#scale-vs-suitability

#scale-vs-suitability

In 1966, BCG found that a company's unit production costs would fall by typically 20 to 30 percent in real terms for each doubling of 'experience,' or accumulated production volume.

Super shoes and ultralight gear make a difference, but with new advancements in artificial intelligence (AI) that can look at our running form and compare it to the ideal, analyze our nutrition intake from a simple photo and help us plan our diets, and offer guidance on training and recovery, the interwovenness of technology and running is only set to increase.

When you take the leap of faith to bring your vision, your idea, to life and start your company, you wear many hats and take on many tasks. You develop the business plan and deck pitch, help build a great product or service offering, create and implement the marketing strategies, make sales, handle customer service and get take-out for everyone during the late nights they're working.

But if you're innovating within your industry, it's a problem you should expect and prepare for because it means having to operate in two realities-the internal reality where you know the challenges in your industry and how you're going to solve them, and the external reality where nobody else has recognized the problem that needs to be solved. In a highly regulated industry like healthcare, safety, and stability create an inertia that often works against innovation.

At that point, backpressure and load shedding are the only things that retain a system that can still operate. If you have ever been in a Starbucks overwhelmed by mobile orders, you know the feeling. The in-store experience breaks down. You no longer know how many orders are ahead of you. There is no clear line, no reliable wait estimate, and often no real cancellation path unless you escalate and make noise.

What happens under the hood? How is the search engine able to take that simple query, look for images in the billions, trillions of images that are available online? How is it able to find this one or similar photos from all that? Usually, there is an embedding model that is doing this work behind the hood.

Your AI pilot showed 94% accuracy improvements. The LLM is yielding solid results. You're getting defunded anyway. The reason? You solved a problem AI can solve. Your budget-holder needed you to solve theirs. Companies launch AI pilots that produce results, then stall at scale. The team's diagnosis: "They don't get it." What's really going on: These projects never earned budget-holder buy-in.