Establishing clear usage and utility telemetry from AI features justifies substantial development costs and cloud infrastructure investments.
Seems like every vendor is pushing an AI-washed story these days. Generative AI hype is at an all-time high thanks to ChatGPT, Midjourney, and many newer startups and projects. Companies that fear missing out on the next big thing are spending money on AI functionality as a loss leader, or slapping an LLM chatbot in front of their apps.
It makes me wonder: Wait a minute, is this really going to help customers? And, how are we supposed to start making money from so much AI hype?
For most companies, building out a complete AI stack would be prohibitively expensive and resource-intensive. Who has the money and resources to literally design their own AI, and how would the new venture make enough money to stay afloat, considering massive R&D and infrastructure costs?
Overinvestment in AI Is Not the Answer
Right now, venture capital is on the hunt for any startup claiming to revolutionize machine learning or use GenAI in some novel way. Tech titans are placing big bets on tools in the sector. Will another project even come close to getting $10 billion dollars for a minority share like OpenAI got from Microsoft?
You never know what might happen up there in the stratosphere. For the rest of our efforts here on Earth, we can run experiments, but we will only be able to meaningfully adopt AI once it provides business value for customers.
Eventually, the bills will come due on so many AI projects, and there will be a great reckoning that will divide the products that can find a market niche and help companies capture revenue, from those that won’t.
The cost of infrastructure will get really high, really fast, as will the cost of GPUs, data ingress/egress, and architecture. Companies large and small will struggle to find any experienced AI modeling experts or machine learning data scientists willing to start working on yet another project.
A Supply Chain of Loosely Coupled Composite AI Applications
If enterprises want to see a return on their AI investments, they must prove that their chosen strategy is applicable to real-world business and societal problems, rather than serving as window dressing.
Since we can’t depend on just one form of AI, to get there, we’ll need composite AI – a supply chain consisting of multiple AI suppliers, with multiple models and multiple training data sets working together in a loosely coupled fashion, based on the right fit for the job.
Read the whole article on DZone here: https://dzone.com/articles/monetizing-generative-ai-starts-with-metering
©2024 Intellyx B.V. Intellyx is editorially responsible for this content, and no AI was used to write the article. At the time of writing, Amberflo is an Intellyx customer. Image source: Adobe Image Express.
