Will AI make technology obsolete?

Indeed, we are witnessing an unprecedented technological upheaval, arguably the most profound and certainly the swiftest in history.

This AI surge holds immense promise: automating intricate processes for massive productivity boosts, expediting scientific discoveries in fields from medicine to materials science, and broadening access to specialized knowledge in vital sectors like healthcare and education. Pioneers are already leveraging it to streamline mundane tasks, often referred to as “vibe-coding.”

From this perspective, it truly appears to be an exhilarating era.

But is it always so? The potential drawbacks are equally apparent. Forecasters and commentators have raised alarms about widespread job displacement, cognitive decline, sophisticated cyberattacks, and even more severe consequences.

Conversely, it can feel like a daunting period to navigate.

Given your readership of this publication, it’s reasonable to assume a general interest in and understanding of technology. It’s increasingly evident, however, that technology at large faces significant challenges from the rapid expansion of AI.

How AI is harming technology

The proliferation of AI is impacting a broad spectrum of technologies, products, and services, leading to issues such as obsolescence, setbacks, and increased costs. Specifically, the AI sector is currently:

1. Sparking severe chip shortages. Leading RAM manufacturers like Samsung, SK Hynix, and Micron are reallocating their production capabilities towards High-Bandwidth Memory (HBM), crucial for AI systems. This redirection is causing scarcity in standard DRAM and NAND chips, essential components for devices ranging from smartphones and laptops to medical equipment.
2. Inflating hardware costs. The scarcity of memory components is making the production of non-AI electronics considerably more expensive. By early 2026, the cost of conventional computer memory and SSDs had significantly risen, primarily because the industry is prioritizing the more lucrative AI-specific chips over consumer-grade components. This trend is even prompting an increase in refurbished laptop purchases, as new models become less affordable for many.

3. Causing delays in GPUs and related devices. The escalating need for AI computational power, predominantly dependent on Graphics Processing Units (GPUs), has led to substantial backorders for these processors. Consequently, devices that utilize GPUs for their intended graphical processing functions are also facing significant delays.
4. Generating widespread, COVID-era supply issues. The redirection of chip supplies towards AI infrastructure is disrupting the rollout of non-AI hardware. Essential power and automotive chips are now in short supply, impacting sectors from car manufacturing to household appliances. This situation is reminiscent of the widespread shortages experienced during the COVID-19 pandemic.
5. Redirecting startup investments. Non-AI startups are finding it increasingly difficult to secure funding. Venture capital is being channeled almost entirely into AI-focused enterprises, compelling founders outside the AI space to pivot their strategies or incorporate “AI-first” elements (a practice known as “AI washing”), even when such integration is not genuinely required.
6. Depleting talent from research institutions. While a symbiotic relationship has long existed between university tech research labs and the broader tech industry, AI is now distorting this dynamic. Private AI firms are luring away leading academic researchers and engineers with exceptionally high salaries. This trend significantly weakens university departments and non-AI research facilities, endangering the future supply of skilled professionals for vital areas such as conventional software engineering.
7. Deterring graduates from non-AI tech careers. With companies increasingly focusing on AI, entry-level positions in other technological domains are being reduced. In the US, job postings for new graduates in these roles decreased by 35% between 2023 and 2025. This not only disrupts traditional career progression paths but also discourages young individuals from entering tech professions outside of AI.
8. Amplifying the threat of cyberattacks. Malicious entities are leveraging AI to target non-AI systems. AI empowers even less experienced hackers to execute highly sophisticated assaults. Technologies capable of voice cloning and generating deceptive identities are bypassing established security protocols, thereby overstraining conventional IT defense mechanisms.
9. Forging a novel digital divide. A widening gap is emerging between tech-savvy individuals, developers, and those proficient in using AI for tasks like “vibe-coding,” and individuals who are less technical or less inclined to adopt these new AI tools.
10. Alienating the public from the tech sector. Public goodwill towards Silicon Valley is eroding, partly due to the AI industry’s perceived excesses: a demanding “996” work culture, widespread job insecurity, the proliferation of “AI slop,” exorbitant salaries, escalating electricity consumption, and environmental concerns stemming from new data centers. Additional factors include the unapproved use of personal data and copyrighted material for model training, alongside a deluge of deepfakes, misinformation, and low-quality AI-generated content flooding social platforms such as Facebook.
11. Diminishing the market for traditional applications. The conventional software market is evolving, with a move towards “vibe-coding” – users opting to create personalized, ephemeral applications via AI platforms such as Replit, Lovable, and Cursor, rather than subscribing to paid apps. Gartner forecasts a 25% reduction in mobile app usage by consumers, who will increasingly depend on generative AI assistants to manage tasks without needing to navigate multiple distinct applications, even independently of “vibe-coding.” Regardless of the approach, the app development landscape faces significant disruption.
12. Jeopardizing factual integrity. AI chatbots are redefining search engines by delivering concise answers directly, bypassing the traditional list of links. This change deprives publishers of crucial website traffic and the revenue necessary for their survival, thereby decreasing both the motivation and financial capacity for generating and disseminating new knowledge (or “facts,” broadly speaking). Simultaneously, this model often presents erroneous information as factual, which is detrimental to the technology sector itself—an industry fundamentally reliant on education, ongoing learning, and the creation of new information.

These developments certainly paint a bleak picture, and their immediate consequences appear unfavorable. What remains uncertain, however, is the long-term ripple effect of the AI revolution and whether it will ultimately offer a net positive or negative impact on the non-AI technological communities, businesses, initiatives, and cultural elements we have cherished for many years.

For now, it’s crucial to maintain a pragmatic perspective on the escalating challenges AI poses to the broader tech landscape, even for those who enthusiastically embrace its advancements.

AI disclosure: I don’t use AI to do my writing. The words you see here are mine. I do use Gemini 3 Pro via Kagi Assistant (disclosure: my son works at Kagi) as well as both Kagi Search and Google Search to fact-check. I used a word processing application called Lex, which has AI tools, and after writing the column, I used Lex’s grammar checking tools to hunt for typos and errors and suggest word changes.