Industry executives and experts share their predictions for 2025.  Read them in this 17th annual VMblog.com series exclusive.

Two and a half years after ChatGPT helped usher in the era of generative AI, enterprises are moving from dabbling to doing as they tap extensive data libraries to customize large language models and create text, image, video and agentic AI applications. 

According to IDC, global enterprise spending on AI solutions is projected to reach $307 billion in 2025, and grow at a 29% compound annual growth rate to $632 billion by 2028. The cumulative global economic impact of AI through 2030 is estimated at a staggering $19.9 trillion, potentially driving 3.5% of global GDP by the end of the decade.

Optimism about achieving a return on investment is growing as early adopters such as Accenture and ServiceNow report significant productivity gains from mining their own data to gain actionable insights.  

The world's industries have a lot of data to work with. The amount of industry stored data is estimated to be in the neighborhood of 120 zettabytes - the equivalent of trillions of terabytes, or more than 120x the amount of every grain of sand on every beach around the globe. 

From humanoid robots to AI agents and sustainable data centers, NVIDIA executives' AI predictions offer a glimpse into a future where AI is deeply integrated into various aspects of our lives and industries:

IAN BUCK
Vice President of Hyperscale and HPC

Inference drives the AI charge: As AI models grow in size and complexity, the demand for efficient inference solutions will increase.

The rise of generative AI has transformed inference from simple recognition of the query and response to complex information generation - including summarizing from multiple sources and large language models such as OpenAI o1 and Llama 450B - which dramatically increases computational demands. Through new hardware innovations, coupled with continuous software improvements, performance will increase and total cost of ownership is expected to shrink by 5x or more.

Accelerate everything: With GPUs becoming more widely adopted, industries will look to accelerate everything, from planning to production. New architectures will add to that virtuous cycle, delivering cost efficiencies and an order of magnitude higher compute performance with each generation.

As nations and businesses race to build AI factories to accelerate even more workloads, expect many to look for platform solutions and reference data center architectures or blueprints that can get a data center up and running in weeks versus months. This will help them solve some of the world's toughest challenges, including quantum computing and drug discovery.

Quantum computing - all trials, no errors: Quantum computing will make significant strides as researchers focus on supercomputing and simulation to solve the greatest challenges to the nascent field: errors.

Qubits, the basic unit of information in quantum computing, are susceptible to noise, becoming unstable after performing only thousands of operations. This prevents today's quantum hardware from solving useful problems. In 2025, expect to see the quantum computing community move toward challenging, but crucial, quantum error correction techniques. Error correction requires quick, low-latency calculations. Also expect to see quantum hardware that's physically colocated within supercomputers, supported by specialized infrastructure.

AI will also play a crucial role in managing these complex quantum systems, optimizing error correction and enhancing overall quantum hardware performance. This convergence of quantum computing, supercomputing and AI into accelerated quantum supercomputers will drive progress in realizing quantum applications for solving complex problems across various fields, including drug discovery, materials development and logistics.

KARI BRISKI
Vice President of Generative AI Software

A symphony of agents - AI orchestrators: Enterprises are set to have a slew of AI agents, which are semiautonomous, trained models that work across internal networks to help with customer service, human resources, data security and more. To maximize these efficiencies, expect to see a rise in AI orchestrators that work across numerous agents to seamlessly route human inquiries and interpret collective results to recommend and take actions for users.

These orchestrators will have access to deeper content understanding, multilingual capabilities and fluency with multiple data types, ranging from PDFs to video streams. Powered by self-learning data flywheels, AI orchestrators will continuously refine business-specific insights. For instance, in manufacturing, an AI orchestrator could optimize supply chains by analyzing real-time data and making recommendations on production schedules and supplier negotiations.

This evolution in enterprise AI will significantly boost productivity and innovation across industries while becoming more accessible. Knowledge workers will be more productive because they can tap into a personalized team of AI-powered experts. Developers will be able to build these advanced agents using customizable AI blueprints.

Multistep reasoning amplifies AI insights: AI for years has been good at giving answers to specific questions without having to delve into the context of a given query. With advances in accelerated computing and new model architectures, AI models will tackle increasingly complex problems and respond with greater accuracy and deeper analysis.

Using a capability called multistep reasoning, AI systems increase the amount of "thinking time" by breaking down large, complex questions into smaller tasks - sometimes even running multiple simulations - to problem-solve from various angles. These models dynamically evaluate each step, ensuring contextually relevant and transparent responses. Multistep reasoning also involves integrating knowledge from various sources to enable AI to make logical connections and synthesize information across different domains.

This will likely impact fields ranging from finance and healthcare to scientific research and entertainment. For example, a healthcare model with multistep reasoning could make a number of recommendations for a doctor to consider, depending on the patient's diagnosis, medications and response to other treatments.

