US Expands AI Compute Access For Businesses

In 2026, American innovation is changing quickly as federal programs lower the costs of specialized hardware. For a long time, only a few tech giants had access to high-performance computing, which made it hard for smaller companies to experiment or build new products. Now, new public-private partnerships are giving startups and mid-sized firms the computing power they need to compete worldwide. As a result, the government is moving from just regulating the industry to actively shaping a more open and accessible tech economy.   

Removing Barriers To Computing For Small Businesses 

A key part of this change is the National AI Research Resource (NAIRR), which has moved from a test program to a permanent part of the US infrastructure. In early 2026, NAIRR increased its support for the private sector, focusing on startups and small businesses. This means new companies can avoid spending huge amounts on GPUs or expensive cloud services. By offering affordable access to top-quality computing, the program helps companies succeed based on their ideas and technology, not just their funding.  

Regional compute hubs are now being set up across the Midwest and South, spreading the AI economy beyond traditional tech hubs. These hubs give local businesses, such as manufacturers and healthcare providers, fast access to powerful computers so they can train their own models on-site. This is especially important for edge AI, where data needs to be processed close to where it is collected for quick results. By placing these resources across the country, the US is building a stronger, more balanced tech industry.  

Protecting Security And Independence For Private Companies 

As more companies gain access to computing power, the government has established sovereign stack rules to protect domestic firms’ intellectual property. The 2026 update to the CHIPS Act now requires secure, isolated environments in public computing centers. This lets businesses in sensitive fields such as defense and biotech use powerful computers without risking their private data. These cleanroom setups help companies stay competitive while leveraging government-supported infrastructure.  

The Department of Commerce has started the American AI Exports Program to help companies bring these secure technologies to international markets. The program makes it easier for US businesses to export complete AI solutions, including the computing systems behind them. By connecting US computing access with trade policy, the government is helping Americans’ standards for safe and ethical AI become the world standard. For startups, this makes it easier and more affordable to grow from local development to global markets.  

The Economic Ripple Effects Of Computational Abundance 

We can already see the effects of greater computing power in how quickly many US industries are working. For example, small energy companies now use shared high-performance computers to run grid simulations that used to take months. In agriculture, startups are using large datasets and vision-language models to identify crop pests more accurately than ever before. These examples show that access to computing is more than just a technical need. It is a key driver of productivity across many fields.  

Wider access to computing is also helping people build new skills. As more companies use advanced models, there is a growing need for workers trained in AI, which has led to new training programs connected to these computing centers. This combination of better technology and skilled workers is fueling ongoing innovation. By making advanced computing more affordable, the US is helping its economy stay strong, even as global supply chains and politics change.  

A New Era of Inclusive Innovation 

Moving forward, shared access to computing is opening up new opportunities in American technology. By making high-performance computing accessible to everyone, the US is helping more people create tools that address both local and global problems. This approach shows that lowering barriers to creativity increases creativity, benefiting the entire economy. As these programs grow, the goal will be to ensure that advanced computing continues to support the goals of all American businesses.  

This report on the National AI Research Resource provides an update on how this program is becoming a lasting national resource to support AI research and innovation in small businesses. 

Source: NSF Artificial Intelligence Research Resource: the NAIRR Operations Center (NAIRR-OC) 2026 

Best AI Compliance Tools for US Businesses (2026)

In 2026, using spreadsheets for managing compliance with laws regarding artificial intelligence (AI) will not only be inefficient but also dangerous. As laws governing the use of AI continue to evolve rapidly in the U.S., more and more businesses are being required to adopt enforced accountability measures. More regulatory entities, especially the U.S. Securities and Exchange Commission (SEC), are increasing their scrutiny of AI-related matters, including transparency and disclosure requirements, as well as risk reporting, forcing businesses to comply with regulations affecting the use of AIs. 

In addition, regulatory frameworks, such as the National Institute of Standards and Technology’s (NIST) Artificial Intelligence Risk Management Framework (AI RMF 1.0), are becoming the accepted standard for implementing responsible AI governance. These frameworks include guidelines based on four key components Governance (G), Mapping (M), Measurement (M), and Management (M) that require businesses to continually monitor and document their use of AI systems. 

Businesses will have difficulty meeting compliance standards due to manual processes, such as manually tracking instances of “Shadow AI” across different functional areas of a business and manually mapping datasets to multiple compliance requirements; thus, making it an impossible challenge without automated solutions. 

This is where tools designed to manage AI compliance come into play. Companies such as Vanta, Drata, and Secureframe are changing how U.S. companies approach governance. By automating the audit workflow, integrating with cloud-based systems, and providing near-real-time visibility into compliance, these companies will change how businesses manage compliance from a reactive to a proactive approach. 

How to Choose an AI Compliance Platform (2026) 

Prior to making any comparisons between platforms, it is important to understand what is actually important in 2026. There are multiple types of compliance platforms available; not all are required for AI-specific compliance or risks. 

1) NIST AI RMF Mapping 

An effective platform should directly align with the NIST AI RMF and provide automated mapping that averages across the four primary functions of the AI RMF: Govern, Map, Measure, and Manage. 

2) Shadow AI Detection 

An increasing risk is employees using unapproved AI applications, which expose sensitive data and use AI safely outside their company. The most approved platforms provide the capacity to log usage and directory. 

3) Real-Time Monitoring and Audit-Ready 

Regulators expect ongoing compliance, and annual auditing is no longer adequate. A compliant platform will include real-time dashboards and automate the collection of evidence. 

4) API Integration 

To be considered compliant, all modern platforms must have robust API integrations with cloud service providers, HRIS systems, version control systems, and data pipeline systems. 

5) Compliance Reporting 

There is increasing scrutiny from organizations such as the Federal Trade Commission, and the terminology used in compliance reporting must produce easy-to-use, exportable reports that clearly show compliance efforts. 

Vanta vs Drata vs Secureframe: 2026 Comparison 

1. Vanta 

Vanta is a leader in automating compliance, especially for startups and mid-size businesses. 

Top features: 

  • Automation for SOC 2, ISO 27001, and AI Governance Workflows 
  • Deep Cloud Platform Integrations such as AWS, GitHub, and Google Workspace 
  • User-Friendly Dashboards For Real-Time Tracking of Your Compliance 

AI Capabilities (2026): 

Vanta’s recent investment in AI includes NIST AI RMF mapping and risk documentation. One of Vanta’s key value propositions is its ability to prepare companies for audit in less time, which will help rapidly growing businesses. 

