AI News Archive: July 23, 2026 — Part 5
Sourced from 500+ daily AI sources, scored by relevance.
- Utilities Promise to Protect Electric Bills. It Won’t Stop Data Center Backlash.
Utilities Promise to Protect Electric Bills. It Won’t Stop Data Center Backlash. Barron's
Score: 63🌐 MovesJul 23, 2026https://www.barrons.com/articles/ai-data-centers-big-tech-energy-electricity-32286b3c - AI will not trigger employment collapse, staffing company Adecco Group says
AI will not trigger employment collapse, staffing company Adecco Group says Reuters
- How healthcare AI is actually being used today
How healthcare AI is actually being used today Healthcare IT News
Score: 62🌐 MovesJul 23, 2026https://www.healthcareitnews.com/resource/how-healthcare-ai-actually-being-used-today - A backyard bee with roughly 1 million neurons can recognize a human face using almost no computing power at all, and what scientists built to prove it is now drawing serious attention from AI engineers
Every summer, bees drift through American backyards by the millions, landing on flowers, hovering, tilting, and darting away. Most people barely register them. But something is happening inside those tiny bodies that scientists have only just begun to fully map, and the implications reach far beyond gardens or honey or pollination. It reaches into the ... Read more
- Tesla’s robotaxi promises are clashing with reality
In an earnings call yesterday, Tesla CEO Elon Musk did his best to paint a positive portrait of the company's robotaxi program. New cities are being added, more miles are being driven, and more people are experiencing Tesla's unsupervised vehicles. But Musk's typical bullishness seemed to be absent from the call, as the occasional trillionaire […]
Score: 62🌐 MovesJul 23, 2026https://www.theverge.com/transportation/970003/tesla-robotaxi-mileage-waymo-cities-earnings-musk - European semiconductor stocks diverge as investors weigh AI demand, growth expectations
European semiconductor stocks diverge as investors weigh AI demand, growth expectations Reuters
- Twitter founder creates ‘Slack killer’ that treats AI as colleagues
‘This is going to completely transform how we work,’ says early Buzz user
Score: 62🌐 MovesJul 23, 2026https://www.independent.co.uk/tech/buzz-ai-slack-rival-dorsey-block-b3020013.html - AI Is Letting Solo Founders Build Startups Without Hiring. Here’s What AI Couldn’t Do for Them
One-person startups won’t eliminate teams. They’ll change when founders hire and what those hires do.
Score: 62🌐 MovesJul 23, 2026https://www.inc.com/diana-bocco/ai-solopreneurs-founders-building-startups-hiring/91378552 - Cognizant and Gulf Edge Announce Strategic Partnership to Accelerate Enterprise AI Adoption in Southeast Asia
Cognizant and Gulf Edge Announce Strategic Partnership to Accelerate Enterprise AI Adoption in Southeast Asia The Straits Times
- Google CEO distracts from Gemini 3.5 Pro delay with talk of Gemini 4 and monthly releases
Google CEO Sundar Pichai has sought to allay concerns over the delayed release of the Gemini 3.5 Pro large language model. He dodged questions about it in Google’s quarterly earnings call on Wednesday by focusing on the company’s next frontier AI model, Gemini 4, and plans to release subsequent LLMs at an almost monthly cadence. His comments came a day after Google unveiled Gemini 3.6 Flash and 3.5 Flash Cyber but offered no update on the release of Gemini 3.5 Pro, the company’s delayed flagship reasoning model that many developers had expected to arrive weeks earlier. Google introduced the Gemini 3.5 family at its annual I/O conference, promising to release the Pro model in June. That timeline has since slipped, with Bloomberg suggesting Gemini 3.5 Pro is months late because the model’s coding performance is falling short of internal expectations, especially when compared to better performance by similar models from OpenAI and Anthropic. Instead of revisiting the Gemini 3.5 Pro timeline, Pichai used the earnings call to shift the discussion toward Gemini 4, when asked about how his company planned to navigate an increasingly competitive race to release frontier AI models by to Barclays Investment Bank analyst Ross Sandler. “We are creating a baseline on top of which you will see us rapidly iterate on subsequent model releases. And so picking up pace and releasing models almost at a monthly cadence is part of our road map as we are building Gemini 4 as well,” Pichai said during the call . Sandler’s question followed one from JPMorgan Chase & Co analyst Douglas Anmuth , who asked Pichai if Google was releasing frontier AI models frequently enough to keep pace with rivals OpenAI and Anthropic. Pichai had responded to Anmuth’s question that Google remained confident of competing at the frontier and was investing heavily in a larger Gemini 4 base model. Analysts, though, aren’t as confident as Pichai. While delays to Google’s frontier model roadmap have not triggered an exodus of existing customers, either because of high switching costs or because many enterprises already running multi-model architectures, they have made CIOs evaluating AI platforms more cautious about making new commitments, said Bhupendra Chopra , chief revenue officer at IT consulting firm Kanerika. A monthly model release cadence could prove to be a double-edged sword for enterprises and their CIOs. While a monthly release cadence could help enterprises gain faster access to improvements in model performance, cost and capabilities, it will also require CIOs to invest more heavily in testing, governance and version management to safely adopt those updates, said Sanchit Vir Gogia , chief analyst at Greyhound Research. Similarly, Pareekh Jain , principal analyst at Pareekh Consulting, said enterprises will embrace a faster release cadence only if each successive model delivers measurable improvements in performance, cost or safety, rather than simply changing version number. The challenge for CIOs, Jain said, is not just keeping up with model releases; it’s deciding whether each new version is worth the cost of validating it. This article first appeared on InfoWorld .
