A trillion dollars of useful change
I’m building Symbolia for manufacturing and heavy industry within a roughly $30 trillion industrial economy. MM13 My goal is $1 trillion of additional annual customer benefit, agreed and realised through verified optimisation. At our 10% share, reaching that goal would create a $100 billion annual success-fee opportunity, before fixed assurance fees.
That ambition begins with machines already at work. A factory can produce more with the equipment it owns, use less energy and waste less material. AI will make improvements easier to propose. Symbolia will help engineers establish which changes meet their requirements and put the useful ones into service.
Charge for maintained assurance
Our first customers will be manufacturers operating robotic equipment. I’ll begin with bespoke engagements, working with the engineers who change the machines and the operations leaders who benefit from better production.
We will charge a fixed fee to check changes and keep their evidence current, plus 10% of agreed, realised benefit. Onboarding and the work of turning requirements into precise checks will be priced explicitly, along with unusually expensive computation. The fixed fee supports a dependable service; the success fee shares in the improvements it helps make possible.
Suppose a factory improves its production process and saves $1 million a year in energy and materials, after the costs of making the change. Once the agreed measurements confirm that benefit, Symbolia earns $150,000 and the customer keeps $850,000 before fixed assurance fees. The same model applies to agreed gains from additional sales or reduced downtime.
Before work begins, we agree how to measure the benefit and deduct the costs of achieving it. The customer decides whether to deploy. The requirements a change must satisfy stay independent of our fee.
Build once, use again
The world graph is the business asset I intend to build. It holds maintained knowledge of machine behaviour, the conditions where that knowledge applies and the evidence behind it.
A robot family modelled for one engagement can help us understand the next installation. A controller’s timing behaviour need not be reconstructed for every customer. Engineers can spend more of their time on what has changed, using earlier work wherever its conditions hold.
That is how I expect margins to improve. The customer pays for dependable improvement while each comparable job requires less specialist work. Private information remains under the customer’s control; sharing requires permission.
Invest ahead of the next customer
As we learn which knowledge gets used repeatedly, I’ll invest in building more of it. That means understanding common components, gathering evidence and developing checks before a customer needs them. We can spread that investment across many installations while maintaining the knowledge they depend on.
The opportunity grows with the graph. Understanding another component lets us serve more machines; understanding how components interact lets us assess more valuable changes. Revenue from today’s work can fund knowledge that improves tomorrow’s service.
Prove the economics in use
The programme starts with a real robot arm, then a compatible installation. I’ll compare total delivery cost at the same assurance standard, including failed attempts, computation, review, customer effort and maintenance. The working model shows how specialist time affects the margin. Investment in the graph must repay the cost of building and maintaining it through useful coverage or savings on new installations held out from its development.
A customer who gains from the work and returns for the next change gives us a reason to expand. Reusable knowledge gives us the means.
I want each improvement to make the next one easier to achieve.