China has created an international organization to set standards and introduce regulation for AI, inviting 28 other countries to join — but the US, a leading AI powerhouse is not part it.

The World Artificial Intelligence Cooperation Organization (WAICO) was established by 29 countries, including China, Russia and Brazil, at a ceremony in Shanghai, China, on July 16. Notably absent are the US, the European Union and its member states, the UK, Japan and South Korea.

Chinese AI companies have made a concerted effort to provide an alternative to US dominance. While the US is clearly ahead, Chinese enterprises are looking to narrow the gap in various areas: the open-weight model market, AI cyber protection and open source AI.

WAICO has been some years in development and has been designed to set some universal guidelines in AI. Researchers say WAICO differs in three ways from other initiatives to create global AI organizations: membership open to any sovereign state, there is no regime-type test for entry, and its agenda is built around development and the global capability divide.

The signing ceremony to create WAICO comes just days after Demis Hassabis, CEO of Google DeepMind, called on the US to take a lead in global AI regulation. “The US is well-positioned to take the first step in developing such a framework. It could establish a new Standards Body modelled on a federally overseen public-private partnership or self-regulatory organization, much like the Financial Industry Regulatory Authority (FINRA), with a board that includes independent leading technical experts and open-source representatives,” Hassabis wrote.

Microsoft’s big lead over AI competitors like Google and others has vanished, and the company is now playing catch-up. As a result, Microsoft’s stock has tanked in the last year — down roughly 23% compared to a year ago, due mainly to its massive AI spending and an inability to monetize Copilot. 

The company clearly needs to do something. And last week it did, though not what you might expect. It laid off 4,800 people, a little more than 2% of its worldwide workforce, with its Xbox division hit hardest. And it’s not reducing its massive spending on AI data centers or other AI-related costs.

The New York Times explained the cuts this way: “It is Microsoft’s latest employee culling as it plows tens of billions of dollars into the infrastructure for building artificial intelligence.”

Was cutting back on gaming (while still going all-in on AI) the right move for Microsoft? To answer that, let’s take a look at the details of the company’s July layoffs.

A year of layoffs

The recent cuts come in the wake of larger Microsoft workforce reductions over the last year or so. In May 2025, the company laid off 6,000 employees, about 3% of its workforce. Then a few months later, it laid off 9,000 more, about 4% of its workers. In both rounds of cuts, the company’s gaming division was hit — though it wasn’t the primary target.

This year, in April and May, the company rolled out its first voluntary retirement program for US employees. Approximately 3,000 people took the money and ran.

Then came last week, when Microsoft primarily targeted gaming. When the cuts take full effect over the next year, 2,850 gaming employees will be let go. In addition, Microsoft is cutting loose several of its gaming studio brands, which will become independent companies or be sold to buyers.

The layoffs hit the two remaining gaming studios, Activision Blizzard, which makes the big-selling games Call of Duty and Candy Crush, and ZeniMax Media, which publishes series including Fallout and The Elder Scrolls. Three years ago, in 2023, Microsoft bought Activision Blizzard for $69 billion. That followed its purchase of ZeniMax Media in 2020 for $7.5 billion. Both seemed like sizable acquisitions at the time. 

Compared to Microsoft’s AI spending now, they’re chump change.

Follow the money

A memo sent to employees about the July layoffs by Amy Coleman, Microsoft executive vice president and chief people officer, made clear the layoffs were more about AI than they were about gaming. 

Of the cuts, she wrote: “The “why” is this: our business is changing because the world around it is changing. The way technology is built, deployed, and used is transforming faster than at any point in my time here. Our customers’ needs are shifting, the business models that serve them are shifting, and that means the work itself — what we do, where we focus, and how we’re organized — has to transform, too.

“Our customers are navigating this same shift, and they’re counting on us to help them through it.”

That last sentence is an oblique reference to the early July launch of the Microsoft Frontier Company, which will embed 6,000 engineers inside customers’ businesses to help them more effectively deploy AI. The cost: $2.5 billion.

