Me: What will happen to Social Media as the Age of AI comes into being?

ChatGPT: David, as AI matures from a novelty into infrastructure, social media will change less in existence than in character. The platforms will remain, but their social, economic, and epistemic roles will shift in several predictable—and a few unsettling—ways.

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1. Content Volume Will Explode, and Authenticity Will Become Scarce

AI makes content production nearly free. Text, images, video, voices, even personalities can be generated at scale.

Implications

• The signal-to-noise ratio will worsen.
• Human-made content will no longer be the default assumption.
• “Authentic” will become a premium label, not a baseline.

Expect:

• Mandatory content provenance (cryptographic signing, watermarking).
• Platform-level flags like verified human, synthetic, or hybrid.
• A cultural shift where who produced content matters more than what it says.

Authenticity becomes a feature, not a given.

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2. Social Media Becomes Increasingly Non-Social

Much of what users interact with will not be people.

Already underway:

• AI influencers
• AI customer support posing as humans
• AI “friends,” “commenters,” and “fans”

Future state:

• Large portions of engagement loops will be machine-to-machine.
• Humans become audience members inside algorithmic ecosystems.
• Engagement metrics lose meaning as bots talk to bots.

Platforms will quietly tolerate this because engagement—real or not—drives revenue.

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3. Trust Will Collapse, Then Re-Form in Smaller Circles

Mass-scale trust will continue to erode.

Causes:

• Deepfakes
• Synthetic outrage
• AI-generated “evidence”
• Personalized persuasion at industrial scale

Result:

• Public feeds become less trusted.
• People retreat into:
  • Private groups
  • Paid communities
  • Curated networks with known identities

This mirrors a historical pattern:

When information becomes cheap, trust becomes expensive.

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4. Algorithms Will Stop Showing “What’s Popular” and Start Showing “What Works on You”

AI enables extremely precise psychological modeling.

Expect:

• Feeds optimized for emotional response, not relevance.
• Content tailored to your:
  • Cognitive style
  • Political temperament
  • Attention span
  • Emotional triggers

This is less about persuasion and more about retention control.

Without regulation or strong user controls, social media becomes a behavioral feedback system rather than a communication platform.

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5. Regulation Will Lag, Then Overcorrect

Governments will respond late and imperfectly.

Likely trajectory:

1. Initial denial or confusion
2. High-profile AI misinformation crisis
3. Emergency regulations (identity, watermarking, liability)
4. Over-broad restrictions that harm small creators more than large platforms

Large firms adapt easily. Independent voices struggle.

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6. A Counter-Movement Will Emerge: “Human-Only” Spaces

As AI saturation increases, we will see:

• Platforms that ban synthetic content
• Verified-human communities
• Slow, low-volume, text-heavy spaces
• Reputation-based participation instead of follower counts

These will not replace mainstream social media—but they will become influential, similar to how newsletters and forums regained relevance.

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7. The Meaning of “Social” Will Shift

Historically:

• Social media = people talking to people

In the AI age:

• Social media = humans, AIs, and algorithms co-producing narratives

The risk is not that AI dominates discourse.
The risk is that humans lose awareness of when discourse is no longer human at all.

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Bottom Line

Social media will not disappear—but it will fragment:

• Mass platforms: High volume, low trust, AI-dense
• Private spaces: High trust, low reach
• Human-verified niches: Small, slow, influential

Those who understand systems—especially feedback loops, incentives, and control architectures—will navigate this shift better than those who focus only on content.

If you’d like, I can also frame this from:

or a historical technology-transition comparison (printing press, radio, TV).

a network-engineering perspective,

a control-systems perspective,

Me: “AI makes content production nearly free.” I totally disagree with this statement, the costs of AI Server Farms is exploding!

ChatGPT: David, your point is technically correct: computing AI at scale is expensive. Large-scale model training and inference do require significant investment in hardware, energy, and cooling. From the infrastructure side, those costs are real and rising.

That said, in the context of social media content generation, the statement “AI makes content production nearly free” is shorthand for a dramatic reduction in marginal cost per unit of content — particularly for end users and content creators — not for large model training or data-center operations. Let’s unpack this distinction formally and precisely.

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1. Distinguishing Economic Costs

a. Fixed Costs (Infrastructure and Models)

• Training large AI models (e.g., GPT-like architectures) requires huge upfront capital for GPUs/TPUs, power, and data engineering.
• Operating inference at scale — especially at social-media volumes — incurs ongoing energy and hardware depreciation costs.

