Brandon Griffin’s advice to the Class of 2026 lands at a pivotal moment: as graduation season approaches, students are stepping into a labor market that is already being reshaped by generative AI, agentic assistants, and new hiring workflows — and the rules for getting started have changed.
The basic message in the USA TODAY column is straightforward and urgent: learn to work with AI, don’t lean on it as a substitute for learning, and protect your ethics and craft while staying flexible. The piece points to routine early‑career tasks — drafting reports, building slide decks, summarizing meetings — that are increasingly automatable, and argues students must develop the higher‑value human skills that remain hard for machines: judgment, creativity, contextual storytelling, and the ability to manage AI as a team member.
This view sits squarely within a wider conversation about the new workplace blueprint Microsoft calls the “Frontier Firm,” where human–agent teams are the default and the new managerial skill is being an “agent boss” — someone who can direct, train, and verify AI agents. Microsoft’s 2025 Work Trend Index explicitly describes this shift and quantifies leader and employee attitudes toward managing AI agents.
That doesn’t mean humans are out of a job. It means the composition of valuable work is shifting. Employers increasingly prize workers who can guide AI — set the problem, evaluate outputs, catch hallucinations, and translate raw results into strategic choices. Microsoft’s research and product announcements now center on that human‑in‑the‑loop leadership model.
Meanwhile, hiring platforms and recruiters are experimenting with AI too. LinkedIn and other job marketplaces are deploying tools that score matches and flag qualification gaps; some product teams promote “Jobs Match” features that tell applicants whether a role is a fit before they apply. That shifts the signal landscape: it’s not only applicants using AI — hiring systems increasingly use AI to filter and rank. This double‑sided AI adoption can create mismatches and subtle biases (including so‑called self‑preference biases in models evaluating outputs from models). Be ready to show verifiable, demonstrable skills beyond optimized documents.
In short: embrace generative AI, but don’t outsource judgment. Build a portfolio that shows both tool fluency and human judgment. Protect your ethics, and push institutions to give every student fair access to the skills and tools that will define early careers for the next decade.
Source: USA Today Class of AI – My advice to students stepping into a work world with AI
Background
The basic message in the USA TODAY column is straightforward and urgent: learn to work with AI, don’t lean on it as a substitute for learning, and protect your ethics and craft while staying flexible. The piece points to routine early‑career tasks — drafting reports, building slide decks, summarizing meetings — that are increasingly automatable, and argues students must develop the higher‑value human skills that remain hard for machines: judgment, creativity, contextual storytelling, and the ability to manage AI as a team member.This view sits squarely within a wider conversation about the new workplace blueprint Microsoft calls the “Frontier Firm,” where human–agent teams are the default and the new managerial skill is being an “agent boss” — someone who can direct, train, and verify AI agents. Microsoft’s 2025 Work Trend Index explicitly describes this shift and quantifies leader and employee attitudes toward managing AI agents.
Why this matters now: AI is not hypothetical
Generative AI is no longer experimental background noise. Practical tools such as ChatGPT, Google Gemini, and Microsoft 365 Copilot are embedded into everyday workflows — drafting, summarization, rapid data exploration, and prototype creation. Those capabilities make the old “starter work” that newcomers used to rely on for learning far less scarce and valuable. As a result, entry‑level pipelines are under pressure: fewer people may be hired for first‑pass drafting or research because AI can perform those tasks faster and cheaper. Independent analyses and community reporting show the same pattern: task‑level automation is real, and younger workers are among the most exposed.That doesn’t mean humans are out of a job. It means the composition of valuable work is shifting. Employers increasingly prize workers who can guide AI — set the problem, evaluate outputs, catch hallucinations, and translate raw results into strategic choices. Microsoft’s research and product announcements now center on that human‑in‑the‑loop leadership model.
What the USA TODAY advice gets right — and where it needs nuance
Strengths of the message
- Pragmatic balance. The column emphasizes mastery over fear: learn AI tools, but don’t let them do your thinking. That practical stance is crucial for employability in 2025.
