“Mind the Gap” – The Man in the Middleware
The evolution of software has experienced a few consistent patterns over the years, with a few disruptors here and there that drive a new methodology, or new economics. One consistent trend has been that the introduction of a useful technology, such as a virtual machine monitor, often generates significant enthusiasm. However, it frequently ends up relegated to middleware, which either requires a solution higher up the stack (like Broadcom/VmWare VSphere) to maintain market momentum and customer retention, or gets absorbed into the lower layers of the platform or hardware. The base technology is useful, but doesn’t have a direct touch to the customers/users, and ends up bundled with other innovations that do.
With that background, everyone has been watching the battle for domination of worldwide AI capital investments amid claims that “agentic AI” may hollow out the inherent value of SaaS services. Others continue the never-ending hype surrounding new model development to justify ‘build it and they will come’ data center investments. This news cycle, there has been a lot of traffic on Deepseek, small language models, and OpenAI’s SUPER agents against their “Benchmarkgate.” That is a lot of jargon for Wall Street begging AI providers “take my money, please,” and the largest model competition between a handful of players who want an unfair share of that capital/investment. It seems to be independent of the revenue base of paying customers who are still working to find the AI 10x ROI to their businesses. There are other economic challenges – if OpenAI is losing money on every query after seemingly randomly selecting $200/month as their Pro business model, how long can their business model hold before prices must change? Does an enterprise need a model that can answer world history questions, or quotes from Shakespere, or just a trained-on-that company-data model that can accelerate time to value for their specific enterprise processes? If a business does not need a model that can answer world history questions, does it need “superintelligence”?
There is a clear and practical application for the automation and streamlining that AI, as part of a business process, can bring. The challenge for the underlying model itself is that another process has to invoke it, whether a browser/app (ChatGPT), or the front end of a business process/query (ex:ETL). For consumer facing businesses, LLMs function as middleware that drives revenue growth in advertising and shopping businesses. The helpful system recommending things based on my browsing or shopping history? AI functions and models running in the background. This business isn’t new and radical – different types of applied AI have driven shopping operations for years. In other enterprises, for significant value for AI to be realized, the debate on agentic/ SaaS and the importance of a superintelligence model comes down to what interacts with the AI function – customers or front-end processes. Agentic AI in particular breaks up workflows in ways that abstract which model is running a function or process. The model or more likely, models, underpinning the agentic AI workflows work in the background.
An analyst told me recently that “models don’t matter”, which may be an exaggeration since data bases aren’t sexy anymore, but are critical to getting great results from AI deployments.
One test on whether becoming middleware is a likelihood is how close the funding pitch sounds like the Southpark Underwear Gnome business plan:
Their business plan is broken into three simple steps:
- step 1: Steal underpants
- step 2: ???
- step 3: Billions and billions of profit.
While I see signs that models themselves are becoming background functions using the Southpark test, the SaaS/Agentic debate where both options offer user facing features and AI capabilities “in the workflow” is the real battlefield for companies wanting to capitalize on the AI transformation. My theory on which model wins comes down to “users likely do not care how the underlying architecture was constructed”, so the advantage goes to ease of use.
What are your thoughts about where SaaS and Agentic have advantages or disadvantages?
The evolution of software has experienced a few consistent patterns over the years, with a few disruptors here and there that drive a new methodology, or new economics. One consistent trend has been that the introduction of a useful technology, such as a virtual machine monitor, often generates significant enthusiasm. However, it frequently ends up relegated to middleware, which either requires a solution higher up the stack (like Broadcom/VmWare VSphere) to maintain market momentum and customer retention, or gets absorbed into the lower layers of the platform or hardware. The base technology is useful, but doesn’t have a direct touch to the customers/users, and ends up bundled with other innovations that do.
With that background, everyone has been watching the battle for domination of worldwide AI capital investments amid claims that “agentic AI” may hollow out the inherent value of SaaS services. Others continue the never-ending hype surrounding new model development to justify ‘build it and they will come’ data center investments. This news cycle, there has been a lot of traffic on Deepseek, small language models, and OpenAI’s SUPER agents against their “Benchmarkgate.” That is a lot of jargon for Wall Street begging AI providers “take my money, please,” and the largest model competition between a handful of players who want an unfair share of that capital/investment. It seems to be independent of the revenue base of paying customers who are still working to find the AI 10x ROI to their businesses. There are other economic challenges – if OpenAI is losing money on every query after seemingly randomly selecting $200/month as their Pro business model, how long can their business model hold before prices must change? Does an enterprise need a model that can answer world history questions, or quotes from Shakespere, or just a trained-on-that company-data model that can accelerate time to value for their specific enterprise processes? If a business does not need a model that can answer world history questions, does it need “superintelligence”?
There is a clear and practical application for the automation and streamlining that AI, as part of a business process, can bring. The challenge for the underlying model itself is that another process has to invoke it, whether a browser/app (ChatGPT), or the front end of a business process/query (ex:ETL). For consumer facing businesses, LLMs function as middleware that drives revenue growth in advertising and shopping businesses. The helpful system recommending things based on my browsing or shopping history? AI functions and models running in the background. This business isn’t new and radical – different types of applied AI have driven shopping operations for years. In other enterprises, for significant value for AI to be realized, the debate on agentic/ SaaS and the importance of a superintelligence model comes down to what interacts with the AI function – customers or front-end processes. Agentic AI in particular breaks up workflows in ways that abstract which model is running a function or process. The model or more likely, models, underpinning the agentic AI workflows work in the background.
An analyst told me recently that “models don’t matter”, which may be an exaggeration since data bases aren’t sexy anymore, but are critical to getting great results from AI deployments.
One test on whether becoming middleware is a likelihood is how close the funding pitch sounds like the Southpark Underwear Gnome business plan:
Their business plan is broken into three simple steps:
- step 1: Steal underpants
- step 2: ???
- step 3: Billions and billions of profit.
While I see signs that models themselves are becoming background functions using the Southpark test, the SaaS/Agentic debate where both options offer user facing features and AI capabilities “in the workflow” is the real battlefield for companies wanting to capitalize on the AI transformation. My theory on which model wins comes down to “users likely do not care how the underlying architecture was constructed”, so the advantage goes to ease of use.
What are your thoughts about where SaaS and Agentic have advantages or disadvantages?
