12 New Technology Trends Transforming Business in 2026

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Reignite Growth Despite the Global Slowdown

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Executive Summary: New Technology Trends to Watch in 2026

1. Agentic AI in Operations: Over 1500 startups and USD 2.8 B VC funding in H1 2025; developer adoption of agentic frameworks up 920%.
2. Small Language Models (SLMs) at the Edge: On-device models cut cloud costs by 70%, with Qualcomm NPUs hitting 45 TOPS and Apple’s models generating 30 tokens/sec on-device.
3. AI Governance Platforms & Model Risk Management (MRM): North America holds 33.2% share; EU AI Act drives compliance and lifecycle oversight growth.
4. Energy-Efficient & Hybrid Computing: Advanced cooling cuts power use 40%, while liquid-cooling and hybrid chips improve efficiency per watt.
5. Spatial Computing for Field Work & Training: AR training cuts onboarding time 75% and improves accuracy 33%; hardware affordability accelerates adoption.
6. Polyfunctional Robotics & Fast-Learning Automation: Industrial robotics to reach USD 60.6 B (2030); AI-robotics at USD 77.7 B (2030) with 28.3% CAGR.
7. Disinformation Security & Content Integrity: Detection tools reach 94-96% accuracy; C2PA & CAI frameworks onboard 5000+ members for content verification.
8. Post-Quantum Cryptography (PQC) Readiness: Governments mandate migration by 2026-2030, with AWS and EU adopting hybrid quantum-safe encryption.
9. Data Products & AI-Native Platforms: 65% of firms monetize APIs, 46% to invest in metadata tools to govern AI workflows.
10. Sector-Specific GenAI (Regulated Workloads): 53% of Am Law 200 firms use Legal AI; healthcare AI market to hit USD 187.7 B (2030) at 38.6% CAGR.
11. Advanced AI Hardware & Chip Supply: US CHIPS Act allocates USD 52.7 B, EU funds EUR 43 B; NVIDIA FY 2025 revenue up 114% YoY to USD 130.5 B.
12. Bio-Digital & Materials Crossovers: Bioconvergence market to reach USD 260.3 B (2033) at 7.9% CAGR; synthetic biology USD 35.6 B (2035) at 22.6% CAGR.

Frequently Asked Questions (FAQs)

What technology is trending right now?

Generative AI is trending, with about 78% of organizations using AI in at least one business function. The adoption rose from 33% in 2023 to 71% in 2024.

What is the next big technology?

Agentic AI (autonomous systems that plan and execute multistep workflows) is emerging as the next big technology. Companies such as AWS, Google, and Microsoft are investing in their development.

12 New Technology Trends to Look Forward to in 2026

1. Agentic AI in Operations

Agentic AI systems operate independently across digital workflows and apply reasoning and planning to make decisions based on context rather than follow fixed rules or prompts.

Market Momentum

The agentic AI market is projected to grow from USD 7.06 billion in 2025 to USD 93.20 billion by 2032. This reflects a CAGR of 44.6% over the forecast period.

As of 2025, Prosus mapped more than 1500 agentic AI startups worldwide. In the same period, global venture capital investment in these startups reached USD 2.8 billion during the first half of the year.

Greylock, Sequoia, and Andreessen Horowitz have invested heavily in agentic AI. Accel, General Catalyst, and other venture firms have also backed several startups in this space.

Moreover, Agentic AI is expected to represent 10% of all AI funding rounds this year, which signals growing investor interest.

Agentic AI’s initial five-year (2024-2029) CAGR is 175% and it outpaces traditional generative AI’s 90% CAGR during its first five years (2022-2027).

The recent strategic deals include ServiceNow’s USD 2.85 billion acquisition of Moveworks in March 2025 and NiCE’s USD 955 million acquisition of Cognigy in August 2025.

Key Drivers & Enablers

• Developer adoption of agentic AI frameworks, such as AutoGPT, BabyAGI, OpenDevin, and CrewAI, rose 920% between early 2023 and mid-2025. LangChain and CrewAI appear in more than 1.6 million GitHub repositories.
• Meanwhile, API-rich enterprise systems support real-time execution across CRM, ERP, and analytics platforms to simplify operations and reduce manual intervention.
• Further, rising cost pressures and ongoing labor shortages continue to drive interest in autonomous workflows. The sectors facing supply chain disruptions are adopting these solutions more quickly.
• In parallel, tools that support compliance and observability are strengthening trust in AI decisions. To scale agentic systems, companies need real-time monitoring, audit trails, role-based access, and governance controls.