Limitations: 

  • Less Customization Options for Enterprise Organizations 
  • More Limited AI Risk Modeling Capabilities Compared to Competitors 

2. Drata 

Drata is known for its continuous compliance and enterprise-class functionality. 

Top features: 

  • Real-Time Control Monitoring 
  • Advanced Automation for Evidence Collection 
  • Strong Audit Workflow Reporting 

AI Capabilities (2026): 

Drata has made a significant investment in AI governance functionality through risk-scoring models and automated control mappings aligned with the NIST frameworks, enabling improved insight into overall system-level risks. 

Limitations: 

  • More Time Required to Learn to Use Than Competitors 
  • High Cost For Small Teams Compared To Competitors 

3. Secureframe 

Secureframe is a flexible, scalable, and customizable compliance solution. 

Strengths: 

  • Wide variety of compliance frameworks supported 
  • Intelligent vendor management tools for managing third-party vendor risk 
  • Ability to customize workflows 

AI Capabilities (2026):   

Secureframe is a leader in detecting “shadow AI” and assessing vendor risk, and provides businesses with the ability to track where shadow AI is being used across their entire ecosystem of AI solutions and how those tools interact with sensitive data. 

Limitations: 

  • Complex interface 
  • Somewhat less intuitive/streamlined onboarding process. 
Feature Vanta Drata Secureframe 
NIST AI RMF Mapping Yes Advanced Yes 
Shadow AI Detection Basic Moderate Advanced 
API Integrations Extensive Extensive Extensive 
Ease of Use High Medium Medium 
Enterprise Scalability Medium High High 
Pricing (2026) $$ $$$ $$$ 

Why It Is Important Now 

The regulatory environment is rapidly changing, requiring greater disclosures and transparency from public companies regarding technology-related risks, such as AI-based systems. The U.S. Securities and Exchange Commission’s current guidance suggests these disclosures must be made through regulated filings to provide security holders with complete and accurate information. Along the same lines, the National Institute of Standards and Technology’s ongoing updates to its AI security guidance reinforce this need by promoting the adoption of structured risk management practices for organizations that rely on AI. 

Two industry leaders, Vanta and Drata, assert that automation is no longer a choice but a necessity for businesses to remain competitive in today’s marketplace. Their resources support continuous monitoring to reduce audit fatigue and accelerate compliance preparation. 

Conclusion 

Select Vanta if you are a startup or a small- to mid-sized company looking for a simple, efficient solution. Select Drata if you need more automated processes with enterprise-grade capabilities, or greater insight into your company’s AI-related compliance risk than Vanta provides. Also, select Secureframe only if you require stronger risk detection and management of third-party vendors than either of these two products offers. 

Ultimately, your selection of the best automated compliance solution will depend on your company’s size, the level of risk you are currently exposed to, and the regulatory obligations your company faces. However, as we approach 2026, the only certainty is that manual compliance will no longer be a viable option under any circumstances.

Source-Newsroom 

AI Data Center Cooling: Liquid Versus Air Systems

Air cooling hits a hard limit at 41.3 kW per rack. Above this point, the amount of air required to remove heat exceeds any practical system’s capacity, leading to noise problems and unstable temperatures that engineering cannot fix. Liquid cooling offers better thermodynamics, but the price is steep. Retrofitting costs $2–3 million per megawatt. Deciding between air and liquid cooling affects not only infrastructure budgets, but also competitiveness in AI markets where even milliseconds matter.  

December 2025 update: This year, liquid cooling moved from a cutting-edge option to a standard practice. The data center liquid cooling market reached $5.52 billion in 2025 and is expected to grow to $15.75 billion by 2030. Now, 22% of data centers use liquid cooling, making it a core part of infrastructure. Direct-to-chip cooling leads with 47% market share. Microsoft started rolling out liquid cooling across Azure campuses in July 2025 and is testing microfluids for future use. Colovore opened a $925 million facility that supports up to 200 kW per rack. New AI chips like NVIDIA H100/H200 and AMD MI300X produce over 700 W per GPU, which air cooling cannot handle. As a result, hybrid systems that use both air and liquid cooling are becoming the norm.  

Data centers worldwide consume 460 terawatt-hours of energy each year, and cooling accounts for 40% of that in traditional setups. NVIDIA’s latest GPU roadmap shows power use doubling every two years, reaching 1,500 watts per chip by 2026. Organizations now face a turning point: small improvements to air cooling cannot keep up with the rapid rise in heat. The choices made now will set operational costs for the next ten years.  

Microsoft invested $1 billion to retrofit its facilities for liquid cooling after finding that air-cooled systems could not handle GPT training workloads. Amazon Web Services uses both methods, relying on air cooling for storage and CPU tasks and liquid cooling for GPU clusters. These different strategies show that no single cooling technology fits every need, and making the wrong choice can leave companies with costly unused equipment.  

The Physics Behind It All 

Air holds 3,300 times less heat per unit volume than water under normal conditions. This fact shapes every cooling choice in today’s data centers. To move one kilowatt of heat with air, you need 100 cubic feet per minute (CFM) of airflow with a 10-degree Fahrenheit temperature rise. For a 40-kilowatt rack, that means 4,000 CFM, which is about as fast as the wind in a Category 2 hurricane in the cold aisle.  

Water’s specific heat capacity is 4.186 kJ/kg · K. So just one gallon can absorb as much heat as three thousand cubic feet of air. At a flow rate of 10 gallons per minute, you can cool a 100-kilowatt heat load with a 20-degree Fahrenheit temperature rise. To do the same with air, you need 10,000 CFM, which would be extremely noisy at 95 decibels, and use 25 kilowatts just for the fans. As equipment gets denser, water’s advantage only grows.  

Heat transfer coefficients clearly show the difference. Air-to-surface convection ranges from 25 to 250 W/m².K depending on the air velocity. Water-to-surface convection is much higher, from 3,000 to 15,000 W/m².K, which is about 60 times better and allows for much smaller heat exchangers. When liquid contacts the chip directly through cold plates, the rate exceeds 50,000 W/m².K, approaching the best possible conductive heat transfer.  

Temperature differences amplify these benefits. Air cooling requires a 30 to 40 degree Fahrenheit gap between the incoming air and the component to move enough heat. Liquid cooling works with just a 10 to 15 degree Fahrenheit difference, which keeps the components cooler, reduces leakage current, and makes them more reliable. According to Arrhenius equation modeling, lowering the operating temperature by 10 degrees Celsius can double the component’s lifespan.  

Altitude and humidity also limit the effectiveness of air cooling. For example, Denver’s high elevation lowers air density by 17%, so you need more airflow to get the same cooling. In high-humidity environments, condensation can form when cold air meets warm surfaces, causing serious damage to equipment. Liquid cooling doesn’t depend on surrounding air, so it works reliably anywhere from Death Valley to the Himalayas.  