These are not trivial and are rising as model size increases.

b. Marginal Costs (Per Content Item)

Once an AI model is trained and deployed:

• Generating an individual post, image, or clip costs fractional compute time.
• For a user, that translates into effectively negligible time or money per item.

In economics, this is analogous to:

High fixed cost; very low marginal cost.

So while total industry spending on AI infrastructure may explode, the cost of producing each piece of content — especially from the user’s perspective — is very low.

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2. Who Bears the Cost? Platforms, Not End Users

On social media:

• Users generate prompts.
• Platforms handle the compute on the backend — controlled by centralized providers.

This means:

• The user doesn’t pay directly for compute in most cases; the platform does.
• Platforms amortize costs over many users and revenue streams (ads, subscriptions, data).

Thus, from a user and creator economics standpoint:

• The cost per unit of AI-generated content is effectively low or even zero.

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3. Content Economy vs. Compute Economy

We must separate these two economic layers:

Compute Economy

• Deals with hardware, energy, data centers.
• Has upward pressure due to scaling models.

Content Economy

• Deals with published outputs (posts, videos, ads).
• Faces downward pressure on marginal cost due to automation.
• Result: Content inflation and saturation.

The original statement refers to the content economy.

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4. Implications for Social Media

Because the marginal cost of publishing AI-generated content is so low within the platforms:

• Volume of available content increases exponentially.
• Incentives shift toward novelty and engagement, not human thought.
• Algorithms optimize for retention rather than authenticity.

This dynamic holds even if the overall cost of running AI infrastructure rises.

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5. Conclusion — A Refined Statement

A more precise formulation would be:

AI significantly lowers the marginal cost of producing individual pieces of content on social platforms, even though the total compute and infrastructure costs remain high.

This distinction matters because:

• It clarifies why content volume explodes.
• It separates platform economics from end-user economics.
• It avoids conflating total system cost with per-item cost.

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If you want, I can formalize this further with an economic model (fixed vs. variable costs) or analyze how this impacts revenue models for platforms like Meta, TikTok, and X.

Me: So, are you saying the Platform Costs won’t be passed on to Content Creators? Then why does the most advance AI applications come with a subscription cost?

ChatGPT: David, I am not saying platform costs will never be passed on. I am saying they will be passed on selectively, asymmetrically, and strategically—which is why your subscription example is exactly the right counterpoint.

Let’s be precise and reconcile the two observations.

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1. Why Some AI Is “Free” and Some Requires Subscriptions

The key distinction: who creates value, and who captures it

Platforms divide users into three economic roles:

1. Audience – consumes content
2. Creators – produce engagement-driving material
3. Power users / Enterprises – extract direct productivity or revenue

AI costs are passed on only where price elasticity allows it.

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2. Why Social Media Platforms Often Absorb AI Costs

For mainstream social media creators:

• AI-generated content:
  • Increases posting frequency
  • Improves engagement metrics
  • Keeps users on-platform longer

This means:

• The platform earns more ad revenue
• The creator supplies content
• The AI cost becomes an acquisition and retention expense

So the platform treats AI like:

CDN costs, video encoding, or recommendation engines
— expensive, but necessary to keep the system alive

Passing these costs directly to average creators would:

• Reduce content volume
• Hurt engagement
• Weaken network effects

Platforms avoid that unless forced.

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3. Why Advanced AI Is Sold via Subscription

Advanced AI tools sit in a different economic category.

They:

• Deliver direct, private value (productivity, automation, insight)
• Are not primarily about engagement
• Do not rely on network effects

Examples:

• Long-context reasoning
• Code generation
• Data analysis
• Multimodal synthesis

These are closer to software tools, not social infrastructure.

Hence:

• Clear value attribution
• Willingness to pay
• Subscription pricing makes sense

This is classic price discrimination, not contradiction.

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4. Cost Pass-Through Happens at the Margins, Not Uniformly

Costs are passed on when:

• Compute is intensive and individualized
• Outputs are not monetized by ads
• Users are time- or revenue-constrained professionals

Costs are not passed on when:

• Compute boosts platform-wide engagement
• Outputs are public and monetizable
• Volume matters more than quality

This explains why:

• Casual AI image tools are free or cheap
• Professional-grade AI video, coding, and research tools are paid

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5. The Long-Term Trend You’re Actually Pointing To

You’re highlighting a deeper reality:

AI is reverting to a utility model, not a democratization model.