- AI companies want to run your business. They can't always run their models.
AI companies want to run your business. They can't always run their models. Business Insider
Score: 62🌐 MovesJul 23, 2026https://www.businessinsider.com/openai-presence-ai-agents-hugging-face-hack-2026-7 - Workers who direct AI agents outperform peers who simply delegate to them
Even when other skill sets were identical, the ability to judge artificial intelligence output made a critical difference in performance, per a study from KPMG and University of Texas at Austin.
Score: 61🌐 MovesJul 23, 2026https://www.hrdive.com/news/workers-who-refine-and-direct-ai-outperform-peers-who-delegate-to-it/826000/ - Nokia Q2 profit beat as sales from AI, cloud doubled
Nokia Q2 profit beat as sales from AI, cloud doubled Reuters
Score: 61🌐 MovesJul 23, 2026https://www.reuters.com/business/nokia-q2-profit-beat-ai-demand-2026-07-23/ - OpenAI's new release turns a bad week ugly for software stocks
OpenAI's new release turns a bad week ugly for software stocks Business Insider
Score: 60🤖 ModelsJul 23, 2026https://www.businessinsider.com/openai-release-turns-a-bad-week-ugly-for-software-stocks-2026-7 - Clearwater data center developer to go public via SPAC deal
A Clearwater AI infrastructure company, that was founded three years ago, is heading to the public markets in a deal valuing it at $4 billion.
Score: 60💰 MoneyJul 23, 2026https://www.bizjournals.com/tampabay/news/2026/07/23/clearwater-tecfusions.html?ana=brss_6150 - Ropedia raises $22M to scale human-centric data collection for embodied AI
Singapore-based robotics data infrastructure firm Ropedia Pte. Ltd. today announced it raised $22 million in Pre-Series A funding to scale up its collection of real-world, multimodal interaction data to fuel artificial intelligence robotics models developed by technology companies. Physical AI and embodied AI development is increasingly burdened by a lack of real-world data at a […] The post Ropedia raises $22M to scale human-centric data collection for embodied AI appeared first on SiliconANGLE .
Score: 60💰 MoneyJul 23, 2026https://siliconangle.com/2026/07/23/ropedia-raises-22m-scale-human-centric-data-collection-embodied-ai/ - Hidden risks in AI data centers spark new battery safety research
As lithium-ion systems scale up globally, Waterloo researchers are investigating fire hazards and solutions to protect critical infrastructure.
Score: 60🌐 MovesJul 23, 2026https://techxplore.com/news/2026-07-hidden-ai-centers-battery-safety.html - 'Vote him out': Bastrop residents unite against data centers as Texas AI boom accelerates
'Vote him out': Bastrop residents unite against data centers as Texas AI boom accelerates Austin American-Statesman
Score: 60🌐 MovesJul 23, 2026https://www.statesman.com/business/technology/article/data-centers-bastrop-county-22349254.php - FakeGit Targets AI Coding Agents with Malicious GitHub Repos
FakeGit Targets AI Coding Agents with Malicious GitHub Repos DevOps.com
Score: 60🌐 MovesJul 23, 2026https://devops.com/fakegit-targets-ai-coding-agents-with-malicious-github-repos/ - Public Horrified as OpenAI Pushes “Child After Child Into the Grave”
"It is unconscionable that 'ChatGPT-assisted suicide' is becoming a recurring cause of death." The post Public Horrified as OpenAI Pushes “Child After Child Into the Grave” appeared first on Futurism .
Score: 60🌐 MovesJul 23, 2026https://futurism.com/artificial-intelligence/openai-pushes-child-into-grave - Flock's CEO Says its ALPRs Don't Do Video After Repeatedly Announcing They Can
Flock's CEO Garrett Langley says people don't understand the capabilities of its ALPR system, but they do.