Air Cooling Technologies and Their Limits 

For 40 years, traditional raised-floor air-cooling was the standard in data centers because it was simple and reliable. Computer room air conditioning (CRAC) units push cold air under raised floors, which then move it through perforated tiles into the cold aisles. Servers pull in this air and release it into the hot aisles. This setup works well for three to five kilowatts per rack, but once loads exceed 15 kilowatts, hot-air recirculation becomes too much for the system to handle, causing cooling to fail.  

Hot-aisle and cold-aisle containment makes cooling more efficient by preventing hot and cold air from mixing. Using plastic curtains or solid panels to separate these zones helps maintain temperature differences, which boosts cooling performance. When done right, containment can cut cooling energy use by 20 to 30 percent and increase cooling capacity by 40 percent. Google’s data centers have achieved a PUE of 1.10 with advanced air-cooling and full containment, demonstrating what’s possible when technology is used effectively.  

In-row cooling places refrigeration units closer to the servers, shortening the air path and reducing fan energy use. Vertiv’s CRV series places cooling units between server racks and can handle up to fifty-five kilowatts per unit. Schneider Electric’s in-row coolers offer similar capacity and use variable-speed fans that adjust to the heat load. This method works well for medium-density setups, but it needs one cooling unit for every two or three server racks, which takes up floor space.  

Rear-door heat exchangers are among the best air-cooling options for higher server densities. These units, which can be passive or active, attach to the back of server racks and cool the hot air before it enters the room. MotiveAir’s chilled door can handle up to seventy-five kW per rack by circulating chilled water. This technology maintains the usual airflow while removing heat at the source. However, installing these exchangers needs careful alignment, and the extra door weight can be a problem for older racks.  

Direct expansion (DX) cooling removes the need for chilled water systems by sending refrigerant straight to the cooling units. This simplifies and improves efficiency for smaller data centers. However, the risk of refrigerant leaks and limited ability to scale have slowed its use. Facebook stopped using DX cooling after leaks led to several facility evacuations and switched to water-based systems instead.  

Liquid Cooling’s Expanding Taxonomy 

Single-phase direct-to-chip cooling is the most common liquid-cooling method today because it is reliable and relatively simple. Cold plates attached to CPUs and GPUs circulate coolant at 15 to 30 degrees Celsius, removing 70 to 80 percent of the server’s heat, while fans remove the rest. AC attacks in the rack CDU system can support 120 kW per rack and include backup pumps and leak detection. This technology needs only minor changes to servers, so it can be added to existing setups without replacing hardware.  

Two-phase direct-to-chip cooling uses refrigerant phase changes to remove more heat. The coolant boils at about 50 degrees Celsius on the chip’s surface, and the vapor carries away the heat. ZutaCore’s waterless DLC cools up to 900 watts per GPU using low-pressure refrigerant R-1234ze. Because boiling is self-regulating, it maintains steady temperatures even when heat loads change. However, the system is complex and refrigerant costs are high, limiting its use.  

Single-phase immersion cooling cools servers by fully submerging them in a dielectric fluid, eliminating the need for air cooling. GRC’s IceRAQ systems use synthetic oil to maintain an inlet temperature of 40 to 50 degrees Celsius. Submerge SmartPod uses a similar method with biodegradable fluids and can handle 100 kW in just 60 square feet. Immersion cooling eliminates the need for fans, reduces failure rates, and enables very high server density. However, the fluids cost $50 to $100 per gallon, and servicing the equipment can be difficult, which slows adoption.  

Two-phase immersion is the most advanced cooling technology available. 3M’s Novec fluids boil at carefully controlled temperatures between 34 and 56 degrees Celsius, keeping component temperatures steady. Microsoft’s Project Natick demonstrated that two-phase immersion can handle heat fluxes of 250 W/cm², which is 10 times higher than air cooling can manage. BitFury uses 160 megawatts of two-phase immersion cooling for cryptocurrency mining, demonstrating that the method can scale up despite the fluids costing $200 per gallon.  

Hybrid approaches combine technologies for optimized cooling. Liquid cooling handles high-power components, while air cooling manages memory storage and networking equipment. HPE’s Apollo systems use this approach with direct-to-chip cooling for processors and traditional air cooling for anything else. The strategy balances performance and cost, but requires managing two parallel cooling infrastructures.  

Moving Ahead Calls for Careful Planning. 

Choosing the right cooling technology is a key decision that impacts all parts of data center operations. This choice shapes how you design your facility, pick equipment, and run daily tasks, and stay competitive for years to come. It’s important to consider not only what you need now, but also how your workloads, regulations, and technology might change in the future.  

Air cooling still works well in certain situations, such as enterprise data centers with moderate power needs, edge sites with limited space, and locations that only occasionally require high power. Because air cooling is a mature technology, costs are predictable, and expertise is readily available. New advances in containment, airflow, and heat recovery help keep air-cooling useful even within its physical limits.  

Liquid cooling is now essential for AI systems, high-performance computing, and any setup with more than 40 kW per rack. Its efficiency becomes even more valuable as energy costs and carbon taxes go up. Companies that switch early benefit from higher density, better reliability, and lower operating costs, which can offset the higher upfront investment.  

Introl guides organizations through cooling technology choices with full assessment, design, and implementation services. Our engineers review your current setup, plan for future needs, and create migration strategies that minimize disruptions. Whether you want to improve air cooling or move to liquid cooling, we offer solutions that balance performance, cost, and risk for your global operations.  

The real question is not if you should use liquid cooling, but when and how to make the switch. Companies that stick with air cooling will see higher costs and lose their edge as workloads grow. Those who adopt liquid cooling now will be ready for a future where high computing power sets leaders apart. The science is clear, so the decision is up to you.

Source: Liquid Cooling vs Air Cooling for AI Data Centers: 2025 Analysis 

Best Agentic AI Platforms for US Enterprises (2026)

The move from passive chatbots to autonomous systems has reshaped corporate digital strategy. In 2026, US organizations will expect AI agents that execute multi-step workflows, interact with legacy software, and make bounded decisions. This shift has created a competitive market for specialized providers and large ecosystem players. Choosing the best agentic AI platforms for US enterprises (2026) requires balancing orchestration capabilities, security, and integration with existing software.  

