Per-seat pricing only works when your users are human. But when agents become the primary users of software, that model collapses.

Executives aren't evaluating software against software anymore. They're comparing the combined costs of software licenses plus labor against pure outcome-based solutions. Think customer support (per resolved ticket vs. per agent + seat), marketing (per campaign vs. headcount), sales (per qualified lead vs. rep). That's your pricing umbrella—the upper limit enterprises will pay before switching entirely to AI.

enterprises are used to deterministic outcomes and fixed annual costs. Usage-based pricing makes budgeting harder. But individual leaders seeing 10x efficiency gains won't wait for procurement to catch up. Savvy managers will find ways around traditional buying processes.

This feels like a generational reset of how businesses operate. Zero upfront costs, pay only for outcomes—that's not just a pricing model. That's the future of business.

The winning strategy in my books? Give the platform away for free. Let your agents read and write to existing systems through unstructured data—emails, calls, documents. Once you handle enough workflows, you become the new system of record.

One of the most impactful uses of LLM technology lies in content rewriting, which naturally capitalizes on these systems’ robust capabilities for generating and refining text. This application is a logical fit, helping users enhance their content while engaging with a service.

Similar to summarization but incorporating an element of judgment, features like Microsoft Team CoPilot’s call transcript summaries distill extensive discussions into essential bullet points, spotlighting pivotal moments or insights.

The ability to ‘understand’ nuanced language through summarization extends naturally into advanced search functionalities. ServiceNow does this by enabling customer service agents to search tickets for recommended solutions and to dispel jargon used by different agents.

Rather than merely focusing on content creation or manipulation, emerging applications of these systems provide new perspectives and predict outcomes based on accumulated human experiences. The actual value of these applications lies not merely in enhancing efficiency but in augmenting effectiveness, enabling users to make more informed decisions.

complexity can make it difficult for both domain novices and experts alike to learn how to use the interface. LLMs can help reduce this barrier by being leveraged to prove assistance to the user if they’re trying to accomplish something, but don’t exactly know how to navigate the interface.The user could tell the program what they’re trying to do via a text or voice interface, or perhaps, the program may be able to infer the user’s intent or goals based on what actions they’ve taken so far.Modern GUI apps are slowly starting to add in more features for assisting users with navigating the space of available commands and actions via command palettes; popularised in software iA Writer and Superhuman.

for executing a sequence of tasks as part of a complex workflow, LLM powered interfaces afford a richer opportunity for learning and using complex software.The program could walk them through the task they’re trying to accomplish by highlighting and selecting the interface elements in the correct order to accomplish the task, along with explanations provided.

Expert interfaces that take advantage of LLMs may end up looking like they currently do - again, complex tasks require complex interfaces. However, it may be easier and faster for users to learn how to use these interfaces thanks to built-in LLM-powered assistants. This will help them to get into flow faster, improving their productivity and feeling of satisfaction when using this complex software.

unlike Clippy, these new types of assistant would be able to act on the interface directly. These actions will be made in accordance to the goals of the person using them, but each discrete action taken by the assistant on the interface will not be done according to explicit human actions - the goals are directed by he human user, but the steps to achieve those goals are unknown to the user, which is why they’re engaging with the assistant in the first place

With iA Writer 7 you can manually mark ChatGPT’s contributions as AI text. AI text is greyed out. This allows you to separate and control what you borrow and what you type. By splitting what you type and what you pasted, you can make sure that you speak your mind with your voice, rhythm and tone.

As a dialog partner AI makes you think more and write better. As ghost writer it takes over and you lose your voice. Yet, sometimes it helps to paste its replies and notes. And if you want to use that information, you rewrite it to make it our own. So far, in traditional apps we are not able to easily see what we wrote and what we pasted from AI. iA Writer lets you discern your words from what you borrowed as you write on top of it. As you type over the AI generated text you can see it becoming your own. We found that in most cases, and with the exception of some generic pronouns and common verbs like “to have” and “to be”, most texts profit from a full rewrite.

we believe that using AI for writing will likely become as common as using dishwashers, spellcheckers, and pocket calculators. The question is: How will it be used? Like spell checkers, dishwashers, chess computers and pocket calculators, writing with AI will be tied to varying rules in different settings.

We suggest using AI’s ability to replace thinking not for ourselves but for writing in dialogue. Don’t use it as a ghost writer. Because why should anyone bother to read what you didn’t write? Use it as a writing companion. It comes with a ChatUI, so ask it questions and let it ask you questions about what you write. Use it to think better, don’t become a vegetable.