Spotlighting an Innovator: OpenAI

OpenAI, based in the US, raised USD 40 billion in March 2025 at a post‑money valuation of USD 300 billion. SoftBank led the round with a USD 30 billion commitment, and participation from Microsoft, Thrive Capital, Coatue, and Altimeter. The company offers Operator, which is integrated into ChatGPT as a ChatGPT agent that uses its own browser to perform assigned tasks.

2. Small Language Models at the Edge

SLMs are compact AI systems that run on local or edge devices rather than cloud servers to process tasks with contextual accuracy while using limited compute, memory, and energy.

Market Momentum

The global edge AI market is expected to reach USD 66.47 billion by 2030. It will grow at a compound annual rate of 21.7% between 2025 and 2030.

Meanwhile, the SLM sector is valued at USD 0.93 billion in 2025. It is projected to reach USD 5.45 billion by 2032, with a CAGR of 28.7%.

Developers are optimizing small models to match large language model performance. For example, Microsoft’s Phi-3 Small (7B) scores 75.5 on MMLU benchmarks and outperforms Mistral 7B and Llama 3 8B while maintaining the same inference speed.

Besides, Mistral 7B costs 62.5% less than Llama 3 8B for input tokens and 66.7% less for output tokens, which makes it a practical option for enterprise deployment.

Further, Apple Intelligence runs a 3-billion-parameter model directly on iPhones. It generates 30 tokens per second on the iPhone 15 Pro model, which is trained in 16‑bit and quantized to 4‑bit for efficiency.

Similarly, Qualcomm’s Snapdragon X Elite NPU delivers 45 trillion operations per second of on‑device processing power. This establishes a new benchmark for laptop AI performance.

Samsung’s Galaxy S25 series integrates Google Gemini with on‑device visual AI. With this, users are able to perform real‑time photo analysis and multi‑modal interactions without relying on cloud transmission.

Key Drivers & Enablers

• Regulations such as the EU AI Act and GDPR encourage local processing of sensitive data. The AI Act requires safeguards for high‑risk systems and mandates real‑time monitoring to support human oversight. Moreover, it supplements GDPR with exceptions for biometric identification, bias detection, and real‑time recording in high‑risk contexts.
• Operational efficiency also drives enterprise adoption. On‑device models reduce latency and cut cloud costs in continuous inference workloads.
• Open‑weight platforms enable industry customization. Hugging Face hosts thousands of pre‑trained models across natural language, computer vision, and audio domains. At the same time, Ollama allows organizations to run models locally with full privacy and offline functionality.

Spotlighting an Innovator: SiMa

US-based SiMa raised USD 85 million in August 2025, which brought its total funding to USD 355 million and was led by Maverick Capital with participation from StepStone Group. The company develops hardware and software for Physical AI deployment. Its MLSoC Modalix and Palette suite support vision, transformers, and generative AI within a unified architecture.

3. AI Governance Platforms & Model Risk Management

AI governance platforms provide structured oversight across the AI lifecycle and cover model design, training, deployment, and monitoring to promote transparency, auditability, and compliance.

Market Momentum

IMARC Group projects the AI governance market to reach USD 4.3 billion by 2033. It will grow at a compound annual rate of 36.71% between 2025 and 2033.

Model risk management (MRM) spending is also rising, and the market is expected to reach USD 6.5 billion by 2033, with a CAGR of 13.2%.

The average banks use 175 quantitative models for different activities, from settling loan rates to fraud detection. Moreover, 79% institutions with assets above USD 250 billion often have GenAI live or in the pipeline.

Risk distribution is nearly balanced across high, moderate, and low categories. Current Expected Credit Loss (CECL) models for loan loss, Asset/Liability Management for interest rate risk, and Bank Secrecy Act/Anti-Money Laundering (BSA/AML) models for anti‑money laundering are the most common high‑risk types.

Moreover, the EU AI Act, US AI Bill of Rights, and similar regulatory frameworks in Japan and Canada are driving enterprise demand for governance solutions.

Since August 2, 2025, rules for general purpose AI models, governance structures, and penalties have applied. Organizations deploying high‑risk AI must meet requirements for technical documentation, risk management, and human oversight by August 2, 2026.

