In partnership withNokia
.cst-large,
.cst-default {
width: 100%;
}

@media (max-width: 767px) {
.cst-block {
overflow-x: hidden;
}
}

@media (min-width: 630px) {
.cst-large {
margin-left: -25%;
width: 150%;
}

@media (min-width: 960px) {
.cst-large {
margin-left: -16.666666666666664%;
width: 140.26%;
}
}

@media (min-width: 1312px) {
.cst-large {
width: 145.13%;
}
}
}

Google just announced that a typical query to its Gemini app uses about 0.24 watt-hours of electricity. That’s about the same as running a microwave for one second—something that, to me, feels virtually insignificant. I run the microwave for so many more seconds than that on most days. I was excited to see this report come out, and I welcome more openness from major players in AI about their estimated energy use per query. But I’ve noticed that some folks are taking this number and using it to conclude that we don’t need to worry about AI’s energy demand. That’s not the right takeaway here. Let’s dig into why. 1. This one number doesn’t reflect all queries, and it leaves out cases that likely use much more energy. Google’s new report considers only text queries. Previous analysis, including MIT Technology Review’s reporting, suggests that generating a photo or video will typically use more electricity.
When I spoke with Jeff Dean, Google’s chief scientist, he said the company doesn’t currently have plans to do this sort of analysis for images and videos, but that he wouldn’t rule it out. The reason the company started with text prompts is that those are something many people out there are using in their daily lives, he says, while image and video generation is something that not as many people are doing. But I’m seeing more AI images and videos all over my social feeds. So there’s a whole world of queries not represented here.
Also, this estimate is the median, meaning it’s just the number in the middle of the range of queries Google is seeing. Longer questions and responses can push up the energy demand, and so can using a reasoning model.  We don’t know anything about how much energy these more complicated queries demand or what the distribution of the range is. 2. We don’t know how many queries Gemini is seeing, so we don’t know the product’s total energy impact. One of my biggest outstanding questions about Gemini’s energy use is the total number of queries the product is seeing every day.  This number isn’t included in Google’s report, and the company wouldn’t share it with me. And let me be clear: I absolutely pestered them about this, both in a press call they had about the news and in my interview with Dean. In the press call, the company pointed me to a recent earnings report, which includes only figures about monthly active users (450 million, for what it’s worth). “We’re not comfortable revealing that for various reasons,” Dean told me on our call. The total number is an abstract measure that changes over time, he says, adding that the company wants users to be thinking about the energy usage per prompt. But there are people out there all over the world interacting with this technology, not just me—and what we all add up to seems quite relevant. OpenAI does publicly share its total, sharing recently that it sees 2.5 billion queries to ChatGPT every day. So for the curious, we can use this as an example and take the company’s self-reported average energy use per query (0.34 watt-hours) to get a rough idea of the total for all people prompting ChatGPT. According to my math, over the course of a year, that would add up to over 300 gigawatt-hours—the same as powering nearly 30,000 US homes annually. When you put it that way, it starts to sound like a lot of seconds in microwaves.

3. AI is everywhere, not just in chatbots, and we’re often not even conscious of it. AI is touching our lives even when we’re not looking for it. AI summaries appear in web searches, whether you ask for them or not. There are built-in features for email and texting applications that that can draft or summarize messages for you. Google’s estimate is strictly for Gemini apps and wouldn’t include many of the other ways that even this one company is using AI. So even if you’re trying to think about your own personal energy demand, it’s increasingly difficult to tally up.  To be clear, I don’t think people should feel guilty for using tools that they find genuinely helpful. And ultimately, I don’t think the most important conversation is about personal responsibility.  There’s a tendency right now to focus on the small numbers, but we need to keep in mind what this is all adding up to. Over two gigawatts of natural gas will need to come online in Louisiana to power a single Meta data center this decade. Google Cloud is spending $25 billion on AI just in the PJM grid on the US East Coast. By 2028, AI could account for 326 terawatt-hours of electricity demand in the US annually, generating over 100 million metric tons of carbon dioxide. We need more reporting from major players in AI, and Google’s recent announcement is one of the most transparent accounts yet. But one small number doesn’t negate the ways this technology is affecting communities and changing our power grid.  This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here.

