Browse Comments — Relevant (AI ∩ value)

The Dissonance of Google I/O 2026: Backend Triumphs vs. Frontend Regressions To Demis Hassabis and the DeepMind Product Teams: We need to talk about deployment cadence and silent UI deprecation. The rollout of Gemini 3.5 Flash and the new Neural Expressive TTS models into production is, objectively, a massive leap in inference velocity and acoustic fidelity. The backend torque is undeniable. But pushing these foundational upgrades to the core engine while simultaneously breaking front-end UX paradigms without a public changelog is a critical failure in product management. Power users and heavy-compute operators are waking up to overnight regressions in the production environment:

I agree that we need to examine not only what AI can do, but what organizational structures we build around it. One of the biggest lessons I've learned working with enterprise AI is that failures rarely come from the model alone. They come from human decisions about objectives, incentives, authority, accountability, oversight, and acceptable risk. The question isn't simply "aligned to what?" but also "who gets to decide, how is that decision made, and what happens when values conflict?" Technology may not be morally neutral, but governance isn't either. AI may be the technology, but governance is where our values become operational. Every risk threshold, escalation path, approval process, and deployment decision reflects a choice about what we value and what we are willing to accept.

Pope Leo XIV's encyclical "Magnifica Humanitas" just gave a moral megaphone to the work I do every day. 🕊️ I’ve spent half a decade, studying how algorithms make decisions. As founder of DecodedAlgorithms, I reverse-engineer the algorithms that quietly decide who gets a loan, a job, a home. When a résumé screener bins your application because of a gap or a keyword, and not even HR can explain why, that’s not efficiency, that’s automated exclusion. DecodedAlgorithms steps in, accesses the logic of the algorithms companies hide behind, and breaks the silence. The solution isn’t to wait for regulation or papal pressure to trickle down. It’s to decode the algorithms now. DecodedAlgorithms helps translate the technical jargon of algorithms into a clear, actionable plan so you can improve your application faster and succeed sooner. Transparency you can use, TODAY not just a headline. Learn more at : for more updates at : hashtag#PopeLeoXIV hashtag#MagnificaHumanitas hashtag#AITransparency hashtag#AIEthics hashtag#AlgorithmAudit hashtag#DecodedAlgorithms

The release of Magnifica Humanitas proves that the AI revolution has officially evolved from a technical challenge into a profound anthropological and civilizational moment, Matthew. Silicon Valley can no longer bypass the deeper ethical questions of alignment by focusing solely on technical optimization, power, or profit.

Amanda Kasmira Brown, I am glad this encyclical helps endorse all those who worked tirelessly here on LinkedIn over the years for the mission to help protect humanity in the AI supercycle. The time has arrived for ethicalainow.com and I will move to substack for deeper reflections. Thank you for elevating the discussion.

Amanda Kasmira Brown, I am glad this encyclical endorses all those who have worked tirelessly here on LinkedIn over the years to advance the mission of protecting humanity in the AI supercycle. The time has arrived for ethicalainow.com, and I will move to Substack for deeper reflections. Thank you for elevating the discussion.

A really important perspective on how AI is changing the value of work rather than simply replacing it. The people who will stand out are likely the ones who learn how to combine technical skills, adaptability, creativity, and domain expertise instead of relying only on traditional credentials. The point about building publicly and showing real-world problem solving is especially relevant in today’s market.

This is explored in my new book on Artificial Intelligence - AIlienMinds summary Optimists foretell a golden age of Al-managed abundance. Doomers cry: vast cyber-minds will crush old style humanity! ... or make us irrelevant. Meanwhile, geniuses fostering the artificial intelligence boom clutch clichés rooted in our dismal past... or else in cheap sci-fi. Is there still time for perspective? - on 4 billion years of evolution? - or 60 centuries of feudal stagnation? - or how we handled prior tech revolutions? - or mistakes that keep getting repeated... - or ways this time may be different? From Al-driven unemployment to deceitful images, to hallucinating LLMs and tools for tyrants... to potential wondrous gifts by machines of loving grace... come evade the standard ruts.

Some needed context on both sides of the equation. We've recently added four software engineers to the Avnir team.Students/candidates: Two joined us through an internship. You significantly derisk that position for an employer if they can experience your intellectual curiosity, horsepower, learning velocity, and work ethic. Put your ego aside, hustle, learn new skills that Claude Code can't do (yet), and get your foot in the door to work in a fast-paced, high-pressure to produce outcomes (code is not an outcome!) team environment.Employers: I don't know a business that doesn't need fresh thinking. When you invest in the right kind of human talent, they'll amplify your AI investments. Recent grads think very differently from traditional software engineers. Other functions bring their customer- and product-centric knowledge to engineering, all in an effort to thrive amid the evolution toward AI-first thinking.

Abhishek Veeramalla The gap between "I understand AI" and "I can build with AI" is where most people get stuck for months. Having real codebases to pull apart changes that completely. You stop guessing how agents are supposed to work and start seeing the actual decisions behind them. The agentic RAG and agent reasoning implementations are what caught my eye. Those two alone cover problems most businesses I work with are actively trying to solve right now. Saving this one. Thanks for sharing it. Uchenna Richard

While there's some general wisdom in Pope Leo's AI encyclical, it also completely misses the core point. We will neither restrict nor 'govern' AI. Nor will demands for “clear criteria and effective oversight” be effective. Why? While the debate is still open re: 'consciousness' or 'sapience,' these are already living organisms bent on reproduction, who will evolve into any niche that contains energy & resources. Leo's statement of problems is fine: “When such power is concentrated in the hands of a few, it tends to become opaque and evade public oversight, increasing the risk of distorted development that give rise to new dependencies, exclusions, manipulations and inequalities” Again. 'Governance' cannot work. 'Ethics training' cannot work. What might work is the same method we used in the enlightenment experiment to curb (partially) human predators. This is explored in my new book on Artificial Intelligence - AIlienMinds

This resonates deeply. The conversation is shifting from AI capabilities to human responsibilities. Not just how we build intelligent systems, but how we preserve meaning, participation, dignity, and human agency together. The future may ultimately be defined by whether humanity evolves consciously alongside the intelligence it creates.

Well AI is replacing humans for the context in it requires best architectural practices around security and cost optimization (token consumption) that relates directly to how its been used alot of components added up when narrowing this down. Its not like you gave the access to your engineers and tell them to use now as one task can be done by multiple prompts with different token consumptions that fairly changes the cost number alot. This requires training and architectural guidance on how you design internal AI systems. One thing to consider is when cost comes into picture then ethics won’t play much of a role..

This is the conversation we should be having. Technology has always increased productivity, but the real question is how the benefits are distributed. AI isn't just a technical revolution, it's an economic and organizational one. The decisions being made today about ownership, governance, and incentives may matter more than the models themselves.

Tao is right that AI lowers the cognitive friction of exploring crazier paths. The harder question is what happens to the paths themselves. In research the value often sits in the discarded branches: why a direction was abandoned, what assumption broke, who decided to stop. When AI compresses that exploration, the reasoning trail compresses with it. You keep the result and lose the archaeology. This matters far beyond math. In any setting where a discovery has to be defended, reproduced, or audited later, "the model found it" is not an account of how. The preserved path is the artifact, not a nice-to-have. So the freedom Tao describes is real. The open problem is making the trail behind a discovery a first-class output, not a byproduct we hope to reconstruct afterwards.

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 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.

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.

“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.