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For the past two years, artificial intelligence has felt oddly flat. Large language models spread at unprecedented speed, but they also erased much of the competitive gradient. Everyone has access to the same models, the same interfaces, and, increasingly, the same answers. What initially looked like a technological revolution quickly started to resemble a utility: powerful, impressive, and largely interchangeable, a dynamic already visible in the rapid commoditization of foundation models across providers like OpenAI, Google, Anthropic, and Meta. That flattening is not an accident. LLMs are extraordinarily good at one thinglearning from textbut structurally incapable of another: understanding how the real world behaves. They do not model causality, they do not learn from physical or operational feedback, and they do not build internal representations of environments, important limitations that even their most prominent proponents now openly acknowledge. They predict words, not consequences, a distinction that becomes painfully obvious the moment these systems are asked to operate outside purely linguistic domains. The false choice holding AI strategy back Much of todays AI strategy is trapped in binary thinking. Either companies rent intelligence from generic models, or they attempt to build everything themselves: proprietary infrastructure, bespoke compute stacks, and custom AI pipelines that mimic hyperscalers. That framing is both unrealistic and historically illiterate. Most companies did not become competitive by building their own databases. They did not write their own operating systems. They did not construct hyperscale data centers to extract value from analytics. Instead, they adopted shared platforms and built highly customized systems on top of them, systems that reflected their specific processes, constraints, and incentives. AI will follow the same path. World models are not infrastructure projects World models, systems that learn how environments behave, incorporate feedback, and enable prediction and planning, have a long intellectual history in AI research. More recently, they have reemerged as a central research direction precisely because LLMs plateau when faced with reality, causality, and time. They are often described as if they required vertical integration at every layer. That assumption is wrong. Most companies will not build bespoke data centers or proprietary compute stacks to run world models. Expecting them to do so repeats the same mistake seen in earlier AI-first or cloud-native narratives, where infrastructure ambition was confused with strategic necessity. What will actually happen is more subtle and more powerful: World models will become a new abstraction layer in the enterprise stack, built on top of shared platforms in the same way databases, ERPs, and cloud analytics are today. The infrastructure will be common. The understanding will not. Why platforms will make world models ubiquitous Just as cloud platforms democratized access to large-scale computation, emerging AI platforms will make world modeling accessible without requiring companies to reinvent the stack. They will handle simulation engines, training pipelines, integration with sensors and systems, and the heavy computational liftingexactly the direction already visible in reinforcement learning, robotics, and industrial AI platforms. This does not commoditize world models. It does the opposite. When the platform layer is shared, differentiation moves upward. Companies compete not on who owns the hardware, but on how well their models reflect reality: which variables they include, how they encode constraints, how feedback loops are designed, and how quickly predictions are corrected when the world disagrees. Two companies can run on the same platform and still operate with radically different levels of understanding. From linguistic intelligence to operational intelligence LLMs flattened AI adoption because they made linguistic intelligence universal. But purely text-trained systems lack deeper contextual grounding, causal reasoning, and temporal understanding, limitations well documented in foundation-model research. World models will unflatten it again by reintroducing context, causality, and time, the very properties missing from purely text-trained systems. In logistics, for example, the advantage will not come from asking a chatbot about supply chain optimization. It will come from a model that understands how delays propagate, how inventory decisions interact with demand variability, and how small changes ripple through the system over weeks or months. Where competitive advantage will actually live The real differentiation will be epistemic, not infrastructural. It will come from how disciplined a company is about data quality, how rigorously it closes feedback loops between prediction and outcome (Remember this sentence: Feedback is all you need), and how well organizational incentives align with learning rather than narrative convenience. World models reward companies that are willing to be corrected by reality, and punish those that are not. Platforms will matter enormously. But platforms only standardize capability, not knowledge. Shared infrastructure does not produce shared understanding: Two companies can run on the same cloud, use the same AI platform, even deploy the same underlying techniques, and still end up with radically different outcomes, because understanding is not embedded in the infrastructure. It emerges from how a company models its own reality. Understanding lives higher up the stack, in choices that platforms cannot make for you: which variables matter, which trade-offs are real, which constraints are binding, what counts as success, how feedback is incorporated, and how errors are corrected. A platform can let you build a world model, but it cannot tell you what your world actually is. Think of it this way: Eery company using SAP does not have the same operational insight. Every company running on AWS does not have the same analytical sophistication. The infrastructure is shared; the mental model is not. The same will be true for world models. Platforms make world models possible. Understanding makes them valuable. The next enterprise AI stack In the next phase of AI, competitive advantage will not come from building proprietary infrastructure. It will come from building better models of reality on top of platforms that make world modeling ubiquitous. That is a far more demanding challenge than buying computing power. And it is one that no amount of prompt engineering will be able to solve.