Companies are adding policy management, lineage tracking, and bias detection modules to strengthen governance.

Key Drivers & Enablers

• Enterprise accountability is increasing. Boards and compliance teams need visibility into model behavior, data provenance, and oversight mechanisms.
• Additionally, model observability stacks are advancing. Integration of machine learning operations (MLOps) and LLM operations (LLMOps) tools supports continuous validation and retraining under governance requirements.
• Third‑party risk controls are becoming essential. Firms using external or foundation models add vendor audit layers to reduce dependency and protect intellectual property.

Spotlighting an Innovator: CalypsoAI

US-based CalysoAI raised USD 23 million in Series A‑1 funding in June 2023. Paladin Capital Group led the round with support from Lockheed Martin Ventures, Hakluyt Capital, and Expeditions Fund. CalypsoAI develops technology to test, validate, and monitor internal and third‑party AI applications before deployment.

4. Energy-Efficient & Hybrid Computing

Energy-efficient computing involves designing and using computer systems that complete tasks with minimal energy use and reduced environmental impact, while maintaining reliable performance.

Whereas hybrid computing combines different computing models to use their distinct strengths, such as integrating on‑premises infrastructure with cloud services or merging analog and digital components.

Market Momentum

The global green data centers market is projected to reach USD 240 billion by 2030. It is expected to grow at a CAGR of about 18% between 2025 and 2030.

Cloud high‑performance computing (HPC) markets are expanding at similar rates. The cloud HPC market is forecast to reach USD 24.80 billion by 2031, with a CAGR of 16.8%.

In parallel, hybrid architectures are gaining traction, and more than 67% of businesses plan to improve computing agility through cloud infrastructure.

According to the International Energy Agency’s April 2025 report, global electricity demand from data centers will more than double by 2030. And, the demand is expected to reach about 945 terawatt-hours.

Cloud providers are also committing to renewable energy goals. Google aims to run all data centers and offices on renewable energy by 2030. It is also investing over USD 5 billion in 5 gigawatts of clean‑energy projects during the next decade.

Microsoft targets its 100/100/0 commitment by 2030, which means matching 100% of electricity consumption, 100% of the time, with zero‑carbon energy purchases. The Swedish region already uses renewable energy for every hour of consumption.

Key Drivers & Enablers

• The rising AI energy footprint is a major driver. ChatGPT queries on average consume up to 10x the electricity that a Google search does. By 2030, AI‑related data centers could use 3-4% of global electricity, matching the power needs of some nations.
• Sustainability mandates and regulatory pressures are also accelerating adoption. Enterprises face ESG reporting requirements to reduce emissions from digital infrastructure. Carbon pricing encourages investment in low‑emission operations, while net‑zero mandates push renewable‑powered facilities.
• Besides, advanced cooling systems support higher‑density deployments. Precision liquid cooling lowers energy use by up to 40% and reduces water consumption by up to 96%. This also minimizes stranded capacity and improves compute resource utilization.
• Finally, chip diversification enhances task‑specific efficiency. Hybrid compute stacks combine CPUs, GPUs, NPUs, and quantum simulators to improve performance. Intel, NVIDIA, and AMD are integrating hybrid accelerators to deliver better efficiency per watt.

Spotlighting an Innovator: Cerebras Systems

US-based Cerebras Systems raised USD 1.1 billion in September 2025. Fidelity Management & Research Company and Atreides Management led the round. The firm develops wafer‑scale AI chips that shorten training time and lower power consumption compared to conventional GPUs.

5. Spatial Computing for Field Work & Training

Spatial computing combines augmented reality (AR), virtual reality (VR), and mixed reality (MR) with real‑world data to create interactive, 3D environments that support field operations.

Market Momentum

Research and Markets projects the spatial computing market to reach USD 421.2 billion by 2030. It is expected to grow at a CAGR of 21.6%.

The industrial and manufacturing segment led the augmented reality market in 2024. AR applications supported real‑time equipment maintenance, remote assistance, and assembly line monitoring to reduce downtime and improve worker performance.

In 2025, Apple’s Vision Pro and Meta Quest 3 expanded enterprise adoption of mixed reality. The pricing and deployment strategies differed, with Meta Quest 3 launched at USD 500, while Meta Quest 3S lowered the entry point further at USD 300.

Meanwhile, Apple Vision Pro M5 maintained a USD 3499 price point and targeted premium use cases that require high visual fidelity.