That sounds like a substantial amount of money. But it’s only a drop in the bucket of how much money the company plans to spend on AI. In April, Microsoft told investors it would spend $190 billion on data centers and other AI infrastructure this year, a 60% increase over what it spent last year. At the same time, Microsoft said it would shrink its workforce.

Its latest layoffs are clear-cut evidence of that. It’s also evidence that the company recognizes how badly Xbox has performed, and that it needed to do something about it. 

In early June, Microsoft sent a memo to everyone in its Xbox division entitled “Next 100 Days: XBOX Reset.” The memo laid out the problems with its ailing game business and pulled no punches. It noted that beyond the $69 billion the company spent three years ago to buy Activision, “Over the past five years, we have spent over $20 billion on ongoing investments in our content, platform, and hardware subsidy, but our annual revenue has declined nearly half a billion during that time. Going forward, this cannot continue.” 

The layoffs and spinoffs were the first steps. They won’t be the last.

There’s no doubt this is just the beginning of Microsoft’s disinvestment in gaming. The issue isn’t just that the company’s investments haven’t paid off. It’s that Microsoft’s AI ambitions are so large and expensive that it can no longer afford to seriously fund gaming.

Ultimately, it was the right thing to do, at least from a business perspective. The future is AI. It’s not in gaming.

So, for the foreseeable future at Microsoft, when it comes to AI — the sky’s the limit. But when it comes to gaming, things look much less rosy.

It’s been several years since generative AI and large language models (LLMs) took the world by storm. LLMs surpassed earlier natural-language systems at generating text, while diffusion models enabled generating images, music, and videos.

These generative AI models work well in the digital world, but on their own, they have limited capabilities to comprehend the three-dimensional physical world and other spaces. This includes the objects occupying an area, how they relate to each other, tracking movement, and answering complex questions requiring an understanding of dimensions, distances, motion, and collisions.

Spatial intelligence is an AI capability that allows models to reason about three-dimensional space. These models can generate 3D scenes of the world and other spaces. This content can then be displayed through traditional renderers, game engines, or AR/VR systems that use spatial computing techniques. But it’s the spatial intelligence model’s ability to connect natural language with 3D models that has the most applications in robotics, manufacturing, construction, and other physical environments.   

Dr. Fei-Fei Li, often called the godmother of AI, published a manifesto on how spatial intelligence is AI’s next frontier, contrasting it with LLMs. “While current state-of-the-art AI can excel at reading, writing, research, and pattern recognition in data, these same models bear fundamental limitations when representing or interacting with the physical world,” wrote Dr. Li. “Our view of the world is holistic—not just what we’re looking at, but how everything relates spatially, what it means, and why it matters. Understanding this through imagination, reasoning, creation, and interaction—not just descriptions—is the power of spatial intelligence.”

The concept of spatial intelligence isn’t new and was described in Howard Gardner’s book, Frames of Mind, in 1983. Recent breakthroughs, including the launch of World Labs’ Marble and its $1 billion funding round, and competing approaches from Google’s Genie 3 and Nvidia Cosmos, should put spatial intelligence and world models on more R&D road maps.

What are spatial intelligence models?

It’s important to develop AI literacy and understand the terminology and concepts related to the physical world and 3D AI technologies: 

• Spatial intelligence encompasses specialized approaches such as geospatial models for mapping the physical world and building information modeling (BIM) for modeling physical structures. It also extends to generative 3D, robotics, and physical reasoning applications.
• World models are a class of neural network architectures and are currently a prominent approach to building spatial intelligence.
• Digital twins are live, virtual replicas of physical assets that combine 3D models with real-time sensor data. Spatial intelligence, an emerging capability of digital twins, adds natural-language prompting, generative scenario exploration, and physics-aware reasoning.
• Spatial computing refers to digital content anchored in and interacting with physical space, sensed and rendered in three dimensions and delivered through AR/VR and mixed-reality systems.

“Spatial intelligence models go beyond pixels to understand the 3D structure of the world—how objects are positioned, how they move, and how they interact,” says David Fattal, founder and CTO at Leia. “This enables applications like more realistic video generation, spatial computing interfaces, and AI systems that can reason about physical environments. As real-world 3D data becomes more available, these models will become foundational to the next generation of visual AI.”

