AI in 2026: Efficiency, artifacts and ownership

A look at the key themes in AI suggests the industry is maturing into a new phase. Where raw capability was once the only metric, the focus is now shifting to cost-efficiency through token optimisation. At the same time, new tools are emerging that move beyond simple generation to create fully usable artifacts, like complete websites. This progress, however, brings the complex issue of AI ownership and intellectual property into sharp focus as a critical business risk.

The AI Daily Brief highlights three interconnected trends pointing to the next stage of AI adoption for businesses: a focus on efficiency, the emergence of more capable agentic tools, and the growing urgency of the intellectual property debate.

Token efficiency takes centre stage

The conversation around AI performance is shifting from pure capability to token efficiency. Tokens are the units of data (words, characters, or parts of words) that models process. Optimising their use is becoming a primary goal for both model developers and the businesses that use them.

This signifies a maturation of the market. Previously, the race was simply to build the largest, most powerful models. Now, the emphasis is on achieving the best possible output for the lowest possible computational cost. This means developing smarter, more compact models and more sophisticated prompting techniques to reduce waste and improve speed.

From code snippets to 'Codex Sites'

The brief points to the emergence of tools like Codex Sites, which signal a leap forward in agentic AI capabilities. These are systems that don't just generate code or content, but produce a complete, usable artifact from a high-level prompt.

Instead of a developer asking an AI for a code snippet and then manually integrating, testing, and deploying it, a tool like Codex Sites could take a brief like "Create a landing page for our new winter campaign" and deliver a live, hosted website. This automates the entire workflow, not just a single task within it.

The AI ownership debate intensifies

As AI systems become more capable and generate more commercial value, the question of who owns the output is no longer a theoretical debate for lawyers. It has become a pressing business risk.

When an AI agent, or a chain of agents, creates a new product design, a marketing campaign, or a business process improvement, the intellectual property (IP) chain is unclear.

• Does the user who wrote the prompt own it?
• Does the company providing the AI model have a claim?
• What about the owners of the vast datasets the model was trained on?

By 2026, with AI-generated content being central to many business operations, this ambiguity represents a significant source of potential legal and commercial conflict.

These three themes are not separate; they represent a cohesive picture of an industry entering its next phase of maturity. For business owners and operators, understanding this shift is critical for strategic planning, investment, and risk management.

Efficiency directly impacts your bottom line. Treating AI usage like a utility, measured and optimised for cost, is becoming standard practice. The days of open-ended experimentation without regard to token consumption are ending. Businesses that master token-efficient workflows will have a significant cost advantage, allowing them to scale their AI initiatives sustainably. The ROI calculation for AI is moving beyond potential productivity gains to include the direct operational cost of APIs and processing.

Agentic tools change the nature of work, not just the tools for work. The move from task automation (e.g., ChatGPT writing an email) to workflow automation (e.g., Codex Sites building a website) is profound. It redefines job roles. The value of an employee is less about their ability to perform a manual task (like coding a button) and more about their ability to strategically direct an AI agent to achieve a business goal. This requires a shift in skills, training, and how you structure your teams and processes. It fundamentally changes how work gets done, moving people from production to direction and quality assurance.

Ownership is now a board-level risk. The legal ambiguity around AI-generated IP is a liability that can no longer be ignored or delegated solely to the IT department. If you cannot confidently assert ownership over the output of your AI systems, you cannot protect your competitive advantage. Innovations, designs, and marketing materials created with AI could be vulnerable to challenge or replication. Your agreements with AI vendors are now critical legal documents that determine the defensibility of your own intellectual property. This uncertainty impacts everything from product development to capital raising and potential acquisitions.

Business leaders should respond to these trends proactively, not reactively. The focus should be on building a durable, defensible, and efficient AI strategy.

1. Conduct an AI Cost and Efficiency Audit. Start treating your AI usage like any other core utility. Work with your technology team to measure token consumption across different models and use cases. Identify areas of high cost or inefficiency and pilot newer, more token-efficient models or prompting techniques. Make a clear connection between AI usage and business value to justify the cost.

2. Pilot a True Agentic Workflow. Move beyond simple chatbots and text generators. Identify a single, well-defined business process that involves multiple steps—like creating marketing collateral, onboarding a new employee, or generating a sales proposal. Experiment with tools and platforms that can automate the entire workflow, not just one piece of it. This will provide valuable insight into how roles, processes, and skills will need to change in your organisation.

3. Perform a Legal Review of Your AI Posture. This is no longer optional. Engage legal counsel with expertise in technology and intellectual property. Your goals are to:

   • Review all AI vendor terms of service: Understand exactly what rights you have to the outputs and what data rights you are granting the vendor.
   • Develop a clear internal policy: Create guidelines for employees on the acceptable use of AI tools, especially concerning the input of proprietary company data and the handling of AI-generated content.
   • Establish an IP strategy: Define your organisation's position on owning, protecting, and defending IP that has been created or co-created with AI systems.