Bracing for Impact

AI can outlearn humans in most routine tasks. Mass displacement is likely. When a technology can ingest unfathomable amounts of data, and outperform humans at pattern‑spotting, persuasion and hardware design, calling it merely “another tool” is whistling past the graveyard.

Oxford Economics estimates 47 million U.S. jobs may vanish by 2030. The Hollywood writers’ strike of 2023 was an early tremor: ChatGPT could draft B‑plot dialogue cheaper than junior staff. McKinsey’s 2025 survey shows code‑gen tools now cut software feature delivery time by 40 %.

This is before you take into account anthropologist David Graeber arguing in his book 'Bulllshit Jobs' that up to 40 % of work is “socially useless.” AI’s deflationary push may at the very least just expose which of those jobs need to go.

Challenges

• Loss of income for millions.
• Identity crisis: “What am I for?”
• Risk of widening inequality.

Possible Solutions

• Universal Basic Income: stipend for necessities.
• Skill refocus: creativity, caregiving, research.
• Community work tokens: reward local contributions.

Valu positions personal AIs and community economies as engines for new, meaningful roles.

Cheap Abundance & Deflation

When robots handle production and logistics, marginal costs fall toward zero. DeepMind’s AlphaFold predicted 200 million protein structures for the cost of compute, slicing years off pharma R&D. When a trillion‑parameter model can draft blueprints and source parts lists, the marginal cost of new products trends toward zero - good for consumers, brutal for legacy industry margins.

Upshot: A world where basics are cheap or free - but only if ownership of the AI stack is broad, not captive to monopolies.

Concentrated Compute Power

Training top‑tier AI requires massive GPUs, energy, and data. Training GPT‑4 reportedly cost >$100 million in GPU time. That narrows the field to:

1. Cloud hyperscalers (AWS, Google, Azure)
2. State labs (US DoD, China’s BAAI)
3. A handful of open‑source insurgents pooling compute (LAION)

Just as oil pipelines shaped 20th‑century geopolitics, GPU supply chains will shape the 21st. The 2024 US chip embargo against China targeted not weapons but AI accelerator boards.

Risks

• Policy capture: corporations outmaneuver governments.
• Single‑point failures: model bias affects billions.
• Digital colonialism: smaller nations become customers, not creators.

Valu’s Verus chain distributes AI workloads and rewards nodes, diluting the power gap.

Perception Engineering

Attention markets optimise for engagement, not truth. TikTok’s algorithm, fine‑tuned for watch‑time, accidentally became a precision instrument for memetic warfare. During the 2022 Ukraine invasion, fake videos of “ghost fighters” went viral hours after the first missiles — a digital fog that blurred truth long enough to sway public support.

• Short viral clips beat long nuance.
• AI fine‑tunes the feed per user to maximise screen time.
• Result: fractured realities and extreme tribes.

Counter‑measures

• Verifiable sources.
• Paid, consent‑based messaging.
• Personal AI gatekeepers that value accuracy over clicks.

Coping playbook

• Upskill on judgement: critical thinking remains scarce.
• Own your data: feed a personal LLM that works for you.
• Diversify capital: equities, tokens, time‑ownership in DAO projects.
• Push for audit trails: model cards, dataset citations, verifiable hashes.

Charting a humane future means steering AI toward broad benefit before its gravity pulls governance, economy and culture into a narrow orbit around whoever owns the biggest cluster.

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