Key Drivers & Enablers

• Immersive workforce training is reducing onboarding time. For example, Boeing technicians using HoloLens to guide electrical wiring achieved faster task completion and a 33% improvement in accuracy. The training time per person fell by up to 75%.
• Digital twins combined with spatial computing improve monitoring and predictive maintenance. Integrating IoT data with 3D visualization further enables real‑time asset performance tracking.
• Enterprise adoption frameworks support scalable development. Unity, Unreal Engine, and NVIDIA Omniverse provide tools for building AR and VR solutions.

Spotlighting an Innovator: Varjo

Finland-based Varjo received EUR 12 million in funding from Business Finland. The grant supports Varjo’s EUR 44 million research and development program focused on next‑generation XR training systems. The company builds high‑resolution mixed reality headsets used in automotive, aerospace, and defense for simulation and research and development (R&D).

6. Polyfunctional Robotics & Fast-Learning Automation

Polyfunctional robots learn and perform multiple tasks using adaptive AI and multimodal inputs. These systems combine perception, reasoning, and dexterity to operate in unstructured environments.

Market Momentum

AI‑enabled robotics is expanding faster than traditional industrial robotics. The AI robots market is forecast to reach USD 77.73 billion by 2030, with a CAGR of 28.3% from 2024 to 2030.

Moreover, humanoid robots are emerging as another important segment. The market is expected to reach about USD 18 billion by 2030, with a CAGR of 40% between 2025 and 2030.

According to World Robotics 2025, 542 000 industrial robots were installed in the last year. This is more than double the number recorded ten years earlier, and the annual installations exceeded 500 000 units for the fourth consecutive year.

“The new World Robotics statistics show 2024 the second-highest annual installation count of industrial robots in history – only 2% lower than the all-time high two years ago,”

– Takayuki Ito, President of the International Federation of Robotics.

Key Drivers & Enablers

• Labor shortages and an aging workforce are increasing demand for autonomous and adaptive systems in logistics, manufacturing, and healthcare. In developed economies, geopolitical tensions and trade barriers are also pushing enterprises to explore humanoid robots as strategic AI solutions.
• Rapid training cycles are reducing deployment timelines. Reinforcement learning and simulation‑based training allow robots to acquire new skills faster than traditional programming methods.
• Further, affordable sensors and actuators support wider deployment. The falling prices for LiDAR and tactile sensors make multi‑capable robots more accessible for industries.
• Integration of foundation models improves task generalization. Large multimodal models, like RT‑2 by Google DeepMind, enable robots to perform diverse tasks.

Spotlighting an Innovator: Figure AI

US-based Figure AI raised USD 675 million in February 2025 at a valuation of USD 2.6 billion. The company designs general‑purpose humanoid robots using its Helix AI platform. These robots perform physical tasks in homes and industries to support labor and automation needs.

7. Disinformation Security & Content Integrity

Disinformation security protects digital ecosystems from AI‑generated misinformation, deepfakes, and manipulated media.

Market Momentum

The deepfake AI market reached USD 1.14 billion in 2025. It is projected to grow to USD 8.11 billion by 2030, reflecting a 48.06% CAGR during the forecast period.

Meanwhile, the AI detector market is expected to increase from USD 0.58 billion in 2025 to USD 2.06 billion by 2030, at a CAGR of 28.8%.

According to Deloitte’s Center for Financial Services, generative AI could drive fraud losses in the US to USD 40 billion by 2027. This figure would rise from USD 12.3 billion in 2023, representing a 32% CAGR.

In 2024, 25.9% of executives in a survey reported at least one deepfake incident targeting their organizations. Besides, half of the respondents expect attacks to increase.

Moreover, the EU AI Act mandates content traceability and labeling for AI‑generated media. Article 50 requires that such content be marked in a machine‑readable format and detectable as artificially generated or manipulated.

At the same time, Adobe, Microsoft, Google, and OpenAI have joined the Coalition for Content Provenance and Authenticity (C2PA) and the Content Authenticity Initiative (CAI).

CAI membership exceeded 5000 organizations by 2025, including technology firms, media outlets, camera manufacturers, and civil society groups.