Monitoring the built environment

To better understand spatial intelligence, let’s consider physical infrastructure such as bridges and buildings. The American Society of Civil Engineers estimates a $9.1 trillion investment is needed from 2024 through 2033 to achieve a state of good repair. When maintenance and monitoring lag, it can lead to major failures such as the 2022 collapse of the Fern Hollow Bridge in Pittsburgh.

Spatial intelligence and the development of digital twins may help identify issues earlier and prioritize where investments are needed. “Spatial intelligence models serve as the 4D digital blueprints for our built environment, allowing us to visualize and predict the complex interactions between aging assets and the shifting ground beneath them,” says Patrick Cozzi, chief platform officer at Bentley Systems. “By synthesizing disparate geospatial data into a living digital twin, these models provide the foresight necessary to mitigate the hidden risks of structural fatigue and subsurface instability.”

There’s a significant challenge in bridge health monitoring and transitioning from manual, infrequent structural inspections to leveraging sensors, digital twins, and spatial intelligence. Cozzi adds, “This integration of continuous field data moves beyond static documentation, empowering agencies to evolve from reactive repairs to proactive, resilient asset management that safeguards the long-term integrity of our most critical public systems.”

Avoiding collisions

Bridges are largely static, but the real world is increasingly being occupied by autonomous systems such as self-driving cars, robots, and drones. And where there are moving systems, there is a risk of collisions.

“Spatial intelligence models are AI systems that reason about the physical world by combining vision, sensor data, and contextual cues to understand space, motion, and object relationships,” says Sudeep George, CTO at iMerit. “The value of spatial intelligence models lies not just in perceiving an environment, but in enabling machines to act within it safely and in real time. That is especially important in robotics and autonomous systems, where decisions must be made in complex, multimodal, fast-changing settings.”

To see one example, this tutorial for simulating robotic environments combines Nvidia Isaac Sim, an open source robotics reference framework, with spatial intelligence in Marble from World Labs. 

Today’s collision detection systems, such as those used in autonomous vehicles, typically rely on modules for sensing, perception, planning, and control. Spatial intelligence models may offer improvements by assessing the collision risks of unidentified objects or by tracking objects that move out of sensor view. For example, Waymo’s World Model, built on Genie 3, is a simulator that generates complex weather conditions and other critical safety events.

For an out-of-this-world example, James Urquhart, field CTO and technology evangelist at Kamiwaza, has delivered several examples of spatial intelligence applications, including one for satellite collision detection and conflict analysis. Urquhart says, “Models that specialize in these types of data sets, as well as the physics and geography of the real world, enable faster and more accurate decision-making for tasks that depend on them.”

Applying spatial intelligence

Recent spatial intelligence announcements include creating 3D worlds from image or text prompts with Marble and simulating water physics, lighting, weather, and animal behavior with Genie 3. But difficulties remain in bringing spatial intelligence to physical-world use cases.

“Spatial intelligence and world models are laying the groundwork for future AI agents that will be able to interact in and with our physical world,” says Jason Corso, cofounder and chief scientist at Voxel51. “These models are significantly more challenging to develop and test, largely because the data underlying their development is complex, and it’s hard to handle all of the combinatorics involved in the physical world.”

In addition to learning the models and prototyping with them, development and data leaders need to review the data assets that will feed spatial intelligence models. “Spatial intelligence models translate location signals into a structured understanding of the real world, but they’re only as reliable as the data beneath them,” says Dan Adams, executive vice president and general manager of Enrich at Precisely. “The real unlock isn’t the model—it’s the reference layer with persistent identifiers, confidence metadata, and source lineage that lets AI reason about places, not just match strings.”

Even once applications are developed, there will be infrastructure challenges in deploying them at the edge. Ali Kayyam, principal research scientist at BrainChip, says, “The key to unlocking spatial intelligence at scale is having the low-power, event-driven hardware that can run it at the sensor in real time where it matters most.”