Start your AI query engine: With enterprises and research organizations sitting on petabytes of data, the challenge is gaining quick access to the data to deliver actionable insights.

AI query engines will change how businesses mine that data, and company-specific search engines will be able to sift through structured and unstructured data, including text, images and videos, using natural language processing and machine learning to interpret a user's intent and provide more relevant and comprehensive results.

This will lead to more intelligent decision-making processes, improved customer experiences and enhanced productivity across industries. The continuous learning capabilities of AI query engines will create self-improving data flywheels that help  applications become increasingly effective.

CHARLIE BOYLE
Vice President of DGX Platforms

Agentic AI makes high-performance inference essential for enterprises: The dawn of agentic AI will drive demand for near-instant responses from complex systems of multiple models. This will make high-performance inference just as important as high-performance training infrastructure. IT leaders will need scalable, purpose-built and optimized accelerated computing infrastructure that can keep pace with the demands of agentic AI to deliver the performance required for real-time decision-making.

Enterprises expand AI factories to process data into intelligence: Enterprise AI factories transform raw data into business intelligence. Next year, enterprises will expand these factories to leverage massive amounts of historical and synthetic data, then generate forecasts and simulations for everything from consumer behavior and supply chain optimization to financial market movements and digital twins of factories and warehouses. AI factories will become a key competitive advantage that helps early adopters anticipate and shape future scenarios, rather than just react to them.

Chill factor - liquid-cooled AI data centers: As AI workloads continue to drive growth, pioneering organizations will transition to liquid cooling to maximize performance and energy efficiency. Hyperscale cloud providers and large enterprises will lead the way, using liquid cooling in new AI data centers that house hundreds of thousands of AI accelerators, networking and software.

Enterprises will increasingly choose to deploy AI infrastructure in colocation facilities rather than build their own - in part to ease the financial burden of designing, deploying and operating intelligence manufacturing at scale. Or, they will rent capacity as needed. These deployments will help enterprises harness the latest infrastructure without needing to install and operate it themselves. This shift will accelerate broader industry adoption of liquid cooling as a mainstream solution for AI data centers.

GILAD SHAINER
Senior Vice President of Networking 

Goodbye network, hello computing fabric:  The term "networking" in the data center will seem dated as data center architecture transforms into an integrated compute fabric that enables thousands of accelerators to efficiently communicate with one another via scale-up and scale-out communications, spanning miles of cabling and multiple data center facilities.

This integrated compute fabric will include NVIDIA NVLink, which enables scale-up communications, as well as scale-out capabilities enabled by intelligent switches, SuperNICs and DPUs. This will help securely move data to and from accelerators and perform calculations on the fly that drastically minimize data movement. Scale-out communication across networks will be crucial to large-scale AI data center deployments - and key to getting them up and running in weeks versus months or years.

As agentic AI workloads grow - requiring communication across multiple interconnected AI models working together rather than monolithic and localized AI models - compute fabrics will be essential to delivering real-time generative AI.

Distributed AI: All data centers will become accelerated as new approaches to Ethernet design emerge that enable hundreds of thousands of GPUs to support a single workload. This will help democratize AI factory rollouts for multi-tenant generative AI clouds and enterprise AI data centers.

This breakthrough technology will also enable AI to expand quickly into enterprise platforms and simplify the buildup and management of AI clouds.

Companies will build data center resources that are more geographically dispersed - located hundreds or even thousands of miles apart - because of power limitations and the need to build closer to renewable energy sources. Scale-out communications will ensure reliable data movement over these long distances.

ANDREW FENG
Vice President of GPU Software 

Accelerated data analytics offers insights with no code change: In 2025, accelerated data analytics will become mainstream for organizations grappling with ever-increasing volumes of data.

Businesses generate hundreds of petabytes of data annually, and every company is seeking ways to put it to work. To do so, many will adopt accelerated computing for data analytics.

The future lies in accelerated data analytics solutions that support "no code change" and "no configuration change," enabling organizations to combine their existing data analytics applications with accelerated computing with minimum effort. Generative AI-empowered analytics technology will further widen the adoption of accelerated data analytics by empowering users - even those who don't have traditional programming knowledge - to create new data analytics applications.

The seamless integration of accelerated computing, facilitated by a simplified developer experience, will help eliminate adoption barriers and allow organizations to harness their unique data for new AI applications and richer business intelligence.

Editor's note: The figures on AI solutions spending are from IDC's "IDC FutureScape: Worldwide Generative Artificial Intelligence 2025 Predictions" report, Doc # US52632924, published in October 2024. The data on the economic impact of AI are from IDC's press release titled "IDC FutureScape: The AI Pivot Towards Becoming an AI-Fueled Business,,", published in October 2024.