Tech Trends 2025: Complete CB Insights Report Analysis

Overview: 14 Tech Trends Reshaping Industries in 2025

CB Insights’ annual Tech Trends report has become essential reading for investors, executives, and strategists seeking to understand which technologies are gaining real traction across industries. The 2025 edition identifies 14 trends spanning financial services, healthcare, enterprise technology, AI, and industrials — each backed by proprietary data on funding, M&A activity, commercial maturity, and corporate adoption signals.

What makes this year’s report particularly compelling is the convergence of multiple technology waves. Artificial intelligence — and specifically generative AI — threads through virtually every trend, from wealth management and drug discovery to corporate M&A strategy and robotics. But the report goes beyond AI hype to highlight where technology is creating measurable business impact, where commercial maturity remains nascent, and where the most significant investment opportunities lie.

The trends fall into five sectors: Financial Services (4 trends covering AI advisors, agent commerce, crypto, and fintech M&A), Healthcare & Life Sciences (3 trends on AI diagnostics, RNA therapeutics, and caregiving robots), AI (4 trends on M&A, explainability, open-source dynamics, and the global AI race), Enterprise (2 trends on spatial computing and retail personalization), and Industrials (2 trends on data centers and space investment). This analysis dives deep into the most impactful themes and what they mean for business leaders.

The Cyborg Wealth Advisor: AI Transforms Financial Advisory

The concept of the “cyborg wealth advisor” — where AI augments rather than replaces human financial advisors — represents one of the most commercially mature tech trends for 2025. The data is compelling: senior financial advisors currently spend just 17% of their time with clients, with 70% consumed by back-office and middle-office tasks. Top performers who manage to spend 26% of their time client-facing generate over $1 million in annual revenue, demonstrating the direct revenue impact of reclaiming advisor time through AI.

Morgan Stanley’s wealth management business provides the most prominent validation of this trend. Following deeper AI integration, the firm posted record quarterly net revenue of $7.3 billion in Q3 2024, representing a 14% year-over-year increase. The bank has deployed AI-powered tools for meeting preparation, compliance documentation, and client engagement — demonstrating that AI can create measurable revenue impact in wealth management at scale.

Despite this promise, adoption remains uneven. Nearly half of wealth managers are still in the learning phase with AI, while 62% of firms cite the lack of regulatory guidelines as a top obstacle. This creates a significant competitive advantage for early movers. CB Insights identifies over 50 AI-focused wealth tech startups that have validated their products commercially, with the majority in the “Deploying” stage or later — signaling that the ecosystem of solutions is ready for broader adoption. For deeper context on how financial institutions are leveraging technology, see our analysis of the JPMorgan Annual Report 2024.

AI Agents Are Given Money to Spend

Perhaps the most forward-looking tech trend for 2025 is the emergence of AI agents with payment capabilities — autonomous systems that can reason, make decisions, and execute financial transactions without human intervention for each step. While still nascent, the infrastructure for agent commerce is being built by both startups and established players at remarkable speed.

The fundamental challenge is that current payments infrastructure is designed for humans — and specifically designed to keep bots out. Online payments require human identity verification through bank accounts and credit cards, creating a barrier for autonomous AI systems. Stripe’s Issuing product addresses this directly by allowing developers to generate single-use virtual cards with spend controls that agents can use for purchases.

Crypto is emerging as the first AI payment rail, circumventing the need for human identity verification. Skyfire and Coinbase are building infrastructure for agent-to-agent transactions using blockchain-based wallets. Google CEO Sundar Pichai demonstrated an AI agent performing a shopping return end-to-end at Google I/O 2024, while Amazon has outlined plans for autonomous AI shopping agents where users could “give it a budget.”

Before autonomous spending can scale, however, an “agent trust layer” must emerge. CB Insights identifies identity verification and access management as the first big market opportunity — essentially building the “driver’s license” for AI agents. Companies like Anon (developer toolkit for agent authentication), Okta (async authentication for AI agents), and Skyfire (AgentID digital wallet) are early movers in this critical infrastructure layer. This trend intersects significantly with the broader discussion of decentralized finance and how blockchain technology enables new financial paradigms.

Crypto Takes More Steps Toward the Mainstream

Cryptocurrency has returned to executive conversations in a meaningful way, with quarterly mentions of crypto and related terms on corporate earnings calls rebounding to over 1,200 in Q3 2024. Payments leaders remain “crypto-curious,” with major players including Visa, Mastercard, Stripe, PayPal, and Block all making strategic partnerships, acquisitions, or investments in digital currencies during 2024.