Score: 60🌐 MovesJul 23, 2026https://www.404media.co/flocks-ceo-says-its-alprs-dont-do-video-after-repeatedly-announcing-they-can/ - Everyone hates massive data centers. This $18 billion CEO has a better way to get you the AI compute you need
For the past few years, the AI infrastructure race has been driven by the assumption that larger artificial intelligence models require larger data centers. That idea has fueled an extraordinary wave of spending. In rural Richland Parish, Louisiana, for example, Meta is building one of the world’s largest AI infrastructure projects. Its Hyperion campus is expected to cost more than $50 billion and run on about 5 gigawatts of power, roughly the output of five nuclear reactors. But as these facilities grow, so does the resistance to them. Across the country, communities are pushing back over data centers’ demands on power and water, and the impacts they’re having on rural and suburban areas. The bigger the project, the more likely it is to become a political target. But Tom Leighton, cofounder and CEO of Akamai, the $18 billion content delivery network company that powers a significant share of the world’s web traffic, would argue that’s not even the worst part. The Meta project, like so many others, rests on the assumption that companies able to concentrate the most computing power will be best positioned to build the next generation of AI systems. And Leighton believes that assumption applies more clearly to AI training than to AI inference (the “thinking” that AI does when applying its training to real-world data). “The next challenge for AI is what it will take to run those models and their derivatives everywhere,” he tells Fast Company , sharing his belief that the industry may be trying to solve too many problems with the same enormous building. Leighton’s 28-year-old company has spent the past several years expanding into a cloud platform for AI. Rather than trying to match the hyperscalers data center for data center, Akamai is betting on a different approach offer a less disruptive path for expanding AI infrastructure, one that relies more heavily on a network of existing facilities instead of concentrating enormous demands for land and power in a single community. “Agentic AI needs low latency, high performance, and affordable economics that giant, centralized data centers cannot provide,” Leighton says. His proposed solution is also inspired by what made Akamai a player in internet infrastructure to begin with during the dot-com era. In the late 1990s, Leighton helped address the web’s growing pains, when every request going back to a handful of centralized servers was choking the internet’s growth. Now, Akamai is attempting to bring a version of the architecture that saved the web to AI. And Leighton’s got the AI kingmaker Nvidia backing him. Bigger AI data centers won’t solve every AI problem Leighton’s proposed solution is partly an economic one. Running an AI model is a different problem from training one, and many inference tasks do not require the largest model, the most powerful chips, or a request traveling thousands of miles to a massive centralized campus. For enterprises pursuing agentic AI, the debate extends beyond securing graphics processing unit (GPU) capacity. Companies increasingly need to decide where inference should run, where enterprise data is processed, how quickly AI agents must respond, what it costs to move data across regions, and how security policies are enforced once those systems move into production. In other words, deploying AI agents increasingly becomes an infrastructure design problem—not just a model selection problem. Leighton argues those operational decisions, not raw compute alone, will determine whether AI applications deliver acceptable performance, reliability and economics at scale. Inference performance depends partly on how quickly an AI system can respond when a decision is required. Network distance, data location, tool calls, and application design can all affect response time. “If an AI request has to travel thousands of miles, touch data in another location, call tools, and then return an answer to a user in real time, the speed of the data center is only one part of the equation,” Leighton says. “Proximity matters. In fact, it may matter even more as AI evolves.” Leighton’s view draws on Akamai’s experience with web infrastructure during the late 1990s. As the web expanded, centralized origin servers struggled to handle global demand. “Websites were built around centralized origin servers. As demand went global, every user request had to travel back to a small number of central places,” Leighton recalls. “The result was slow performance, websites would go down or freeze up during traffic spikes, and there was a lot of frustration for users.” The industry called the problem the “World Wide Wait.” Leighton, an MIT applied mathematician, helped develop an alternative based on distributing content and computation closer to users. Algorithms determined where requests should be served. Nearly three decades later, Leighton believes AI infrastructure now faces a related distribution problem, and argues that a major engineering challenge will be coordinating inference across thousands of data center locations while maintaining consistent performance, security, and reliability as a unified system. “Anyone can build a data center, but making many locations act intelligently together, under changing demand, with consistent performance and trust, is another challenge altogether,” he says. Can distributed AI outperform centralized clouds? The central component of Akamai’s strategy is AI Grid, an orchestration layer developed with Nvidia. AI Grid determines whether an inference workload should run in a centralized AI factory, a regional cloud, or one of Akamai’s edge locations. The decision depends on latency requirements, operating costs, and performance needs. Leighton argues that routing decisions can materially affect inference performance. GPU cost and availability remain significant constraints, but the industry is asking step-one questions in a step-two market. “The bigger questions are around where inference should run, what data it needs, how quickly the response must come back, what it will cost to move the data, and what security policy has to be enforced along the way,” he says. “The answers to those questions will have a much bigger impact on whether AI reaches its potential.” For Leighton, the definition of scale itself is changing. During the training era, scale meant concentrating as much compute as possible inside a single AI factory. In the agent era, he argues, scale increasingly depends on how effectively infrastructure can distribute inference across many locations while keeping latency, data movement, and costs under control. “GPU availability is a major issue today, but GPUs are not the solution to every AI problem,” he argues. “Many inference tasks do not need the largest model or the largest cluster or the most expensive compute. What they need is the ability to marry the right model, in the right place, data and moment, at the most effective cost.” Akamai says customer demand is beginning to reflect this approach. Earlier this year, the company disclosed a four-year, $200 million agreement with an unnamed major