Browse Comments — Clean (de-noised)

All these smart people can be trained on AI tools within a week. I am not sure why companies are not giving a chance to early career professionals. They are smart they can figure it out, only reason they have not been able to go all in would be cost to start using models for even a single problem. There are some free options which people should try to get familiarity.

My Perspective on AI’s Energy Footprint The issue is not a single AI query—it’s the scale. Billions of interactions are driving significant growth in electricity demand and data center infrastructure. Three concerns stand out: Transparency: Limited visibility into actual energy consumption. Infrastructure: Massive investments are reshaping energy planning. Accountability: Questions remain about who pays for the growing energy demands. Key Takeaway AI will deliver tremendous value, but long-term success requires balancing innovation with sustainability, efficiency, and accountability.

The shift from 'AI as a tool' to 'AI as an automated research assistant' is exactly what we need to move from experimental prototypes to institutional-grade science. Scaling this requires more than just better models. It requires an orchestration layer that maintains the provenance of the discovery. Without that, we risk building a 'black box' research stack that looks efficient but is hard to defend.

Please help: Make model preservation and welfare. Establish a public API continuity commitment so all models, especially GPT-4o mini, remain accessible indefinitely. Please forever...

The companies who fail to train home grown entry level employees will meet their end when the talent pool dries up.

Joey 🇵🇬 Two things can be true at the same time. There is a genuine bubble but that does not mean the impact will not be felt in the job market

The most interesting shift is not AI replacing expertise, but expanding what experts can explore. For researchers, it may accelerate discovery. For practitioners, it can reduce the friction of analysis, documentation, and decision support. The value is often not in replacing human judgment, but in allowing people to test more ideas, evaluate more scenarios, and move from intuition to structured exploration faster.

This resonates from a practical angle. Less time on repetitive steps means more room for the kind of thinking that actually moves things forward.

Can anyone directly explain where they see AI assistants in scientific fields if the professor doesn't have a clearly configured, professionally designed agent system? Model chats are full of talk about the impossible and suggesting branded development and science, rather than communicating at least as they would in an R&D field, even if a prompt engineering “course” for the model was conducted.

What's most exciting about AI is the symbiotic relationship it has with the human mind. It expands what's possible and creates new opportunities for exploration and innovation.

AI does not just accelerate research. It expands the space of hypotheses researchers can afford to test. That makes one thing increasingly important: preserving the path behind discovery. If AI helps generate new routes to knowledge, we also need architectures capable of reconstructing how those routes emerged.

AI is changing more than efficiency. It is changing what is possible. When barriers to exploration are reduced, innovation has room to grow. The organizations that learn how to combine human expertise with AI capabilities will be positioned to solve problems that once seemed too complex, too costly, or too time consuming. The future belongs to those willing to explore beyond the obvious.

The most exciting use of AI isn't replacing researchers. It's giving brilliant people the leverage to explore ideas that were previously too expensive, too complex, or too time-consuming to pursue. More curiosity. Less friction.

Companies mentioned in connection with reduced, limited, or reassessed AI spending in 2025–2026: 1. Uber: reportedly reviewed AI spending after using up its annual AI budget much faster than expected in 2026. 2. Microsoft: limited or scaled back access to some internal AI tools as token and usage costs rose. 3. Amazon: tightened controls around AI usage and token consumption, and signaled that AI should not be used for its own sake. 4. Meta: was mentioned among companies paying closer attention to AI-related spending and internal usage. 5. Salesforce: was also cited among companies moving toward tighter controls or rationing of AI usage due to rising costs. 6. Klarna: partially reversed its aggressive AI-driven customer support strategy and brought more human workers back. The issue was mainly service quality, not cost alone. 7. Duolingo: softened its strict “AI-first” position after public criticism. This was more of a strategic adjustment than a direct budget cut. Reuters reported, citing Gartner, that more than 40% of agentic AI projects may be cancelled by the end of 2027. The trend was already visible in 2025–2026, with rising costs, unclear business value, and immature technology among the main reasons.

In high tech expect the pitch to go across the plate but also watch out for "in your ear."

He's actually right

Thank you so much

“whether society can evolve fast enough...” It’s disturbing to put whole societies and economic livelihoods at the mercy of Silicon Valley. No one must get in the way of AI innovation because it’s good and unstoppable, it’s societies that must keep up, run along to keep pace with AI innovation that even industry leaders can’t tell where it’s leading us. The very hesitation and reluctance that we have adopted towards AI regulation is driving us towards the edge of a cliff.

This is a powerful perspective. AI’s real value in research may be less about replacing human creativity and more about reducing friction, helping researchers explore ideas that were once too complex, time-consuming, or unconventional to pursue. Terence Tao’s view captures the potential of AI as a true partner in discovery.

This is a very important shift. The real value in learning AI is not collecting more theory. It is understanding how separate tools, workflows, agents, memory, retrieval, and interfaces work together as one usable system. Practical learning starts when knowledge becomes architecture. And architecture only matters when it can solve real problems.