Scale by Subtraction (Part 3): The Economics of AI Product Strategy

#techstrategy #llmops #engineeringeconomics #aiproductstrategy

In Part 1 (Scale by Subtraction: An Engineering Leader’s View on Practical AI | by Imran Siddique | Dec, 2025 | Medium), we applied “Scale by Subtraction” to AI Architecture (removing the RAG bloat).

In Part 2 Scale by Subtraction (Part 2): Redefining the Engineering Organization | by Imran Siddique | Dec, 2025 | Medium), we applied it to the Organization (removing the “Junior/Senior” labels).

Now, in the final chapter, we tackle the most dangerous trap of all: The Strategy.

As Engineering Leaders, we are under immense pressure to “add AI” to everything. But adding features is easy; making the economics work is hard. If we aren’t careful, we end up building features that annoy users and bankrupt the margin.

Here is how we apply “Scale by Subtraction” to Product Strategy and Economics.

8. The “Chatbot” Fatigue vs. Invisible AI

The Hype: Add a Copilot to everything! Every app needs a chat window on the right side where the user can ask questions about their data.

The Reality:

We are reaching Chatbot Fatigue.

Users don’t want to have a conversation with their accounting software or their toaster. They want the software to just do the work. Adding a chat interface to a task that used to be a single click is not innovation; it is friction.

The Strategy (Scale by Subtraction):

“Scale by Subtraction” in Product means Subtracting the Conversation.

If I have to prompt the AI to do something, that is often a failure of design.

Lazy AI: A chat window where I have to type, “Summarize this ticket.”
Invisible AI: I open the ticket, and the summary is already there in the “Notes” field.

We need to focus on the Job to be Done. If the job is “Fix the error,” the AI should highlight the error and suggest the fix inline. It shouldn’t wait for me to open a side panel and ask, “How do I fix this?”

The Lesson:

The best AI is the one you don’t talk to. Stop building Chatbots; start building Predictive Interfaces that subtract the need to ask.

9. Token Economics & The “Invisible Tax”

The Hype: AI is a premium luxury. We should slap a ‘Copilot +$20’ sticker on our pricing page and pass the token costs directly to the user.

The Reality:

In the very near future, AI will be as ubiquitous as the database.

We don’t charge customers a separate line item for “SQL Storage Fees” or “Carbon Offsets.” We shouldn’t charge them separately for AI. It creates friction and makes the product feel disjointed. “Everything is AI anyway” — treating it as a luxury add-on is a short-term strategy.

The Strategy (Scale by Subtraction):

If we absorb the cost (which we should), we must be ruthless about Efficiency.

“Scale by Subtraction” applies to the Feature List :

We subtract features where Cost > Value. (Do not auto-generate a summary for every log file if the user only opens 1% of them).
We use “Just-in-Time” Intelligence. We only run the inference when the user signals intent or when it provides high value.

The Lesson:

Don’t pass the complexity of your cloud bill to your customer. Absorb the cost but survive the margin squeeze by subtracting waste. Only build AI where it solves a real problem.

10. The “Moat” is Not the Model

The Hype: To have a competitive moat, we need to train our own Foundation Model. We can’t rely on OpenAI or Google; we need to own the ‘Brain’.

The Reality:

Unless you have billions of dollars and a data center the size of Texas, you are not competing on Base Models.

The model is rapidly becoming a commodity — a utility like electricity. Spending engineering cycles trying to pre-train or heavily fine-tune your own LLM is often just vanity R&D.

The Strategy (Scale by Subtraction):

“Scale by Subtraction” means we subtract the “Researcher” ambition.

We don’t build the engine; we build the car. Our IP (Intellectual Property) is not the LLM. Our IP is the Context , the Constraints , and the Evaluation Rigor.

Be LLM Agnostic: We build for Interoperability. Today we use GPT-4; tomorrow we might switch to Claude 3.5 or Llama 4 because it’s cheaper or faster. If your architecture is tightly coupled to one vendor’s prompt format, you are already legacy.
Asynchronous by Design: LLMs can be slow. We build asynchronous architectures that handle the “thinking” in the background, so the user never waits on a spinning loader.

The Lesson:

Don’t fall in love with the Model. Fall in love with your Data. The Model is just the CPU; your Data is the software that runs on it.

Conclusion: The “Scale by Subtraction” Manifesto

Across this three-part series, the common thread is simple: Resistance to Addition.

In the AI era, it is incredibly easy to add complexity. You can generate infinite code, infinite text, and infinite features. But as Engineering Leaders, our job is not to see how much we can add. It is to see how much we can remove while still solving the customer’s problem.