Leading Platforms for Ecosystem Integration 

Organizations using specific software stacks often choose native agentic solutions. Microsoft Copilot Studio is a leading option for enterprises on Azure and Microsoft 365. Its main advantage is graph grounding, enabling agents to autonomously access data from Teams, SharePoint, and Outlook to perform tasks such as scheduling meetings or synthesizing documents. For integrated internal productivity, Microsoft delivers the fastest time-to-value among the best AI platforms for US enterprises (2026).  

Salesforce has also transformed customer-facing operations with its Agentforce platform. By grounding agents in the Data Cloud, Salesforce enables CRM-native autonomy, allowing agents to research leads, update opportunities, and personalize outreach without human input. The platform maintains a single source of truth and includes a strong trust layer to protect sensitive customer data. For sales- and service-focused teams, it is often preferred to keep intelligence close to revenue data.  

Orchestrating Internal Operations 

ServiceNow AI agents have established a leading role in IT and HR service delivery. They efficiently manage complex back-office workflows, including technical incident resolution and employee onboarding across departments. Integration with the Configuration Management Database (CMDB) offers operational context beyond what generalist models provide, making these agents essential for large enterprises seeking to automate internal processes and service requests.  

The Rise Of Platform-Agnostic Specialists 

Many enterprises prefer not to be restricted to a single ecosystem, which has driven the growth of specialized orchestration-first platforms. In 2026, Lumay emerged as a secure, vendor-neutral operating system for autonomous agents. Its smart flow engine enables companies to build agents that operate across diverse systems, such as linking legacy SAP ERP with modern Slack communication hubs for organizations with fragmented technology stacks. Lumay delivers the integration needed for comprehensive automation.  

IBM Watsonx Orchestrate remains the preferred platform for highly regulated sectors such as finance and defense. IBM’s emphasis on verifiable inference and auditability ensures all agent decisions are logged and accessible to compliance officers. The platform supports hybrid cloud and on-premises deployments, addressing data sovereignty concerns that can hinder AI adoption in the public sector. In 2026, IBM is recognized as the gold standard for governed mission-critical operations among US enterprises.  

Developer-Centric Frameworks for Custom Builds 

High-growth tech firms and organizations with substantial engineering teams are adopting pro-code frameworks such as LangGraph and CrewAI. These tools offer building blocks for complex multi-agent systems, enabling agents to research, critique, and format outputs collaboratively. This level of control supports the development of proprietary IP and customized AI agent behaviors. Although these frameworks require advanced technical skills, they deliver strong ROI through flexibility and the absence of per-seat licensing fees typical of commercial platforms.  

Strategic Selection Criteria for 2026 

Platform selection should address the company’s primary bottleneck. To reduce IT ticket volume, ServiceNow offers unmatched operational depth. For increasing sales velocity, Salesforce’s CRM native intelligence is the most effective. Many enterprises now use a two-tier strategy: ecosystem native agents for routine tasks and specialist platforms like Luna for complex cross-departmental orchestration.  

Leading agentic AI platforms for US enterprises in 2026 stand out for their ability to manage exceptions effectively. Instead of failing on errors, top systems involve a human supervisor with a concise summary of the issue. The human-in-the-loop capability has enabled autonomous agents to become integral to business operations. As the market matures, reliability and security of execution are now prioritized over the novelty of autonomy.  

In 2026, organizations must view AI as a digital workforce rather than a feature. Leading US enterprises treat agent platforms as long-term infrastructure investments, not short-term productivity tools. With scalable solutions from Microsoft and Salesforce and specialized governance from IBM and Lumay, building an autonomous enterprise is now achievable. Success depends on aligning platform strengths with the organization’s unique operational needs. 

Sources: Latest in artificial intelligence 

Oracle and AWS Collaborate to Expand Multicloud Networking

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Best HBM Memory Vendors for AI Systems (2026)

High-bandwidth memory (HBM) is a modern form of DRAM that stacks chips and uses wide connections to achieve very high data rates. Because it is compact and energy-efficient, it handles large datasets well.  

Industries such as artificial intelligence, gaming, data centers, and advanced graphics use HBM to achieve faster computing, improved performance, and lower power consumption. This article highlights the main companies driving HBM technology around the world.  

The Big Three HBM Manufacturers 

The following are the top three high-bandwidth memory companies, often known as the Big Three.  

  1. SK Hynix 

Based in South Korea, SK Hynix leads the global HBM market and is expected to retain over 50% of the market share, which stood at 62% in Q2 2025  

SK Hynix became a leader by starting early with stacked DRAM design and building a strong partnership with Nvidia, which uses SK Hynix’s HBM3E and HBM4 memory in its AI accelerators.  

In early 2025, SK Hynix completed the world’s first 12-layer HBM4 samples and plans to begin mass production later that year. HBM4 offers over 2 TB of bandwidth and employs advanced techniques such as MR/MUF to enhance cooling and stability.  

With HBM demand expected to grow by about 30% each year until 2030, SK Hynix is investing heavily in new memory factories and research to maintain its market leadership.  

  1. Micron Technology 

Micron Technology, based in the United States, entered the HBM market after its Korean competitors, but has quickly caught up. By Q2 2025, Micron’s market share reached 21%, putting it ahead of Samsung Electronics and demonstrating its growing influence in the industry. Micron sent HBM4 36 GB 12HI samples to key customers for next-generation AI platforms. Made with its advanced 1b DRAM process, HBM4 has a 2048-bit inference interface, data rates over 2 TB, and is more than 20% more power-efficient than HBM3e.  

Micron also provides HBM3E12 high memory for NVIDIA’s Blackwell and AMD’s MI350 platforms. The company plans to boost HBM4 production in 2026 to support its customers’ new AI system launches.  

  1. Samsung Electronics 

Samsung Electronics remains a major player in the HBM industry, using its large manufacturing capacity and advanced processes to stay competitive. In Q2 2025, Samsung held 17% of the HBM market.  

Although Samsung dropped to third place in Q2 2025, it is using its manufacturing strengths to try to catch up. At SEDEX 2025, Samsung presented its sixth-generation HBM (HBM4) products, highlighting their high speed.  

With HBM4 rolling out on a large scale, analysts believe Samsung’s market share could rise to over 30% by 2026.  

Leading Companies Using HBM Technology. 

Here are some top companies making the most of HBM technology.  

  1. Advanced Micro Devices (AMD) 

Advanced Micro Devices (AMD) has quickly adopted new memory technologies to improve computing efficiency. It was one of the first to use HBM in mainstream products, starting with early Radeon graphics cards, and continues to improve stacked memory in its latest data center solutions.  

AMD’s Instinct MI300 accelerator family demonstrates the importance of HBM for high-performance computing. The MI300A model combines CPU and GPU cores in a single package, with 128 GB of HBM3 memory and a peak bandwidth of 5.3 TB.  