The standout development was Stripe’s $1.1 billion acquisition of stablecoin payments platform Bridge — the largest crypto acquisition by exit valuation in history. Stablecoins, which offer the programmability of crypto with price stability, have emerged as the most practical use case for digital currencies in payments. This acquisition signals that established fintech leaders see stablecoins not as speculative assets but as practical infrastructure for global payments.

However, CB Insights provides an important reality check: the broader crypto sector remains commercially immature. Blockchain startups that raised funding in 2024 lag significantly behind AI startups in commercial readiness, with 27% still in the “Emerging” stage compared to 19% for AI companies. Only 8% of funded blockchain companies have reached the “Scaling” stage, compared to 16% for AI. This data suggests that while crypto infrastructure is advancing, enterprises should carefully vet rather than blindly prioritize blockchain investments.

Compressed Fintech Valuations Create M&A Opportunities

Fintech exit valuations have fallen dramatically from their 2021 peak, and this compression is creating strategic acquisition opportunities for well-capitalized buyers. The median M&A exit valuation for fintech companies dropped from $146 million in 2021 to around $80 million in 2024 — though signs of a rebound are emerging with strong exits like Stripe’s purchase of Bridge.

The picture is even starker when measured per employee: the median VC-backed M&A valuation per employee fell to $1.1 million for fintech companies, below the $1.3 million cross-sector average. This suggests that fintech teams haven’t maintained the productivity or growth metrics that justified higher valuations during the boom years — but the underlying talent and technology remain valuable.

CB Insights identifies over 120 fintech startups with healthy business scores (Mosaic 600+) that show signs of potential growth headwinds (5%+ headcount reduction and no funding in two years). Many of these companies have strong products and teams but may have hit growth plateaus, making them attractive acquisition targets. The report notes that 2024 acquisitions increasingly reflect opportunistic consolidation, with acquirers purchasing cash-burning startups at attractive valuations to capture synergies, as illustrated by Shift4’s acquisition of Revel. This aligns with broader fintech market dynamics.

AI Transforms Disease Management and Early Detection

Artificial intelligence is entering a new phase in healthcare, moving beyond administrative optimization to enable earlier disease detection — sometimes before symptoms even appear. Companies using AI to evaluate patient symptoms saw a surge of activity in 2024, with multiple equity investments and acquisitions after just one deal in 2023. Google Ventures’ investment in Ubie’s Series D-II round signals big tech’s growing interest in this space.

AI-enabled early disease detection startups are targeting a broad range of conditions: Alzheimer’s disease, heart disease, cancer, liver disease, eye disease, and more. Some companies, like Lucem Health (backed by Cedars-Sinai, Mayo Clinic, and AWS), enable providers to mine existing health datasets across entire patient populations to proactively identify high-risk individuals without direct testing. This shift from reactive to proactive disease management could fundamentally change healthcare economics.

CB Insights identifies the highest-momentum startups in this space, led by companies like Whiterabbit.ai (cancer detection, Mosaic 831), Cleerly (heart and lung disease, Mosaic 801), and Qure.ai (tuberculosis and lung cancer, Mosaic 783). These companies have established meaningful business relationships with major healthcare institutions and pharmaceutical companies, indicating that the market is moving beyond pilot programs toward broader clinical deployment.

RNA Therapeutics: Investment Floodgates Open

The RNA therapeutics market has taken off dramatically since 2016, with investment floodgates now fully open. Three leading approaches — RNA interference (RNAi), antisense oligonucleotides (ASOs), and messenger RNA (mRNA) — account for 80% of RNA therapeutics in development. The mRNA technology validated during COVID-19 vaccine development has accelerated investment across all RNA modalities, as researchers and investors recognize the potential to target traditionally “undruggable” proteins.

Alnylam Pharmaceuticals stands as the sector leader, with five FDA-approved RNAi therapies and 15+ programs in its pipeline. Notably, the company is advancing from liver-targeted therapies to brain-targeted treatments for central nervous system diseases, including programs for Alzheimer’s disease, Huntington’s disease, and ALS. This expansion into the CNS represents a fundamental breakthrough in RNA delivery technology that could open entirely new therapeutic frontiers.

