Previously, compute and data were the limiting factors in AI development. Now, the challenge is scaling the generation of high-quality, human-expert data needed to train frontier models for complex cognitive tasks that go beyond simply processing the public internet.

The next major evolution in AI will be models that are personalized for specific users or companies and update their knowledge daily from interactions. This contrasts with current monolithic models like ChatGPT, which are static and must store irrelevant information for every user.

Web3's principles of identity and value transfer were sound, but it lacked the AI-driven intelligence that powered Web2 giants like Google. It focused on rigid logic contracts instead of intelligent, adaptive systems, and tried to bootstrap economic incentives before creating real value.

Today's AI boom is fueled by scaling computation, which is a known engineering challenge. The alternative, embedding nuanced, human-like inductive biases, is far harder as it requires a deep understanding of the problem space. This difficulty gap explains why massive models dominate AI development over more targeted, efficient ones—scaling is simply the more straightforward path.

AI model capabilities have outpaced their value delivery due to a fundamental design problem. Users are inherently scared and distrustful of autonomous agents. The key challenge is creating interaction patterns that build trust by providing the right level of oversight and feedback without being annoying—a problem of design, not technology.

While language models are becoming incrementally better at conversation, the next significant leap in AI is defined by multimodal understanding and the ability to perform tasks, such as navigating websites. This shift from conversational prowess to agentic action marks the new frontier for a true "step change" in AI capabilities.

Previous enterprise software, like SAP or Salesforce, only required users to learn its functions. AI is different because it's a partner you must also teach. The quality of its output depends entirely on the quality of your instruction, requiring a new meta-skill of co-evolution with technology.

As models mature, their core differentiator will become their underlying personality and values, shaped by their creators' objective functions. One model might optimize for user productivity by being concise, while another optimizes for engagement by being verbose.

Human intelligence leaped forward when language enabled horizontal scaling (collaboration). Current AI development is focused on vertical scaling (creating bigger 'individual genius' models). The next frontier is distributed AI that can share intent, knowledge, and innovation, mimicking humanity's cognitive evolution.