Key Drivers & Enablers

• Generative media are proliferating. Industry data shows that 75% of video marketers use AI tools for video creation, and AI‑generated content is increasingly applied in marketing. As adoption grows, so do reputational risks.
• Brands face financial and legal exposure from misuse of synthetic content, especially in finance, healthcare, and politics. Global deepfake incidents increased 900% between 2023 and 2025. In North America, incidents surged 1740% year‑over‑year between 2022 and 2023.
• Initiatives like C2PA and CAI are building global frameworks to verify digital content origins. Hardware manufacturers are embedding provenance at the source. Canon, Nikon, Sony, Fujifilm, and Logitech have joined these initiatives.
• Multimodal systems combine audio and visual analysis to achieve 92.9% accuracy in controlled testing. Real‑time detection models analyze voice, video, and behavioral patterns to reach 94-96% accuracy rates.

Spotlighting an Innovator: Clarity

Israeli startup Clarity raised USD 16 million in seed funding in February 2024, led by Walden Catalyst Ventures and Bessemer Venture Partners. Its technology runs a security overlay during each stage of the hiring process. It provides recommendations on potential AI‑driven deception directly within enterprise systems such as applicant tracking systems (ATS), human resources information systems (HRIS), and IT service management (ITSM) to aid in preventing insider threats.

8. Post-Quantum Cryptography Readiness

Post‑quantum cryptography readiness focuses on developing and deploying encryption algorithms resistant to quantum computer attacks.

Market Momentum

Markets and Markets projects the global PQC market to grow from USD 420 million in 2025 to USD 2.84 billion by 2030. This represents a compound annual growth rate of 46.2%.

On August 13, 2024, the US National Institute of Standards and Technology (NIST) finalized three post‑quantum cryptography standards.

FIPS 203 introduces a module‑lattice‑based key‑encapsulation mechanism (ML‑KEM) derived from CRYSTALS‑Kyber for general encryption. FIPS 204 establishes a module‑lattice‑based digital signature algorithm (ML‑DSA) based on CRYSTALS‑Dilithium as the primary signature standard. FIPS 205 defines a stateless hash‑based digital signature algorithm (SLH‑DSA) from SPHINCS+ as a backup method.

Meanwhile, the European Union has set a coordinated transition framework. Member States issued a PQC roadmap in June 2025 requiring all nations to begin migration by the end of 2026. The critical infrastructure must complete the transition by 2030.

In addition, investment in quantum technologies increased in 2024. Startups worldwide secured about USD 2 billion in funding, up from USD 1.3 billion in 2023, which marks a 50% rise.

Key Drivers & Enablers

• Recent algorithmic breakthroughs have shortened the timeline for cryptographically relevant quantum computers. Google Quantum AI research published in May 2025 estimates that RSA‑2048 encryption could be broken in less than one week using under one million noisy qubits.
• Government mandates are also shaping adoption. The US National Security Memorandum 10 (NSM-10), issued in May 2022, sets a national strategy prioritizing migration to quantum‑resistant cryptography across federal systems.
• Industry consensus supports hybrid cryptographic frameworks as transition strategies. These approaches combine classical and post‑quantum algorithms to create defense‑in‑depth by requiring adversaries to break both methods simultaneously.
• Major cloud providers have moved from research to deployment of post‑quantum capabilities. Amazon Web Services, for example, has implemented hybrid post‑quantum TLS using the ML‑KEM algorithm, based on CRYSTALS‑Kyber, across services such as AWS Key Management Service, Certificate Manager, and Secrets Manager.

Spotlighting an Innovator: QNu Labs

Indian startup QNu Labs secured INR 60 crore in Series A funding in July 2025. India’s National Quantum Mission led the investment. The company offers quantum‑safe cybersecurity solutions, including Quantum Key Distribution (QKD), Quantum Random Number Generators (QRNG), and Post‑Quantum Cryptography (PQC). These technologies protect critical sectors such as finance, healthcare, telecommunications, and defense from quantum computing risks.

9. Data Products & AI-Native Platforms

Data products and AI‑native platforms represent a shift in enterprise data strategy, where they transform data assets into modular, reusable, and governed products that remain accessible across business units.

Market Momentum

By 2033, the data‑centric AI platform market is projected to reach USD 44.2 billion. It is expected to grow at a CAGR of 23.2% from 2025 to 2033, driven by the rising focus on data quality, governance, and automation in AI initiatives.

Data observability platforms form a key infrastructure layer supporting these efforts. According to DataIntelo, the global market for such platforms is projected to reach USD 9.8 billion by 2033, with a CAGR of 21.3%.