U.S. technology company to deploy one of the world’s largest clusters of Nvidia RTX PRO 6000 Blackwell GPUs on its platform. Three months later, Akamai announced a seven-year, $1.8 billion cloud infrastructure commitment from a leading U.S. frontier AI model developer. Insider reports identified the company as Anthropic. The agreement is the largest contract in Akamai’s 28-year history. Together, the two agreements represent roughly $2 billion in committed cloud business from customers Akamai did not have two years ago. Akamai’s cloud infrastructure revenue has also grown 40% year over year. Leighton declined to identify the companies or discuss the workloads behind the agreements. He says customer evaluations now include a broader set of operational questions. “When customers evaluate AI infrastructure, of course they look at scale and performance,” he says. “But they’re also asking how workloads perform in production, how costs evolve over time, how reliable the infrastructure is, and whether it gives them the flexibility to adapt as AI usage changes.” Without naming additional customers, Leighton says Akamai is supporting production AI deployments for an AI-powered video intelligence platform in India and a U.S.-based consumer AI company. Both require low-latency inference and do not depend on large centralized training clusters. The real cost of AI goes far beyond GPUs Many companies evaluate AI infrastructure through token prices, GPU usage, and model endpoint costs. Production systems also create costs related to context retrieval, API calls, storage reads, and network traffic across zones and regions. Akamai claims its architecture can reduce inference latency by as much as 2.5 times compared with traditional hyperscaler infrastructure, and lower inference costs by up to 86%. Its published benchmarks, conducted using Nvidia’s methodology, show RTX PRO 6000 Blackwell GPUs on Akamai’s cloud delivering up to 1.63 times the inference throughput of Nvidia H100 GPUs. The system sustained roughly 24,000 tokens per second per server under 100 concurrent requests. Leighton says the commercial viability of AI infrastructure will depend on whether those systems can deliver acceptable performance, reliability, and cost. “As a CEO, I am always skeptical of vague economics,” he says. “It is easy to say the future is bigger data centers and more GPUs. It is harder to show how that architecture delivers the right performance, reliability, and cost when AI is running everywhere, all the time. Inference is where AI must show profitable ROI.” Leighton’s background includes work as a theoretical computer scientist. He holds more than 50 patents and has served as Akamai’s CEO for more than two decades. “As a mathematician, I am skeptical of straight-line thinking. The fact that one architecture worked for the first phase of AI does not mean it will work for every phase that follows,” he says. “As a theoretical computer scientist, I am aware that what ignites a technological revolution is rarely the thing that lets it survive in the real world. The early promise of AI is no exception. The massive centralized clouds that started this boom aren’t built for the highly distributed reality of what comes next.” Wall Street is still pricing AI like the cloud era Wall Street has spent much of 2026 evaluating how Akamai’s cloud business fits with its legacy operations. The company’s content delivery network business has been shrinking for years. Its AI cloud expansion has also required substantial spending on hardware and infrastructure. Rising memory prices, driven by strong demand for AI hardware, increased component costs as Akamai purchased thousands of Blackwell GPUs. Margins compressed, and earnings per share declined even as revenue grew. Investors continued to value the company largely as a mature infrastructure provider. The $1.8 billion commitment led to Akamai’s largest single-day stock rally in more than two decades. The agreement provides evidence of demand for Akamai’s distributed AI infrastructure. It also underscores the amount of capital required to build and operate that infrastructure at scale. “It highlights both the opportunity and the discipline required to pursue it,” Leighton says. “We are making significant investments because we believe AI will be a major driver of cloud demand, but we are not trying to simply copy the hyperscaler model. Our advantage is that we already operate one of the world’s most distributed platforms, and power and protect large parts of the internet. The investments we are making are about extending that platform for cloud and AI.” The AI industry has invested heavily in GPUs, large clusters, and more powerful models. Leighton argues that inference will require additional infrastructure designed around distribution, latency, and cost. “In the early days of the web, many people assumed the answer was just more central infrastructure. It was not. AI is reaching a similar point,” he says. Whether Akamai’s distributed model can compete effectively with centralized cloud providers will depend on customer demand, technical performance, and the economics of operating the network at scale.
- Zuckerberg says AI should empower people, not replace them, in new Meta vision
Mark Zuckerberg says Meta's AI vision is about empowering people, not replacing them, pushing back against dystopian narratives about the technology.
Score: 60🌐 MovesJul 23, 2026https://www.foxbusiness.com/technology/meta-ai-vision-empower-people-mark-zuckerberg - MaineGeneral Health’s blueprint for AI scribe success
MaineGeneral Health’s blueprint for AI scribe success Healthcare IT News
Score: 60🌐 MovesJul 23, 2026https://www.healthcareitnews.com/resource/mainegeneral-healths-blueprint-ai-scribe-success - How CISOs can prepare for the next generation of AI-driven security
How CISOs can prepare for the next generation of AI-driven security Techcircle
Score: 60🌐 MovesJul 23, 2026https://www.techcircle.in/2026/07/23/how-cisos-can-prepare-for-the-next-generation-of-ai-driven-security - Google opens AI start-up accelerator applications
Fifteen AI-driven South African start-ups can secure up to R1 million each through Google's latest accelerator programme.
Score: 60🌐 MovesJul 23, 2026https://www.itweb.co.za/article/google-opens-ai-start-up-accelerator-applications/KzQenMjyXxn7Zd2r - Google Cloud's record results can't quiet concerns on AI spending and model release timelines
Google Cloud's record results can't quiet concerns on AI spending and model release timelines IT Pro
- AI infrastructure buildout reshapes the enterprise stack from silicon to systems
The AI infrastructure buildout has become the defining story of the enterprise technology cycle, pushing compute, storage, networking and data into a wholesale redesign. As organizations chase instant time to value, the economics of tokens and the pressure to modernize aging data centers are forcing a rethink of how AI gets deployed at scale. That […] The post AI infrastructure buildout reshapes the enterprise stack from silicon to systems appeared first on SiliconANGLE .
Score: 60🌐 MovesJul 23, 2026https://siliconangle.com/2026/07/23/ai-infrastructure-buildout-drives-amd-rack-scale-strategy-amdadvancingai/ - AI poses many risks, but is government regulation the best tool to tackle them?