- Ethics and integrity. Warning against “cheating” with real‑time answer tools is prescient. The Cluely episode — a high‑profile app that suggested live answers during interviews — is a live case study in the reputational and ethical risks of outsourcing human judgment to AI. Business reporting shows Cluely’s rapid early adoption triggered both fascination and swift controversy.
- Attention to new roles. The idea of becoming an “agent boss” — someone who manages AI coworkers — is already being baked into corporate strategy and product roadmaps. Microsoft’s trend index shows leaders expect training and agent‑management responsibilities to rise in the next five years.
Necessary caveats and missing nuance
- Not all entry work is disappearing. Automation is task‑level, not role‑level. Many entry roles have interpersonal, physical, or unpredictable judgment components where AI remains an assistive technology rather than a replacement. Historical technology shifts compress some tasks while creating new entry paths. Community reporting suggests a realignment more than an immediate collapse.
- Access and equity are unresolved. AI fluency is a new axis of inequality. Institutions with budgets to buy enterprise Copilot or education instances can protect student data and enable safe classroom experiments; smaller institutions or underfunded students risk falling behind. Institutions and employers must invest in equitable access and retraining.
- Employer practices lag product hype. Vendors and early adopters frequently promote high ROI and productivity numbers; real enterprises still wrestle with integration, governance, and auditability. Organizations are hiring for new AI roles even while they explore whether agents can be deployed safely and reliably.
The “agent boss” and why it should be a resume line
Microsoft frames the coming era around the “agent boss” archetype: workers who can design prompts, assemble multi‑agent workflows, set guardrails, and audit outputs. The practical implication for new graduates is simple: technical familiarity alone is insufficient; employers will pay for demonstrated competence in structuring and supervising AI. That encompasses:- Designing safe prompts and guardrails (data masking, privacy filters).
- Integrating outputs into decision processes (verifying sources, using citations).
- Monitoring and debugging agent behavior (audit logs, error handling).
- Communicating how AI was used and why; showing provenance and human oversight.
Lessons for students and early‑career professionals
1) Become AI‑literate — deliberately
- Learn the basics of prompt design and model limitations. Treat AI outputs as drafts that must be interrogated.
- Practice verification workflows. Always check facts, dates, and metrics returned by a model; ask for sources and validate them.
- Use enterprise education instances where possible: they come with governance and data protections that public consumer models often lack.
2) Preserve and document your human contribution
If AI helps you produce a slide deck, a research summary, or a marketing plan, document what you did to shape and verify that output. Future employers ask for proof you understand, not just a finished artifact. Simple practices that demonstrate ownership:- Keep a short note explaining your prompt strategy and edits.
- Annotate where you corrected or expanded the AI’s answers.
- Produce a one‑paragraph synthesis that shows your judgment or next steps.
3) Build skills AI can’t own
Focus on capabilities that remain durable:- Contextual judgment: turning data into strategy.
- Interpersonal influence: leading meetings, negotiating, and client empathy.
- Creative synthesis: framing problems, telling persuasive stories.
- Domain depth: rare, specialized knowledge that agents can’t invent credibly.
4) Use AI to scale your learning, not substitute for practice
Treat AI as a coach: use it to generate practice problems, mock interviews, or study flashcards — then work through the reasoning yourself. That approach leverages AI’s speed while protecting the cognitive skill-building that employers prize. Educators and institutions increasingly recommend this mode of integration.Hiring, interviews, and the ethics test
The Cluely controversy is a concrete warning. Tools that feed real‑time answers during interviews create a moral and practical minefield. Early press coverage documents both rapid user adoption and serious issues: latency, hallucinations, privacy concerns, and institutional responses (suspensions, policy clarifications). Using AI to prepare for interviews — mock answers, research, rehearsal — is one thing. Using AI to perform during an interview is another; it risks institutional sanctions and long‑term damage to reputation.Meanwhile, hiring platforms and recruiters are experimenting with AI too. LinkedIn and other job marketplaces are deploying tools that score matches and flag qualification gaps; some product teams promote “Jobs Match” features that tell applicants whether a role is a fit before they apply. That shifts the signal landscape: it’s not only applicants using AI — hiring systems increasingly use AI to filter and rank. This double‑sided AI adoption can create mismatches and subtle biases (including so‑called self‑preference biases in models evaluating outputs from models). Be ready to show verifiable, demonstrable skills beyond optimized documents.