Category:
E-Commerce
Most managers are using AI the same way they use any productivity tool: to move faster. It summarizes meetings, drafts responses, and clears small tasks off the plate. That helps, but it misses the real shift. The real change begins when AI stops assisting and starts acting. When systems resolve issues, trigger workflows, and make routine decisions without human involvement, the work itself changes. And when the work changes, the job has to change too. Lets take the example of an airline and lost luggage. Generative AI can explain what steps to take to recover a lost bag. Agentic AI aims to actually find the bag, reroute it, and deliver it. The person that was working in lost luggage, doing these easily automated tasks, can now be freed to become more of a concierge for these disgruntled passengers. As agentic AI solves the problem, the human handles the soft skills of apologizing, and offering vouchers to smooth the passengers transition to a new locale that was disrupted by a misplaced bag, and perhaps going a step further to make personal recommendations for local shops to pick up supplies. With AI moving from reporting information to taking action, leaders can now rethink how jobs are designed, measured, and supported to best maximize on the potential of the position and the abilities of the person in it. According to data from McKinsey, 78% percent of respondents have said their organizations use AI in at least one business function. Though some are still applying it on top of existing roles rather than redesigning work around it. 1. When tasks disappear, judgment becomes the job Many roles are still structured around task lists: answer tickets, process requests, close cases. As AI takes on more repeatable execution, what remains for humans are exceptions, tradeoffs, and judgment calls that dont come with a script. Take for example a member of the service team at a car dealership. Up until now the majority of their tasks have been scheduling appointments, sending follow-up emails, making follow-up calls and texts. Agentic AI can remove the bulk of that work. Now that member of the team can make the decisions that require nuance and critical thinking. They know that the owner of a certain vehicle is retired and has trouble getting around. They can see that their appointment is on a morning when it might snow. The human then calls the customer and rebooks them for when the weather is more favorable. These sorts of human touches are what will now set this dealership apart and grow customer loyalty. 2. Measure what humans now contribute As AI absorbs volume, measuring people on speed and responsiveness pushes them to compete with machines on machine strengths. Instead, evaluation should reflect what humans uniquely provide: quality of judgment, ability to prevent repeat issues, and stewardship of systems that learn over time. In the example above, the service team member at the car dealership could now be assessed not by number of appointments set, or cancellations rescheduled, but by outcomes such as customer satisfaction, and repeat business. The KPIs should be in-person or over the phone touch points with a customer to up-sell, or suggest better services that their vehicle will need. 3. Human accountability for AI work When AI is involved, ownership has to be explicit. Someone must own outcomes, even if a system takes the action. Someone must own escalation rules, workflows, and reviews. Without that clarity, AI doesnt reduce friction, it just shifts it to the moment something goes wrong. In the car dealership example, a human should still be overseeing the AI agents doing the work and ensuring that its done well. If there are problems, they should be able to catch them and come up with solutions. One of the biggest risks with AI isnt failure, its neglect from humans overseeing the overall strategy and bigger goals that the AI is completing. Systems that mostly work fade into the background until they dont. Teams need protected time to review where AI performed well, where it struggled, and why. Looking ahead This shift isnt theoretical. Klarna has publicly described how its AI assistant now handles a significant share of customer service interactions, an example of how quickly AI moves from support tool to frontline worker. Once AI is doing real work, the old job descriptions stop making sense. Roles, accountability, metrics, and oversight all need to be redesigned together. AI improves fastest when humans actively review and guide it, not when oversight is treated as an afterthought. The next phase of work isnt about managing people plus tools. Its about designing systems where expectations are clear, ownership is explicit, humans focus on meaningful decisions, and AI quietly handles the rest. If leaders dont redesign the job intentionally, it will be redesigned for them, by the technology, by urgent failures, and by the slow erosion of clarity inside their teams.