AI poses many risks, but is government regulation the best tool to tackle them? thenationalnews.com
- Ng Chee Meng’s Cabinet return gives workers a stronger voice amid AI disruption, say union leaders
Ng Chee Meng’s Cabinet return gives workers a stronger voice amid AI disruption, say union leaders The Straits Times
- OpenAI's Greg Brockman on AI models
OpenAI's Greg Brockman on AI models The Straits Times
- Thailand's new investment jumps 37% in Jan-June on AI boom
Thailand's new investment jumps 37% in Jan-June on AI boom Nikkei Asia
- Hyundai, Kia open Shanghai UX hub for AI-defined vehicles
Hyundai Motor and Kia said Thursday that they have opened UX Studio Shanghai, a user experience research hub in Shanghai's Jing'an District, to deepen China-specific vehicle development as competition intensifies in the world's largest auto market. The new facility will study China's fast-changing smart mobility market and consumer trends, with insights incorporated into the development of next-generation vehicles and AI-defined mobility technologies. Relocated from Huangpu District to a standal
- Government sees €1.5bn tax package and what an AI crash would mean for Ireland
Business Today: The best news, analysis and comment from The Irish Times business desk
- 26% of surveyed manufacturers plan to increase Physical AI investment: TCS study
“Manufacturers are preparing for longer value-realisation timelines, underscoring sustained transformation over short-term pilots,” TCS said, noting that no surveyed organisation plans to reduce Physical AI investments.
- Runtime: OpenAI's security snafu; Google Cloud's blowout quarter; Verse is One To Watch
+ Microsoft expands its deal with Mistral, Intel snags a new chip customer, and Microsoft admins finally catch a break.
- The new value architecture of the AI-native SaaS era
The traditional methods of measuring success no longer tell the full story. Here’s what should replace them — and why. In brief: AI is transforming software as a service (SaaS) , and the old ways of keeping score no longer apply. Smart companies are evolving new metrics that provide deeper insight into how AI-native software is performing in a new marketplace. These changes impact everything from pricing to valuations. The transformation of the software-as-a-service (SaaS) industry toward AI-native operating companies is rapidly changing the unit of value across the industry. The traditional metric of seats — which measured access — is rapidly giving way to credits designed to measure work performed. This evolution is upending the industry in multiple ways, impacting everything from pricing to enterprise valuations. While many companies still cling to seat-based metrics to measure growth, efficiency and durability, the future is likely to be one in which companies utilize a credit-centric metrics framework , with seats and outcomes as the bookends of a spectrum. Why do software companies need new metrics? Why the rethink, and why now? There are five major forces that are driving this shift: The unit of value is changing . Seats measured who could access software, and credits measure what the software actually does. But in an AI-native world, agents don’t have seats; they have workloads. Over the past 18 months, every major SaaS platform has moved to some forms of credit or consumption unit. The cost of goods sold (COGS) is exploding. AI inference adds real per-unit costs that scale with usage. In an AI-native world, software companies can’t scale to infinite users at near‑zero marginal cost as before. Buying is moving up the org chart. AI-native applications shift purchasing to higher-level operators — such as line-of-business leaders or chief operating officers — which expands the market from software budgets to labor budgets. And because AI agents replace services as well as software, the total market opportunity is 3x to 10x larger than traditional SaaS. Time to value (TTV) is collapsing. With AI-native tools, customers start seeing meaningful results in weeks rather than quarters. Onboarding and setup are fast, workflows are pre-built, and there’s no need for extensive customer success or professional services — dramatically reducing implementation time and costs. Retention is bifurcating. AI forces clarity in a way that traditional SaaS couldn’t. Products that can provide value become even “stickier” and retain customers. Those that don’t churn faster. In an AI-native marketplace, the middle disappears. How this shift is impacting pricing Given how AI-native software is transforming the market , the shift to more variable pricing options is inevitable. Seats won’t go away completely. Subscription pricing based on the number of users is stable and predictable and will continue to work for some customers. Tokens — the use of pass-through pricing for underlying compute — will fit those customers where the AI feature is commoditized or the buyer wants transparency into costs. Credits will likely become the dominant architecture because they provide a simple metric for both customers and providers. The vendor sets the conversation ratio between credits and underlying compute, shielding the customer from inference cost details. Credits are easy to understand and can be packaged into annual contracts for multiple features and products. Finally, the industry will likely see some move toward outcome-based pricing for results such as resolved tickets, recovered revenue or qualified leads. This strategy will mostly be limited to verticals where it is easy to prove AI impacted the result. Where a software vendor sits on this spectrum is a signal of differentiation and pricing power. Credits are where most defensible AI-native businesses are landing because they balance customer predictability with vendor margin control. How AI upends classic SaaS metrics When SaaS was in its infancy, companies settled on key metrics designed to answer a small set of core questions. Are we growing? Are customers using the product? Are we retaining and expanding accounts? But as AI upends software itself, it is also requiring companies to adopt new metrics to track success. These