I Hate AI
The Technology Everyone Complains About and Uses Anyway
In broadband, skepticism is a survival skill.
This industry is built on engineering, reliability, and real-world problem solving. Many professionals have watched buzzword after buzzword pass through product roadmaps and conference agendas, digital transformation, big data, IoT, cloud-first strategies, and numerous other “revolutions” that often delivered more marketing than material change on the ground.
The reaction to AI’s sudden “I’m here” in demos, keynotes, vendor pitches, and press releases is understandable. “I hate AI” has become a common statement.
It is not an anti-technology stance. It is fatigue with exaggerated promises, fear-driven headlines, and the suggestion that one category of software can solve every business challenge. The fatigue is earned.
Something undeniable is happening, though, people are using AI and doing it a lot. Not just developers or technology companies, but ordinary users with everyday problems. People ask whether they should see a doctor, how to fix their tomato plants, or which paint to buy. Parents use AI tools to explain algebra. Small business owners rely on them to draft communications, summarize contracts, and organize information.
These are not futuristic scenarios. They are mundane questions that used to go to a neighbor, a manual, or a search engine. Increasingly, they go to AI.
If so, many people claim to dislike AI, why are they using it? Because it helps. Not perfectly, and certainly not as a replacement for doctors, engineers, accountants, teachers, or broadband professionals. It helps because it offers a faster way to frame a problem, understand options, and approach decisions slightly better informed.
People in this industry already understand that. Technology only matters when it solves real problems.
What the industry actually needs
For broadband, the real question is not whether AI will replace people. Operators will use these tools to make their people more effective.
For years, critical functions such as network operations, customer experience, infrastructure management, content delivery, and business strategy have often operated as separate silos. Data exists everywhere; insight tends to live in pockets.
AI begins to add value when it helps connect those pockets:
- A network engineer using pattern detection across large datasets to surface emerging issues.
- A customer care agent seeing likely root causes and recommended actions while still applying empathy and professional judgment.
- An operations leader combining telemetry, ticket history, and customer feedback to prioritize investments.
AI can surface information. People still determine what matters and what to do next.
Broadband is well positioned for this shift. Networks already connect people, devices, content, and data. AI represents another analytic layer on top of that foundation, one that can help operators understand what is happening, predict what may fail, and intervene before minor issues escalate into major service events.
Used thoughtfully, this can translate into:
- Smarter network operations
- Faster issue resolution
- More efficient use of infrastructure
- Better subscriber experiences
- Fewer truck rolls
- Stronger business insights
- More time for work that truly requires human judgment
This is an extension of existing practice. Predictive maintenance, network analytics, anomaly detection, automated ticket routing, and Wi-Fi optimization have been part of the toolkit for years. AI has not suddenly appeared; it is becoming more accessible, more visible, and applicable to a wider set of operational and customer-facing workflows.
Broadband powers the AI economy
There is another aspect of the AI story that is often underappreciated: AI depends on connectivity.
Every interaction with an AI platform, every query, every response, every model update starts as traffic on a network. Students, businesses, healthcare providers, employees, and consumers all rely on broadband infrastructure to access these tools.
For many years, broadband was framed primarily as entertainment infrastructure: streaming video, online gaming, and faster downloads. Those remain important, but they no longer capture the full value proposition.
Today, broadband:
- Connects students to digital classrooms, labs, and specialized instruction.
- Connects businesses to collaboration platforms and cloud-based productivity tools.
- Connects patients to telehealth, remote monitoring, and specialist care.
- Connects communities to economic opportunity.
Increasingly, it also connects all those users to AI-powered tools that help them learn, decide, and operate more efficiently.
Without broadband, AI is a laboratory capability. With reliable broadband, it becomes a broadly accessible resource. That distinction has competitive, educational, and economic implications. Operators are not just delivering speed; they are delivering access to the next generation of digital tools.
We have seen this before
The current debate about AI may feel unprecedented, but history suggests a familiar pattern.
When personal computers entered the workplace, offices still relied heavily on typewriters, filing cabinets, paper ledgers, calculators, and large pools of administrative staff. Documents were typed, edited, copied, mailed, and filed manually.
PCs changed that. Word processors transformed document creation. Spreadsheets changed how organizations analyzed information. Email reshaped communication and decision cycles. Tasks that once required days of manual effort could often be completed in hours.
Some roles diminished or disappeared. At the same time, entirely new categories of work emerged: IT professionals, software developers, network administrators, cybersecurity specialists, data analysts. The work did not vanish; it evolved.
Accounting is another good example. The profession moved from paper ledgers and calculators to spreadsheets, then to Excel and dedicated accounting software, and eventually to cloud-based platforms that automated much of the repetitive bookkeeping.
Each step prompted resistance from those who had mastered the prior tools and processes. Yet accountants did not disappear. The nature of their work shifted toward analysis, planning, forecasting, compliance, and advisory services activities that are more complex to automate and more valuable to organizations.
The same dynamic has played out across many office and support roles. As firms adopted PCs, software, and then cloud applications, job requirements increasingly emphasized digital skills rather than eliminating positions outright.
AI is likely to follow a similar trajectory.
In education AI is not the problem
The next generation of AI users, kids, are ahead of us but tension is visible in education. Today, many schools are focused primarily on catching students who use AI tools, often relying on detection software and stricter enforcement policies. These measures may deter some, but they do not address the underlying challenge and are unlikely to scale as the AI tools improve.
AI is not the problem. It is that many teaching assessment models were designed for an era when such tools did not exist.
Students still need to learn to think critically: to evaluate information, identify errors, recognize bias, question assumptions, and apply knowledge in real-world contexts. Knowing how to obtain an answer is not the same as understanding it.
In an environment where software can generate plausible responses on demand, skills related to questioning, verification, and judgment become more important.
That means educators also must adapt or go back to the basics. Growing up we didn’t have computers, that wasn’t that long ago—at least to me! Assessment and grading practices will need to evolve, placing greater emphasis on discussions, presentations, oral examinations, project work, and in-class assignments that reveal how students think, not just what they submit.
Technology changes what can be automated, and the most valuable parts of human work shift toward judgment, creativity, and problem solving.
AI will not replace broadband professionals
The same principles apply directly to broadband. AI can scan logs and identify anomalies, but it cannot walk the plant, read the nuances of a customer interaction, or balance performance, budget, and regulatory constraints. It can suggest probable root causes, but it cannot fully account for local context, history, and community expectations.
- AI can identify impairments. People fix those impairments.
- AI can automate routine tasks. People bring experience and context.
- AI can process data. People make durable decisions.
The industry still relies on professionals who understand their networks, their markets, their customers, and their teams.
Change is not painless. Some roles will change and some responsibilities will shift. Many professionals will need to integrate new tools into well-established workflows. Those concerns are valid and merit serious attention from leaders and policymakers.
However, opting out of these tools does not halt their adoption. It simply increases the risk that organizations and individuals will have to catch up later under more pressure.
So when I hear someone say, “I hate AI”…
The skepticism is understandable. The hype is loud, but the point is simple: use the tools where they help and keep people making the calls where it matters.
Brady Volpe
brady.volpe@volpefirm.com
Brady Volpe is Chief Product Officer at OpenVault and founder of The Volpe Firm, Inc., and Nimble This (now OpenVault). With more than 30 years in broadband cable and telecommunications, he has led product development and successful technology launches. His expertise spans high-speed data, DOCSIS®, PNM, HFC, machine learning, and PMA. Through his blog, podcast, and livestream, he connects engineering, product strategy, and practical industry experience to advance broadband innovation.
image: Shutterstock
