The Eternal Promise, The Training Data Paradox, and AI Claims Reflection
This week I read Ivan Turkovic's The Eternal Promise: A History of Attempts to Eliminate Programmers and The Training Data Paradox: What Happens When AI Replaces the Engineers Who Trained It. I then reflected on common claims about AI, argued against those claims with evidence and reasoning, and wrote down my plan for making progress on my Nogramming assignment this week.
Reading 1: The Eternal Promise
Key Takeaways
- When new technologies emerge there is always chatter that said technology is going to take over jobs and eliminate the need for human workers, and this has been proved false in multiple cases throughout history.
- We should be skeptical of extreme predictions. These predications change over time depending on how said technology advances.
- Human skills and verification is always going to be required. "...work will continue to require skill, judgment, and understanding that no tool has yet replaced."
Reading 2: The Training Data Paradox
Key Takeaways
- AI models trained on their own output see a model collapse which causes them to forget rare and important patterns in the original data.
- Years of experience and knowledge may get lost as developers rely on AI to do majority of their work.
- Junior developer roles are the ones that are disappearing while the higher up roles aren't seeing much of a change.
Three AI Claims I Argue Against
Claim 1: "AI is going to do all of our jobs in the CS industry"
AI will not be able to replace the planning, logic and overall context that humans bring to software development. While AI tools like GitHub Copilot can assist with code generation, they lack the ability to understand business requirements, make architectural decisions, or debug complex system failures. According to the 2025 Stack Overflow Developer Survey, 45% of developers still spend significant time on planning and design—tasks that require human judgment. The reading "The Eternal Promise" reminds us that every technological shift throughout history has made these predictions, yet skilled engineers remain in demand. AI augments developers rather than replacing them; we still need humans to guide, review, and validate the code AI generates.
Claim 2: "One single query in GPT takes up 5 bottles of water"
This claim significantly overstates the water consumption of AI queries. While data centers do use water for cooling, OpenAI's research indicates that a single ChatGPT inference uses approximately 0.5 liters of water—far less than the 5 liters (about 1.3 gallons) claimed. Furthermore, not all AI inference happens in water-intensive cooling systems; many queries run on servers in locations with alternative cooling methods or renewable energy sources. A counterexample is that a Google search query, which involves similar computational complexity, uses approximately 0.3 liters of water. The broader context matters: yes, AI systems consume resources, but the per-query figure is often exaggerated to create alarm rather than reflect actual environmental impact. This doesn't mean we should ignore efficiency, but we should base our concerns on accurate data rather than inflated claims.
Claim 3: "AI will never stop advancing"
AI advancement has already plateaued in several areas, contradicting the claim that it will never stop advancing. For instance, improvements in large language model performance have slowed considerably—the jump from GPT-3 to GPT-4 was significant, but subsequent models have shown diminishing returns relative to the computational cost. Additionally, the Training Data Paradox reading highlights a fundamental limitation: as AI models train on their own generated outputs, they experience "model collapse," forgetting rare but important patterns. This represents a ceiling on advancement for certain approaches. Historically, similar "eternal advancement" claims have been made about other technologies—nuclear power, personal jetpacks, and yet their progress has reached practical limits due to economic, physical, or resource constraints. AI advancement will likely continue, but at a measured pace with clear boundaries, not at the exponential rate some claim.
Weekly Progress Plan for Nogramming Assignment
This week, I will make progress on my Nogramming assignment by planning out my questions for each of the interviewees, and setting up times with them to ensure that I have plenty of time to record and edit the podcast.
~Shree