Where to get started

My suggestions for developers looking to get hands-on with spatial intelligence and world models:

• To try out Marble, review their API documentation and case studies, and then experiment with a developer-focused React application.
• Review the Nvidia Cosmos developer hub, documentation, and cookbook of case studies and learning paths.
• You can get an overview of Genie 3, but access is currently restricted through Project Genie, which requires a Google AI Ultra subscription.

The model of the forward deployed engineer is sweeping through enterprise IT like a gold rush, and I’m concerned that many companies don’t understand what they’re signing up for.

Let’s start with the headline numbers. AWS announced a $1 billion investment in a new Forward Deployed Engineering organization. Google Cloud committed $750 million to expand similar programs. Microsoft has been running Azure-focused embedded engineering teams for years, including partnerships with Accenture to scale forward deployed engineering practices. All three are pitching the same story: We’ll send engineers to work directly with your teams, help you deploy AI, and accelerate your digital transformation. You get top-tier technical talent for free, and we get to partner with you on your journey.

It sounds reasonable on the surface. It sounds collaborative, even generous. But I’ve been in this industry long enough to know that when a multi-billion-dollar company offers you something for free, they’re sure to get much more than they give.

What you actually get

The forward deployed engineer model isn’t new. The consulting industry has been doing some version of it for decades. What makes this different is the scale and the direct financial incentive behind it.

These engineers work for the cloud provider. They’re not your employees. They’re not independent consultants. They’re technically excellent professionals who are being paid to solve your immediate problems while simultaneously building relationships and architectures that favor their employer’s ecosystem. Think about it from their perspective. Those forward engineers are evaluated on whether customers succeed with their employer’s platform. They’re rewarded when enterprises adopt more services from that platform. Their career advancement depends on making AWS, Google Cloud, or Microsoft Azure the obvious choice for all of your technical decisions.

This isn’t a criticism of the individual engineers. Many of them are genuinely talented and genuinely want to help. But they’re operating within a system that rewards specific outcomes, and those outcomes align with the vendor’s financial interests, not necessarily yours.

The problem no one talks about

Here’s what I see happening at enterprises right now. A company decides they need help deploying AI. A cloud provider offers to embed engineers at no additional cost. Those engineers work alongside internal teams, make architectural recommendations, and help build out systems. Six months later, the company has a production AI system running on a single cloud platform, built by people with deep expertise in that specific platform.

The problem? Nobody evaluated whether that platform was actually the best choice for the business. Nobody looked at alternatives. Nobody asked whether a multicloud architecture or best-of-breed approach might deliver better results at lower cost.

The engineers embedded in these programs are not going to recommend that you split your workloads across providers. They’re not going to suggest you use open source tools where they make sense. They’re not going to point you toward a competitor when their employer’s solution will work well enough. That’s not how these programs are designed to function. What you’re getting is optimized architecture for a single cloud brand, not optimized architecture for your business.

The financial reality will hit

The bills are going to come due, and they’re going to be painful. I’ve watched this pattern play out before. When enterprises lock into a single cloud provider through these embedded engineering programs, they often discover two or three years later that they’re paying premiums that their more independent-thinking competitors avoided.

The reasons are straightforward. When you’re architecting systems around a single platform, you naturally fall into usage patterns that favor that platform’s pricing structures. You use their managed databases instead of portable alternatives. You adopt their AI services instead of evaluating third-party options. You build workflows that only work within their ecosystem. And when it comes time to renegotiate or benchmark against alternatives, you find that migrating would cost more than accepting whatever pricing they offer.

I’ve spent the past decade helping companies untangle from these situations. I’ve seen organizations with cloud bills 15 to 20 times higher than they should be, unable to migrate because their entire AI infrastructure is built on proprietary services that only work on one platform. The forward deployed engineer programs are accelerating this problem. They’re making it easier to get into these situations and harder to get out.

Think before you commit

Before you accept one of these programs, consider these three recommendations.

First, require independent architecture oversight from day one. Hire or engage architects who work for your company, not for your cloud provider. They should evaluate every recommendation made by embedded engineers against business requirements and compare options across providers. This isn’t about being suspicious of the engineers. It’s about ensuring that decisions are made with your interests in mind.

