AI News Archive: July 21, 2026 — Part 5
Sourced from 500+ daily AI sources, scored by relevance.
- Google Bets Water Stewardship Can Ease AI Data Center Concerns
Google plans to replenish more freshwater than its data centers consume by 2030 as it expands water stewardship efforts amid growing AI infrastructure scrutiny. The post Google Bets Water Stewardship Can Ease AI Data Center Concerns appeared first on TechRepublic .
Score: 55🌐 MovesJul 21, 2026https://www.techrepublic.com/article/news-google-water-stewardship-ai-data-centers/ - AI tech proactively prevents subway door entrapment accidents
A research team led by Professor Jo Woon Chong of the School of Electronic and Electrical Engineering at Sungkyunkwan University (SKKU), in collaboration with researchers from KAIST and Texas Tech University in the United States, has developed the Passenger Movement Estimation System (PMES). This AI-based system predicts passenger movements using CCTV footage to prevent subway door entrapment accidents before they occur.
Score: 55🌐 MovesJul 21, 2026https://techxplore.com/news/2026-07-ai-tech-proactively-subway-door.html - Samsung’s humanoid robot ambitions are real, but factories come first
Samsung wants to build humanoid robots, but its new RX division is starting with practical factory machines while consumer models and robot butlers remain distant ambitions without a launch timeline.
Score: 55🌐 MovesJul 21, 2026https://www.digitaltrends.com/computing/samsungs-humanoid-ambitions-are-real-but-factories-come-first/ - Meta’s AI Push Threatens Shopify’s Core Business, Analyst Warns
Meta’s AI Push Threatens Shopify’s Core Business, Analyst Warns Barron's
- TerraByte AI expands its ‘Earth Search Engine’ with satellite imagery partnership and interactive features
Startup upgrades 'Earth Search Engine' to open new paths to high-resolution satellite imagery and data-driven insights. Read More
- Temporal takes on the chaos behind enterprise AI agents
With more agents comes more complexity — an issue Temporal Technologies Inc. aims to address. Founded in 2019, Temporal offers an open-source durable execution platform that ensures artificial intelligence agents work as designed. Becoming available on Amazon Web Services Marketplace has given the company a wider reach, with big-name clients hoping to better manage their agentic […] The post Temporal takes on the chaos behind enterprise AI agents appeared first on SiliconANGLE .
Score: 55🌐 MovesJul 21, 2026https://siliconangle.com/2026/07/21/temporal-agentic-ai-durable-execution-awsmarketplaceseries/ - Local AI clustering with Dell's Pro Max GB10 — connecting two Nvidia Grace Blackwell to scale out AI compute at home
We paired up and tested a pair of Dell's Pro Max with GB10, to see what a small cluster of Nvidia's Spark silicon can do. At $6332 each, as of writing, it's still an expensive prospect, but far cheaper and more desk-friendly than a big server box full of GPUs and the other necessary high-end hardware.
- BlueWave-ai technology to regulate electric vehicle charging in Ontario
The Ottawa-based company signed a deal with four Ontario utilities that aims to smooth the electricity load used by EV chargers
- Inflection AI is Shaping the Future of Personal Intelligence
Inflection AI is Shaping the Future of Personal Intelligence Toronto Star
- It’s time to rebuild the house: The AI-driven transformation of enterprise
Somewhere around 2017, at a family dinner in my hometown in Gujarat (India), an uncle asked me what I actually did for a living. I said “cloud consulting,” and he had no idea what that meant. So I reached for an analogy, and it worked so well that I’ve been using it for a decade […] The post It’s time to rebuild the house: The AI-driven transformation of enterprise appeared first on e27 .
Score: 55🌐 MovesJul 21, 2026https://e27.co/its-time-to-rebuild-the-house-the-ai-driven-transformation-of-enterprise-20260719/ - Why Do AI Systems Misbehave?
AI systems are increasingly impressive, which makes their failures all the more baffling. This blog dives into the causes of AI misbehavior. The post Why Do AI Systems Misbehave? appeared first on Center for Security and Emerging Technology .
