Understanding AI Tokens: The Hidden Economics Behind Your Next Software Feature
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When I first encountered the concept of “tokens” in AI systems, I was completely baffled. Here I was, someone who had spent years building and scaling software products, and suddenly I was faced with a completely new way of thinking about costs and usage. It reminded me of my early days as a professional dancer, when I learned that every movement had both artistic value and precise technical requirements.
Just as dance taught me that memorable performances combine creative vision with underlying mechanics, I’ve discovered that building successful AI-powered products requires grasping both the transformative potential and the economic realities of how these systems work. Today, I want to share what I’ve learned about tokens (the fundamental unit of AI computation) and why understanding them is crucial for any executive considering AI integration.
Why Tokens Matter More Than You Think
AI tokens are the individual beats in the rhythm of artificial intelligence. They are discrete units that represent how AI systems process and generate language. Each token typically represents a word, part of a word, or individual characters, depending on how the system is designed.
Here’s where it gets interesting for those of us building software products: unlike traditional software licensing, where you pay per user or per seat, AI systems charge based on token consumption.
In traditional SaaS, a customer who logs in once a month pays the same monthly fee as someone actively using your software eight hours a day. With AI-powered features, the economics flip entirely – your costs scale directly with how intensively customers use your AI capabilities.
The Two-Part Structure: Input and Output Tokens
AI systems work with a two-part structure that’s crucial to understand: input tokens and output tokens. Think of this like the preparation and performance phases in dance – both essential, but requiring different levels of energy and resources.
Input tokens represent everything the AI needs to “understand” before responding – your user’s question, conversation history, system instructions, and relevant context data. If you’re building an AI feature that helps customers write marketing copy, input tokens include their brand guidelines, previous campaigns, target audience information, and their specific request.
Output tokens represent the AI’s generated response – the actual content, recommendations, or analysis it produces. These are the headlines, body text, and suggestions the AI creates for your customer.
Here’s what surprised me most: output tokens typically cost significantly more than input tokens. Generation requires more computational resources than comprehension, which means your costs aren’t just based on how much information you feed the system, but heavily weighted toward how much content you ask it to create.
A customer requesting a simple recommendation might consume a few hundred tokens, while someone wanting detailed analysis could easily consume thousands. This has profound implications for product strategy and pricing.
The Economics Revolution: Plummeting Costs Create New Opportunities
One of the most exciting developments I’ve witnessed is the dramatic reduction in token costs. When I first started exploring AI integration, GPT-4 tokens cost around 0.03 per 1,000 input tokens and 0.06 per 1000 output tokens in early 2023. Today, those same tokens cost roughly 0.0025 and 0.01, respectively – an 85-90% reduction in less than two years.
This cost reduction is transformative. Features that were economically unfeasible eighteen months ago are now commercially viable at scale. Real-time personalisation, sophisticated content generation, and complex analysis that would have been prohibitively expensive are now within reach of companies of all sizes.
However, the cost reduction also means that competitive advantages based solely on AI access or affordability will be temporary. The sustainable competitive advantage lies in how you combine AI with your unique data, domain expertise, and customer relationships.
Rethinking Commercial Models: Beyond Per-Seat Pricing
This shift to token-based economics requires fundamentally rethinking how we structure commercial models. In my experience leading product organisations, this has been one of the most challenging aspects of AI integration – not the technology itself, but figuring out pricing that aligns costs with customer value.
Traditional SaaS pricing is beautifully simple: charge per user per month, maybe with different tiers. But AI-powered features create variable costs that scale with usage intensity rather than user count. A single power user could consume significantly more tokens than dozens of light users.
The most successful approaches I’ve observed involve hybrid models that combine predictable base pricing with usage-based components for AI features. One approach offers a foundation platform fee covering core features and basic AI allocation, with additional charges for intensive usage. Another strategy involves value-based pricing tiers that bundle AI capabilities into packages for different customer segments.
The key insight is that token costs should be invisible to customers while being carefully managed internally – this is somewhat similar (to a point) to the API economy.