The MI300X, designed for AI and high-performance computing, increases memory to 122 GB of HBM3, making it one of the largest memory setups in the industry today.  

  1. NVIDIA Corporation 

NVIDIA is central to global HBM demand because its AI accelerators require substantial memory bandwidth to run thousands of GPU cores simultaneously. The company uses HBM3 and HBM3E technologies to meet these needs.  

The NVIDIA H100 Tensor Core GPU, widely used in AI and cloud systems, uses HBM3 stacks. The newer H200 adds HBM3E for even faster data. SK Hynix mainly supplies these memory stacks for NVIDIA, and Samsung Electronics is expected to provide more as production grows.  

  1. Intel Corporation 

Intel’s use of HBM shows how important memory bandwidth is for different types of computing. Instead of relying solely on parallel processing like GPUs, Intel combines x86 CPUs, Xe GPUs, and AI accelerators, all of which benefit from faster on-package memory.  

The HBM Future and Market Trends 

The HBM market is evolving quickly as technology advances. These changes are pushing high-bandwidth memory companies to explore new opportunities. Here’s what’s happening:  

  • HBM4 and beyond: high bandwidth memory has entered a new phase. In April 2025, JEDEC released the HBM4 standard, which features a 2048-bit interface and transfer speeds of almost two TBs per stack. HBM4 doubles the throughput of HBM3E and offers better energy efficiency and scalability for AI and data centers.  
  • Continued expansive demand drivers: HBM is now used beyond just GPUs. It’s being adopted in AI accelerators, ASICs, and high-performance CPUs, all of which require fast, low-latency data handling. Analysts expect HBM shipments to exceed 30 billion gigabytes in 2026, driven by growth in AI infrastructure projects.  
  • Market outlook: The future looks bright for the HBM industry. SK Hynix predicts a strong 30% annual growth rate through 2030, and the HBM market reaching several billion dollars as demand for AI training and inference grows.  

HBM’s future is tied to the fast progress of AI data center technology and new packaging methods.

Sources: What’s New 

Sk Hynix

Find what you need through Micron.com.

NVIDIA Rubin Versus Blackwell: AI Performance Compared

The Rubin platform launching in 2026 signals a major shift in machine intelligence. Instead of focusing only on training power, the industry is now moving toward efficient large-scale inference. Robin builds on its predecessor’s achievements in trillion-parameter models by streamlining the data pipeline for agentic AI. The new design treats the data center rack as a single computing unit, leveraging advanced memory and fast connections to eliminate legacy bottlenecks. To really understand how Nvidia Rubin compares to Blackwell in AI performance, it’s important to look closely at the hardware improvements that change how tokens are generated and processed at scale.  

Architectural Foundations: Transistor Density And Process Nodes 

The main difference between these two architectures starts with the silicon. Blackwell used a custom 4NP process to fit 208 billion transistors into a dual die design. Rubin almost doubles this with 336 billion transistors made using TSMC’s advanced 3NM (N3) process. This extra complexity makes room for more specialized logic units, especially in the Tensor cores, which handle most of the matrix multiplication. As a result, Rubin can run many more operations at once without using more power.  

The 2026 architecture goes further than just increasing transistor count. It adds third-generation transformer engines that support NVFP4, a four-bit floating-point format. This doubles inference speed compared to the eight-bit precision used before. Blackwell started using low-precision training, but Rubin improved this for the reasoning phase of AI, where models handle longer chains of thought. Thanks to these hardware upgrades, companies can run more complex models without using much more energy or hardware.  

Memory Subsystem: HBM4 and Unprecedented Bandwidth 

Memory bandwidth has often limited AI performance, especially as models now use million-token context windows. Blackwell systems used HBM3e memory, offering up to eight TBs of bandwidth and 192 GB per GPU. Rubin goes even further, using BioRubin HBM4, which provides 22 TB of bandwidth and 288 TB of capacity. This 2.75 times speed boost helps avoid the memory wall that can slow large language models during inference.  

Switching to HBM4 lets the NVIDIA Rubin versus Blackwell comparison focus on goodput, which means the real productive work a system does. With 288 GB of fast memory per chip, the Rubin GPU can store larger portions of a model’s KV and cache them locally. This reduces the cost of data transfers between GPUs, thereby reducing delays in real-time tasks. For teams using mixture of experts (MOE) models, this large memory pool means that routing decisions occur in microseconds rather than milliseconds.  

Interconnect Evolution: NVLink 6 and Rack-Scale Coherence 

Communication between chips is another key part of the 2026 performance upgrade. Blackwell used NVLink 5, which gave each GPU 1.8 TB/s of two-way bandwidth. The new Rubin GPUs use sixth-generation NVLink, raising this to 3.6 TB/s. This faster connection is important for NVLink 72 rack-scale systems, where 72 GPUs work together as one large computing unit. With double the interconnect bandwidth, most enterprise workloads no longer experience the usual distributed computing shadows.  

System-Wide Integration: The Vera CPU Advantage 

One major change in 2026 is the new Vera CPU, which replaces the Grace CPU used in Blackwell systems. Vera is built to manage the step-by-step reasoning and data tasks needed by autonomous agents. It connects directly to Rubin GPUs via 1.8 TB of NVLink, eliminating the PCIe bottleneck. This close connection enables the CPU to handle checkpointing and data preparation without interrupting the GPU’s intensive training or inference.  

Inference Efficiency and Token Economics 

For enterprises in 2026, cost per token is a key metric. NVIDIA says the Rubin platform can cut inference costs by up to ten times compared to Blackwell-class systems. This improvement comes from using disaggregated inference and the NVFP4 precision. By running the prefill and decode phases on hardware designed for each task, Rubin uses energy more efficiently. As a result, companies can now run advanced reasoning models that were previously too costly to operate at scale.  

Training is now much more efficient, as the new platform requires only 1/4 as many GPUs to train a diverse set of expert models. Using less hardware lowers AI factory costs and makes it easier to manage cooling and power. Developers benefit from faster iteration and can test bigger models in the same amount of time. According to NVIDIA Rubin versus Blackwell, all performance comparison analysis, the 2026 architecture is designed for a future where AI is always available, not just a tool.  

Future-Proofing the AI Factory 

Looking ahead to 2027, choosing between these platforms depends on your long-term goals. Blackwell is still strong for standard training and established LLM workflows. Rubin, on the other hand, is built for the next wave of AI, including agentic AI and large-scale reasoning. With liquid cooling and exascale performance, Rubin is set to power the next generation of AI super factories. The right choice depends on whether your organization is focused on current needs or preparing for more complex autonomous workflows in the future.  