- IT leaders confident but cooked when it comes to rogue AI agents
A large majority of IT and security leaders are confident in their teams’ ability to detect when an AI agent has gone rogue, but few are able to take quick action to mitigate the fallout when an agent exceeds its intended scope. Nine in 10 IT and security leaders surveyed by IT observability vendor WanAware believe in their capabilities to find malfunctioning agents, but only 26% acknowledge that they can trace the downstream impact within minutes. Over 45% say it would take hours to understand the full impact of an agent incident. That delay between detection and mitigation can be a huge problem, says Jeffrey Collins , WanAware’s CEO. The survey suggests IT leaders are overconfident about their ability to control agents, he adds. And here, timing is critical, Collins says, given that malfunctioning agents can lead to major outages and data breaches — damage that can start within seconds, he notes. “That’s truly the gap here. It’s not if you understand it; it’s when you understand it,” Collins says. “If your average time to just knowing about an event is measured in days, weeks, or months, you have a serious problem right now.” While it’s not always easy to tell whether an agent has gone beyond its scope, it’s even harder to tell the downstream impacts, he adds. “What’s been affected if one machine was compromised, either from our own AI usage as a customer or from someone else’s, what else could happen, and how can we understand that quickly?” Collins asks. Machine speed Kevin Paige , field CISO at IT solutions provider C1, agrees that time is of the essence when an AI agent malfunctions. “The problem is that agents move at machine speed, so the gap between an agent malfunctioning and you catching it isn’t measured in minutes, it’s measured in actions,” he says. “Every minute it’s wrong it’s still working, and because it’s usually running on borrowed standing credentials, the damage spreads across everything those credentials can reach before anyone can pin it on the agent.” In many cases, organizations with rogue agents don’t find out from their own detection tools , but from customers, auditors, or broken downstream systems, he says. “That’s the worst way to learn,” Paige adds. “The longer-term cost is trust, because one incident like that and the business pulls back on agents entirely, so failing to contain a malfunction fast is also what stalls adoption.” The problem with detecting rogue agents is that many organizations have built in visibility but not control, he says. “When an agent goes out of scope it’s rarely dramatic,” Paige adds. “Usually, it’s using access it legitimately has, for a purpose nobody signed off on, which means your access model doesn’t even flag it. So you find out after the fact, and you fix it by hand.” IT teams can stop agents that exceed their scope, but only if controls were built in before the agent was deployed, adds Chris Camacho , COO of Abstract Security. “Every agent should have its own identity, narrowly scoped permissions, and a complete audit trail,” he says. “Just as important, organizations need the ability to immediately revoke that identity or suspend the agent without manually hunting through multiple consoles during an incident.” Part of the challenge is that an agent’s activity is spread across identities, cloud platforms, SaaS applications, APIs, and security tools that were not designed to tell a complete story, Camacho says. Security teams often have to piece together events from multiple basic questions such as, what did the agent access, and what changed? “Most organizations know where they’ve deployed AI agents,” he adds. “That’s very different from knowing exactly what an agent did after something unexpected happens.” The organizations that most successfully manage agents won’t be the ones that deploy the most, he says. “They’ll be the ones that can explain every action an agent took, prove it operated within policy, and stop it immediately when it doesn’t,” he adds. Confidence isn’t reality The survey’s results make sense to Joe Brinkley , director of offensive security research and community at pentest firm Cobalt. The high confidence in detecting malfunctions is compliance paperwork, whereas the minority of respondents who can detect problems quickly is the reality on the ground, he says. “Tracing agent impact fast is brutal,” Brinkley says. “These systems do not run on fixed code paths. They use nondeterministic reasoning across a web of different APIs. Traditional logs only catch isolated events. They completely miss the full execution chain.” By the time an anomaly alert hits, an agent has already executed multiple downstream actions, he adds. In some cases, agent malfunctions are related to data flow vulnerabilities, such as when a prompt injection from an untrusted input such as a malicious email overwrites the system instructions, he says. “We need to be clear about the actual technology; the AI is not waking up angry,” Brinkley says. “The agent suddenly thinks its official job is to dump your database. It spends tokens as fast as possible to do that.” Agents are also vulnerable to loop failures, when they hit API errors and try to self-correct, he adds. “It hits that same broken endpoint 10,000 times in two minutes,” he says. “It drains your budget and causes a self-inflicted denial of service. It is an automated wrecking ball moving faster than your monitoring can log it.” Brinkley recommends that IT leaders put “hard kill” switches at the API layer to stop agents going out of scope. “You can stop it, but soft guardrails are useless,” he says. “Do not try to patch the prompt or filter the text. You have to treat the agent like a compromised user account. Pull the OAuth tokens and kill the access immediately.”
Score: 55🌐 MovesJul 21, 2026https://www.cio.com/article/4198035/it-leaders-confident-but-cooked-when-it-comes-to-rogue-ai-agents.html - Canva wants you to have full control over the design – Code 2.0 just does the heavy lifting for you
AI chatbots are rubbish at making edits to your designs – Canva Code 2.0 automatically creates elements for you to amend yourself.
- Banking Payments Architecture, Real-Time Rails, AI, And Commercial Trends
Real-Time Payments, Orchestration Engines, And AI Fraud Detection Drive Capital Efficiencies Commercial banking is undergoing a structural metamorphosis, shifting from rigid, batch-processed legacy systems to a model of autonomous, event-native finance. The convergence of ISO 20022-native data fabrics and agentic AI is transforming payments from simple capital transfers into high-fidelity data signals that power real-time […]
Score: 55🌐 MovesJul 21, 2026https://www.forrester.com/blogs/banking-payments-architecture-real-time-rails-ai-and-commercial-trends/ - What's New in Snowflake Cortex Agents for Enterprise AI
Learn how new Cortex Agents capabilities simplify building, deploying, and operating enterprise AI agents with managed orchestration, execution, and governance.