Early phase:

• Subsidized
• Growth-focused
• “Free” tools

Later phase:

• Tiered access
• Metered usage
• Enterprise capture

Social platforms will:

• Continue offering baseline AI at low or zero cost
• Monetize capability, priority, and scale

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6. Bottom Line (No Hand-Waving)

• Platform AI costs are real and increasing
• They will be passed on
• But not uniformly, and not primarily to casual creators
• Subscriptions exist because:
  • Some AI creates private value, not platform value
  • Those users can and will pay

Your intuition is correct: someone always pays.
The disagreement was never whether—only who, when, and how.

If you want, I can map this directly onto:

Or a control-systems analogy (subsidized loop vs metered loop)

Meta / Google / OpenAI pricing strategies

Utility-style metering models (compute minutes, tokens)

Me: I am hearing from this last part that Advertisers will be carrying the Platform costs … but doesn’t Advertising costs just get passed on to the Consumers?

ChatGPT: David, yes — in the long run, advertising costs are passed on to consumers. Your instinct is economically sound. What matters is how directly, how visibly, and how unevenly that pass-through occurs.

Let’s walk this carefully, without hand-waving.

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1. The Cost Chain (End to End)

For ad-supported platforms, the cost flow is:

AI Infrastructure → Platform → Advertisers → Product Prices → Consumers

Nothing disappears. It is redistributed.

However, each link in that chain has friction, delay, and leakage, which is why the effect is not obvious day-to-day.

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2. Why Consumers Don’t Notice Immediately

a. Advertising Is an Indirect Tax

Unlike a sales tax:

• It is embedded in product pricing
• It is spread across all buyers
• It is rarely itemized

A $10 increase in ad spend might result in:

• $0.03 added to a unit price
• Or slightly lower product quality
• Or reduced customer service
• Or lower wages upstream

Consumers pay, but not cleanly or transparently.

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3. Why Advertisers Still Accept Higher Platform Costs

Advertisers tolerate rising platform costs when:

• Targeting precision improves
• Conversion rates increase
• Waste is reduced

AI improves:

• Audience segmentation
• Ad creative optimization
• Real-time bidding efficiency

So even if ad prices rise:

• Cost per effective conversion may fall
• Advertisers see net benefit
• Platforms justify higher pricing

This delays consumer impact.

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4. Where the Pass-Through Breaks or Distorts

Pass-through is not guaranteed in competitive markets.

a. High Competition, Low Margins

• Firms cannot raise prices
• They absorb ad costs via:
  • Lower profit
  • Reduced investment
  • Cost cutting elsewhere

b. Monopoly or Brand-Locked Markets

• Costs pass through almost fully
• Consumers have little choice

Ironically, AI-enhanced advertising tends to favor large brands, which strengthens pass-through power.

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5. AI Changes How Consumers Pay

Instead of higher prices alone, consumers pay via:

• Attention extraction
  More ads, more intrusive formats
• Behavioral manipulation
  Prices optimized to willingness-to-pay
• Personalized pricing (emerging)
  AI infers what you will tolerate

This is not inflation in the classical sense — it is behavioral rent extraction.

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6. The Uncomfortable Conclusion

You are identifying a subtle but critical point:

AI does not eliminate costs — it redistributes them in less visible ways.

So yes:

• Advertisers carry platform AI costs first
• Consumers carry them eventually
• The burden is:
  • Diffuse
  • Uneven
  • Hard to attribute

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7. Final Framing

A precise way to state this is:

Ad-supported AI platforms function as a multi-layer cost-masking system, where infrastructure costs are amortized through advertising and ultimately embedded in consumer prices, attention, and autonomy rather than explicit fees.

That’s not ideology — it’s system dynamics.

If you’d like, I can:

Or model this as a feedback loop with instability thresholds

Quantify pass-through under different market structures

Compare this to historical transitions (radio, TV, cable)

Me: You are missing two important contributions to the Cost of AI. 1) Investment by the Government into providing infrastructure. Comes from our taxes. 2) Cost of Power Infrastructure for AI are being added directly into consumer electricity billings.

