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AI evolves faster than ours vocabulary to describe it. We may need some new words. We have “cognition” about how a single mind thinks, but we don’t have a word to describe what happens when human intelligence and artificial intelligence work together to perceive, decide, create and act. Let’s call this process intelligence.
Intelligence is not a feature; it is the organizing principle of the next wave of software where humans and AI operate within the same shared business model. Today’s systems treat AI models as something you invoke from the outside. You act as a “user,” requesting responses or integrating a “human in the loop” step into agent workflows. But this is evolving towards continuous co-production: people and agents shape decisions, logic and actions together, in real time.
Read on to discover the three forces driving this new paradigm.
A unified ontology is just the beginning
In a recent letter to shareholdersAlex Karp, CEO of Palantir, wrote that “all the value of the market will go to chips and what we call ontology” and argued that this change is “only the beginning of something much larger and more significant.” By ontology, Karp means a shared model of objects (customers, policies, assets, events) and their relationships. It also includes what Palantir calls the “kinetic layer” of an ontology that defines the security actions and permissions connecting objects.
In the SaaS era, each enterprise application creates its own object and process models. On top of a multitude of legacy systems and often chaotic models, businesses face the challenge of putting it all together. This is important and difficult work, with redundancies, incomplete structures and missing data. The reality: No matter how many data warehouse or data lake projects are commissioned, few companies succeed in creating a consolidated enterprise ontology.
A unified ontology is essential for today’s world. agentic AI tools. As organizations connect and federate ontologies, a new software paradigm emerges: agentic AI can reason and act across providers, regulators, customers, and operations, not just within a single application.
As Karp describes it, the goal is to “connect the power of artificial intelligence to real-world objects and relationships.”
Global models and continuous learning
Current models may contain extensive context, but holding information is not the same as learning from it. Continuous learning requires the accumulation of knowledge, rather than resetting with each refresher.
To achieve its goal, Google recently announcement “Nested Learning” as a potential solution, directly anchored in the LLM architecture and existing training data. The authors do not claim to have solved the challenges of building global models. But Nested Learning could provide them with the raw ingredients: a durable memory with continuous learning built into the system. The full stop would make reconversion obsolete.
In June 2022, Yann LeCun, Chief AI Scientist at Meta, created a plan for “autonomous artificial intelligence” which presented a hierarchical approach to using joint integrations to make predictions using global models. He called the technique H-JEPA, and later say frankly: “LLMs are good at manipulating language, but not good at thinking.”
Over the past three years, LeCun and his colleagues at Meta have put H-JEPA theory into practice with the open source V-JEPA and I-JEPA models, which learn image and video representations of the world.
The personal intellectual interface
The third force in this agentic, ontology-driven world is the personal interface. This places people at the center rather than as “users” on the periphery. It’s not another app; it is the primary way a person participates in the next era of work and life. Rather than treating AI as something we visit through a chat window or API, the personal intelligence interface will always be active, aware of our context, preferences, and goals, and capable of acting on our behalf across the federated economy.
Let’s analyze how all this is already falling into place.
In May, Jony Ive sold his AI device company io to OpenAI to accelerate a new category of AI devices. He noted at the time: “If you create something new, if you innovate, there will be unintended consequences, some will be wonderful, some will be harmful. Even though some of the less positive consequences were unintended, I still feel a responsibility. And the manifestation of that is a determination to try to be useful.” In other words, having a good personal intelligence device means much more than an attractive business opportunity.
Apple is looking beyond LLMs for on-device solutions that require less processing power and result in less latency when building AI applications to understand “user intent.” Last year they created UI-JEPAan innovation that shifts to “on-device analysis” of what the user wants. This directly affects the business model of today’s digital economy, where centralized profiling of “users” turns intent and behavior data into vast sources of revenue.
Tim Berners-Lee, the inventor of the World Wide Web, recently said: “The user has been reduced to a consumable product for the advertiser…there is still time to build machines that work for humans, not the other way around.”" Shifting user intent to the device will drive interest in a secure personal data management standard, Solidwhich Berners-Lee and colleagues have been developing since 2022. The standard is ideally suited to pair with new personal AI devices. For example, Inrupt, Inc., a company founded by Berners-Lee, recently combined Solid with Anthropic’s MCP standard to Agent wallets. Personal control is more than a feature of this paradigm; it is the architectural guarantee allowing systems to acquire the capacity to learn and act continuously.
Ultimately, these three forces are moving and converging faster than most realize. Corporate ontologies provide the nouns and verbs, research into global patterns provides lasting memory and learning, and the personal interface becomes the authorized point of control. The next era of software is not coming. It’s already there.
Brian Mulconrey is Senior Vice President of Sureify Laboratories.