Moving from Blackwell to Rubin is more than a simple hardware upgrade. It is a complete redesign of the AI compute stack. The 2026 platform doubles memory bandwidth, increases transistor density, and improves low-precision inference, setting a new standard for private and public clouds. The last generation showed that AI could scale, but this one shows it can also be efficient, secure, and cost-effective worldwide. This leap in technology means the 2026 infrastructure is ready to support the next decade of AI progress. 

Source: Data Centers for the Era of AI Reasoning 

AI Repatriation Strategy: Moving From Cloud To Sovereign Infra

Cloud repatriation is increasingly common in enterprise IT. Most of us have heard the saying, “Data has gravity.” It’s used so much in tech that it’s almost a cliché. Still, the main point stands out: once your data is in the cloud, moving back on-premises is tough.  

More companies are finding strong reasons to move their data back from public clouds to on-premises systems. High costs, sometimes sixty-five to seventy percent more than on-premise data sovereignty issues, and the need to keep data close to AI projects are making organizations realize the cloud is not always the best choice.  

Here are the main reasons enterprises are moving their data back on-premises, along with tips for making the migration smooth and affordable  

Cost 

Cost is the primary driver of cloud repatriation. On-premises storage can be 65-70% cheaper than public cloud storage over 5 years. These numbers are real and are changing how companies plan their IT.  

Let’s take a look at a real example.  

Suppose your company needs to store 10 PiB (petabytes) of data starting from 0 and adding the same amount each month for 5 years. That means you’ll reach 10 PiB after 60 months. A quick calculation shows:  

  • You would add about 170.6 TiB each month.  
  • At the standard storage price of $0.021 per GB per month, this adds up to $6.7 million over five years.  

This is much more expensive than on-premises storage, even after accounting for space, power, cooling, and management.  

And these clouds, these cloud costs don’t even include extra fees like:  

  • API charges,  
  • minimum object size charges,  
  • and retrieval charges from lower-cost storage classes (both per-object and per-GiB charges).  

On average, these extra fees make up about 8% of a monthly cloud storage bill. You don’t have these charges with on-premise storage, so cloud storage really does cost more.  

You might wonder about discounts. Usually, enterprise customers can get fifty percent or more off the list price for on-premises infrastructure compared to only ten to thirty percent of public cloud pricing.  

Then there are egress fees, which are often the most debated part of cloud pricing. You pay these fees whenever you move data out of the cloud or use it with outside services. If you want to analyze your cloud data with another provider’s AI tools, you’ll pay. If you move data between clouds for backup, you’ll pay again. If you combine cloud data with on-premises data for analytics, the costs keep adding up.  

Cloud providers often say they use a cost-plus model where you pay for the operating costs you use. That sounds fair, but it doesn’t apply to egress charges. The cost of data entering the cloud is the same as the cost of data leaving the cloud. We all know these charges exist in the cloud, but not on premises.  

The Sovereignty Imperative 

The second main reason for cloud repatriation is data sovereignty. This term covers many concerns. For example, when your data is in the cloud, do you know exactly where it is stored? Some cloud providers let you choose a region or a facility, but not the specific location. Usually, all facilities in a region are located in a single US state or county. Still, could your data end up outside your country if it travels over a network that passes through a country you want to avoid? Would you know if your data is governed by the laws of your business’s country or the country where the data is stored? There are many unknowns, and for those responsible for company data, these uncertainties may not be acceptable.  

What we do know is that cloud providers will hand over your data if they receive a court-ordered subpoena, as long as the country where your data is stored follows the rule of law. If not, anything can happen. You don’t need to debate whether cloud storage has backdoors, security flaws, or if providers access your data without permission. These are real concerns and sometimes lack evidence. For example, Google reads your email (see Google’s privacy policy), but only for security and spam protection.  

Making the Move: Practical Repatriation 

Moving large datasets can feel overwhelming, but today’s tools and standards make it much easier. There are three key principles for a successful repatriation.  

First, use the available migration tools. Cloud providers actually offer advanced utilities to help move data both into and out of their platforms. For example, AWS DataSync is built for large-scale data transfers and works efficiently. These tools can make the migration process much simpler.  

Second, use standard APIs. Amazon’s S3 API is the most common standard for cloud storage, and even other cloud providers support S3-compatible interfaces. On-premises solutions like Cloudian HyperStore also use these APIs. Because of this standardization, applications can easily move between environments with minimal cloud code changes, often just updating the endpoint URL.  

Third, plan for egress fees. Many cloud providers now have policies that waive these charges for customers who are moving their data out. If you don’t qualify for these policies, a quick return-on-investment check usually shows that the migration costs are offset by future savings.  

One of the biggest benefits of cloud repatriation is that your applications stay compatible after moving data to S3-compatible on-premise storage. Your existing applications can use it right away. You don’t need major rewrites, since the same APIs work both in the cloud and on-premises.  

Best Practices 

  • Use native migration tools (e.g., AWS DataSync).  
  • Stick to S3-compatible storage for seamless app portability.  
  • Negotiate waived egress fees. Many providers offer them for departures.  
  • Map workloads to cost/resilience tiers in advance.  

Regional Cloud Service Providers: Your Opportunity Awaits 

This trend opens up big opportunities for regional cloud providers. Local providers can give enterprises the simple operations they expect from public cloud while also addressing cost and data sovereignty issues that global providers often can’t.  

Regional providers can stand out by offering repatriation as a service, including migration costs in new hosting contracts. Since no application code changes are needed and migration tools are easy to use, this is a strong market opportunity for providers who focus on value instead of just size.  

Mission Possible! 

Cloud repatriation isn’t about turning away from innovation. It’s about making smart choices that fit your long-term business goals. Lower costs, better data control, and an easier migration all make a strong case for bringing your data back in-house.  

Moving data can be challenging, but it’s possible. With the right strategy, tools, and partners, companies can move away from public clouds and build data systems that meet their needs rather than just following what vendors offer. 

Source: Cloud Repatriation: Moving Your Data from the Cloud to On Premises 

US AI Chip Policy Update: What It Means for Startups

In 2026, the rise of American AI leadership has made regulations more complex, changing how new companies get the chips they need to survive. While most attention is on exporting high-end GPUs like the NVIDIA Blackwell series, the US AI chip policy update and what it means for startups looks at important changes in both domestic supply and international trade that affect young tech firms. Starting in April 2026, the US Department of Commerce launched a global gatekeeper system. This new approach replaces the old blanket bans with a more targeted tiered review process. The update is more than just a trade policy. It changes the competitive landscape for startups working at the cutting edge of AI.  