Score: 55🌐 MovesJul 21, 2026https://www.snowflake.com/content/snowflake-site/global/en/blog/snowflake-cortex-agents-enterprise-ai-scale - Environment-free Synthetic Data Generation for API-Calling Agents
Training API-calling large language model (LLM) agents demands massive amounts of high-quality trajectories. However, collecting such data at scale typically requires fully implemented environments with executable APIs and realistic, pre-populated backend databases, creating a major bottleneck for scalability. To overcome this, we propose an environment-free synthetic data generation approach that leverages LLMs as on-the-fly digital world models. Given only API specifications, our method generates trajectories mimicking interactions between an agent and a stateful environment. Specifically…
- Indian companies look to Chinese LLMs as AI costs bite
DeepSeek and others are winning on price, but concerns over foreign dependence loom.
Score: 55🌐 MovesJul 21, 2026https://kr-asia.com/indian-companies-look-to-chinese-llms-as-ai-costs-bite - Should AI Learn From Every Conversation?
The human brain rewires itself constantly and building that into AI has risks.
Score: 55🌐 MovesJul 21, 2026https://www.inc.com/komninos-chatzipapas/should-ai-learn-from-every-conversation/91376029 - Roblox launches AI-powered Build tool, expands Hindi support across platform
Roblox launches AI-powered Build tool, expands Hindi support across platform
- AWS VP Who Led Agent-Building Service Departs After Eight Months
AWS VP Who Led Agent-Building Service Departs After Eight Months The Information
Score: 55🌐 MovesJul 21, 2026https://www.theinformation.com/briefings/aws-vp-led-agent-building-service-departs-eight-months - Nvidia shows off DLSS 5 with three AI modes for different levels of detail — upscaler can switch between models in real-time
DLSS 5 gets a second showing with Nvidia opening up the upscaler to object-level tweaking for developers with three different models.
- Personalizing Airbnb search by learning from the guest journey
How we built a Transformer-based sequence model that encodes years of guest behavior to surface the right listings at the right time. By: Daochen Zha , Chun How Tan , Xin Liu , Bin Xu , Han Zhao , Xiaowei Liu , Jun Shi , Tracy Yu , Hui Gao , Huiji Gao , Liwei He , Michael Kinoti , Stephanie Moyerman , and Sanjeev Katariya Introduction Planning a trip on Airbnb rarely happens in a single session. A guest searching for a place to stay in San Francisco might browse dozens of listings over several days, leaving behind a trail of views. Typically, over a period of years, that same guest will have accumulated many previous bookings, reviews, and the occasional cancellation. Taken together, these events reveal a great deal about what that guest values in a stay. For years, Airbnb’s search ranking captured this through hand-crafted features: aggregated statistics such as total past bookings or average listing price. These worked well, but as the feature count grew into the hundreds, the approach became harder to scale and increasingly limited in expressiveness. In this blog post, we describe how we built a sequence modeling system that encodes the full guest journey using a Transformer, learning richer representations of guest preferences to deliver more personalized search results. An example of a guest journey, which is typically long, exploratory, and complex. Challenges Event sequences per guest present three core challenges. First, they are dominated by listing views, which account for the vast majority of all events — some guests accumulate hundreds of thousands of them — making raw sequences computationally intractable to model directly. The distribution of event types, with the majority being listing views. Second, unlike social media platforms, which optimize for engagement, Airbnb optimizes for booking conversion. Bookings are rare, compared to events, and deliberate, whereas a listing view could reflect genuine intent or simply idle browsing. Building a model that generalizes from these sparse, noisy signals to surface the listings most relevant to a guest is fundamentally different from social media engagement modeling. Third, training a sequence model on hundreds of millions of search-label pairs is expensive; targeted optimizations are needed to reduce costs and improve the throughput of model training. Solution Designing the guest sequence How to tackle this challenge, which is also a rare opportunity to use Transformer technology to improve guest experience? After research into similar use cases, we came up with a plan of action. Rather than modeling all events uniformly, we split the guest sequence into two parts: The long-term sequence captures infrequent but informative events from the past seven years: bookings, reviews, cancellations, and other past interactions. We cap this sequence at 80 events. The short-term sequence captures listing views from the past 21 days. We cap the short-term sequence at 200 events. The 80-event and 200-event capping thresholds are set so that only the longest 2% of sequences are truncated. Together, the two sequences give the model both depth from a guest’s booking history and the immediacy of their most recent browsing activity. Events are encoded using a shared feature pool with a unified embedding table [1] for high-cardinality IDs such as listing and host identifiers, along with hierarchical geographic IDs. An overview of our solution. Making training & serving efficient We’ve implemented three strategies that, together, deliver roughly a 4x improvement in training throughput. The most impactful is batching of searches. Consider a guest who performs three searches — at T=4, T=7, and T=10 — interspersed with events at other timestamps. Because our encoder uses a causal mask, it processes the sequence cumulatively. The embedding produced at position T=3 captures everything the guest has done up to that point, making it the correct sequence representation for the search at T=4. Similarly, no events are ignored between searches: the embedding at T=6 captures the entire sequence from T=1 through T=6 to serve the search at T=7, and the embedding at T=9 captures the full history up to T=9 to serve the search at T=10. Rather than running the encoder three separate times from scratch, we run it once over the full event sequence and route each search to its corresponding intermediate embedding, allowing three searches to share a single encoder forward pass. An illustration of batching of searches. The other two strategies reduced padding waste. (Data is processed in fixed chunks, and empty space in actual data is filled with zeros, referred to as “padding.”) To minimize padding within batches, we