Second, demand a clear exit strategy before you begin. Ask the cloud provider to document which proprietary services you’re using, what migration paths exist, and what the cost would be to move to an alternative platform. If they can’t provide that information, or if the migration costs seem impossibly high, that’s a sign that you’re building technical debt that will be very expensive to service later.

Third, benchmark your costs continuously. Set up internal processes to compare your cloud spending against industry benchmarks and against what your competitors might be paying for similar workloads. Don’t wait until your contract renewal to discover that you’re paying premium prices. Monitor expenses from the beginning, and be willing to challenge your cloud provider if you’re not getting value that justifies the cost.

The bottom line

The forward deployed engineers are solving real problems. Enterprises genuinely struggle with AI deployment, and having experienced engineers available to help is valuable. I’m not suggesting these programs are fundamentally bad. However, they’re being marketed as neutral partnerships when they’re actually strategic sales programs designed to lock enterprises into specific platforms. The helpful engineers showing up at your office are building dependencies that will be very difficult to break. The “free” technical assistance is being funded by margins on services you’ll be buying for years.

Go in with your eyes open. Use these programs but add your own independent oversight. Build architectures that you could leave if you needed to. And don’t let the immediate satisfaction of having problems solved today blind you to the financial consequences that will arrive tomorrow.

For all its potential, generative AI, on the whole, churns out a lot of junk.

Yet employees are becoming ever more reliant on this “workslop” masquerading as high-quality material, says a recent Harvard Business Review blog. They become lazy and less productive, quality control goes off the rails, and integrity and trust begin to erode.

Experts urge enterprises to act now, before they lose control entirely.

“When slopification happens at scale and in sequence across a business’s processes, those processes themselves — and their outputs — start to deteriorate,” Matthias Holweg, professor at the University of Oxford’s Saïd Business School, and analyst Thomas H. Davenport argue in the post. “Eventually, people start to lose trust in the processes that they rely on to do their jobs.”

They call this organization-level phenomenon “knowledge decay.”

Three key challenges

Holweg and Davenport identify three key challenges to address when it comes to AI-generated content in the workplace, to ensure knowledge decay isn’t occurring: verification, validation, and entropy.

Verification requires “disentangling” authentic human content from AI-generated content that could contain glaring errors, they note.

This can be time-intensive and involve critical thinking and additional research, and in many cases the effort negates the gains gleaned from AI use. In the case of hiring, for example, in addition to using AI to write their resumes and CVs, tailoring their prompts to the AI ranking algorithms to ensure they rise to the top of the queue, some candidates are getting crafty, using AI clandestinely to generate responses to interview questions in near-real time.

All this taken together can result in candidates who are subpar or simply not a good fit for an organization. As a result, recruiters may then have to spend more time doing on-site interviews where AI access isn’t available, Holweg and Davenport contend.

Another issue is knowledge validation: confirming how and where humans have provided real value when AI is used in a workflow. For instance, a consulting firm can easily use AI to create standard written reports and PowerPoint slides, while their clients are paying for expert human insights.

“Human experts now have to justify not only the quality of the output submitted, but also that actual human intellectual work has produced it,” Holweg and Davenport emphasize.

Finally, knowledge entropy is like a “risky AI-based game of telephone”: As knowledge is passed through an AI again and again in an iterative process, it moves further away from the original “ground truth” data that was used to create it in the first place, they point out.

“The greater the number of iterations of content through an LLM, the more it will depart from the original,” Holweg and Davenport note. LLMs are probabilistic, “context-agnostic” statistical models that provide next-word-prediction outputs. They have “no conception of fact or truth and simply predict the most likely outputs,” they write.

And when, in some cases, large language models (LLMs) are trained on synthetic data created by other models, the authors write, bigger problems emerge. When this manufactured data subsequently repeatedly goes through the model, it can affect the its accuracy and variability. This is known as “generative inbreeding” or model collapse.