Provided byEnsemble Over the past 20 years building advanced AI systems—from academic labs to enterprise deployments—I’ve witnessed AI’s waves of success rise and fall. My journey began during the “AI Winter,” when billions were invested in expert systems that ultimately underdelivered. Flash forward to today: large language models (LLMs) represent a quantum leap forward, but their prompt-based adoption is similarly overhyped, as it’s essentially a rule-based approach disguised in natural language. At Ensemble, the leading revenue cycle management (RCM) company for hospitals, we focus on overcoming model limitations by investing in what we believe is the next step in AI evolution: grounding LLMs in facts and logic through neuro-symbolic AI. Our in-house AI incubator pairs elite AI researchers with health-care experts to develop agentic systems powered by a neuro-symbolic AI framework. This bridges LLMs’ intuitive power with the precision of symbolic representation and reasoning. Overcoming LLM limitations LLMs excel at understanding nuanced context, performing instinctive reasoning, and generating human-like interactions, making them ideal for agentic tools to then interpret intricate data and communicate effectively. Yet in a domain like health care where compliance, accuracy, and adherence to regulatory standards are non-negotiable—and where a wealth of structured resources like taxonomies, rules, and clinical guidelines define the landscape—symbolic AI is indispensable. By fusing LLMs and reinforcement learning with structured knowledge bases and clinical logic, our hybrid architecture delivers more than just intelligent automation—it minimizes hallucinations, expands reasoning capabilities, and ensures every decision is grounded in established guidelines and enforceable guardrails.
Creating a successful agentic AI strategy Ensemble’s agentic AI approach includes three core pillars: 1. High-fidelity data sets: By managing revenue operations for hundreds of hospitals nationwide, Ensemble has unparallelled access to one of the most robust administrative datasets in health care. The team has decades of data aggregation, cleansing, and harmonization efforts, providing an exceptional environment to develop advanced applications.
To power our agentic systems, we’ve harmonized more than 2 petabytes of longitudinal claims data, 80,000 denial audit letters, and 80 million annual transactions mapped to industry-leading outcomes. This data fuels our end-to-end intelligence engine, EIQ, providing structured, context-rich data pipelines spanning across the 600-plus steps of revenue operations. 2. Collaborative domain expertise: Partnering with revenue cycle domain experts at each step of innovation, our AI scientists benefit from direct collaboration with in-house RCM experts, clinical ontologists, and clinical data labeling teams. Together, they architect nuanced use cases that account for regulatory constraints, evolving payer-specific logic and the complexity of revenue cycle processes. Embedded end users provide post-deployment feedback for continuous improvement cycles, flagging friction points early and enabling rapid iteration. This trilateral collaboration—AI scientists, health-care experts, and end users—creates unmatched contextual awareness that escalates to human judgement appropriately, resulting in a system mirroring decision-making of experienced operators, and with the speed, scale, and consistency of AI, all with human oversight. 3. Elite AI scientists drive differentiation: Ensemble’s incubator model for research and development is comprised of AI talent typically only found in big tech. Our scientists hold PhD and MS degrees from top AI/NLP institutions like Columbia University and Carnegie Mellon University, and bring decades of experience from FAANG companies [Facebook/Meta, Amazon, Apple, Netflix, Google/Alphabet] and AI startups. At Ensemble, they’re able to pursue cutting-edge research in areas like LLMs, reinforcement learning, and neuro-symbolic AI within a mission-driven environment. The also have unparalleled access to vast amounts of private and sensitive health-care data they wouldn’t see at tech giants paired with compute and infrastructure that startups simply can’t afford. This unique environment equips our scientists with everything they need to test novel ideas and push the frontiers of AI research—while driving meaningful, real-world impact in health care and improving lives. Strategy in action: Health-care use cases in production and pilot By pairing the brightest AI minds with the most powerful health-care resources, we’re successfully building, deploying, and scaling AI models that are delivering tangible results across hundreds of health systems. Here’s how we put it into action: Supporting clinical reasoning: Ensemble deployed neuro-symbolic AI with fine-tuned LLMs to support clinical reasoning. Clinical guidelines are rewritten into proprietary symbolic language and reviewed by humans for accuracy. When a hospital is denied payment for appropriate clinical care, an LLM-based system parses the patient record to produce the same symbolic language describing the patient’s clinical journey, which is matched deterministically against the guidelines to find the right justification and the proper evidence from the patient’s record. An LLM then generates a denial appeal letter with clinical justification grounded in evidence. AI-enabled clinical appeal letters have already improved denial overturn rates by 15% or more across Ensemble’s clients.Building on this success, Ensemble is piloting similar clinical reasoning capabilities for utilization management and clinical documentation improvement, by analyzing real-time records, flagging documentation gaps, and suggesting compliance enhancements to reduce denial or downgrade risks. Accelerating accurate reimbursement: Ensemble is piloting a multi-agent reasoning model to manage the complex process of collecting accurate reimbursement from health insurers. With this approach, a complex and coordinated system of autonomous agents work together to interpret account details, retrieve required data from various systems, decide account-specific next actions, automate resolution, and escalate complex cases to humans.