At the Gartner Data & Analytics Summit 2025, highly consumable data products were identified as a strategic trend. Organizations are recognizing the need to deliver assets that remain accessible, reusable, and scalable across business units.

Gartner research also shows that 46% of organizations plan to invest in active metadata tools within the next two to three years. Metadata is viewed as the foundation for AI trust and automated governance.

Meanwhile, AI‑native business applications, including predictive analytics suites and autonomous data agents, are creating new cross‑functional monetization models. In parallel, cloud providers such as Snowflake, Databricks, and Google Cloud are driving platform convergence by embedding data governance and MLOps into unified architectures.

Key Drivers & Enablers

• Decentralized data ownership is advancing through data mesh patterns and product‑based governance frameworks, which enable federated control and reduce organizational silos.
• Streaming pipelines and API‑first design accelerate decision loops and reduce latency in AI systems. The Postman 2025 State of the API Report notes that 65% of organizations generate revenue from APIs to show how well‑designed programs move beyond cost centers to become profit drivers.
• AI lifecycle management supports sustainable automation. Unified observability tracks model drift, bias, and performance metrics. Modern platforms integrate MLOps capabilities across the lifecycle, from development to deployment and monitoring.

Spotlighting an Innovator: Polars

Dutch startup Polars raised EUR 18 million in Series A funding in September 2025, led by Accel and participation from Bain Capital Ventures. Polars provides a Rust‑based data processing library built on the Apache Arrow columnar format. This design enables fast, parallel computation for data infrastructure and analytics.

10. Sector-Specific GenAI (Regulated Workloads)

Sector‑specific generative AI applies domain‑trained models to regulated industries such as finance, healthcare, legal, and public administration to integrate compliance, auditability, and explainability into their design.

Market Momentum

The vertical AI market, which includes industry‑specific solutions, is projected to reach USD 115.4 billion by 2034. It is expected to grow at a CAGR of 24.5%, with North America holding 37.1% of the market. Besides, the US share alone is valued at USD 3.8 billion.

Financial services, life sciences, and legal sectors are driving the adoption of domain‑specific generative AI. The banking, financial services, and insurance (BFSI) sector accounted for 21.5% of the vertical AI market in 2024.

McKinsey estimates that generative AI could add USD 200-340 billion in annual value across banking if use cases are fully implemented.

In legal services, 53% of Am Law 200 firms have purchased Legal AI tools. In addition, 43% report a dedicated budget line for generative AI solutions. Law firms are deploying domain‑tuned models for contract review and case prediction.

The global AI market in healthcare is projected to reach USD 187.69 billion by 2030. It is expected to grow at a CAGR of 38.62% from 2025 to 2030, supported by applications in medical imaging, clinical documentation, drug discovery, and patient monitoring.

By 2027, half of enterprise AI models will be domain‑specific. This trend is driven by demands for accuracy, cost efficiency, and regulatory alignment.

Major technology providers are also advancing domain‑focused platforms. For instance, Google Cloud introduced MedLM in December 2023, a family of healthcare foundation models built on Med‑PaLM 2 and available through the Vertex AI platform.

Key Drivers & Enablers

• Data governance frameworks impose requirements for accuracy and traceability. For example, HIPAA mandates privacy and security protections for healthcare AI systems. At the same time, Basel III sets banking rules for risk management and capital adequacy.
• Domain‑tuned models deliver performance and cost benefits compared to general‑purpose models.

“Small, task-specific models provide quicker responses and use less computational power, reducing operational and maintenance costs.”

– Sumit Agarwal, Vice President Analyst at Gartner

• Explainable AI integrates interpretability layers to support regulatory audits in high‑stakes industries. Under GDPR’s right to explanation, organizations must provide clear reasoning for automated decisions, particularly in loan applications, insurance underwriting, and healthcare diagnostics.

Spotlighting an Innovator: Owkin

France-based Owkin raised USD 180 million from Sanofi in November 2021. The company develops an AI platform that connects data scientists, clinicians, academic researchers, and pharmaceutical firms. It creates global datasets while protecting patient information through federated learning within hospital infrastructure.

11. Advanced AI Hardware & Chip Supply

Advanced AI hardware includes specialized processors, accelerators, and computing systems to manage intensive AI workloads efficiently. At the same time, governments and enterprises are focusing on semiconductor supply chain resilience.

