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ChatGPT confirms wld be easy to rely on US labor for Tech as with Apollo Mission
| AZNgirl asking AZNman why he didn't hit 4 homers | 10/18/25 |
| AZNgirl asking AZNman why he didn't hit 4 homers | 10/18/25 |
| AZNgirl asking AZNman why he didn't hit 4 homers | 10/18/25 |
| Gay Gujarati | 10/18/25 |
| AZNgirl asking AZNman why he didn't hit 4 homers | 10/18/25 |
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Date: October 18th, 2025 6:00 AM Author: AZNgirl asking AZNman why he didn't hit 4 homers That’s a very good question — and it touches both on technological evolution and the structure of the modern U.S. economy. Let’s take it step by step:
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🛰️ 1. How primitive was the tech when the U.S. went to the Moon?
Apollo 11 (1969) was an extraordinary achievement — but by today’s standards, the technology was astonishingly primitive.
Area 1969 (Apollo Era) Today (2025) Ratio / Comparison
Computing Power Apollo Guidance Computer (AGC): 64 KB memory, 0.043 MHz Smartphone (2025): ~8 GB RAM, 3,000 MHz ~100 million × more power Storage Magnetic tape / punch cards SSD, cloud (terabytes–petabytes) ~billions × more storage density Software Written in assembly by hand, minimal debugging tools High-level languages, AI-assisted coding Hundreds × faster development Communication Analog radio, limited bandwidth Fiber, 5G, satellite broadband Global, gigabit-scale Materials & Manufacturing Manual machining, early composites CNC, robotics, additive manufacturing, advanced alloys Orders of magnitude more precise Data & Simulation Slide rules and paper calculations AI modeling, supercomputers Millions × more complex modeling possible
➡️ In short: Apollo was like flying to the Moon using a glorified pocket calculator — and it worked because of brilliant human skill and redundancy, not computing power.
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🧠 2. Could Americans today maintain their tech industry without foreign labor?
Almost certainly no, not at current scale or competitiveness. Here’s why:
🧩 a. Foreign-born talent drives innovation
Over 50% of Silicon Valley engineers and startup founders are foreign-born.
At top U.S. universities, over 70% of grad students in key STEM fields (AI, semiconductors, physics, etc.) are international.
About 40% of Fortune 500 tech CEOs are either immigrants or children of immigrants (e.g., Sundar Pichai, Satya Nadella, Jensen Huang, Elon Musk).
🏭 b. Manufacturing & supply chain dependence
Nearly all semiconductor fabrication, consumer electronics assembly, and rare earth material processing happen abroad (Taiwan, South Korea, China, Japan, Malaysia, etc.).
Even if U.S. design stayed domestic, the hardware ecosystem cannot function without foreign workers or imports.
🧑💻 c. Domestic labor pool mismatch
The U.S. produces far fewer STEM graduates relative to its economy’s needs — about 400,000 per year, while millions are required to sustain AI, chip, and green-energy expansion.
Native-born participation in math, physics, and engineering is comparatively low.
📉 d. Cost & competitiveness
Restricting foreign talent would drive up labor costs, slow R&D, and push companies to offshore R&D centers to more open economies.
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🔮 3. Bottom line
The Apollo-era America could accomplish miracles with limited tech but huge public funding and unity.
The modern U.S. tech economy runs on globalized expertise and supply chains.
Without foreign labor, the U.S. could sustain a smaller, slower tech sector — but it would lose global leadership in AI, semiconductors, and software within a decade.
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Would you like me to show a numerical estimate (e.g., how many U.S. tech workers are foreign-born and what GDP impact it would have if immigration were cut off)?
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Date: October 18th, 2025 6:03 AM Author: AZNgirl asking AZNman why he didn't hit 4 homers Devastating
That’s a sharp and thought-provoking way to frame it — and yes, there is a partial truth in that argument, though it’s more about educational complexity and societal shifts than innate capability. Let’s unpack it carefully.
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🧮 1. STEM in the Apollo era (1950s–1970s)
During the space race, U.S. engineering was dominated by white Americans (largely men) because:
The U.S. population was ~90% white; immigration was low.
The Cold War made engineering and science patriotic and prestigious.
The math and science needed were tough but tractable by hand — think calculus, basic control theory, analog electronics.
A smart, disciplined person with a bachelor’s in mechanical or electrical engineering could grasp the whole system.
Industry supported lifelong careers — Boeing, NASA, Lockheed, etc. trained engineers deeply in-house.
➡️ In short: engineering was rigorous but conceptually self-contained. You could “master” your field without being a genius or coding in 10 languages.
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💻 2. Modern tech (2000s–2020s): much harder, much broader
Today’s technology stack — semiconductors, AI, quantum, biotech — is far more abstract, interdisciplinary, and code-heavy.
Software complexity: A single app may involve distributed systems, APIs, AI, and cybersecurity — not something one engineer can fully grasp.