bucketize sequences by length. And to eliminate padded searches from the ranking model, we use sparse calculation of searches. The serving design reflects the same efficiency principle. Running a multi-layer Transformer encoder over a full guest sequence — spanning up to seven years of events and 21 days of views — at query time would be expensive. Instead, we decouple the two stages of inference. The sequence encoder runs as a daily batch job, processing event history and storing the resulting embeddings for guests who have generated new events. When a guest performs a search, the ranking model retrieves their stored embedding in real time and combines it with the live search query, then uses the information to produce scores for the retrieved listing candidates. This split keeps latency low at serving time, while still incorporating the full depth of a guest’s history into every ranking decision. An illustration of model serving. Three milestones Before rolling out any changes, we rigorously evaluate them using A/B testing, where guests are randomly but consistently assigned to either our current model or a new model. These tests typically run for three weeks, during which we closely monitor key business metrics and “guardrail” metrics to ensure there are no unintended negative side effects. A new model is only launched if there is strong, statistically proven evidence that it improves the guest experience without failing any safety checks. Following this standard, we rolled out the system in three stages. First, we tested the system with the long-term sequence alone, establishing that sequence-learned guest representations could meaningfully improve our existing ranking system. Second, we added the short-term view sequence, which introduced data scaling challenges that are addressed by the efficiency strategies above. Third, we introduced a setwise ranker [ 2 , 3 ], co-trained with the sequence encoder. Unlike a pointwise ranker that scores each listing independently, the setwise ranker considers a set of candidates together, allowing it to reason about relative differences across listings. Combined with the rich guest representations from the sequence encoder, it can make more personalized ranking decisions by jointly understanding the guest’s travel history and how the retrieved listings compare to each other. Results Offline, the long-term sequence alone improved Normalized Discounted Cumulative Gain (NDCG) of booking labels by +0.44% over our existing system. Adding the short-term view sequence brought the combined improvement to +1.48%. The setwise ranker added a further +2.3% improvement in offline ranking quality. In total, the improvement added up to 3.78%. At Airbnb, where the ranking model has been refined over more than a decade, a 0.3% improvement is considered significant. This substantial improvement means our model can rank the listings a guest likes higher, which may potentially help guests find what they want more easily. Online A/B tests confirmed the gains. With the long-term sequence alone: +0.31% uncanceled bookers (guests who have booked), +0.38% views. Adding the short-term sequence: +0.55% uncanceled bookers, +0.82% uncanceled nights, +0.90% views — all statistically significant. The rise in views suggests that the model is effectively surfacing listings that capture guests’ interests, which directly translates into the higher booking conversions we observed. The setwise ranker delivered a further +0.28% uncanceled bookings and +0.32% booking requesters. We also applied the system to promotional email listing ranking. Using the same sequence embeddings, without any architecture changes, produced +0.16% uncanceled bookers, +0.23% uncanceled nights, and +5.04% email clicks — demonstrating that the guest representations generalize beyond search. Conclusion What we built learns directly from the full arc of a guest’s relationship with Airbnb; their past trips, recent browsing activity, and everything in between. Moving from hand-crafted feature aggregation to learned sequence representations gives our ranking system access to a kind of guest understanding that was previously out of reach: not just what a guest has done across their history at Airbnb, but what those actions suggest about what they are looking for right now. The results across search ranking and promotional emails, which are two independently iterated surfaces, validate that this signal is both powerful and general. The work is far from finished. We are exploring near-real-time sequence embedding updates, richer event types such as wishlists and map interactions, target-aware ranking, generative recommender, and continued improvements to setwise ranking. Each of these directions builds on the same foundation: the more faithfully we can represent the guest journey, the better we can connect guests with homes they will love. For a deeper technical treatment, see our KDD ’26 paper JourneyFormer: Encoding Airbnb guest journey with sequence modeling [4]. Interested in learning more about our technical journey? Browse our previous publications to see how our systems have evolved. If tackling these kinds of challenges excites you, explore our open roles . Acknowledgments We would like to especially thank the following people for their great collaboration and for continuing to advance sequence modeling at Airbnb (listed alphabetically): Ashish Jain, Gil Forsher, Hao Li, Haozhen Ding, Jiawei Yao, Kedar Bellare, Linyun He, Mingyang Xu, Pallavi Adusumilli, Pengyu Hou, Shashank Dabriwal, Sid Reddy, Sophie Wang, Tanya Piplani, Vijay Velagapudi, Yangbo Zhu, Yan Zhang, Yi Li, Yiwei Wang, Yuli Han, and Zhiwei Wang. References [1] Coleman, Benjamin, et al. “Unified embedding: Battle-tested feature representations for web-scale ML systems.” Advances in Neural Information Processing Systems, 2023. [2] Tang, Jie, et al. “Learning to Comparison-Shop.” ACM International Conference on Information and Knowledge Management. 2025. [3] Haldar, Malay, et al. “Beyond Pairwise Learning-To-Rank At Airbnb.” ACM International Conference on Information and Knowledge Management. 2025. [4] Zha, Daochen, et al. “JourneyFormer: Encoding Airbnb Guest Journey with Sequence Modeling.” ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2026. All product names, logos, and brands are property of their respective owners. All company, product, and service names used in this website are for identification purposes only. Use of these names, logos, and brands does not imply endorsement. Personalizing Airbnb search by learning from the guest journey was originally published in The Airbnb Tech Blog on Medium, where people are continuing the conversation by highlighting and responding to this story.