Your customers should experience AI value without worrying about underlying computational costs. The product strategy should abstract token management into business value metrics that customers understand.
The Strategic Advantage of Self-Hosting
Organisations that reach sufficient AI usage volume often consider hosting their own AI models rather than relying entirely on external services. This decision is like choosing between renting studio space versus building your own dedicated facility – both have merits, but ownership provides control and long-term economics.
When you use external AI services, you pay for every token processed, including inefficient queries and suboptimal processing. Self-hosted models allow sophisticated optimisation techniques including prompt caching, model fine-tuning, and intelligent request routing that can reduce effective token costs by 60-80% while improving response times.
The technical requirements are significant – specialised infrastructure, GPU clusters, and team expertise in AI operations. But for companies with substantial AI usage, the economics become compelling. More importantly, self-hosting provides control over performance, customisation capabilities, and optimisation for your specific use cases.
Fine-Tuning: Teaching AI Your Company's Signature Style
One of the most powerful concepts in AI product development is fine-tuning – or in simpler terms – training existing AI models on your specific data and use cases. This is where the dance analogy becomes particularly useful: fine-tuning is like teaching AI your company’s signature choreography.
When learning new dance styles, you don’t start from scratch. You build on fundamental techniques but adapt them to specific requirements. Ballet requires different precision than contemporary dance, which differs from jazz. The foundation is similar, but the expression is uniquely suited to each form.
Fine-tuning works similarly. You start with a powerful general-purpose AI model, then train it on your domain data, successful examples, and desired outcomes. The result is an AI system that understands your industry context, speaks your customers’ language, and generates responses aligned with your brand and business objectives.
I’ve seen this create remarkable competitive advantages. A travel company could fine-tuned models on their booking data and customer preferences. The resulting AI would understand seasonal patterns, destination preferences, and complex travel decision-making processes. Competitors could access the same base technology, but couldn’t replicate the domain-specific intelligence.
Creating Natural AI Experiences
One overlooked aspect of AI integration is user experience design. This is where performance background becomes invaluable – creating great AI experiences is about managing expectations and building engagement over time.
When AI systems take time to process complex requests, users need to understand what’s happening and why it’s worth waiting. Showing the AI’s “thinking” process – indicating it’s analysing data or generating personalised recommendations – actually increases user satisfaction and perceived value.
Streaming responses, where users see content appearing in real-time, create dynamic engagement. Rather than waiting for complete responses, users can begin interacting with content as it’s generated. This is particularly effective for longer-form content where the AI is building complex recommendations.
The key insight is that AI experiences should feel collaborative rather than transactional. Users should feel like they’re working with an intelligent partner that understands their needs and is actively helping them succeed.
The Compound Effect of AI Investment
As I reflect on my journey from professional dance to building AI-powered products, both domains reward compound investment over time.
Daily practice builds capabilities that compound into extraordinary performances.
In AI product development, the combination of data accumulation, model improvement, and user experience refinement creates capabilities that become more valuable and differentiated over time.
The companies succeeding with AI integration view it as a long-term capability investment rather than a short-term feature addition.
- They’re building data flywheels where better AI attracts more users, generating more data that enables better AI.
- They’re developing domain expertise that makes their AI uniquely valuable to their customer base.
For executives and entrepreneurs considering AI integration, start with a clear understanding of your unique advantages – your data, domain expertise, and customer relationships – and think about how AI can amplify these strengths. The token economics are just the foundation; the real value comes from creating experiences that your customers can’t get anywhere else.
The future belongs to companies that can work effectively with AI – understanding its capabilities, economics, and potential while creating solutions that are both technically excellent and deeply meaningful to their customers.
The economics are becoming more favorable every month, the technology more accessible, and the competitive advantages are there for companies willing to invest in learning this new discipline.
The stage is set, and the economics have never been more favourable.
The question isn’t whether AI will transform your industry – it’s whether you’ll be leading the transformation or following someone else’s playbook.