map out every AI company backed by big tech and their strategic venture arms in 2023 with key takeaways below.

CB Insights customers can use this search to stay updated on every big tech AI investment, including 2024 deals. 

Key takeaways

1) Big tech continues to pile in on AI infrastructure, nabbing stakes in the next generation of AI startups.

As companies rush to harness AI’s potential, big tech wants to put itself right at the heart of AI value chains.

Some of the largest AI rounds backed by the group in 2023 went to leading foundation models and other development platforms: OpenAI ($10B corporate minority from Microsoft), Anthropic ($2.6B in funding over multiple rounds backed by Google and Amazon), and Databricks ($500M Series I backed by Nvidia’s NVentures with follow-on participation from AWS and Microsoft). 

2) Rivalries in cloud computing drive activity, while some startups bring in multiple big tech backers to remain neutral.

Due to the capital-intensive nature of AI development, startups are linking up with big tech companies like Google, Microsoft, Amazon, and Nvidia to access their cloud infrastructure, chips, and dollars.

In turn, big tech companies are fueling their rival cloud computing and chip businesses.

3) Healthcare and industrials lead big tech’s vertical AI investments.

Nvidia backed 8 AI startups in healthcare & life sciences in 2023, with 7 of these focusing on AI drug discovery, as the chipmaker doubles down on the sector.

Applications in materials development, manufacturing, and warehousing are also drawing the attention of big tech as the sector races to automate operations.

For example, the Amazon Industrial Innovation Fund invested in AI-powered robotics safety startup Veo Robotics in April 2023. Meanwhile, just last month, Microsoft’s M12 and NVentures backed humanoid robotics startup Figure’s $675M Series B. 

4) An emerging focus area is AI companions and agents.

Companies in this category like Inflection are working to make interacting with computers as conversational as it is to talk to people.

A number of companies backed by big tech in 2023, like Adept and Imbue, are building “autonomous agents” — LLM-powered bots that can independently reason and execute tasks.

In this vein, in January 2024, the Amazon Alexa Fund backed a round to AI agent startup MultiOn.

5) Nvidia bursts onto the scene.

Overall, Nvidia has ramped up its activity more significantly — from backing 5 AI startups in 2022 to 32 in 2023 — than any other tech giant as it embeds itself deeper in the generative AI ecosystem.

Apple and Meta, which historically have made fewer investments compared with other giants and do not have venture arms, did not publicly disclose any AI startup investments in 2023. 

Looking ahead

Large acquisitions have been key for big tech to bring in new revenue sources as well as launch new product lines.

But with regulatory pressure hobbling big tech’s acquisition activity, expect these players to lean more heavily on AI partnerships and investments, as well as product launches, in the coming months. 

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In the past 2 years, it has established key partnerships with tech giants like Microsoft, Nvidia, and Google to give its clients access to custom generative AI capabilities, AI toolkits, and more.

Accenture also recently announced it will invest $3B in AI by 2026, including doubling its AI talent to 80,000 employees. As part of this investment, the consulting giant announced the launch of a network of generative AI studios to help its clients deploy the tech.

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The divide is playing out among big tech companies vying to gain an edge in the great AI race.

Meta launched its open-source large language model (LLM), Llama 2, earlier this year, while Google and OpenAI have taken closed approaches so far.

The private LLM developer market is also split.

Proponents of open-source AI — where source code is available for public use — highlight advantages such as:

• Increased model flexibility & customizability
• Accelerated innovation that comes with a large developer community
• Transparency into AI models, data, and code vulnerabilities

But opponents of open-source AI fear it will be misused to fuel cyberattacks, AI-generated hate speech, and more — and highlight closed-source LLMs’ performance edge, additional development resources from deep-pocketed backers, and greater support from the companies that developed them.

While developers debate the philosophy of open vs. closed, early LLM adopters appear to be more interested in the day-to-day practicalities of what companies are offering.

For this buyer we spoke with, OpenAI’s ease of use stands out. But open-source models are attractive for their potential cost-savings.

OpenAI

OpenAI’s developer API and the developer experience is definitely the best. It’s really managed, super clean APIs, well documented, and has the most integrations. There was a big push toward using open-source models and then fine tuning them with your own data. That’s still a big thing. We’re actually evaluating that for some of the public data set stuff because it’s a lot cheaper, especially if you add in more huge amounts of data versus a giant OpenAI model. — Partner, Early-stage VC firm

Read the full transcript here.

This OpenAI customer, who is also considering Hugging Face and Meta’s Llama 2, indicates that open-source may have better ROI, even if performance is weaker.

OpenAI

Meta’s invention… it may be cheaper than the OpenAI [model] because it’s open source. Then we believe the performance of the Hugging Face and also the Llama 2 is also comparable to the OpenAI [model]. Maybe just a little bit weaker than that, but maybe the overall…ROI is quite a good deal. — Cloud, Data & AI Lead at Fortune 500 company

Read the full transcript here.

The open-source AI development market map

Beyond open-source foundation models, a number of vendors have emerged over the years to offer open-source tools for different parts of the AI development process.

For example, in LLM application development, Rasa provides an open-source conversational AI framework. 

Rasa Technologies

We considered several other vendors in this space before choosing Rasa Technologies, such as IBM and Watson, LivePerson, Eva from Amplify, and Sprinklr’s conversational bot solution…We leveraged Rasa Technologies as the natural language understanding (NLU) on the back end…The pros for us of using Rasa Technologies were the price model and our ability to grow with the product, the quality we got for it, and the support. We were able to own our own data and our own environment and set things up how we wanted it. — Senior Manager, Fortune Global 500 company

Read the full transcript here.

Dig in with the full map. 

Our AI research director recently dove into the landscape in our Future of Large Language Models briefing  — download the recording for free here. 

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But OpenAI is only one of the players building the foundational layers for generative AI applications and garnering heavy investor interest. For example, in May 2023, Cohere raised a $270M Series C round while Anthropic secured a $450M Series C round with backing from Google and Salesforce Ventures, among others.

Using CB Insights data, we explore key trends in the LLM developer landscape, including:

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The startup reached a $1B valuation after raising $150M of Series D funding in March 2022. However, the company is reportedly now looking to sell itself for significantly less. Toughening market conditions have put pressure on high-valued unicorns, forcing valuations to contract and driving investors away from late-stage rounds. 

Rapid’s troubles could be a sign of things to come for other highly-valued companies in the API management space.

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Copilots are designed to work alongside humans, automating tedious tasks or providing suggestions to allow users to finish things up more quickly.

There are generative AI-powered copilots emerging for a host of applications, but one of the most prominent is software development. Since 2021, Microsoft-owned GitHub has offered a copilot for coders that taps into OpenAI‘s models, while Amazon recently launched a competing service called CodeWhisperer. But the tech giants face a growing slate of rivals vying for the attention of software developers.

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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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