- Tesla’s Problem Is Opposite of Big Tech: Not Enough AI Spending
After hearing Tesla Inc.’s seemingly countless promises about artificial intelligence, autonomous driving and robotics, Wall Street wants the company to start putting its money where its mouth is.
- Moore Threads packs 256 GPUs into its MTT C256 system
Moore Threads, a Chinese GPU developer, demonstrated its MTT C256 system at WAIC 2026, linking 256 GPUs into a single data-center-scale computing unit. The system uses a one-layer Scale-up network for all-to-all communication across the 256 cards and is housed in two standard racks. Moore Threads says the network achieves sub-microsecond latency. The company also […]
Score: 54🌐 MovesJul 21, 2026https://technode.com/2026/07/21/moore-threads-packs-256-gpus-into-its-mtt-c256-system/ - Concerns grow over AI giants' hidden debts
Alphabet, Amazon, Meta, Microsoft, and Oracle have accumulated $1.65 trillion in debt from the data center buildout, up eightfold in four years.
Score: 54🌐 MovesJul 21, 2026https://www.semafor.com/article/07/21/2026/tech-stocks-rebound-after-ai-bubble-fear-induced-slump - China's 'open' AI is a terrible business, and nothing like open-source software
China's 'open' AI is a terrible business, and nothing like open-source software Business Insider
Score: 54🌐 MovesJul 21, 2026https://www.businessinsider.com/china-ai-boom-terrible-business-open-weight-models-2026-7 - CoreWeave’s AI-Native Cloud Faces the Storm
CEO Michael Intrator says the global AI build-out requires capital on a scale rarely seen, but that he is on the “right side” of a generational change.
Score: 53🌐 MovesJul 21, 2026https://www.wsj.com/cio-journal/coreweaves-ai-native-cloud-faces-the-storm-bef81999?mod=rss_Technology - Hisense Unveils AI-Powered Growth Strategy to Shape the Next Era of Intelligent Living
Hisense Unveils AI-Powered Growth Strategy to Shape the Next Era of Intelligent Living The Straits Times
- How AI may drive union-resistant tech workers to the bargaining table
Tech workers are increasingly unionizing, trading Silicon Valley’s myth of exceptionalism for collective bargaining to contest the corporate deployment of artificial intelligence For decades, the technology industry was a fortress that labor unions couldn’t breach. Tech workers already had cushy compensation packages, dream benefits like unlimited vacation and free lunch, and a flat corporate hierarchy that made engineers feel as powerful as their bosses, all of whom dressed down in sneakers and hoodies. So why unionize? Now, that fortress is cracking from the inside. Unions have become increasingly popular for tech employees. After months of mass layoffs tied to artificial intelligence and mounting anxieties about how it’s being deployed, some tech workers say they’ve been saddled with higher workloads while facing the threat of job loss caused by the very products they’re building. Workers from Google DeepMind and Meta in the UK are also objecting to how their companies’ AI products are being used, such as for military purposes or to monitor employee productivity . Those same workers are now attempting to unionize. Continue reading...
Score: 53🌐 MovesJul 21, 2026https://www.theguardian.com/technology/2026/jul/21/ai-tech-workers-unionize - How labor unions are tackling AI and midterm elections
How labor unions are tackling AI and midterm elections marketplace.org
Score: 53🌐 MovesJul 21, 2026https://www.marketplace.org/story/2026/07/21/the-aflcios-strategy-for-ai-and-the-midterm-elections - 70% Logistics Cost Cut? | Why Custom AI Models are the Future
SummaryView Transcript Every logistics company operates uniquely, so why settle for one-size-fits-all AI? Sushanth Raman, CEO of Pallet, introduces custom AI models designed specifically for individual businesses. Discover how sovereign AI slashes execution costs by 70-80%, enhances data privacy, and drives tangible ROI. Learn why owning your intelligence is critical for future supply chain success. […] The post 70% Logistics Cost Cut? | Why Custom AI Models are the Future appeared first on FreightWaves .