Market Momentum

Coherent Market Insights estimates the global AI chips market at USD 83.80 billion in 2025. It is expected to reach USD 459 billion by 2032, reflecting a CAGR of 27.5%.

Deloitte’s 2025 Global Semiconductor Industry Outlook reports that generative AI chips accounted for more than 20% of total chip sales in 2024, reaching about USD 125 billion.

McKinsey analysis suggests that semiconductors may capture 40-50% of the total value in the emerging AI technology stack. This marks a significant opportunity for value creation.

Semiconductor rebalancing initiatives are reshaping global supply chains. The US CHIPS and Science Act, signed on August 9, 2022, authorized USD 280 billion in new funding. Of this, USD 52.7 billion was allocated to manufacturing, research, and workforce development.

The Boston Consulting Group and the Semiconductor Industry Association estimate that by 2032, the United States will hold 28% of the global market for advanced logic chips. Its share of global fabs is projected to grow to 14%.

Major industry players are also investing in next‑generation AI accelerators. NVIDIA reported revenue of USD 130.5 billion for fiscal year 2025, ending January 2026, reflecting 114% year‑over‑year growth.

Key Drivers & Enablers

• AI compute demand continues to rise. Training frontier models requires resources that have expanded steadily over the past 15 years. Research from the Institute for Progress shows that training compute has increased about 5x each year, leading to a billion‑fold overall growth.

• Heterogeneous architecture is improving efficiency. Hybrid integration of CPUs, GPUs, NPUs, and specialized accelerators optimizes task‑specific workloads across environments ranging from cloud data centers to edge devices.
• Finally, chip supply diversification is reducing risk. Multinational partnerships address geopolitical vulnerabilities and production bottlenecks that became evident during the COVID‑19 pandemic and the subsequent semiconductor shortage.

Spotlighting an Innovator: Axelera AI

Dutch startup Axelera AI received up to EUR 61.6 million in March 2025 from the European Union’s EuroHPC Joint Undertaking under the DARE Project. The company builds AI inference solutions for edge computing using proprietary digital in‑memory computing (D‑IMC) and RISC‑V technology. Its Metis AI platform is integrated into Advantech’s ecosystem, with partnerships that include Lenovo, Dell Technologies, and Advantech.

12. Bio-Digital & Materials Crossovers

Bio‑digital and materials crossovers combine biotechnology, nanotechnology, and computational science to create adaptive materials and bio‑integrated systems.

Market Momentum

According to Market.us, the global bioconvergence market is projected to reach USD 260.3 billion by 2033. It is expected to grow at a CAGR of 7.9% between 2024 and 2033.

Meanwhile, the smart materials market continues to expand. It includes piezoelectric materials, shape‑memory alloys, electrochromic materials, and phase‑change materials (PCMs).

NextMSC reports that the smart materials market reached USD 68.78 billion in 2024 and is expected to grow to USD 115.97 billion by 2030, with a CAGR of 9.1%.

DNA‑based data storage is emerging as a major bio‑digital innovation. It offers a theoretical information density of about 10^18 bytes per cubic millimeter – 10^7 times denser than magnetic tape. It also has the potential longevity of thousands of years.

Europe is also expanding capacity. It operates 479 demo, semi‑commercial, and pilot plants, including 90 dedicated to industrial biotechnology. Additionally, Europe has 2362 biorefineries. They represent about twice the precision fermentation capacity of the US.

Key Drivers & Enablers

• Sustainability goals are shaping production models. Biomanufacturing reduces reliance on petroleum‑based materials and supports circular approaches. Synthetic biology enables biological alternatives to chemical processes, with industries pursuing low‑carbon pathways.
• Computational biology advances are accelerating discovery. AI‑driven protein folding and materials modeling shorten validation cycles. AlphaFold, developed by Google DeepMind, predicts 3D protein structures from amino acid sequences with high accuracy.
• Government and venture support are expanding investment. India’s bioeconomy grew from USD 10 billion in 2014 to USD 165.7 billion in 2024, contributing 4.25% to the national GDP.

Spotlighting an Innovator: Genesis

US-based Genesis raised USD 200 million in Series B funding in August 2023. The company develops the Genesis Exploration of Molecular Space (GEMS) platform, which integrates AI and physics to generate and optimize drug molecules. Its Pearl generative diffusion model supports structure prediction and accelerates drug discovery.

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