Tech’s Revolving Door of ‘Must-Haves’ Sparks Skepticism
A veteran Linux developer contends that the tech industry is perpetually chasing the next shiny object, often fueled by marketing hype rather than genuine innovation. This cycle of inflated expectations and eventual disillusionment has been repeating for at least 15 years, with “generative AI” potentially poised to be the next bubble to burst.
The discussion unfolded during a break at the CentOS Connect conference, where a developer—wishing to remain anonymous—shared a candid assessment of the tech landscape. The developer, with nearly four decades of experience, suggested a period of steady progress in the late 20th century gave way to a wave of “bullshit” technologies following the 2008 financial crisis.
Key Takeaways
- Containers, Kubernetes, and the “Cloud” are questioned as solutions in search of problems.
- The “as a Service” model is criticized for relinquishing control over essential infrastructure.
- Blockchain technology and its derivatives are dismissed as largely ineffective.
- “Generative AI” is viewed with skepticism, described as “predictive text turned up to 11.”
The developer identified six technologies as particularly emblematic of this trend: containers, Kubernetes, the “Cloud,” anything “as a Service,” the blockchain, and now, generative AI. The critique wasn’t simply dismissive; it was rooted in a pragmatic view of what truly adds value.
“Sure, containers work, they are handy for testing,” the developer reportedly said. “But they aren’t a deployment method. You shouldn’t need them. Anything that you can run in a container, you can just run on the bare metal, and if you’re not competent enough to get – and keep – that working, then you probably aren’t competent enough to deploy a container either.”
This sentiment extended to Kubernetes, seen as an unnecessary complication for most organizations. “If you don’t need containers, then you don’t need another vastly more complicated tool to deploy those containers,” the developer argued. “The chances are, you are not a vast multinational that must be able to withstand ten million potential new customers visiting your site all at once. It won’t happen, so you won’t lose any of that imaginary business.”
The “Cloud” drew particular scorn, described as “nebulous by name and by nature.” The idea of entrusting sensitive data to third-party providers was likened to “selling homeopathy,” raising concerns about security and control. As one summary put it, “There Is No Cloud – There’s Just Somebody Else’s Computer,” a point underscored as early as 2015.
The “as a Service” model—Infrastructure as a Service, Platform as a Service, and Software as a Service—was criticized for eroding ownership and understanding. “Nobody else will ever care as much about your servers as you will,” the developer asserted. With Software as a Service, the concern was losing track of data and control over the underlying infrastructure.
What’s the core problem with blockchain? It’s the world’s slowest and most-distributed database, making it impractical for many applications.
Blockchain technology, cryptocurrencies, and NFTs were dismissed as largely worthless or based on flawed concepts. Web3 was characterized as “Get ripped off, as a service.”
Generative AI, or large language models, was described as “predictive text turned up to 11,” lacking even basic mathematical abilities. Daniel Stenberg, author of curl, offered a similarly critical perspective. In 2024, Gentoo and NetBSD banned LLM bot slop, further illustrating the growing skepticism.
The timeline of these technologies reveals a pattern. Salesforce.com, founded in 1999, was an early SaaS offering. Amazon Web Services launched in 2002, popularizing cloud computing. Google’s Gmail followed in 2004, bringing SaaS to a wider audience. The Bitcoin whitepaper appeared in 2008, alongside the first version of LXC. Docker arrived in 2013, and Kubernetes in 2014, building on earlier work like Google’s internal “Borg” system. ChatGPT opened to the public in 2022.
While the developer initially estimated a 15-year cycle of hype, the reality appears to be longer. The constant pursuit of the “next big thing” may be driven by a desire for innovation, but it also raises questions about the true value being delivered.
The tech industry’s relentless cycle of innovation and reinvention continues, leaving observers to wonder which technologies will ultimately prove to be lasting solutions and which will fade into obscurity.
