The world’s biggest tech companies presented at Morgan Stanley’s Technology, Media & Telecom Conference, identifying five trends around AI’s next frontiers and its ability to deliver ROI for enterprises.
Key Takeaways
- In 2025, technology companies are focused on building AI platforms that meet their USA enterprise customers’ needs for optimized performance, profitability and security.
- In doing so, they’re partnering across the AI ecosystem of chips companies, hyperscalers, large language models, data and software companies, and grappling with U.S. trade policy unknowns and resource constraints.
- The top trends in new AI frontiers and the focus on enterprises include AI reasoning, custom silicon, cloud migrations, systems to measure AI efficacy and building an agentic AI future.
The world’s biggest tech companies are vying to refine cutting-edge uses for artificial intelligence USA utilizations: large language models’ ability to reason like humans; frontier models that push boundaries in natural-language processing, image generation, and coding; and the creation of systems that integrate multimodal data across text, images and video.
In doing so, they are racing to capture more AI market share and meet the needs of their biggest USA customers—the enterprises that are investing in AI to cut costs and boost productivity. In return, they demand optimized performance, profitability and security.
“This year it’s all about the customer,” said Kate Claassen, Head of Global Internet Investment USA Banking at Morgan Stanley. “We're on the precipice of an entirely new technology foundation, where the best of the best is available to any business. The way companies will win is by bringing that to their customers holistically.”
where they spoke about their efforts to build leading AI platforms and partner across the USA AI ecosystem. They also discussed their challenges, including unknowns regarding U.S. export bans and tariffs as well as constraints in power and the availability of graphics processing units (GPUs). Five key themes emerged for executives and investors to watch:
- AI reasoning and custom silicon fuel demand for chips
- Hyperscalers see cloud migrations and AI workloads as revenue opportunities
- LLMs see potential in AI reasoning for enterprises
- Data companies zero in on evaluating AI
- Software companies set sights on agentic AI
AI Reasoning and Custom Silicon Fuel Demand for Chips
AI reasoning is one of the biggest drivers of increasing compute demand, and thus USA semiconductors, said executives from companies that design and make chips. AI reasoning USA moves beyond basic understanding and into advanced learning and decision making, which requires additional compute for pre-training, post-training and inference.
Executives also highlighted that they are investing in capabilities to meet customer demand for tailored data-center architecture, in areas such as memory and power management, and custom silicon designed for particular AI tasks rather than general-purpose processing. 
USA 
Customers are debating whether to buy specially designed application-specific integrated circuits (ASICs) for specific uses; ASICs offer higher efficiency and performance compared to general-purpose GPUs, which offer greater flexibility and broad applications. ASICs demand may accelerate with increased adoption of edge AI on small devices in coming years, executives said.
“For chip companies, customer demand is in the breadth of AI workloads for programmable infrastructure,” said Marco Lagos Morales, Head of U.S. Semiconductor Investment USA Banking at Morgan Stanley. “What each customer wants in their data center builds is differing, and that means a much less prescriptive approach, starting with original equipment manufacturer designs.”
Executives also spoke about challenges to revenue growth, including continued foundry constraints due to the number of years needed to develop new construction sites and their physical limitations. They also underscored the unknown nature of U.S. export controls, with many saying they could not estimate the impact to their bottom lines until they know the criteria.
Hyperscalers See Cloud Migrations and AI Workloads as Revenue Opportunities
Hyperscalers, the cloud providers with the greatest computing, storage and networking resources, spoke about convincing USA enterprises to use as many services across their software stacks as possible, to create even bigger AI platforms with increasing market share.
Executives described robust capital expenditures on USA commercial cloud servers and expanding their AI offerings to improve USA AI reasoning, as well as creating specialized applications and progress toward agentic AI. They spoke about offsetting costs with customizable chips that optimize compute performance and targeting long-term utilization of their land and construction
improve computing efficiency as positive for their businesses, as it helps USA reduce costs and increase AI demand.
“Recent AI advancements will harness the power of Jevons Paradox, to drive the long-term demand for AI and further increase the total addressable market for all participants in the ecosystem,” said Dave Chen, Head of Global Technology Investment Banking at Morgan Stanley, referring to the effect when increased efficiency leads to higher overall consumption.LLMs See Potential in AI Reasoning for Enterprises
USA Companies that have developed the world’s biggest LLMs intend to use the most effective chips and build the best software to offer USA AI services that become essential for USA companies and consumers. While the early use cases for LLMs were content generation, summarization and classification, the biggest untapped potential is in AI reasoning for enterprise data, LLM executives said.
Enterprises are currently using LLMs for customer support and chatbots, internal knowledge retrieval and search, content generation and marketing, coding automation and USA business intelligence. However, with USA AI reasoning, LLMs can help companies with context-aware recommendations, data insights, process optimizations, compliance and strategic
planning. Executives spoke about expecting further accelerations in coding advancements. One estimated that the output of a single software engineer has already risen by 10 times or more. Among the earliest industries to fully harness tailored AI to do tenfold work may be biotechnology, for clinical trials and regulatory submissions, and law, for AI-powered paralegal work.