Resumes, portfolios, and the rising currency of verified work
The narrative that “resumes will be obsolete” is partly marketing and partly foresight. In practice, resumes are evolving rather than dying. AI‑driven hiring products prioritize signals that can be measured and validated: skills demonstrated in work samples, verified assessments, and real‑time on‑task performance. That means:- Portfolios of live projects, agent configurations, or code repositories are increasingly powerful.
- Short case studies that show your role, the AI tools used, and how you verified outputs are high signal.
- Continuous learning credentials (micro‑credentials, verified certifications) matter more when paired with demonstrable outputs.
For employers and educators: how to preserve the entry pipeline
- Redesign entry roles so training remains embedded. If routine tasks are automated, convert those tasks into supervised learning opportunities where students and new hires validate and correct AI outputs. This preserves mentorship and skill accumulation.
- Invest in equitable access to enterprise or education instances that prevent student data from being used for vendor model training and provide audit trails.
- Make assessment process‑centric: portfolios, live demonstrations, and oral defenses reveal true understanding and discourage shortcutting.
Practical checklist for graduating students
- Embrace AI literacy: take short courses or microcredentials on prompt engineering and model evaluation.
- Build a portfolio of three concrete projects: one technical (data task or agent build), one communications piece (presentation with AI‑assisted research clearly annotated), and one ethical case (how you handled a hallucination or privacy concern).
- Keep a short “AI usage log” for key deliverables: what you asked the model, what you changed, and why. Employers appreciate transparency.
- Practice telling the story of any AI assistance: in interviews, explain how AI helped you produce better work and what you did to ensure accuracy.
- Prioritize people skills. Leadership, collaboration, and clear writing remain hard to automate.
Risks to watch
- Skill atrophy: relying on AI for reasoning tasks can erode core analytical muscles. Counteract this by alternating AI‑assisted and fully manual practice.
- Data privacy and compliance: pasting proprietary or personal data into consumer models can create legal and reputational damage. Prefer enterprise tools for sensitive work and verify vendor contracts.
- Market signaling distortions: as both sides of hiring adopt AI, the system may increasingly favor candidates who share the vendor’s model or toolset. That introduces a class of bias that researchers and policymakers are only beginning to measure. Be prepared to demonstrate domain knowledge beyond polished, AI‑assisted text.
What institutions and policymakers should do
- Support reskilling programs targeted at early‑career workers most exposed to task automation. Surveys show leaders recognize the need to retrain — the policy question is scaling it equitably.
- Promote transparency requirements for tools used in hiring and for any AI systems used to score applicants, including audit trails and contestability for candidates. Independent research and preprints highlight self‑preference and fairness risks that warrant regulatory attention.
- Fund pilots that explore alternative entry paths (micro‑internships, portfolio assessments, supervised agent exercises) and measure whether they preserve learning outcomes and access.
Conclusion
The advice in the USA TODAY column is sound: learn the tools, but cultivate the uniquely human skills that machines can’t reliably provide. The workplace is already changing toward human–agent collaboration, and being able to orchestrate, verify, and lead AI systems — the “agent boss” skill set — will be a differentiator for the Class of 2026. At the same time, shortcuts that trade integrity or learning for immediate gains carry ethical and practical hazards, as the Cluely saga demonstrates. Graduates who treat AI as a force multiplier for learning and who can show documented, verifiable contributions will be the ones employers hire and promote.In short: embrace generative AI, but don’t outsource judgment. Build a portfolio that shows both tool fluency and human judgment. Protect your ethics, and push institutions to give every student fair access to the skills and tools that will define early careers for the next decade.
Source: USA Today Class of AI – My advice to students stepping into a work world with AI