Score: 52🌐 MovesJul 21, 2026https://www.freightwaves.com/news/70-logistics-cost-cut-why-custom-ai-models-are-the-future - Zhipu shares surge 37% as firm builds giant data centre powered by Chinese chips
Shares of Chinese AI giant Z.ai soared 37 per cent in Hong Kong on Tuesday to close at HK$1,219 (US$155), after the company recently completed a giant data centre powered entirely by Chinese chips. Also known as Zhipu, the firm’s share prices rebounded after a week-long drop of more than 40 per cent. According to people familiar with the matter, it had built a giant 1-gigawatt (GW) AI computing centre – a facility that will be used to train and deploy its GLM models. The company has positioned...
- The next enterprise AI frontier is the optimizable company
For the past two years, companies have been trying to add AI to their processes. That sounded reasonable at first. Existing workflows, existing systems, existing data, existing dashboards — now with intelligence attached. Add a copilot here, an agent there, a few automated steps, a model connected to some tools, and wait for productivity to rise. But this approach is starting to reveal its limit: The problem isn’t that AI can’t act. It increasingly can. The problem is that most companies don’t have a formal representation of what their actions mean. A company isn’t a pile of applications. It’s not a CRM, an ERP, a data lake, a Slack workspace, a set of spreadsheets and a collection of dashboards. A company is a causal system: customers, products, contracts, prices, suppliers, employees, approvals, incentives, constraints, risks, processes, and outcomes, all influencing one another over time. If AI is going to optimize the company, it first needs to know what the company is. That’s why the next frontier in enterprise AI isn’t the agent. It’s the ontology. From data to ontology The word ontology can sound unnecessarily philosophical, but the idea is simple: It is a formal model of what exists in a domain and how those things relate to one another. In enterprise terms, that means representing the company not as disconnected rows in databases or documents in repositories, but as a living structure of objects, relationships, permissions, workflows, and actions. A customer has contracts. A contract has terms. A product has dependencies. An order changes inventory. A delay affects satisfaction. A discount affects margin. A support interaction affects retention. That structure matters because AI systems cannot govern or optimize what they cannot represent. This is one reason Palantir’s Ontology has become such an important reference point in enterprise AI. Palantir describes its Ontology, like that, with a capital “O” as if it were a word they’ve somehow invented themselves, as a system that goes beyond data cataloging or schema design, providing a foundation for workflows with metadata, security, and governance. Its architecture material describes the Ontology as the “ dynamic, compounding core of the cybernetic enterprise ,” connecting data, logic, and action into a shared operational model for humans and AI-enabled agents. That is a significant shift. It moves enterprise AI away from “chat with your data” and toward something more serious: AI acting inside a formal model of the business. But once a company has an ontology, a deeper question appears: What is the ontology for? Static ontology is not enough A static ontology tells the system what exists. That is useful. It creates legibility. It lets software and humans refer to the same objects. It makes workflows less dependent on ad hoc integration. It gives agents something more structured than a prompt and something more reliable than a pile of retrieved documents. But companies are not static. A company changes every minute. A customer moves from prospect to active account to renewal risk. Inventory changes. Credit exposure changes. A supplier becomes unreliable. A sales process works in one segment and fails in another. A support policy improves cost metrics and quietly damages trust. A pricing rule increases margin and lowers long-term retention. The real company isn’t the noun. It’s the verb. This is why the distinction between static and dynamic ontology matters. Palantir’s own documentation distinguishes between the semantic elements of the ontology (objects, properties, and links) and what it calls the “ kinetics of the organization ,” defined through action types and functions that enable change while complying with controls and governance. That is already much closer to what enterprise AI needs. But even dynamic ontology may not be the final step. A dynamic ontology can tell the system how the company operates. The next step is an ontology that can improve how the company operates. In other words: an optimizable ontology. The ontology should not just describe the company This is the turning point. If an ontology represents the structure of the company, and if AI systems act through that structure, then the ontology is no longer just a map. It becomes part of the machinery of the company itself. That means the ontology should not merely describe operations. It should allow operations to be optimized. This is where most enterprise AI discussions still fall short. They treat ontology as a semantic layer: a way to connect data, define entities, clarify relationships, and give agents a safer environment in which to act. All of that is necessary. But it is not sufficient. The real opportunity is to turn the ontology into an optimization substrate. Every workflow represented in the ontology should be connected to outcomes. Every action should leave a trace. Every trace should be usable as feedback. Every process should have an explicit objective. Every objective should be measurable. Every measurable outcome should allow the system to learn which actions, configurations, and sequences improve results. At that point, the company is no longer simply using AI: The company is becoming optimizable. From workflows to causal structure Most business processes are still treated as diagrams: boxes, arrows, approvals, handoffs, and exceptions. That’s how humans understand work. It’s not how adaptive systems optimize it. An AI system needs more than a diagram. It needs causal structure. It needs to understand that reducing time in one step may increase rework in another. That shortening a support call may increase churn. That discounting may win the customer but lower the quality of revenue. That automating an approval may increase speed but reduce accountability. That optimizing one department’s KPI may damage the company’s global objective. This is