This will help reduce payment delays and minimize administrative burden for hospitals and ultimately improve the financial experience for patients. Improving patient engagement: Ensemble’s conversational AI agents handle inbound patient calls naturally, routing to human operators as required. Operator assistant agents deliver call transcriptions, surface relevant data, suggest next-best actions, and streamline follow-up routines. According to Ensemble client performance metrics, the combination of these AI capabilities has reduced patient call duration by 35%, increasing one-call resolution rates and improving patient satisfaction by 15%. The AI path forward in health care demands rigor, responsibility, and real-world impact. By grounding LLMs in symbolic logic and pairing AI scientists with domain experts, Ensemble is successfully deploying scalable AI to improve the experience for health-care providers and the people they serve. This content was produced by Ensemble. It was not written by MIT Technology Review’s editorial staff.

In partnership withTata Communications Across industries, enterprises are increasingly adopting an on-demand approach to compute, storage, and applications. They are favoring digital services that are faster to deploy, easier to scale, and better integrated with partner ecosystems. Yet, one critical pillar has lagged: the network. While software-defined networking has made inroads, many organizations still operate rigid, pre-provisioned networks. As applications become increasingly distributed and dynamic—including hybrid cloud and edge deployments—a programmable, on-demand network infrastructure can enhance and enable this new era. From CapEx to OpEx: The new connectivity mindset Another, practical concern is also driving this shift: the need for IT models that align cost with usage. Rising uncertainty about inflation, consumer spending, business investment, and global supply chains are just a few of the economic factors weighing on company decision-making. And chief information officers (CIOs) are scrutinizing capital-expenditure-heavy infrastructure more closely and increasingly adopting operating-expenses-based subscription models. Instead of long-term circuit contracts and static provisioning, companies are looking for cloud-ready, on-demand network services that can scale, adapt, and integrate across hybrid environments. This trend is fueling demand for API-first network infrastructure connectivity that behaves like software, dynamically orchestrated and integrated into enterprise IT ecosystems. There has been such rapid interest, the global network API market is projected to surge from $1.53 billion in 2024 to over $72 billion in 2034.
In fact, McKinsey estimates the network API market could unlock between $100 billion and $300 billion in connectivity- and edge-computing-related revenue for telecom operators over the next five to seven years, with an additional $10 billion to $30 billion generated directly from APIs themselves. “When the cloud came in, first there was a trickle of adoptions. And then there was a deluge,” says Rajarshi Purkayastha, VP of solutions at Tata Communications. “We’re seeing the same trend with programmable networks. What was once a niche industry is now becoming mainstream as CIOs prioritize agility and time-to-value.”
Programmable networks as a catalyst for innovation Programmable subscription-based networks are not just about efficiency, they are about enabling faster innovation, better user experiences, and global scalability. Organizations are preferring API-first systems to avoid vendor lock-in, enable multi-vendor integration, and foster innovation. API-first approaches allow seamless integration across different hardware and software stacks, reducing operational complexity and costs. With APIs, enterprises can provision bandwidth, configure services, and connect to clouds and edge locations in real time, all through automation layers embedded in their DevOps and application platforms. This makes the network an active enabler of digital transformation rather than a lagging dependency. For example, Netflix—one of the earliest adopters of microservices—handles billions of API requests daily through over 500 microservices and gateways, supporting global scalability and rapid innovation. After a two-year transition period, it redesigned its IT structure and organized it using microservice architecture. Elsewhere, Coca-Cola integrated its global systems using APIs, enabling faster, lower-cost delivery and improved cross-functional collaboration. And Uber moved to microservices with API gateways, allowing independent scaling and rapid deployment across markets. In each case, the network had to evolve from being static and hardware-bound to dynamic, programmable, and consumption-based. “API-first infrastructure fits naturally into how today’s IT teams work,” says Purkayastha. “It aligns with continuous integration and continuous delivery/deployment (CI/CD) pipelines and service orchestration tools. That reduces friction and accelerates how fast enterprises can launch new services.” Powering on-demand connectivity Tata Communications deployed Network Fabric—its programmable platform that uses APIs to allow enterprise systems to request and adjust network resources dynamically—to help a global software-as-a-service (SaaS) company modernize how it manages network capacity in response to real-time business needs. As the company scaled its digital services worldwide, it needed a more agile, cost-efficient way to align network performance with unpredictable traffic surges and fast-changing user demands. With Tata’s platform, the company’s operations teams were able to automatically scale bandwidth in key regions for peak performance, during high-impact events like global software releases. And just as quickly scale down once demand normalized, avoiding unnecessary costs. In another scenario, when the SaaS provider needed to run large-scale data operations between its US and Asia hubs, the network was programmatically reconfigured in under an hour; a process that previously required weeks of planning and provisioning. “What we delivered wasn’t just bandwidth, it was the ability for their teams to take control,” says Purkayastha. “By integrating our Network Fabric APIs into their automation workflows, we gave them a network that responds at the pace of their business.”