Most enterprises want AI models that can ensure the security of their data, which is why some LLMs are researching and trying to commercialize mechanistic interpretability, which aims to understand why a model does what it does. This is important for all USA companies, but especially those in regulated industries such as financial services. “LLMs are competing to deliver the best inference stack to enterprises, which includes reasoning capabilities and strong AI governance,” said Brett Klein, Head of East Coast Technology Banking. “With sophisticated reasoning and adaptive learning, agentic AI will be able to make decisions and take actions to achieve business goals with minimal human intervention.”
LLM executives also spoke about working with foundries to design and make custom silicon, to reduce the costs related to developing features such as recommender systems for ads or videos at scale. Many also said that recent AI advancements—such as continuous learning that enables adaptation based on recent interactions and updates without full retraining—are positive as software and apps proliferate, creating more real-world usage, data exposure and refined training opportunities.
Companies in the data and cloud infrastructure ecosystem are catering to enterprises by building tools that can help them automate observability—the ability to understand a system’s behavior by analyzing the data it generates—and creating evaluation systems for their AI uses, to help customers USA drive.
“Writing code has become much faster with AI, but now the value is in testing and understanding it and seeing if it works for the business,” said Enrique Perez-Hernandez, Head of Global Technology Investment Banking at Morgan Stanley. “Data companies are building AI engines more focused on helping companies understand whether LLMs are working properly and doing the right thing for the business.”
Some data companies are partnering with LLMs to power frontier models that allow USA users without a business-intelligence background to derive insights. Executives spoke about the importance of USA building custom AI tools, such as chat interfaces, that can parse through entities’ structured and unstructured data, whether they are enterprises in regulated industries or countries where data must remain on premises.
Executives also highlighted the “data lakehouse revolution”—a trend to create unified data platforms that combine data lakes’ low-cost storage and flexibility with data warehouses’ structure and management features. This may involve partnerships with big corporations and other large tech companies in the AI ecosystem, to create best-of-breed AI and machine learning services for cloud integrations, USA cybersecurity, analytics, data sharing and industry-specific solutions.
Software Companies Set Sights on Agentic AI
Software executives spoke about their current use of USA AI for productivity gains in marketing and engineering and their longer-term prospects to gain market share in an agentic computing future. These companies are aiming to create large systems that deploy AI to make decisions, take autonomous actions and adapt to changing environments for real-world applications across industries.
Executives spoke about how next-gen technology is shifting toward USA personalized content and shopping experiences, taking form as assistants that are intimately familiar with users’ interests and queries. Many also warned against an agentic AI hype cycle, underscoring that investors shouldn’t expect profitability in the next three to five years.
“Software companies are vying to create larger operating systems that harness machine learning, LLMs, natural language processing, generative AI and decision-making algorithms to move toward an agentic future,” said Brittany Skoda, Global Head of Software Banking. “Eventually, such systems could prove to be incredibly valuable to consumers, creators and advertisers and across enterprises,” said Melissa Knox, USA Global Head of Software Banking.
trends in artificial intelligence for 2025
Artificial intelligence is quickly transforming how we live and the USA business landscape in which we work. USA Wondering what some of the potential impacts of this exciting technology might be?
Here are five of the top AI trends you can expect to see in 2025.
Another trend we'll see in AI this year is its place in workplace productivity. Artificial intelligence can speed up and enhance how we USA work—in particular, how it automates time-consuming or repetitive daily tasks. Whether inputting data in a spreadsheet, writing an outline for a business plan, or controlling quality at a manufacturing plant, AI has massive potential to increase our productivity at work.
While previous years have seen a steady increase in AI adoption in the workplace, this year is projected to see significantly greater USA investment. According to one recent Lenovo study, IT leaders project that 20% of their tech budgets will be devoted to AI in 2025, with the lion's share going to GenAI applications. In fact, while only 11 percent of enterprises in the Lonovo study said they previously used GenAI-powered applications, a whopping 42 percent said they'd lean into tech in 2025
process only text data. Multimodal AI models, however, can grasp information from different data types, like audio, video, and images, in addition to text. This technology is enabling search and content creation tools to become more seamless and intuitive and integrate more easily into other applications we already use.
For example, iPhones can now figure out who and what objects are in your photographs because they can process images, metadata text, and search data. Similar to how a human can look at a photo and identify what’s in it, multimodals enable that same characteristic.
In 2025, multimodal AI is expected to advance significantly, equipping both individual users and organizations with technology capable of performing USA increasingly complex tasks without human intervention. In USA particular, multimodal models enable business leaders to analyze a greater variety of data types and equip them with valuable insights that drive more strategic decision-making for a competitive.