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E-Commerce
For decades, America has told a singular story about success, suggesting that the only acceptable path to success is a four-year degree. Any other trajectory was treated as a detour. Fortunately, that story is changing with new, acceptable ways to achieve success. At both the federal and state levels, the U.S. is gradually reinventing its education system to value skills, not just diplomas. From new federal initiatives like Workforce Pell to state-led Education Savings Accounts (ESAs), policy is beginning to catch up to what the economy has been signaling for years. As a country, we need electricians, plumbers, welders, and builders as much as we need white-collar workers. A handful of states now have ESA programs. The main purpose of ESAs is to give parents flexibility with school choice. While ESAs are most widely used for private school tuition, some schools and school networks are now exploring using trades programs, including technical courses, apprenticeships, or industry certifications, as a differentiator to attract parents. There have also been changes to 529 college savings plans, and those funds can be used for short-term credentials and trade-related certificates. These small shifts mark a turning point and are building momentum towards career paths for many, rather than college for all. HANDS-ON EDUCATION For students, the shift can be life-changing. A report from the Southern Regional Education Board found that high school students who take three or more career technical education (CTE) credits had a reduced risk of dropping out. Students who dont always thrive in traditional classroom settings are starting to see that the education system not only values them, but is welcoming them. Ive seen the power of hands-on education at one of our customers, Oklahoma-based Pryor High School Innovation Center, which is utilizing interactive training to drive its HVAC pre-apprenticeship program. The program takes students from zero industry skills to job-ready through a curated pathway of online and in-person trades training. Learning should be more like a set of Lego blocks, and students can build their own pathway by stacking short-term credentials, apprenticeships, and hands-on training programs to suit their strengths. The ability to have a modular, customizable model of learning is emerging in real-time as states like Florida, Arizona, and Texas expand ESAs and workforce grants to fund job-specific education. The flexibility also means faster, stronger pipelines from high school to high-wage work. GOVERNMENT INITIATIVES CAN HELP Career pathways go beyond education and directly translate into national competitiveness. The Inflation Reduction Act and CHIPS and Science Act created significant momentum for the U.S. manufacturing industry, but we need a skilled workforce to make that happen. The new Workforce Pell initiative can help. The rules now expand eligibility to short-term programs, typically just eight to 15 weeks, and directly lead to jobs. The impact could be transformative. The Workforce Pell expansion is expected to bring roughly 100,000 new students into short-term credentialing programs that were previously ineligible for aid. According to the Congressional Budget Office, about $300 million in new Pell funding will flow through the program, with average awards projected at $2,200 per student. The program is slated to take effect in July 2026. Last year, the U.S. Department of Labor announced over $86 million in Industry-Driven Skills Training Fund grants awarded to 14 states, designed to boost innovation, enhance domestic manufacturing and help meet workforce demands nationwide. Of the funding, $20 million will directly support training workers in marine electrical, manufacturing, welding, plus other skilled trades. WHO BENEFITS? While these programs benefit students by providing access to affordable, focused education that leads directly to employment, they also help businesses. Businesses will have access to a stronger, qualified talent pipeline to fill their gaps and replace retiring workers. The programs also help to power a cultural shift were seeing in the perception of skilled trades. For too long, education other than a four-year degree carried a stigma. Fortunately, that mindset is changing. In a recent Harris Poll, 91% of respondents agreed that trade jobs are just as vital to society as white-collar jobs, and 90% said skilled trades offer a faster and more affordable path to a good career. Gen Z has shown an increased interest in the trades, and this year alone, TikTok has virally turned trades like blacksmithing and horseshoeing into career paths. The Skilled Careers Coalition and SkillsUSA partnered with TikTok to influence students’ interest in trade schools, apprenticeships, and high-demand CTE careers. More exposure will go a long way to encourage the next generation of workers to explore and pursue skilled trades. A MORE COMPETITIVE ECONOMY If the federal and state governments continue to align policy and funding with workforce demand, we could see a future where students are able to pursue education tailored to their ambitions and natural aptitudes. Enabling this will do wonders for the economy and deliver a happier, more respectful and proud community. If you ever need a reminder of why this matters, go talk to an electrician or an HVAC technician. You will rarely meet anyone more proud of the role they play in keeping our world running. Forming a new ecosystem that treats education as a lifelong, adaptable tool that is built around outcomes will create, by extension, a more competitive economy. Doug Donovan is CEO and founder of Interplay Learning.
Category:
E-Commerce
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