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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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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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 data management company has seen a surge in demand from enterprises clambering to take advantage of generative AI. The company’s revenue grew to $1.6B for the fiscal year ending in January 2024 — up over 50% YoY — and it has looked to sustain its growth with product expansion and acquisitions. 

One of the company’s biggest recent moves was its acquisition of MosaicML in June 2023 for a whopping $1.3B (at a 65x revenue multiple). The MosaicML team ultimately helped build Databricks’ open-source large language model (LLM), DBRX, which it released in March 2024. 

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For instance, in training GPT-3, OpenAI relied primarily on a Common Crawl dataset containing 45TB of compressed plain text — several times the amount of text found in the Library of Congress. While OpenAI has not disclosed much information around how it trained GPT-4, it’s estimated GPT-4 uses 10x as many parameters as GPT-3.

Even as these models reach sizes of epic proportions, they’re running out of free internet text to train on. As free text becomes less of a competitive differentiator in the coming years, owning proprietary content sources will become all the more valuable.

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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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Customer data platforms (CDPs) can help marketing and data teams reduce their reliance on third-party cookies to track customer journeys for strategic insights and targeted advertisements. 

Major companies are actively evaluating and investing in CDP vendors. For example: 

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But fortunes began to improve dramatically in 2014 with the arrival of CEO Lisa Su, who has helped channel the company’s efforts toward high-growth areas like AI. Its stock price has since climbed 25x.

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The price is substantial and growing. Data breaches now cost nearly $4.5M on average, up 15% over the last 3 years, according to a recent IBM report.

At the same time, regulations such as HIPAA in healthcare, Payment Card Industry (PCI) standards, and the EU’s GDPR impose hefty fines for companies that do not secure their data properly.

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As concern has mounted, responsible AI has been thrown back into the spotlight. 

Responsible AI is an umbrella term for various approaches and solutions that enable bias detection, fairness, explainability, and compliance throughout the AI development process. 

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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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To do so, organizations need to break down data silos, ensure data consistency across different applications, and allow different departments to easily access the data they require.

Ultimately, boosting accessibility enables enterprises to take full advantage of their proprietary data, by using it to improve operations as well as develop AI applications.

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Implementing effective data quality solutions can also be a source of time and cost savings, given data teams spend up to 40% of their time doing quality checks. 

In the market map below, we identify 67 data quality vendors operating across 8 categories. 

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Companies offering weather risk intelligence solutions help organizations across industries — such as aerospace, agriculture, construction, government, insurance, and logistics — better assess and manage the risk of these weather events to ensure operational continuity and protect their assets. These solutions make use of AI, advanced analytics, and forecasting models, and they’re catching the eyes of investors.

Tomorrow.io recently raised an $87M Series E round after launching its second weather satellite into space. Five other weather intelligence companies — ClimateAi, Reask, Truweather Solutions, Salient, and PlanetIQ — have also raised funding in 2023 so far.

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Vector databases provide enterprises with an easy way to store, search, and index unstructured data at a speed, scale, and efficiency that current relational (and non-relational) databases cannot offer.

This is particularly useful for “similarity searches” — e.g., surfacing images similar to an input image, suggesting similar videos, or personalizing e-commerce product recommendations. Vector databases are seeing emerging applications across industries, from drug discovery to anti-fraud and enterprise search. 

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Data clean rooms offer secure data-sharing environments in which advertisers, brands, and retailers can aggregate customer data from walled gardens (like Meta’s and Google’s closed ecosystems) and first-party sources without compromising customers’ privacy.

Businesses can use this customer data for targeted advertising campaigns with enhanced measurement and first-party data enrichment.

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This achievement is partly attributed to its pioneering work in accelerated computing, which uses specialty hardware to complete demanding software tasks much faster than previously possible.

Initially, this effort was primarily focused on gaming — but NVIDIA’s development of parallel processing in 2006 paved the way for new capabilities in scientific and research applications. This breakthrough allowed multiple computations at a given time instead of sequentially, resulting in much faster processing speeds.

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The company built its business by empowering sales professionals with its flagship customer relationship management (CRM) product. But it has also expanded beyond the CRM through strategic M&A and venture capital investing.

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Most recently, in early April, Redis Labs doubled its valuation to $2B when it raised a $110M Series G that included investors Tiger Global Management, Softbank Group, and Technology Crossover Ventures. 

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