The startup ecosystem is equally active. A wave of biotech startups is expanding where and how RNA therapies can be delivered. City Therapeutics raised a $135 million Series A (backed by Regeneron Ventures) for novel RNAi trigger molecules, while Judo Bio focuses on directing siRNA medicines to the kidney for hypertension and endocrine disorders. This diversification of delivery targets — beyond the liver to kidneys, brain, and peripheral nervous system — represents the next frontier for RNA therapeutics investment.

Autonomous Robots Eye Healthcare Caregiving

The U.S. healthcare industry faces a critical staffing crisis: projected shortages of over 139,000 physicians and 63,000 nurses by 2030, coinciding with a rapidly aging population. Advanced robotics are emerging as a potential solution, with applications spanning caregiving support, hospital operations, lab automation, and remote care delivery.

The most futuristic — and potentially transformative — development is the emergence of humanoid robots designed for healthcare applications. Companies like Figure (Mosaic 879), 1X (Mosaic 798), and Agility Robotics (Mosaic 692) are developing highly capable humanoid robots with healthcare and caregiving as target use cases. Tesla CEO Elon Musk has described the Optimus robot’s potential for caregiving: “It can basically do anything you want… babysit your kids, be your friend.” While this vision remains aspirational, the underlying technology is advancing rapidly.

More immediately practical applications are already gaining traction. Diligent Robotics (Mosaic 809) has deployed service robots at Northwestern Medicine, Cedars-Sinai, and Rochester Regional Health that assist clinical staff with non-patient-facing tasks like supply delivery. Histosonics offers automated tissue systems, while EndoQuest develops microrobotic pills for real-time internal imaging. Healthcare leaders should explore partnerships and pilots now to prepare for the next decade of robotic integration.

AI M&A Fuels the Next Wave of Corporate Strategy

AI’s share of corporate tech M&A has doubled since 2020, reaching 7.2% in 2024 — a remarkable acceleration that signals AI is no longer a niche technology bet but a core strategic imperative. Over 300 corporate AI acquisitions occurred in 2024 alone, spanning industries from healthcare to cybersecurity to marketing.

The profile of top AI acquirers has shifted dramatically. In 2020-2021, big tech and gaming companies dominated (Apple, Microsoft, Unity). By 2023-2024, AI infrastructure and data management players lead the pack: Nvidia (7 acquisitions), Snowflake (5), Accenture (4), and Databricks (4). This shift reflects the maturation of the AI ecosystem — from core model development to infrastructure, tooling, and enterprise deployment.

AI chatbots and marketing personalization have emerged as the hottest markets for corporate AI M&A in 2024. Four AI-powered chatbot acquisitions occurred in 2024 year-to-date, driven by enterprises seeking to embed conversational AI into customer-facing operations. Companies like Nvidia, Salesforce, and Thomson Reuters are doubling down on AI acquisitions to embed capabilities across their product offerings. For investors, CB Insights identifies eight AI startups with corporate backers and high M&A probability that could be acquired next, with targets spanning security operations, computer vision, and autonomous coding. This trend connects to broader patterns explored in our coverage of AI enterprise adoption.

LLM Explainability and the Open-Source AI Landscape

Two fundamental dynamics are shaping the AI technology landscape heading into 2025: the growing importance of LLM explainability, and the evolving relationship between open-source and closed AI models.

The “black box” problem — the inability to understand how LLMs arrive at their outputs — becomes increasingly critical as AI enters regulated industries like healthcare, finance, and autonomous vehicles. Anthropic and OpenAI have both published breakthrough research in mechanistic interpretability, identifying millions of specific features inside models and demonstrating the ability to manipulate them. This research is moving from academic curiosity to practical necessity, particularly for medical decisioning (Hims), autonomous vehicles (Telus International), and credit scoring (Equifax).

On the open-source front, the landscape shows a clear bifurcation: closed models dominate at the frontier (GPT-4, Claude, Gemini), while open-source models dominate smaller, more specialized applications. Big tech’s dominant approach is to prioritize closed flagship models while also releasing lighter-weight open models — as exemplified by Apple’s OpenELM, Microsoft’s Phi-3, and Meta’s Llama. This dynamic suggests that the “open vs. closed” debate is less relevant than the question of where each approach creates the most value for specific use cases and deployment contexts.

For enterprises, the practical implication is clear: frontier capabilities will continue to come from closed model providers, but an increasingly capable ecosystem of smaller open models offers flexibility for specialized applications, on-premise deployment, and cost optimization. Companies that develop strategies leveraging both approaches — and that invest in explainability infrastructure — will be best positioned to deploy AI responsibly and at scale.