Steps enterprises can take to prevent AI slop

The two experts, therefore, argue for a “fundamental step change” in the ways models are architected, as well as in establishing explicit rules around how they are used.

Naturally, one of the first, and potentially the hardest, steps is restricting employee AI use: It should only be applied to scenarios where it truly adds value, the authors advise.

For instance, when employees or job candidates are allowed to freely design their CVs, they’ll likely use generative AI to “optimize” their work. To prevent knowledge decay, recruiters should rely on structured documents that require factual responses that an AI can’t generate, for example, asking a candidate about a specific role, projects completed, team members involved, suppliers served, and budgets managed.

When generative AI use is allowed or unpreventable, organizations should define what value is being added and establish clarity around the implications, Holweg and Davenport emphasize. “Content does not need to be entirely human-created, but if AI is being used, be clear why and how,” they write.

In one beneficial scenario, AI such as Copilot or Gemini embedded in standard office software makes it “virtually pointless” to manually create more versions of the content of reports and PowerPoint slides, Holweg and Davenport write.

In another example, in performance evaluations, managers can gather specific, detailed information from team members and customers, then use AI to synthesize that material, rather than generating a “tick-box report” of generic bullet points.

Enterprises should also consider how individual AI use impacts an overall process. For instance, in an interorganizational flow like a revenue cycle, everyone involved should know about and agree on how AI would be used and at what steps in the process. It’s not a question of whether AI is better at a given task, as it increasingly is in some scenarios, but whether it is taking over a task to make things more efficient, Holweg and Davenport write.

Ultimately, however, they posit, public LLMs add “little to no real value,” because they are generic and often contain mistakes. On the other hand, small language models (SLMs) and proprietary models trained on company-specific data can augment human work.

From an architectural perspective, enterprises should track the history of both structured and unstructured data and understand “ground truth” information. Materials like customer interviews provide critical facts, emotions, and context; if AI is used to alter or summarize these, enterprises must identify and record the underlying ground truth data and point back to verifiable, authentic content.

In the end, Holweg and Davenport emphasize, enterprises should establish pragmatic practices now, lest they repeat the mistakes of the past.

“If we fail to address the uncontrolled proliferation of generative AI in our business processes, we are likely to see a rerun of the ‘productivity paradox’ observed with the growth of corporate computing half a century ago,” they write.

Blending ‘human capital’ and ‘token capital’

Other experts also agree on the importance of blending the best of humans with the best of AI.

Microsoft CEO Satya Nadella describes this model as the incorporation of “human capital’ with “token capital.” The former is the human “knowledge, judgment, relationships, ingenuity, and pattern recognition,” while the latter is in built and owned AI capabilities. The opportunity is in melding the two in a learning loop.

In this loop, humans will guide AI systems, set goals, and identify patterns, so AI isn’t “running in circles,” Nadella wrote in an X post. Internal evaluations can determine whether AI is improving when measured against company-specified benchmarks, thus creating institutional memory that is “query-able,” using fewer tokens and saving enterprises money.

Nadella noted: “Every improved workflow generates a better training signal, which accelerates the accumulation of tacit knowledge unique to the firm.”

This article originally appeared on CIO.com.

Enterprises implementing agentic AI face a challenge: Which tools should they allow their agents to use, where can they be found, and how can they be used safely? A new protocol, Agentic Resource Discovery, or ARD, aims to let agents answer those questions for themselves. Behind it are Google, Microsoft, Cisco, Nvidia, Salesforce and others.

ARD aims to standardize the way that tools and services are shared across systems within a corporate domain. For example, when investigating a production problem, an agent may want to query engineering documentation and open support tickets, deployment history and observability systems, all of which could be managed by different registries and across different silos. There is no common layer that pulls them together. ARD has been designed to be that layer.

It operates across two levels. Catalogs and Registries. In the first, an organization publishes a catalog setting out its available capabilities. The Registries layer act as a form of search engine, crawling those published catalogs.

The ARD specification is available now. Organizations are invited to publish their own catalogs using the quickstart guide. After this, they are able to join the community and participate in the evolution of ARD.

This article first appeared on InfoWorld.