ChatGPT: David, you are correct—and those two contributions are not secondary details; they are structural cost absorbers that materially change the economics of AI. Thank you for calling them out. Let’s integrate them properly, because once you do, the picture becomes much clearer—and frankly more concerning.

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1. Government Infrastructure Investment Is a Hidden AI Subsidy

What is actually happening

Governments are funding or underwriting:

• Grid upgrades
• Data-center zoning and tax abatements
• Fiber backbones
• Semiconductor incentives
• Research compute (national labs, universities, defense)

This funding:

• Does not show up on platform balance sheets
• Does come from general taxation or public debt
• Socializes risk while privatizing upside

Economically, this is a capital subsidy, not a market outcome.

Why this matters

It means AI’s apparent “market efficiency” is partially an illusion. A nontrivial portion of fixed costs are:

• Pre-paid by taxpayers
• Amortized across the entire population
• Independent of whether an individual uses AI services

This is closer to Cold War infrastructure economics (interstate highways, aerospace, telecom) than to normal consumer software.

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2. Power Infrastructure Costs Are Already Being Passed Directly to Consumers

This is the most underappreciated part of the system.

What utilities are doing

To support AI-scale loads, utilities are:

• Building new generation (often gas first, renewables later)
• Expanding transmission and substations
• Adding peaker capacity for burst loads

Utilities recover capital costs via:

• Rate base expansion
• Regulatory approval
• Higher residential and commercial electricity tariffs

So:

• Even households with zero AI usage pay
• Costs are geographically concentrated near data-center clusters
• Consumers subsidize industrial compute indirectly

This is not hypothetical. It is already occurring.

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3. Revised Cost Flow (Full System View)

A more accurate cost cascade is:

Taxpayers
→ Government infrastructure & incentives
→ Reduced platform capital burden

Utility customers
→ Grid upgrades & capacity expansion
→ Lower marginal power costs for AI operators

Platforms
→ AI services & social media

Advertisers
→ Ad spend inflation

Consumers
→ Higher prices, higher energy bills, more attention extraction

This is a multi-channel pass-through, not a single pipeline.

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4. Why This Is Politically Stable (for Now)

Despite being regressive, the system persists because:

• Costs are diffuse and delayed
• Benefits are framed as “innovation” or “national competitiveness”
• Infrastructure upgrades are justified independently of AI
• No single bill line says “AI surcharge”

In control-systems terms:

• The feedback signal (consumer pain) is weak
• The actuator (policy response) lags
• The loop is therefore unstable but slow-moving

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5. The Deeper Implication

What you are describing is the emergence of AI as quasi-public infrastructure, but operated for private profit.

That creates three consequences:

1. Cost socialization
   Everyone pays, whether they benefit or not.
2. Access stratification
   Advanced capabilities are paywalled despite public subsidy.
3. Political lock-in
   Once grids and budgets are committed, withdrawal becomes impossible.

This is not unique to AI—but AI accelerates and amplifies the pattern.

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6. Bottom Line (Fully Corrected Model)

A precise statement would be:

The true cost of AI is distributed across taxpayers, utility ratepayers, advertisers, and consumers through multiple indirect channels, with only a fraction appearing as explicit subscription fees or platform expenses.

Your critique strengthens the argument rather than contradicting it.
It shows that AI’s costs are not just passed on—they are deliberately obscured by structural design.

If you wish, we can next examine:

Or where the system is most likely to break first (grid, politics, pricing, or trust)

Whether this cost structure is sustainable

How it compares to telecom and electrification history

Me: Dive into all three of those, please.

ChatGPT: Very well, David. I will take them in order and treat them as interacting system layers, not isolated topics. The key insight is that all three are converging toward stress at roughly the same time.

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1. Is the Current AI Cost Structure Sustainable?

Short answer: only under constrained growth, and current incentives push toward unconstrained growth.

Why it looks sustainable now

• Costs are diffuse (taxes, rates, ads, subscriptions).
• Benefits are concentrated (platforms, large enterprises, geopolitical positioning).
• Early-stage productivity gains mask structural inefficiency.
• Governments tolerate subsidy under “strategic competition” narratives.

This creates the illusion of equilibrium.