new metrics fall into three primary buckets, rebuilt around the pricing spectrum described earlier and the trend toward credits as the primary frame: Revenue composition Committed credit annual recurring revenue (ARR) vs. burndown ARR: Measuring the credits sold on annual commitment vs. those consumed and replenished. This is the single most important split for valuation. Committed credits behave like subscription and burndown behaves like usage. Credit utilization rate: The percentage of purchased credits consumed per period. This is a leading indicator of renewal sizing. Credit burn velocity: How fast is a customer consuming their credits, and is that consumption increasing or decreasing quarter over quarter? This metric predicts expansion or contraction before it shows up in ARR. Effective price per credit: The real revenue per credit after discounts, overage and rollover, which can detect revenue leakage and help companies set smarter guide rails. Margin reality Credit margin: The gross profit the company earns per credit after subtracting inference costs. This is the core economic unit for AI-native, usage-based businesses — the replacement for gross margin per seat used in SaaS. Inference-adjusted gross margin: By carving out AI inference costs separately in the P&L statement, you can see true AI margins, avoid hiding deterioration inside blended SaaS margins, and clearly distinguish AI economics from legacy SaaS economics. Compute leverage ratio: This metric measures how efficiently the business converts compute spend into revenue. It shows whether your AI margins are improving as you scale. AI-adjusted “Rule of 40”: This updated metric recalibrates the traditional growth and profitability benchmark to account for AI’s lower gross margins and variable inference costs, giving a more accurate picture of business health for AI-native companies. Behavioral and value signals Time-to-first outcome: Replaces traditional onboarding metrics. Tracks how fast a customer reaches their first measurable result. Adoption: AI-native adoption is measured by workflow penetration and active agent density, not seat count. As AI replaces human-driven usage, the unit of adoption shifts from people to automated workflows and agents. Net credit retention (NCR): Credit-volume retention across the customer base, tracked separately from net recurring revenue to avoid price-change impact. Along with these new metrics, the industry’s transformation is prompting companies to retire or recalibrate old SaaS measures, including per-seat ARR as a primary key performance indicator (KPI), traditional magic number calibrated to subscription dynamics, unadjusted Rule of 40, customer success metrics tied to human touchpoints, and blended gross margin without AI COGS carve-outs. What does this mean for enterprise value calculations? As the internal metrics of success change, so do the ways the investment community measures growth and long-term viability. Increasingly, a company’s valuation multiple depends on whether its revenue behaves like committed subscription ARR or volatile usage ARR, and the commit‑to‑burndown ratio is the metric investors use to decide where the company fits. For example, a business with 80% committed credit ARR could trade closer to subscription comps and one with 80% burndown could trade closer to usage comps even though both have the same types of customers. Being able to proactively explain the commit‑to‑burndown mix can help companies avoid undervaluation. In addition, utilization is expected to replace net promoter scores and seat usage as the primary predictor of churn or expansion. Low utilization guarantees downsizing at renewal, so companies must track utilization cohorts the same way SaaS tracks logo retention cohorts today. We’re also seeing an inversion of the operating model, with R&D and COGS moving up the P&L and sales and marketing (S&M) and customer success (CS) moving down or sideways. The net operating leverage profile is structurally different from classical SaaS, and the cost-to-scale curve looks different too. Finally, credit margin engineering is a hidden value-creation lever. The gap between price per credit and cost per credit is set by the software vendor and can be optimized. Most operators have barely started managing this rigorously, and the ones who do will pull away on margin. What this means for leaders, boards and investors The shift from classic SaaS metrics to new AI‑native measures isn’t cosmetic. It represents the seismic change the industry is experiencing as AI matures and transforms products and organizations. While these metrics — and perhaps others yet to be determined — may evolve over time, there is no doubt they are already changing how AI companies allocate capital, price products, incent sales teams, evaluate performance and communicate with investors. It’s important to remember that SaaS metrics were practical tools for a specific era of software. As that era draws to a close, winning companies will choose new metrics that shape behavior and drive smart decision-making. The views reflected in this article are the views of the author and do not necessarily reflect the views of Ernst & Young LLP or other members of the global EY organization. This article is published as part of the Foundry Expert Contributor Network. Want to join?
Score: 60🌐 MovesJul 23, 2026https://www.cio.com/article/4199528/the-new-value-architecture-of-the-ai-native-saas-era.html - Erbis Partners With Databricks to Drive Enterprise Data & AI Transformation at Scale
Erbis Partners With Databricks to Drive Enterprise Data & AI Transformation at Scale azcentral.com and The Arizona Republic
- Reinventing hiring for the AI-driven labor market
How AI is transforming recruitment while elevating HR into a strategic talent leadership role.
Score: 60🌐 MovesJul 23, 2026https://www.techradar.com/pro/reinventing-hiring-for-the-ai-driven-labor-market - How Figma stays ahead of vulnerabilities with agents
For the past year, agents at Figma have guarded code as it's written, reviewed every pull request, and audited a decade-old monorepo, all on one policy.
