ChatGPT- May 26
…and after summarising Age of Empire by Karen Hao
My 18 year old son is in university and plans to invest time money effort in tertiary education and then get a meaningful job work and personal life
That’s a very reasonable plan—and still the most reliable path for an 18-year-old today. But the video you shared is useful as a warning about how the rules are shifting, not a reason to abandon that path.
Let me ground this in reality for your son.
🧭 The honest picture for him (2026 onward)
✅ What still works
- A good degree + real skills + work ethic still leads to a solid career
- Most professional jobs are not disappearing
- Employers still value:
- problem-solving
- communication
- reliability
So the “study → job → stable life” model is not broken
⚠️ What has changed (this is where the video is right)
1. Degree alone is no longer enough
- A generic degree = weaker signal than before
- He needs applied capability alongside it
Think:
- internships
- projects
- real-world exposure
2. Entry-level roles are tightening
AI is hitting:
- junior analysts
- basic coding roles
- admin-heavy graduate jobs
So the ladder is still there—but:
- first rung is more competitive
3. Career paths are less linear
Instead of:
degree → job → steady progression
It’s now more like:
degree → experimentation → pivots → growth
4. AI is a tool AND a filter
People who use AI well:
- move faster
- look more capable
People who don’t:
- fall behind quietly
🎯 What your son should actually do (practical strategy)
This is where you can really help him.
1. Pick a “durable skill core”
Not just a subject—a capability
Strong examples:
- problem-solving (engineering, maths, physics)
- structured thinking (economics, data, law)
- human interaction (medicine, psychology, business)
Weaker (alone):
- purely theoretical or generic degrees without application
2. Stack skills early (this is the big one)
By age 21, he should aim to have:
- 1–2 internships or real work experiences
- 2–3 tangible projects (GitHub, portfolio, research, etc.)
- basic AI literacy (using tools, not building models)
This is now table stakes, not exceptional.
3. Learn to work with AI, not compete with it
He should be comfortable:
- using AI for research, drafting, coding help
- validating outputs (critical thinking)
The winning profile is:
human judgment + AI speed
4. Build signal, not just knowledge
Employers now ask:
“What have you done?” not “What did you study?”
Examples:
- built something
- solved a real problem
- contributed to a team
- created something public
5. Choose environments, not just jobs
Early career matters hugely now.
He should prioritise:
- places where he learns fast
- good managers
- exposure to real responsibility
Over:
- just salary or prestige
🧠 Where I’d push back on the video (for your son specifically)
The idea that he’ll end up in a “desperate gig economy” by default is too pessimistic.
That happens mainly if someone:
- drifts through university
- graduates without practical skills
- avoids responsibility or competition
If he is:
- proactive
- curious
- adaptable
He’ll be in the top half of outcomes, which still looks very good.
🧭 The real goal (this is the mindset shift)
Don’t aim for:
“a stable job for life”
Aim for:
“a person who is always employable and adaptable”
That’s the modern version of security.
👨👦 Your role (important)
What actually makes the biggest difference:
- Encourage exploration early (years 1–2)
- Push him (gently) toward doing, not just studying
- Normalize:
- uncertainty
- trying things
- small failures
Not pressure for perfection.
✅ Bottom line
- His plan is still valid and sensible
- The system hasn’t collapsed
- But the bar for success is higher and more practical
If he:
- builds skills alongside his degree
- learns AI as a tool
- gains real experience early
👉 He’ll likely do very well—not struggle.
If you want, tell me what he’s studying (or considering), and I can map out a very specific 3-year game plan for him.
Anthropic Jack Clark - May 26
waystochangetheworld This interview with Jack Clark (co-founder of Anthropic and optimistic Brit) focuses on the potential for AI-driven economic disruption and the necessity of proactive policy-making.
Future Hiring & Economic Trends Identified
The Vulnerability of Entry-Level Roles: Up to 50% of white-collar entry-level jobs could be automated within a few years [00:06].
Interdisciplinary Hiring: AI companies are increasingly hiring people from “humanities” backgrounds (philosophers, political scientists, policy experts) [03:02]. These individuals are hired not for narrow domain tasks, but because AI allows them to execute work that previously required large engineering teams [03:16].
Shift in Firm Structure: We are likely to see firms that “do more with fewer people,” alongside an explosion of small, highly productive entrepreneurial firms enabled by AI acting as a multiplier for individual capability [04:03].
The Persistence of “Human-Centric” Care: Jobs centered on care and development—such as nursing, early childhood education, and hospice care—will remain human-led. These roles are currently undervalued, but a booming AI economy could create the wealth to support expanding these sectors and increasing wages [08:41].
Career Transitions: Future employment will involve frequent, large-scale career transitions. Policy frameworks like wage insurance and overhauled safety nets will be necessary to manage this instability [04:44]. - He raises an interesting point whereby the profits from use of AI made by AI companies should be channel to the “other side of the ledger” via governments in terms of skills retraining
previous videos watched
Synthesizing the three perspectives covered in this conversation (the philosophical “meaning” approach, the economic shift toward flexibility, and the policy-driven “early warning” approach), we can define the future of work through these four pillars:
I. The Disintegration of the “Standard Path”
The traditional 40-year career in a single firm is being replaced by a model defined by continuous transition, project-based work, and “solopreneurship.”
Both technological advancement and economic logic favor firms that remain agile, leading to a rise in decentralized, international, and small-scale operations.
II. The “Human Premium” in a Commoditized World
As AI performs routine cognitive and mechanical tasks, human value is shifting exclusively to:
Sensemaking & Curation: Filtering noise and providing unique perspectives.
Relationship & Trust: High-stakes negotiation, diplomacy, and accountability.
The Experience Economy: Roles where the “product” is human empathy, presence, and genuine care (e.g., teaching, nursing, hospitality).
III. The Crisis of Meaning & Purpose
The transition is not just economic; it is existential. As labor is decoupled from survival and status, humans must shift from being “workers” to “meaning architects.”
Purpose will no longer be assigned by institutions (religion or the industrial machine) but must be generated internally through struggle, curiosity, and contribution to one’s community.
IV. Structural Policy Imperatives
The “AI Revolution” is progressing faster than political discourse can manage. Experts emphasize the need for:
Impartial Monitoring: Third-party economic indices to track early warning signs of unemployment.
New Economic Safety Nets: Rethinking taxation (potentially taxing “compute” as a resource) to fund transitions, training, and the expansion of human-centric public services.
Personal Note: For your son’s path in engineering, these trends suggest that his focus on complex, high-liability, and physical-world systems (Chemical & Environmental Engineering) positions him well within the “human-in-the-loop” category that remains protected from automation. For your own transition, the emphasis on “curation,” “wisdom,” and “meaning-making” supports a move toward advisory or creative roles that leverage your life experience rather than just output-based labor.