The Tiered Threshold: Navigating the New Performance Gaps 

The Bureau of Industry and Security (BIS) released new 2026 guidelines that set clear technical terms for chip exports. Chips with a total processing performance (TTP) below 21,000 and DRAM bandwidth under 6,500 GBs, like the Nvidia H200, now face a case-by-case review instead of an automatic denial. This change aims to keep high-end hardware available to US startups and to allow controlled sales to international markets. For founders, this means mid-tier chips are easier to access for global growth as long as their businesses meet strict security standards.  

On the other hand, the most advanced chips, such as the Blackwell class, are still banned for sale in certain countries for at least 2 years. This gives US startups a clear advantage in training large AI models compared to companies overseas. However, the policy also imposes a 25% tariff on some high-performance chips sent abroad for repair or replacement. Startups now need to plan for potential price increases in their 2026 budgets and schedules, especially if they use global data centers.  

Regulatory Moats: Compliance as a Competitive Edge 

In 2026, a startup’s approach to regulations is just as important as its technical plans. The new chip policy states that any company ordering more than 1,000 high-end GPUs must undergo a review process with major disclosure requirements. Investors now see a founder’s ability to handle BIS approvals as a sign of maturity, especially in Series A and B rounds. Startups that make compliance a core part of their operations are having an easier time getting the limited sovereign compute resources from the US government.  

The policy update also introduces the American AI Exports Program, which prioritizes the provision of full technology packages for international sales. Startups that join groups focused on hardware, data pipelines, and security can gain faster access to federal funding and export licenses. This fast track helps new companies that support the spread of American AI standards. For young startups, joining a trusted group can turn a regulatory challenge into a real advantage when entering markets in allied countries.  

Operational Impacts: Supply Chains and Lead Times 

Although the policy is meant to support domestic growth, the need for individual reviews is delaying early-stage companies’ access to the hardware they need. Founders say funding rounds now take 30 to 45 days longer because venture capital firms are bringing in legal experts to check the startup’s hardware supply chain. The US AI chip policy: what it means for startups recommends keeping flexible procurement contracts to handle these changing rules. Startups should also be prepared for situations in which federal and state regulators may not agree on how to oversee AI.  

The Cost of Sovereignty: Tariffs and Domestic Manufacturing 

One of the main challenges in the 2026 update is a 25% revenue-sharing rule for some high-end chip exports, which amounts to a global tax on American computing power. US data centers mostly do not have to pay these tariffs, but the secondary GPU market is seeing higher prices. Startups are facing 15-20% higher legal and operational costs just to comply with new regulations. This sovereignty tax is meant to ensure that American-made chips do not end up strengthening the military power of rival countries.  

To help with these extra costs, the Department of Commerce is considering a tariff offset program to support domestic manufacturing and research. Startups using chips made at local AI factories could soon qualify for tax credits or direct subsidies to lower their total costs. This gives founders a good reason to keep their main computing sources in the US. For those building local-first or air-gapped AI systems, these incentives could offer a real financial boost and help them compete with bigger tech companies.  

Adapting to the Global Gatekeeper Era 

The US AI chip policy update and what it means for startups show that the days of scaling AI hardware globally without permission are over. To succeed now, startups need both technical skill and an understanding of global politics. Founders should treat chip allocation planning as carefully as they do system design, ensuring they follow evolving federal rules. By being open and meeting the 2026 requirements, startups can get the resources they need to build new intelligent systems. Those who see these rules as a foundation, not just obstacles, will help create a safer and stronger AI economy. 

Source: Office of Science and Technology Policy 

SLM Versus LLM: Which AI Model Improves Enterprise ROI?

As generative AI rapidly advances in 2026, companies began focusing less on model size and more on the value these models deliver. Large language models (LLMs) like GPT-4o and Claude 3.5 first drew attention for their creative abilities. But small language models (SLMs) have become the practical choice for many businesses. Companies are realizing that using huge models for specialized tasks often costs more than it’s worth. Now, the question of whether SLMs or LLMs offer better returns is at the heart of digital transformation plans. To succeed, organizations need to carefully weigh cost, speed, and how well a model fits their specific needs.  

The Architectural Divide: Breadth Versus Depth 

LLMs act as generalists in the digital world, trained on vast amounts of diverse data, so they can handle tasks ranging from writing poetry to solving complex coding problems. Their large size, often with more than one hundred billion parameters, gives them the ability to reason through creative or unclear situations. However, this strength also means they can be slow and expensive to use for broad tasks like market research or brainstorming across a company. LLMs are extremely versatile. They work well as the main brain for jobs that need a wide understanding of human context.  

SLMs, on the other hand, are designed for specific tasks and usually have fewer than ten billion parameters. They are trained on carefully selected high-quality data to excel at tasks such as legal review or medical transcription. Since they are smaller, SLMs can run on regular company servers or even on edge devices, eliminating the need for costly GPU clusters. When it comes to enterprise ROI, SLMs often outperform LLMs for routine structured work. They may not write a screenplay, but they can process thousands of invoices quickly and accurately.  

The Financial Math of Model Selection 

Inference costs are a major reason why more companies are choosing smaller models in 2026. Running a million customer service queries on a top LLM can cost thousands in API fees, while using a distilled SLM for the same task is much cheaper, sometimes reducing costs by up to 100 times. This makes it possible for companies to use AI in every department without blowing up their budgets. For businesses with large amounts of data, the lower total cost of ownership makes SLMs the best choice for long-term profitability.  

Performance And Reliability In Production 

Speed is key to user experience and efficiency. In 2026, businesses, SLMs respond almost instantly, often in just milliseconds, while larger models can take several seconds. This quick response is crucial for real-time applications such as voice assistants or fast fraud detection. When systems are used right away, more people use them, and business processes speed up. This time savings leads to better productivity and a higher return on investment.  

Reliability is another reason SLMs often outperform LLMs in terms of enterprise ROI. LLMs can make mistakes or give wrong answers when asked about specific company data they have not seen before. SLMs trained on a company’s own data operate within a predefined knowledge range. This greatly lowers the chance of errors or confusing answers. In regulated fields like finance or healthcare, this predictability is not just helpful; it is required for compliance.  

Data Sovereignty and Security 

Privacy concerns have led many CIOs in 2026 to choose models that run within their own companies’ networks. Large models often require cloud-based APIs, which means sensitive data must leave the company’s secure systems. SLMs are small enough to run on-site or in a private cloud, allowing companies to retain full control of their data. This setup eliminates additional compliance costs and legal risks associated with using outside providers. For companies focused on security, the peace of mind SLMs offer is an important part of their return on investment.  