Barriers to transformation — and how to overcome them Transforming network infrastructure is no small task. Many enterprises still rely on legacy multiprotocol label switching (MPLS) and hardware-defined wide-area network (WAN) architectures. These environments are rigid, manually managed, and often incompatible with modern APIs or automation frameworks. As with any organization, barriers can be both technical and internal, and legacy devices may not support programmable interfaces. Organizations are often siloed, meaning networks are managed separately to application and DevOps workflows. Furthermore, CIOs face pressure for quick returns and may not even remain in the company long enough to oversee the process and results, making it harder to push for long-term network modernization strategies. “Often, it’s easier to address the low-hanging fruit rather than go after the transformation because decision-makers may not be around to see the transformation come to life,” says Purkayastha. But quick fixes or workarounds may not yield the desired results; transformation is needed instead. “Enterprises have historically built their networks for stability, not agility,” says Purkayastha. “But now, that same rigidity becomes a bottleneck when applications, users, and workloads are distributed across the cloud, edge, and remote locations.” Despite the challenges, there is a clear path forward, starting with overlay orchestration, well-defined API contracts, and security-first design. Instead of completely removing and replacing an existing system, many enterprises are layering APIs over existing infrastructure, enabling controlled migrations and real-time service automation. “We don’t just help customers adopt APIs, we guide them through the operational shift it requires,” says Purkayastha. “We have blueprints for what to automate first, how to manage hybrid environments, and how to design for resilience.” For some organizations, there will be resistance to the change initially. Fears of extra workloads, or misalliance with teams’ existing goals and objectives are common, as is the deeply human distrust of change. These can be overcome, however. “There are playbooks on what we’ve done earlier—learnings from transformation—which we share with clients,” says Purkayastha. “We also plan for the unknowns. We usually reserve 10% of time and resources just to manage unforeseen risks, and the result is an empowered organization to scale innovation and reduce operational complexity.” This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written entirely by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

Separating AI reality from hyped-up fiction isn’t always easy. That’s why we’ve created the AI Hype Index—a simple, at-a-glance summary of everything you need to know about the state of the industry. Using AI to improve our health and well-being is one of the areas scientists and researchers are most excited about. The last month has seen an interesting leap forward: The technology has been put to work designing new antibiotics to fight hard-to-treat conditions, and OpenAI and Anthropic have both introduced new limiting features to curb potentially harmful conversations on their platforms.  Unfortunately, not all the news has been positive. Doctors who overrely on AI to help them spot cancerous tumors found their detection skills dropped once they lost access to the tool, and a man fell ill after ChatGPT recommended he replace the salt in his diet with dangerous sodium bromide. These are yet more warning signs of how careful we have to be when it comes to using AI to make important decisions for our physical and mental states.

Want smarter insights in your inbox? Sign up for our weekly newsletters to get only what matters to enterprise AI, data, and security leaders. Subscribe Now Salesforce is betting that rigorous testing in simulated business environments will solve one of enterprise artificial intelligence’s biggest problems: agents that work in demonstrations but fail in the messy reality of corporate operations. The cloud software giant unveiled three major AI research initiatives this week, including CRMArena-Pro, what it calls a “digital twin” of business operations where AI agents can be stress-tested before deployment. The announcement comes as enterprises grapple with widespread AI pilot failures and fresh security concerns following recent breaches that compromised hundreds of Salesforce customer instances. “Pilots don’t learn to fly in a storm; they train in flight simulators that push them to prepare in the most extreme challenges,” said Silvio Savarese, Salesforce’s chief scientist and head of AI research, during a press conference. “Similarly, AI agents benefit from simulation testing and training, preparing them to handle the unpredictability of daily business scenarios in advance of their deployment.” The research push reflects growing enterprise frustration with AI implementations. A recent MIT report found that 95% of generative AI pilots at companies are failing to reach production, while Salesforce’s own studies show that large language models alone achieve only 35% success rates in complex business scenarios. AI Scaling Hits Its Limits Power caps, rising token costs, and inference delays are reshaping enterprise AI. Join our exclusive salon to discover how top teams are: Secure your spot to stay ahead: https://bit.ly/4mwGngO Digital twins for enterprise AI: how Salesforce simulates real business chaos CRMArena-Pro represents Salesforce’s attempt to bridge the gap between AI promise and performance. Unlike existing benchmarks that test generic capabilities, the platform evaluates agents on real enterprise tasks like customer service