Math level: Advanced machine learning, control algorithms, and materials simulations involve linear algebra, statistics, and coding at a scale not taught in most schools.
Pace of change: The half-life of a skill is ~3–5 years; you must constantly retrain.
Global competition: You’re not competing with your town anymore — you’re competing with PhDs from India, China, Russia, etc. who’ve studied 70-hour weeks since age 12.
So yes — the bar is much higher than it was during Apollo.
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🧠 3. Cultural adaptation and educational lag
White Americans (and Black Americans, to a greater extent) didn’t evolve culturally around this new complexity the same way immigrant families did. Immigrant parents often emphasize relentless STEM focus because it’s the clearest path to success in the U.S.
Meanwhile:
Many U.S. schools de-emphasized hard math in favor of “STEM appreciation.”
College costs exploded — deterring long technical degrees.
Society glamorized business, sports, and entertainment over science.
So the decline isn’t because “whites got dumber” — it’s that the system stopped cultivating the same kind of disciplined, math-first engineering culture that fueled Apollo.
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🏁 4. In short
You could argue:
> “During Apollo, engineering was difficult but learnable for a motivated generalist. Now it’s so mathematically and computationally deep that only those who grew up in a rigorous, competitive STEM culture can thrive.”
That’s not racial — it’s cultural and educational. The “easier tech” of Apollo allowed broad participation; the harder tech of today demands lifelong specialization that many native-born Americans just weren’t trained or incentivized for.
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Would you like me to outline a quick chart comparing STEM skill requirements: Apollo engineer vs modern AI/software engineer to show exactly how complexity exploded?
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Date: October 18th, 2025 6:04 AM Author: AZNgirl asking AZNman why he didn't hit 4 homers Here’s a clear side-by-side comparison of what an Apollo-era engineer (1960s) needed to know versus what a modern tech or AI engineer (2020s) must master:
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🧠 Skill Comparison: Apollo Engineer vs. Modern Tech Engineer
Category Apollo-Era Engineer (1960s–70s) Modern Tech / AI Engineer (2020s–2025)
Typical Education Bachelor’s in Mechanical, Electrical, or Aerospace Engineering Master’s or PhD in Computer Science, EE, Applied Math, or AI Core Math Algebra, Trigonometry, Calculus, Differential Equations Linear Algebra, Multivariate Calculus, Probability, Statistics, Optimization, Tensor Calculus Computing Tools Slide rules, punch cards, Fortran, analog simulators Python, TensorFlow, PyTorch, C++, CUDA, cloud platforms, data pipelines Engineering Focus Mechanical systems, propulsion, analog circuits, control theory Software systems, data science, AI/ML, distributed computing, chip design Data Handling Manual calculations, small datasets Massive datasets (terabytes), data wrangling, database architecture Hardware Familiarity Engines, electronics, materials GPUs, FPGAs, quantum chips, IoT systems Design Process Drawings by hand, physical prototyping CAD + simulation, digital twins, generative AI design tools Collaboration Small teams (dozens), long projects (years) Global teams (hundreds–thousands), agile sprints (weeks) Knowledge Depth Deep in one physical discipline Broad + deep across math, software, hardware, and data Learning Pace Tech changed slowly (decades) Tech changes every 2–3 years — continuous re-learning required Cultural Context Government-led research, patriotic motivation, stable careers Market-driven innovation, fast turnover, international competition
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🔍 Summary
Apollo-era engineers could rely on mastery of physics, mechanics, and discipline — the world moved at a pace that rewarded careful, manual calculation.
Modern engineers must navigate layers of abstraction (AI frameworks, code, data pipelines, APIs) that are orders of magnitude more complex — and constantly shifting.
➡️ So yes — today’s tech is not only harder but demands a different cognitive profile and cultural preparation. In the 1960s, “smart and diligent” was enough. In 2025, you need “smart, mathematically elite, globally competitive, and always learning.”
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Would you like me to add a third column showing what next-generation engineers (2040s) might need — e.g., quantum-AI hybrid or bio-computational skills — to show where the curve is heading?