not a cosmetic distinction. A recent article in Nature Computational Science argues that reliable algorithmic decision-making needs causal reasoning , because decisions inherently involve cause-and-effect relationships and must align computational methods with real-world objectives. That is exactly the enterprise problem: A corporate ontology cannot stop at representation. It has to encode consequences. The most interesting enterprise AI systems will not merely know that a sales opportunity exists, or that a contract is pending, or that a customer has opened three tickets. They will understand how actions on those objects change the probability of outcomes the company cares about: conversion, retention, margin, risk, satisfaction, cycle time, resilience. That is the difference between a semantic ontology and a causal ontology: A semantic ontology tells the system what things mean; a causal ontology tells the system what actions do. And an optimizable ontology goes further: It learns which actions work. KPIs become reward signals Companies already have something that looks like an objective function: KPIs. These are important metrics such as revenue, margin, churn, conversion, customer satisfaction, resolution time, inventory turns, forecast accuracy, fraud loss, compliance incidents, employee retention, time to market, and more. The problem is that in most companies KPIs are downstream measurements. They tell managers what happened after the fact. They’re reported, discussed, explained, occasionally gamed, and then reviewed again next quarter. In an optimizable ontology, KPIs become something more powerful: reward signals. This doesn’t mean blindly optimizing every metric. That would be dangerous. Metrics can conflict. Some are proxies. Some are incomplete. Some are political. Some produce perverse incentives if pursued alone. But that is precisely why the ontology matters: A KPI should not float alone in a dashboard. It should be attached to the process, objects, constraints, and decisions that influence it. It should be placed inside a model of the company that understands trade-offs. It should be connected to other KPIs so that local improvement does not produce global damage. This is where reinforcement learning becomes relevant to enterprise AI: not as a buzzword, and not as a magic layer bolted onto chatbots, but as a mechanism for improving action inside a formally represented business system. A loop acts. The ontology records what changed. The KPI measures whether the outcome improved. The system adjusts. The next action is better informed. That is the basic shape of an optimizable company. Scale changes the meaning of optimization Traditional process improvement is slow because humans have to observe, interpret, redesign, implement, and measure. That works, but it doesn’t scale well across thousands of processes, millions of interactions, and constantly changing conditions. AI changes the speed of the loop. Once enterprise actions are represented in an executable ontology, each interaction becomes an experiment. Each process execution produces a trace. Each trace becomes evidence. Each evidence updates the system’s understanding of what works. The larger the company, the more interactions it generates. The more interactions, the more feedback. The more feedback, the faster the optimization. This is the opposite of how most enterprise systems behave today. In traditional software, scale creates complexity. More customers, more workflows, more exceptions, more countries, more products, and more regulations make the system harder to change. In an optimizable ontology, scale also creates learning fuel. The company becomes more complex, yes, but it also produces more evidence about how that complexity behaves. That’s why the ontology must be executable. A descriptive ontology can help humans understand the business. An executable ontology lets AI act inside the business. An optimizable ontology lets the business improve through action. Those are three very different stages. The missing layer above agents This is why the current obsession with agents is incomplete. Agents are useful. They can plan, call tools, write code, search documents, take actions, and coordinate with other agents. And the conversation is already moving from individual prompts to loops: a recent Business Insider piece describes “ loop engineering ” as the practice of designing recurring systems that guide AI agents instead of requiring a human to prompt every step manually. That is a meaningful shift. But agents and loops still need a world to act inside. Without an ontology, each agent reconstructs the company from prompts, retrieved fragments, tool descriptions, and brittle integrations. That’s why deployments become artisanal. Someone has to explain the business over and over again: what the objects are, what the rules are, which systems matter, who can approve what, what outcomes count, and where the hidden constraints live. A mature enterprise AI architecture should not require every agent to rediscover the company: The ontology should be the shared world. And if that ontology is executable and optimizable, agents become components inside a larger adaptive system rather than isolated actors improvising their way through corporate reality. This also explains why model independence matters. The ontology is the durable asset. Models will change. Agents will change. Interfaces will change. But the company’s representation of itself—its objects, processes, constraints, outcomes, traces, and learned causal structure —should persist . That is where enterprise AI compounds. Digital twins were the preview There’s a useful analogy here with digital twins. NIST describes a digital twin as a computer model of a physical system that can support simulation, monitoring, optimization, and decision support. In a separate publication, NIST says digital twins enable operators to dynamically represent, diagnose, predict, optimize, and control real-world counterparts such as equipment, subsystems, and processes. That is very close to the intuition enterprise AI now needs—except the object is no longer only a machine, a factory line, or a building. The object is the company. A company needs the equivalent of an operational digital twin: not merely a replica of its physical assets, but a representation of its business structure, processes, constraints, decisions, and outcomes. Recent research on digital twins of business processes points in the same direction, describing virtual replicas of real processes that combine process models, real-time data, and simulation capabilities to