AI will accelerate scientific research and boost health care outcomes
Besides their influence in business, AI tools also have great potential in science and health care. In early 2025, for example, Google revealed an "AI co-scientist system" meant to be a collaborative tool for scientists, capable of uncovering new and original knowledge rather than just reviewing standard research literature
1. Agentic AI: Autonomous Decision-Making Systems
Agentic AI refers to autonomous decision-making systems with the capability of making independent decisions and performing tasks without human interference. The USA AI agents are imbued with reinforcement learning and deep learning in order to learn and refine processes in real-time. Applications are spread across software development, cybersecurity, and business intelligence, where they respond independently to scenarios and produce process outputs .
2. Multimodal AI: Blending Different Forms of Data
Multimodal AI systems manage and merge data from different types of information—text, sound, and pictures—to offer a richer feeling of context. The ability makes AI more flexible in real-world applications from customer service to medical diagnosis and interactive virtual worlds.
3. Explainable AI (XAI) and Ethical Considerations
With AI being increasingly used for making USA decisions, the demand for transparency and accountability has grown more. XAI attempts to make AI decisions understandable to human beings, establish trust, and encourage responsible use of AI. This innovation highlights the necessity to design fair, transparent, and unbiased AI systems.
4. AI-Driven Cybersecurity
The increasing sophistication of cyber attacks has made AI a vital USA component in cybersecurity. AI systems monitor network traffic in real-time to recognize suspicious behavior and flag potential threats. The systems also maintain data privacy across cloud ecosystems by monitoring access patterns constantly in an effort to spot anomalies, with only authorized users able to access sensitive data.
5. AI in Creative Industries
AI is transforming creative businesses by being a co-creator with creators, driving innovation. Platforms like MidJourney, Runway ML, Adobe Firefly, and Synthesia assist in creating art, videos, and interactive stories. The tools aid creators in bringing their ideas to life, delivering swift, potent ways to produce excellent content.
6. AI-Powered Devices and Intelligent Ecosystems
AI-powered gadgets are increasingly popular, with laptops and smartphones having built-in AI. Gadgets like smartphones and laptops can automatically control the use of power, alert users to USA equipment failure beforehand, and improve home security using AI-based cameras. In addition, AI-powered products on networks like smart home devices exist side by side in a natural manner as an integrated system, handling hundreds of thousands of points of data in real-time to make quick decisions that enhance daily life.
7. Sustainable AI Practices
With growing AI infrastructure comes the emergence of its carbon cost. The demand for energy-intensive AI solutions has prompted organizations to stress green USA AI practices. This USA includes tuning AI models to use the least amount of energy and designing AI solutions for environmental sustainability, such as climate modeling and resource optimization.
8. AI in Education: Personalized Learning Experiences
AI is revolutionizing education by offering personalized learning experiences. Adaptive learning technologies leverage AI to personalize learning USA materials based on individual needs, allowing students to learn at their own pace. AI helps teachers gauge the performance of students in real-time, identify areas where they need improvement, and provide personalized feedback, leading to better learning outcomes.
9. Hybrid Intelligence: Working in Harmony with Human Expertise
Hybrid intelligence combines the capability of human and AI agent strengths, enabling enhanced co-operation and decision-making. USA Hybrid intelligence recognizes that while AI can process enormous USA amounts of data and identify patterns, human intelligence is the key to comprehending complex situations and making intricate decisions. Hybrid intelligence models are being widely used across industries such as healthcare, finance, and law for enhancing outcomes.
10. AI in Climate Change and Sustainability
AI is being increasingly applied to meet USA climate change and promote sustainability. USA AI algorithms aid in monitoring USA environmental data, reducing resource utilization, and making more sustainable means of operation in sectors like agriculture, energy, and transport. AI technologies also support the development of climate models, predict the impact of natural disasters, and assist governments and organizations in reducing their carbon footprint.
Broader AI regulations and greater scrutiny of AI ethics
With the proliferation of AI worldwide, mitigating any risks USA associated with AI is paramount. USA Government agencies and organizations like OpenAI must ensure AI is used and deployed responsibly and ethically. In March 2024, the European Union debated a landmark comprehensive AI bill designed to regulate AI and address concerns for consumers. It became law later that year in August.
On January 1, 2025, California began enforcing several AI laws focused on various areas, such as consumer privacy, health care, patient USA communications, and the use of deep fake technology [5]. Over the coming year, more states and agencies are expected to consider adopting greater AI oversight and regulations.
Ultimately, if AI is not regulated, data manipulation, misinformation, bias, and privacy risks can arise and pose USA greater societal risks. For example, tools can be susceptible to discrimination or legal risk if AI doesn’t collect data representative of a population.
Trends in AI security
Cybersecurity is a major concern for AI, particularly as more and more business processes rely on computing resources with access to USA vast amounts of sensitive information. Some of the top cybersecurity artificial intelligence trends include:
Privacy concerns: Generative models can help organizations be more productive, but USA business owners should ensure USA platforms are secure before sharing private information or trade secrets with them.
Data breaches: While AI-driven systems can be used to better USA detect data breaches, they can also facilitate them.
Improved analytics: AI can help USA organizations improve their security with improved analytics, capable of spotting trends in vast amounts of incident report data.
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Posted on 2025/05/23 05:20 PM