In the burgeoning age of artificial intelligence, the decades-long reliance on standardized, centralized business applications has become fundamentally unsustainable. According to Michael Recchia, CEO of Adaptnet, a new firm specializing in AI, next-generation computing, networking, and media forensics, the future of enterprise technology hinges on distributing software development and allowing intelligent systems to write and maintain unique code on a per-location basis.
In a recent interview with Slingr.io CEO Grace Schroeder, Recchia—whose career evolved from software engineering to network architecture and fundamental R&D during his tenure at Bell Labs—explained that centralizing software development is incompatible with the needs of modern scale. While custom code once yielded to packaged products and software as a service (SaaS), this standardization forced organizations into accepting "somebody else's road map [and] somebody else's decision making".
The Human Learning Model and Decentralization
The rise of powerful AI enables technology to move "back almost to the metal," allowing for fully customized solutions explicitly written for individual needs.
The HLM is designed to operate as a network that learns like a human being. When placed within a specific domain, the HLM learns that domain and then proceeds to "write the code for that domain". Crucially, this system eliminates the concept of universal software releases and centralized upgrades. Recchia outlined the dramatic implications for the software model:
"It's not going to be this model anymore. Here's a release; please upgrade for everybody. You will have the same set of features at each location that's gone. It's going to be what's required at that location, and the HLM is going to take care of it"
Under this decentralized approach, different sites might communicate with each other, but "each site has its own code maintained by that particular HLM, and it's not necessarily the same release". This enables software to be perfectly tailored to local requirements, thereby shattering the standardized application model. Recchia emphasized that AI will distribute power across the technology landscape, allowing organizations to move away from reliance on centralized vendors:
"I really feel like, you know, AI is ultimately hopefully going to help distribute software like you're going to get away from these gigantic centralized software companies that really drive everything".
AI as a Necessity for Discovery and Scale
The shift away from generic software is not merely a strategic choice for customization; Recchia insists it is now a requirement for continued technological advancement. The human capacity to manage complexity and generate novel solutions has been stretched thin, particularly since R&D organizations no longer prioritize fundamental research in the same manner as Bell Labs once did.
"We need AI now, it's no longer just AI is a neat technology we're going to go use it, it is we actually need it now to move forward," Recchia asserted. "We need it to scale, we need it to generate new ideas, we need it to help us generate new technology. We can't do it now by ourselves. Those days are over".
Recchia provided a vivid example of this capability: the HLM models achieved a major scientific breakthrough by figuring out how to perform "data analytics beyond the way that a human brain does it". This discovery led to the identification of a new neural structure that neuroscientists and neurologists are now utilizing to understand the evolution of the human brain and to research cures for diseases such as multiple sclerosis. The rigid constraints of packaged software prevent fundamental discoveries of this magnitude.
The Reliability Paradox
While AI offers the promise of bespoke, scalable code, it introduces a major reliability challenge, particularly in high-stakes environments like telecom, which demand five or six nines reliability. Recchia explained that sophisticated AI is often used to reduce or eliminate the need for Tier 1 and Tier 2 support staff in network operations (Net Ops). However, when an issue arises, it escalates directly to Tier 3 personnel:
"Tier three doesn't know anything about the code right because the AI wrote it, so that's the challenge," Recchia observed.
The solution, however, is not a retreat to centralized stability. Instead, the firm is developing models for automated Tier 3 and Tier 4 support using AI in net ops. This ensures that even customized, AI-written code maintains reliability, ensuring outages last only "a couple of seconds".
The complexity of modern technological challenges—from achieving near-zero latency networking necessary for remote surgery to tackling the massive power consumption associated with data center build-outs—demands highly specialized, customized solutions. Bespoke AI must replace centralized software because only autonomous, intelligent systems can deliver the speed, customization, and fundamental discoveries required for humanity to make technological leaps forward.
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