guide day-to-day organizational activity. That is the direction of travel. But enterprise AI needs to go one step further. It does not simply need a twin that shows what’s happening. It needs a model through which humans and AI systems can act, learn, and improve. That is the optimizable ontology. World models move from physics to business The phrase world model is usually associated with robotics, autonomous driving, or physical AI: systems that need an internal representation of the environment in order to anticipate consequences and act intelligently. That makes sense. A robot that moves through the physical world needs to know something about objects, space, causality, and time. But companies are worlds too. They’re not physical worlds in the same sense, but they’re operational worlds: partially observable, constantly changing, full of agents, constraints, dependencies, incentives, and delayed consequences. An AI system that acts inside a company without a model of that world is like a robot moving through a warehouse without spatial awareness. It may be powerful. It is not safe. The idea of world models has been central to some of the most important work in reinforcement learning. David Ha and Jürgen Schmidhuber’s “ World Models ” paper explored training agents using compressed representations of their environments. Google DeepMind’s MuZero went further by learning a model of the environment it was playing in and using that model to plan the best course of action. The lesson is not that companies are games: They are not. The lesson is that intelligence becomes much more powerful when actions are connected to outcomes through a model of the environment. Enterprise AI needs the same principle, but applied to organizational reality. Not just “What answer should the model generate?” But “What action should the company take, through which process, under which constraints, toward which objective, and with what expected effect on the rest of the system?” That requires a corporate world model. The company needs to know what it is The hardest part of this transition may be cultural, not technical. Most companies don’t actually have a formal model of themselves. They have org charts, process diagrams, ERP configurations, CRM records, policy documents, data warehouses, dashboards, Slack channels, email archives, and thousands of implicit habits held together by people who know how things really work. That isn’t a world model. It’s an archaeological site. Humans compensate for this because they carry context in their heads. A good manager knows which policy matters, which exception is safe, which customer relationship is fragile, which process is official but ignored, which metric is being gamed, which team is overloaded, and which apparent success is hiding future damage. AI loops don’t know any of that unless the organization makes it explicit. This is why so many enterprise AI deployments still require consultants, integrators, and forward-deployed engineers. Someone has to reconstruct the company for the AI system: what matters, what’s connected, what’s allowed, what counts as success, and where the hidden constraints are. McKinsey’s State of AI in 2025 report makes the broader pattern visible: AI use is widespread, but most organizations have not embedded it deeply enough into workflows and processes to realize material enterprise-level benefits, while high performers are much more likely to redesign workflows and aim for broader transformation. That manual reconstruction is the sign of a missing platform layer. A corporate world model would make that reconstruction persistent, governed, and reusable. Every loop would not need to rediscover the company. Every new agent would not need to be individually briefed on organizational reality. Every workflow would not need to rebuild context from scratch. The company would finally become legible to its own AI systems. From governed decisions to self-improving structure Recent research is already moving in this direction. A 2026 paper on ontology-governed graph simulation for enterprise AI argues that LLM-based agent systems fail when they answer from unrestricted knowledge space without simulating how business events reshape the scenario at hand. Its proposed pipeline—event, simulation, decision—points toward a more structured approach: enterprise decisions derived from an ontology-governed representation rather than from fluent but ungrounded reasoning. That’s important because it shows where the frontier is moving. But the deeper question is not only whether ontology can make decisions safer. It’s whether ontology can make the enterprise itself self-improving. The real promise of enterprise AI is not that every employee gets a better assistant. That’s useful, but it’s not transformation. The real promise is that the company becomes a system capable of improving its own operations continuously. Not through occasional transformation projects. Not through annual process redesign. Not through consultants reconstructing the business one workflow at a time. But through an architecture in which work itself generates the evidence needed to improve work. That requires three layers. First, a formal ontology that represents the company Second, executable workflows that allow AI and humans to act through that ontology Third, optimization loops that connect actions to outcomes and improve the structure over time Remove the first layer, and AI has no stable world. Remove the second, and the ontology remains documentation. Remove the third, and the system cannot learn. Put them together, and enterprise AI stops being a tool attached to the company. It becomes a mechanism by which the company improves. The next question The last wave of enterprise software helped companies digitize what they were. The next wave will help them optimize what they’re becoming. That is a much bigger shift than chatbots, copilots, or agents. It changes the role of software from system of record to system of improvement. It turns KPIs from reporting artifacts into feedback signals. It turns workflows from diagrams into executable, learnable structures. It turns ontology (the real word and its real meaning, not the one capitalized as a commercial term) from description into action. And it raises a question every company will eventually have to answer: Is your organization merely represented in software, or is it optimizable by software? Because the next frontier of enterprise AI will not be the company that has the most agents. It will be the company whose causal structure can learn.
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