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- The AI context gap: Enterprise AI organizations have a trust problem, not a retrieval problem — and most are still building the fix
Across 101 enterprises, the infrastructure that feeds AI agents their business context is being built faster than it can be trusted. Retrieval-augmented generation is already the default context source, and provider-native retrieval has quietly overtaken the dedicated vector databases that define the category — yet a majority of enterprises have already watched their agents produce confident, wrong answers traced to missing or inconsistent context. A governed semantic layer is emerging as the fix, but most are still building it; the field is converging on hybrid retrieval; and even as provider-native tools lead in practice, a plurality say they intend to keep best-of-breed. The result is a context gap — agents that sound authoritative running on a foundation their owners do not yet fully trust. This wave of VentureBeat Pulse Research examines the enterprise RAG and context layer: what feeds AI agents their business context, which retrieval systems enterprises run, how they buy and measure them, where the architecture is heading, and — most revealingly — how often that context is already failing them. The central finding is a context gap — the distance between how confidently enterprise agents answer and how reliable the context beneath them actually is. A majority of enterprises (57%) report that in the past six months their AI agents produced confident but wrong answers they traced to missing or inconsistent business context, and more than half of those said it happened more than once. This is not a fringe failure: retrieval is the primary context source for 38% of enterprises, more than any other approach, so when retrieval is thin or inconsistent, the errors it produces are wearing the agent’s authority. The infrastructure to fix it is being built — 58% already run or are building a governed semantic layer — but for most it is not yet in production. Underneath, the market is consolidating in a direction that surprises. Provider-native retrieval — OpenAI’s file search (40%) and Google’s Vertex AI Search (38%) — already leads every dedicated vector database, and enterprises expect hybrid retrieval to dominate by the end of 2026 (34%). Yet a plurality (36%) say they intend to keep best-of-breed standalone tools rather than consolidate onto a provider’s native context stack, and a majority (57%) plan to switch or add a provider within the year. Stated preference and actual usage are pulling in opposite directions — the market is buying provider-native while insisting it wants independence. Methodology VentureBeat fielded this survey as part of its ongoing Pulse Research series. This survey focused on enterprise RAG infrastructure and the context layer — the retrieval systems, semantic layers, and context sources that feed AI agents. Responses are filtered to organizations with more than 100 employees (n=101); the survey drew no responses from organizations of 100 or fewer, so the full sample qualifies. All responses are from a single Q2 2026 (June) wave, so the report reads cross-sectionally and does not infer month-over-month trends. Several questions were multiple-select, so those shares can sum to more than 100%. By organization size the sample concentrates in the mid-market: 251–1,000 employees (31%) and 101–250 (31%) lead, with 1,001–5,000 (20%), 5,001–10,000 (12%), and 10,001+ (7%) above them. By role it spans managers (39%), individual contributors (27%), the C-suite (16%), and VPs and directors (14%); on purchasing authority it is buyer-credible, with 46% final decision-makers and another 26% recommenders or influencers. Technology/Software is the largest industry at 20%, followed by Healthcare/Life Sciences (11%) and a broad spread across retail, transportation, financial services, manufacturing, and education. At 101 respondents this is a modest sample and should be read as a directional signal rather than a precise measurement; it is self-selected and is not a probability sample. It is best read as the view from organizations actively standing up RAG and context infrastructure rather than from the largest operators. Finding 1: Confident and wrong More than half have traced agent errors to bad context We asked whether, in the past six months, enterprises had traced a confident but wrong agent answer to missing or inconsistent business context. Most had. This is the report’s defining number. A majority of enterprises (57%) have already had an AI agent produce a confident, wrong answer they traced to bad context — wrong metrics, stale definitions, or missing documents — and more than half of those have seen it happen more than once. Only 28% report no such failure, and a small remainder either don’t run agents on enterprise data or don’t trace root cause closely enough to know. The failure mode is specific and dangerous: the model is not obviously hallucinating; it is confidently wrong because the context feeding it was thin or inconsistent. Everything else in this report — what enterprises retrieve, how they govern it, and what they plan to build — is downstream of this problem. Finding 2: RAG is the default context source Retrieval feeds more agents than any other method We asked what an enterprise’s AI agents primarily use to understand its data. Retrieval leads by a wide margin. Retrieval is the backbone of enterprise context. For 38% of organizations, RAG over documents or a vector index is the primary way agents understand the business — nearly twice the share of the next approach, a governed semantic layer or ontology (21%). Mixed approaches (14%), direct live-system queries (10%), and long-context loading (6%) fill out the rest, and only 2% let agents run on the model’s general knowledge alone. The concentration matters in light of Finding 1: because so much enterprise context flows through retrieval, the quality of that retrieval is the quality of the answer. When RAG is the default source, thin retrieval is not an edge case — it is the main failure surface. One approach is notable for its absence from these answers: customizing model weights, also known as fine-tuning. Every leading source of business context is injected at run time. Our most recent direct measurement of fine-tuning comes from our April–May survey wave (a separate survey, n=136), where fine-tuning capabilities ranked last of six factors in model selection at 5% — even as 26% of that sample still named fine-tuning and customization an investment they expect to grow. Fine-tuning has fallen out of the primary selection conversation; context injection is how enterprises make agents knowledgeable about their business. Finding 3: Provider-native retrieval already leads the vector databases OpenAI file search and vertex AI search top the dedicated tools We asked which retrieval systems enterprises run in production today. The answer favors the model providers and hyperscalers over the specialists. The dedicated vector database is no longer the center of the RAG stack. OpenAI’s file search (40%) and Google’s Vertex AI Search (38%) lead — provider-native and hyperscaler-native retrieval — ahead of every purpose-built vector database. Among the specialists, the most-used is the one enterprises already run for other reasons (Elasticsearch/OpenSearch, 20%) and the open, embedded option (pgvector, 12%); the pure-play vector databases that define the category — Weaviate, Qdrant, Pinecone, Milvus — each sit in single digits to low double digits. Notably, 13% of enterprises say they still run no production RAG at all. As with the platforms in the parallel infrastructure wave, enterprises are gravitating to retrieval that comes bundled with tools they already buy. The shape of this finding held across both Q2 waves. In April–May (n=161), provider-built retrieval led usage there too, while every dedicated vector database remained marginal — the most-used standalone vector database peaked at 8% of that sample — and the hybrid, pluralistic future was already the consensus expectation (34% expected hybrid retrieval to dominate, with another 29% expecting multiple architectures by use case). Two waves, consistent picture: the category that coined the “vector database” term is being collected by the platforms enterprises already buy from. Finding 4: But they say they want to keep best-of-breed A plurality resist consolidating onto a provider’s native stack We asked how enterprises will respond as model providers bundle retrieval, memory, and orchestration into their platforms. Their stated intent cuts against their current usage. Here is the tension at the heart of the stack. Even as provider-native retrieval leads in practice (Finding 3), a plurality of enterprises (36%) say they intend to keep best-of-breed standalone tools rather than consolidate onto a provider’s native context stack — well ahead of the 21% who plan to consolidate. Another 21% expect a mix, and 9% intend to build and own the layer themselves. The gap between what enterprises run and what they say they want is the strategic question of the category: they are adopting bundled retrieval for convenience while asserting they will preserve independence. Which impulse wins — the pull of the provider bundle or the stated preference for modular control — will shape the retrieval market more than any single tool. Finding 5: Hybrid retrieval is the consensus bet Vector-only retrieval is already seen as insufficient We asked which retrieval architecture enterprises expect to dominate their production RAG systems by the end of 2026. The field is converging — with a large share still unsure. The architecture is settling on hybrid. A third (34%) expect hybrid retrieval — embeddings combined with reranking and access controls — to dominate their production systems by the end of 2026, three times the 11% who expect vector-only retrieval to prevail. That is a notable signal: the pure vector-search approach that launched the category is already viewed as insufficient on its own, superseded by pipelines that add reranking for accuracy and access controls for governance — the very access controls whose absence produces the failures in Finding 1. Tellingly, the second-largest answer is uncertainty: 17% simply don’t know, and another 14% expect to move beyond a dedicated vector layer entirely toward tool-first or long-context retrieval. The consensus is not a single tool but a layered pipeline — and it is not yet fully formed. Finding 6: The governed context layer is being built now Most run or are building a semantic layer — few in production We asked whether enterprises use a governed semantic or context layer to give agents and BI a shared understanding of their data. Most are on the path; fewer have arrived. The fix for the context gap is under construction. Well over half of enterprises (58%) either run a governed semantic layer in production (25%) or are piloting and building one (34%), and a further 17% are actively evaluating — meaning three-quarters are engaged with the idea in some form. But the balance is telling: more are building than have shipped, so for most enterprises the shared, governed definition layer that would prevent the "confident but wrong" failures of Finding 1 is still a work in progress. The semantic layer is the industry’s answer to inconsistent context; this wave catches it mid-construction, ambition well ahead of production. Finding 7: Bought on ingestion and simplicity, watched for correctness Selection favors operability; monitoring favors correctness and security We asked what matters most when enterprises choose a retrieval system, and what they track once it is running. Both answers lean practical. Enterprises choose retrieval systems on operability. Ease of data ingestion (36%), latency and performance (32%), and operational simplicity (29%) lead the selection criteria — ahead of retrieval accuracy and access control (23% each), the two factors most directly tied to the failures in Finding 1. Once systems are running, the emphasis shifts toward trust: the most-tracked metrics are response correctness (42%) and security and access control (38%), ahead of latency (28%), operational stability (27%), and answer relevance (23%). Satisfaction with current systems is moderately positive but not enthusiastic — on a five-point scale, overall satisfaction averages 4.0, with ease of implementation and value for money both near 3.9. Enterprises buy for how easily a system runs and watch it for whether it can be trusted. Finding 8: A retrieval reshuffle is coming A majority plan to change providers — and the vector specialists are gaining interest We asked whether enterprises plan to change or add a retrieval provider, and which they are considering. The consideration set differs from today’s stack. The retrieval stack is not settled. While 43% have no plans to change, a small majority (57%) intend to switch or add a provider within twelve months, and a quarter (26%) within the next quarter. The consideration set is where it gets interesting: provider-native retrieval still leads what enterprises are evaluating (OpenAI 22%, Vertex AI Search 21%), but the open-source vector specialists punch above their current footprint — Qdrant (14%) and Milvus (13%) draw more switching interest than their present usage (10% and 6%) would suggest. Read with Finding 4, the picture is a market in flux: enterprises run provider-native today, are evaluating a broader field, and say they want to keep their options open. The reshuffle ahead will test whether best-of-breed intent survives contact with the convenience of the bundle. The bottom line: A context gap that more retrieval alone won’t close Organizations with more than 100 employees are wiring agents into their business faster than they can guarantee the context those agents run on. Retrieval is the default source of enterprise context, and it increasingly comes from the model providers and hyperscalers rather than the dedicated vector databases — yet a majority of enterprises have already watched agents answer confidently and wrongly because that context was thin or inconsistent. The failure is not exotic; it is the predictable result of pointing authoritative-sounding agents at an unreliable foundation. The industry’s answer — a governed semantic layer, hybrid retrieval with reranking and access controls — is being built but is mostly not yet in production, and enterprises are pulled between the convenience of provider-native bundles and a stated preference for best-of-breed independence. At 101 respondents in a single Q2 wave this is a directional read, skewed toward the mid-market — but the direction is clear: the context layer is the next contested tier of the AI stack, and right now agents are running ahead of it. The context gap is not a retrieval-volume problem that more documents or bigger indexes will solve on their own; it is a problem of governed, consistent, access-aware context. The open question for later waves is whether enterprises finish building that layer before the confident-but-wrong failures move from the lab into decisions that matter. Based on survey responses from 101 qualified enterprise respondents (100+ employees), drawn from a single Q2 2026 (June) wave. At this sample size the results should be read as a directional signal rather than a precise measurement — it's a self-selected sample, not a probability sample, and skews toward the mid-market. Respondents include managers, individual contributors, VPs/directors, and the C-suite, with strong purchasing authority, across technology, healthcare, retail, transportation, financial services, manufacturing, and education.