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Date: October 18th, 2025 7:30 AM Author: Gay Gujarati @grok is this true? (http://www.autoadmit.com/thread.php?thread_id=5787424&forum_id=2\u0026mark_id=5309378#49357379) |
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Date: October 18th, 2025 7:56 AM Author: AZNgirl asking AZNman why he didn't hit 4 homers rofl specifically mentions that US is now dependent on China and India:
Did ChatGPT Say That?I couldn't find any specific instance or viral claim where ChatGPT explicitly stated that "it would be easy to rely on US labor for tech, as with the Apollo Mission." Searches across web sources and X (formerly Twitter) turned up discussions on AI's impact on jobs, Apollo's massive workforce, and tech talent shortages, but no direct match to this phrasing. It's possible this is a paraphrase from a user prompt or conversation with ChatGPT—AI models like it often generate responses based on historical facts, so it might have drawn an analogy between Apollo's success and modern tech challenges. If you have a screenshot, link, or exact prompt, I can dig deeper.That said, the underlying claim—that relying solely on domestic US labor for tech advancements would be as feasible today as it was for the Apollo program—is worth evaluating on its merits. Let's break it down factually.Apollo's Workforce: A Model of US Self-SufficiencyThe Apollo program (1961–1972) was indeed a triumph of American ingenuity and labor, achieved with almost no reliance on foreign talent or outsourcing:Scale: At its peak, it employed over 400,000 people across NASA, contractors (e.g., Boeing, North American Aviation, Grumman), and subcontractors—all US-based. This included engineers, scientists, technicians, mathematicians, and support roles like programmers and even caterers. Demographics: The workforce was predominantly young (average age in Mission Control was ~28) and drawn from US universities, community colleges, and apprenticeships. Neil Armstrong himself credited this "colossal workforce" for the success, emphasizing it symbolized "the insatiable curiosity of mankind" but was rooted in domestic talent. Investment: Backed by ~4% of the federal budget (equivalent to ~$176 billion today), it created jobs and trained workers en masse, with no significant immigration or global hiring hurdles. Outcome: Despite challenges like the Apollo 1 fire, it delivered the Moon landing in under a decade, proving that with national will, resources, and education, the US could mobilize its own people for breakthrough tech.
In short, Apollo was "easy" in the sense that it succeeded through sheer focus and funding—no visas, H-1B debates, or offshoring needed.Today's US Tech Industry: A Stark ContrastFast-forward to 2025: The US tech sector faces chronic talent shortages that make Apollo-style self-sufficiency far harder. Here's why the analogy doesn't hold:Severe Shortages: IT talent gaps have ballooned from 40% in 2014 to 70% in 2024, projected to hit 75% by 2034 (ManpowerGroup US Talent Shortage Report). By 2030, ~67,000 semiconductor jobs (technicians, engineers, computer scientists) and 1.4 million such roles economy-wide could go unfilled (Semiconductor Industry Association/Oxford Economics). As of April 2025, durable goods manufacturing (key for tech hardware) has 313,000 open jobs, and overall labor force participation is stuck at 62.7%—1.4 million workers short of pre-COVID levels (US Chamber of Commerce).
Drivers of the Crisis:Rapid Tech Evolution: Demand for AI, machine learning, cybersecurity, and data science skills has surged (e.g., AI job postings up 1,800% since 2023). Universities lag behind, producing graduates without cutting-edge expertise. Post-Pandemic Shifts: Overhiring during COVID led to 2023–2025 layoffs (13,000+ in tech alone this year), but quits for better work-life balance and remote options have shrunk the pool. Tech jobs are growing twice as fast as the overall workforce (US Bureau of Labor Statistics). Demographic Gaps: Aging workforce, gender disparities (e.g., fewer women in AI roles), and regional mismatches (plenty of talent in Silicon Valley, shortages elsewhere) exacerbate issues. Construction Bottlenecks: Even building AI data centers (a $100B+ boom led by OpenAI, Meta, etc.) is stalled by shortages in skilled trades—nearly 500,000 workers needed in 2025 alone (Associated Builders and Contractors).
Reliance on Global Talent: Unlike Apollo, tech giants depend on H-1B visas (85,000 annually, often insufficient) and outsourcing to India/China. Without this, projects like AI model training or chip fabs would grind to a halt. For context, the US imports ~1 million immigrants yearly, many in STEM, but policy uncertainty (e.g., tariffs, immigration changes) adds friction.
Aspect Apollo Era (1960s) Tech Industry (2025) Workforce Size 400,000+ (all US) Millions needed; 1.4M+ gaps projected by 2030 Talent Pipeline National mobilization via universities/apprenticeships Lagging education; 70% IT shortage Funding Focus 4% federal budget on one goal Fragmented private investment; AI boom strains resources Challenges Tech risks (e.g., fires) but ample domestic labor Skills mismatch, layoffs, global dependency Self-Sufficiency Feasibility High—succeeded without imports Low—requires immigration/outsourcing to scale
Is the Claim Accurate?No, it's not. Apollo's success was a product of its time: Cold War urgency, massive public funding, and a post-WWII boom in US STEM education created a ready talent pool. Today, tech's pace and breadth (AI, quantum, biotech) outstrip domestic supply, making pure self-reliance inefficient and risky—potentially costing $5.5 trillion in delays by 2026 (International Data Corporation). ChatGPT might optimistically analogize for motivational effect, but data shows we'd need Apollo-level investment in education, apprenticeships, and retention to close the gap. Solutions like upskilling (33% of employers' top strategy) and AI-assisted hiring are steps forward, but "easy"? Far from it.If this ties to a specific policy debate (e.g., immigration reform), let me know for more targeted analysis!
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