The Rise Of Hybrid Strategy 

Many organizations now use model routers instead of picking just one type of AI model. These systems let a small model handle most routine tasks, while only the more complex problems go to a larger LLM. This way, companies avoid using expensive resources for simple jobs. As the saying goes, you don’t use a Ferrari to pick up groceries. This approach helps balance the high cost of LLMs with the efficiency of smaller models. Using this layered setup is a sign of a smart ROI-driven AI strategy today.  

Specialization as a Competitive Advantage 

Fine-tuning a large language model requires significant time, skill, and computing power. In contrast, small models can be updated with new data in just days or even hours. This speed helps businesses quickly adjust their AI tools to new market trends or rules. Companies that can make changes faster have a real advantage over those using slow, inflexible models. Being able to customize AI at a low cost is now a key way for businesses to create value.  

Determining The Best Fit For Your Business 

Choosing between an SLM and an LLM depends on what your business needs. If you want to automate a specific data-heavy task with high accuracy and low cost, an SLM is the better choice. For projects that require creativity, complex reasoning, or long-term planning, an LLM remains the best option. The most successful businesses in 2026 will use different AI models for different jobs, not just one for everything. Matching the model to the task helps make sure every dollar spent on AI adds real value.

Source: Ideas: Steering AI toward the work future we want 

Best AI Data Sovereignty Platforms for US Healthcare

Healthcare Digital highlights the top 10 AI platforms currently used in healthcare to help deliver better care and improve patient outcomes.  

AI platforms are becoming key cloud services in healthcare, helping providers gain faster insights from complex clinical data.  

By adding AI to daily routines, these platforms help clinicians make better decisions, reduce pressure on busy systems, and spend more time with patients.  

With rising costs and growing demands, AI platforms are helping build a stronger, more predictive, and personalized healthcare system.  

Healthcare Digital takes a closer look at the top 10 AI platforms currently used in healthcare.  

10. Butterfly Network 

Headquarters: Massachusetts, US.  

CEO: Joseph DeVivo.  

Year founded: 2011.  

Number of employees: 200.  

Butterfly Network uses AI in its portable ultrasound devices, combining advanced hardware with cloud software and smart imaging features.  

The AI features help improve images, automatically measure, and support clinical decisions right where care is given.  

While this system focuses more on hardware than others, it is playing a growing role in making diagnostic imaging available in primary care, emergency rooms, and remote settings.  

9. Caption AI (Caption Health Part of GE Healthcare) 

Headquarters: California, US.  

CEO: Peter J. Arduini (GE Healthcare)  

Year founded: 2013.  

Tepsen AI offers ultrasound technology powered by AI, enabling even healthcare workers with limited imaging training to obtain high-quality diagnostic images.  

The platform integrates with GE Healthcare’s broader system and uses automated workflows and real-time guidance to make it easier to use.  

Although it has a specialized focus, its use across GE Healthcare provides broad clinical coverage.  

8. PathAI 

Headquarters: Massachusetts, US.  

CEO: Andy Beck.  

Year founded: 2016.  

Number of employees: 300.  

PathAI is an AI platform focused on digital pathology. It uses machine learning to improve diagnostic accuracy and streamline workflow processes.  

This technology helps pathologists and life sciences companies with image analysis, disease detection, and finding biomarkers.  

While PathAI is not as broad as some enterprise platforms, it plays an important role in cancer diagnostics and drug development.  

7. Merative 

Headquarters: Michigan, US.  

CEO: Jerry McCarthy.  

Year founded: 2022.  

Number of employees: 3,000.  

Merative provides data analytics and AI tools for healthcare payers, providers, and life sciences companies.  

Built from IBM Watson Health assets, Merative’s platforms support outcomes research, clinical decision-making, and population health management.  

Although Merative is less focused on clinicians than some newer companies, it is a key analytics partner for healthcare systems.  

6. Trueveta 

Headquarters: Washington, US.  

CEO: Terry Myerson.  

Year founded: 2020.   

Number of employees: 400  

Truveta runs a data platform built from de-identified clinical information collected from health systems.  

Using AI and analytics, the platform supports research, provides insights into population health, and helps develop new therapies.  

Truveta’s main strengths are its deep long-term data and its ability to support collaborations across entire health systems, not just frontline clinical tools.  

5. Tempus 

Headquarters: Illinois, US.  

CEO: Eric Lefkofsky.  

Year founded: 2015.  

Number of employees: 2,300 plus  

Tempus is a precision medicine platform that uses AI to analyze clinical and molecular data, primarily in cancer care.  

This technology helps make personalized treatment decisions, matches patients to clinical trials, and provides research insights.  

By combining genomics, imaging, and real-world data, Tempus has become a leader in data-driven medicine.  

4. Aidoc 

Headquarters: Tel Aviv, Israel.  

CEO: Elad Walach.  

Year founded: 2016.  

Number of employees: 500 plus.  

Aidoc offers an AI platform for medical imaging that helps health systems use, manage, and grow several AI tools within their clinical workflows.  

Its orchestration layer helps prioritize triage and clinical teamwork beyond radiology.  

Aidoc stands out for its strong governance, easy integration, and proven clinical use.  

3. Google Cloud Healthcare 

Headquarters: California, US.  

CEO: Thomas Kurian.  

Year founded: 2008.  

Number of employees: 50,000 plus.  

Google Cloud Healthcare offers an AI-focused platform built around its healthcare API and data engine.  

The platform supports data sharing, health analytics, and advanced machine learning for both clinical and research data.  

Google’s AI technology is strong in large-scale analytics and life sciences uses.  

2. AWS HealthLake 

Headquarters: Washington, USA.  

CEO: Matt Garman.  

Year founded: 2006.  

Number of employees: 125,000 plus.  

AWS HealthLake is a managed platform for storing, processing, and analyzing clinical data using AI and machine learning.  

It uses Fast Healthcare Interoperability Resources standards and supports healthcare AI applications worldwide.  

HealthLake excels at integrating with other systems in the AWS ecosystem, making it a key tool for digital health innovation.  

  1. Microsoft Dragon Copilot 

Headquarters: Washington, US.  

CEO: Satya Nadella.  

Year founded: 1975.  

Number of employees: 220,000-plus.  

Microsoft Dragon Copilot is a top AI platform made for healthcare clinicians.  

It uses clinical intelligence, generative AI, and automated workflows to reduce paperwork and improve both documentation and patient care.  

As part of Microsoft’s larger healthcare and cloud system, the platform is becoming essential for daily clinical work. 

Source: Top 10: AI Platforms in Healthcare