What the Internet Teaches Us About the Future of AI
June 9, 2025
Introduction: AI in the Classroom Isn’t the First Tech Uproar
When OpenAI announced that it would embed ChatGPT across entire university systems—starting with Cal State’s 460,000 students—it sparked both excitement and anxiety. But we’ve been here before. In the late 1990s and early 2000s, the Internet’s arrival in schools caused similar hand-wringing over cheating, information overload, and equity.
Back then, teachers feared students would just copy-paste assignments. Today, they worry about AI hallucinations or outsourcing all thinking. The question then—and now—isn’t whether the technology will be used, but how do we make its use constructive?
To find the answer, let’s look back at the Internet’s integration into education and work—and what it reveals about this new AI-native wave.
The Internet Curve: Hype, Concern, and the Deal We Made
1. The Hype Cycle (1995–2005): A Classroom Revolution
In the early years of Internet access in schools, education leaders spoke of a “digital renaissance.” New teaching methods, global research tools, and email-based collaboration promised to transform classrooms. Governments responded: by 2000, over 95% of U.S. public schools had Internet access (NCES, 2002). The business world followed suit, with corporate training platforms, intranets, and digital knowledge bases booming.
But just like now, the hype outran readiness. The reality was more chaotic: clunky websites, misinformation, and students using Ask Jeeves to “research” Shakespeare.
2. The Concern Phase: Cheating, Attention Spans, and Inequity
Educators feared students would lose research skills, rely too heavily on Google, and plagiarize Wikipedia. Businesses were no different—concerns emerged about email etiquette, cybersecurity, and whether employees could actually focus with broadband at their fingertips.
Eventually, these fears were met with solutions: media literacy became a standard. Tools like Turnitin emerged. Businesses adopted acceptable use policies. And critically, new workflows emerged—digital calendars, collaborative docs, LMS platforms—that fully normalized the Internet.
Education vs. Business: Who Led?
Historically, universities were often first to experiment with tech (see: ARPANET), but businesses were faster to operationalize it at scale. With the Internet, adoption progressed in parallel, but businesses accelerated integration because the ROI was clearer: faster communication, broader markets, more efficient operations.
With AI, it appears we’re seeing the reverse: universities like Ohio State are institutionalizing AI fluency as a baseline literacy skill. Cal State and Duke are deploying campus-wide ChatGPT tools. Meanwhile, many enterprises are still cautiously sandboxing AI for compliance reasons or struggling to move beyond chatbot pilots.
Why Universities and Businesses Must “Give” People AI
It’s tempting to think individuals can just teach themselves AI tools on YouTube and skip the institution altogether. And to some extent, they can.
But here’s the catch: AI alone doesn’t build expertise—AI embedded within real-world systems does. Universities and companies provide three essential elements AI can’t replicate:
- Context – Knowing what matters within a discipline or function.
- Structure – A sequenced path to build complexity over time.
- Validation – A system to test, verify, and improve outcomes.
The same reason the Internet didn’t replace business school is the reason AI won’t make universities or companies obsolete. But both must evolve. Offering AI literacy—just like computer literacy in the ’90s—is now table stakes. It’s about empowering people to think, create, and act with AI embedded in the process, not adjacent to it.
The AI-Native Compact: What We’re Trading
The Internet taught us that digital access levels the playing field—but only when paired with digital fluency. We also learned that every major technological shift brings trade-offs:
| Then (Internet) | Now (AI) |
|---|---|
| + Access to knowledge | + Accelerated productivity |
| – Misinformation | – AI hallucinations |
| + Collaboration tools | + Personalized learning |
| – Privacy and surveillance | – AI data ownership concerns |
The question isn’t whether we accept AI—the question is what deal we’re making.
From AI Fluency to AI Expectations: How to Prepare Your Workplace
Just as Internet fluency transitioned from a resume boost to a job requirement in the early 2000s, AI fluency is now following the same trajectory. But the implications are more profound. Today’s students are graduating not only with exposure to AI—but with the expectation that it will be available to them as infrastructure: always-on, task-aware, and personalized.
They’re used to study bots that summarize readings, chatbots that rehearse job interviews, and tools that draft outlines or write code side-by-side. When they enter the workforce, these students won’t just want AI. They’ll wonder: why doesn’t this job have it?
If your business isn’t preparing for that reality now, you risk onboarding disillusionment, productivity gaps, and talent attrition.
📈 What Should Businesses Start Doing Now?
1. Audit and Align Your Toolset
- Inventory current AI tools: Do you offer employees access to tools like ChatGPT, GitHub Copilot, Notion AI, or Claude?
- Ensure consistency: Standardize access and support for these tools across departments. It’s not enough if one team is AI-native while another still prints PDFs.
2. Establish Internal AI Guidelines and Playbooks
- Provide a clear AI usage policy (especially for regulated industries). (Examples)
- Create role-based use cases: Show sales reps how to generate first-draft emails. Show analysts how to summarize 50-page reports. Show devs how to use copilots for QA testing. (Sample Tools)
- Create a “What AI Can and Can’t Do Here” training module during onboarding.
3. Design Your Onboarding Around AI Readiness
- Offer new hires a “Welcome AI Toolkit” that includes:
- A company-approved generative AI app list.
- Training resources or cheat sheets.
- Example workflows used by top performers.
- Appoint AI champions in each department to offer hands-on help with tool usage.
- Follow industry best practices to ensure coverage of all relevant subjects (Example)
4. Identify Where AI Can Augment (Not Replace)
- Review your job descriptions. Are there roles where AI can handle 20–40% of the workload so your people can focus on higher-value work?
- Deploy AI for internal support: Use chatbots to answer HR, IT, or policy questions instead of relying solely on human support desks.
5. Create Feedback Loops from AI-Native Talent
- When hiring recent graduates, ask how they’ve used AI before and what tools made them more effective.
- Use their responses to continually refine your toolset and training—they are your signal for what’s coming next.
The Opportunity Hidden in the Disruption
Businesses that invest in structured AI enablement today won’t just retain talent—they’ll leapfrog competitors still trapped in manual, permission-gated workflows. The AI-fluent class isn’t a novelty. It’s your next workforce wave.
Meet them with the right tools and mindset, and you don’t just support them—you unleash them.
Want to learn more about how Brimma can help you with your AI planning? Schedule a call!