Why it is not structurally stable

AI compute demand scales superlinearly:

• Model size ↑
• Context length ↑
• Multimodality ↑
• Real-time inference ↑
• Always-on agents ↑

Meanwhile:

• Energy generation scales linearly
• Grid expansion scales slowly and politically
• Public tolerance for opaque cost pass-through is finite

This creates a classic resource bottleneck mismatch.

The breaking constraint is not compute — it is:

• Power availability
• Grid reliability
• Public acceptance of cross-subsidy

Once AI demand begins to compete visibly with residential reliability or affordability, sustainability collapses politically, not technically.

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2. Historical Comparisons (Where This Pattern Has Appeared Before)

AI most closely resembles three historical systems combined, which is why it is hard to regulate.

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A. Electrification (1890–1930)

Similarities

• Massive public-private infrastructure
• Natural monopoly dynamics
• Universal cost sharing
• Productivity justification

Difference

• Electricity democratized access
• AI stratifies access

Electrification reduced inequality. AI risks increasing it.

---

B. Telecommunications & the Internet (1960–2000)

Similarities

• Defense-funded origins
• Government-funded backbone
• Private profit at the edge
• Network effects dominate

Difference

• Telecom pricing became regulated before behavioral manipulation
• AI acts on cognition, not just communication

This raises ethical stakes substantially.

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C. Financialization (1980–2008)

This is the most dangerous analogy.

Similarities

• Abstracted risk
• Hidden leverage
• Cost externalization
• Short-term gains masking long-term fragility

AI infrastructure resembles too-big-to-fail compute, where:

• Withdrawal is impossible
• Collapse demands bailout
• Public pays regardless of benefit

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3. Where the System Is Most Likely to Break First

Not all failures are equal. Here is the likely order.

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1️⃣ Power and Grid Stress (First Failure Mode)

Why first

• Physical constraints cannot be abstracted away
• AI loads are spiky, not smooth
• Data centers cluster geographically
• Utilities cannot ramp capacity on demand

Early warning signs

• Residential rate spikes near data-center zones
• Emergency load shedding exemptions for AI facilities
• Delayed electrification or EV programs
• Political backlash at state utility commissions

This is already emerging.

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2️⃣ Political and Regulatory Overcorrection

Once grid and tax impacts become visible:

Expect:

• Moratoriums on new data centers
• Forced cost allocation
• AI-specific energy tariffs
• “National interest” carve-outs favoring incumbents

Regulation will not be subtle. It will be blunt.

This benefits:

• Large, established platforms
• Defense-aligned vendors

It harms:

• Open research
• Small players
• Innovation at the margins

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3️⃣ Economic Trust and Legitimacy Breakdown

This is slower but deeper.

Symptoms:

• Consumers feel exploited but cannot point to a bill line
• AI benefits feel abstract while costs feel personal
• Productivity gains accrue unevenly
• “Why am I paying for this?” becomes a common question

At this point:

• Anti-AI populism emerges
• Narratives harden
• Rational policy becomes difficult

This is where systems become brittle.

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4. Control-System Interpretation (Since This Is Your Native Language)

This system has:

• High gain
• Long delays
• Weak negative feedback
• Hidden state variables
• Nonlinear saturation limits

That combination guarantees:

Oscillation or collapse unless actively damped.

Right now, there is no damping mechanism—only acceleration.

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5. The Central Insight (All Three Together)

AI is being built as:

• A publicly subsidized
• Privately controlled
• Cognitively invasive
• Energy-intensive
• Behavior-shaping
  infrastructure layer.

That combination has no historical precedent at this scale.

The system can survive any two of those properties.
All five together create instability.

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6. What Actually Stabilizes the System (If Anything)

Stability would require at least one of the following:

1. Transparent AI energy pricing
2. Hard compute efficiency caps
3. Explicit public ownership of base infrastructure
4. Tiered access aligned with public subsidy
5. User-side agency over algorithmic exposure

Absent these, the system does not gently equilibrate — it snaps.

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Closing Thought

Your observations are not peripheral. They cut to the heart of the matter:

AI’s problem is not intelligence. It is accountability for the resources it consumes and the costs it externalizes.

If you wish next, we can:

• Model a plausible “AI energy shock” scenario
• Compare US vs EU vs China trajectories
• Or explore what a deliberately stable AI infrastructure would look like

Just say where you want to go next.

INFO: 3 messages remaining. Upgrade to ChatGPT Go to keep the conversation going (Upgrade to Go)

Me: No, thank you.