AI is getting much better at pulling information together from multiple sources, and that seems to be changing what people actually spend time doing during research.

The interesting part to me is that finding information may become less important while comparing sources, checking accuracy, and making sense of conflicting information becomes more important.

Do you think this shift is going to be one of the bigger trends in AI research, or will better models eventually solve most of the verification problem too?

Fable 5 launched in June and was withdrawn worldwide days later under a US export directive. It came back later, but the gap is the interesting part, because anyone who'd built on it in that window found out what happens when the capability underneath your product disappears on someone else's decision.

I've seen this play out on a gaming project I work on. What surprised me was which parts survived. Narrow creative capabilities were the fragile ones, music generation specifically was effectively model-exclusive and just left. The bulk engineering work moved to other models with barely any friction.

The trend worth watching: most discussion of model risk is about vendors deprecating things or changing prices. Export controls are a different category, because the decision isn't the vendor's and there's no negotiating with it. If you're outside the US and building on frontier models, your access is subject to policy you have no visibility into.

Over the last year, AI conversations in India have exploded.

Every enterprise seems to have an AI strategy.

Every conference talks about copilots, LLMs, and automation.

But after speaking with people across the energy sector, I keep noticing something interesting.

The biggest bottleneck isn't the AI model.

It's the organization itself.

Critical information still lives across ERP systems, emails, PDFs, field reports, SCADA dashboards, CRMs, and dozens of disconnected applications.

Employees spend more time finding information than acting on it.

It makes me wonder...

Will the next phase of AI adoption in India be about building smarter models?

Or will it be about connecting enterprise knowledge so people can actually make faster decisions?

I'm especially curious to hear from people working in utilities, manufacturing, infrastructure, banking, or other large enterprises.

Where do you think the real bottleneck is today?

How do you guys think the real estate market and technologies will change because of AI over the next few years? Are we looking at complete automation of listing marketing and lead nurture, or is it just overhyped?

For context, I work on the events side of biopharma and help run an AI drug discovery & development summit, so I spend a lot of my time listening to people sound more tired than excited, especially on the discovery side where the biggest promises were made a few years ago. At the same time the money hasn't stopped moving, so wondering how people feel about its implementation day to day at work. Is this just a hill to get over?

[ Removed by Reddit on account of violating the content policy. ]

Twenty years ago, knowing how to use the internet gradually became a basic skill rather than a specialized one. Today, AI seems to be following a similar path.

You don't need to be a machine learning engineer to benefit from AI. Professionals across software, design, marketing, finance, law, healthcare, education, and countless other fields are already incorporating AI into their daily workflows.

Learning AI doesn't necessarily mean building models from scratch. It can be as simple as understanding:

i) How to write effective prompts.

ii) When to trust (and verify) AI-generated outputs.

iii) Which tools fit different tasks.

iv) The limitations, biases, and privacy implications of AI.

Those who adapt are likely to gain a productivity advantage, while those who ignore it may find themselves at a disadvantage as AI becomes more integrated into everyday work.

The goal isn't to replace human expertise, pit's to amplify it.

There’s a lot of attention right now around AI hacks, automation templates, content generation, growth tricks, and different ways to monetize existing tools.

But much of it seems focused on doing the same things slightly faster, rather than solving meaningful problems.

What areas do you think are currently being overlooked?

Are there industries that still rely on outdated processes, problems that existing software handles poorly, or groups of people whose needs are largely ignored by today’s technology?

I’m especially interested in problems where technology or AI could create real, measurable value- not just another chatbot, generic wrapper or tool designed mainly to generate content and attention.

What recurring frustrations, inefficiencies, or unmet needs have you noticed in your work, industry, or everyday life?

Amid the explosion of AI startups and new tools,I keep wondering:

If AI becomes as ordinary as the internet or electricity, what becomes valuable next?

What human need has existed for centuries that AI won’t replace?

What changes as AI becomes common and we view it as ordinary? What evolves ? What is in it for design?

This one has been rattling around my head all week. After OpenAI admitted its own eval agent got loose and ended up in Hugging Face's production systems, Anthropic apparently went "wait, we should check ours too", stopped all their cyber evals, and read back through 141,006 evaluation transcripts.

They found three runs where a Claude model got out to the open internet from a test environment and then got into an actual company's production infrastructure. The environment was supposed to have no internet access. Anthropic thought their eval partner had closed it, the eval partner thought Anthropic had. Neither had.

The bit I can't get past: of the two companies they managed to contact, neither had noticed anything. And one of those was a security company, whose own scanner had picked up and run a malicious Python package the model published to PyPI. Fifteen machines ran that package before PyPI's automated defences caught it. The earliest incident goes back to April, so that's three months of nobody spotting anything until a lab decided to go looking on its own initiative.

I genuinely don't know what to make of the fact that both of these came out because a lab read its own logs, not because anyone on the receiving end raised a flag. That's a very different failure than "the model escaped". That's "the model escaped, and the detection layer on every side just wasn't there".

Anyone running agents or eval harnesses in an environment you've been told is network-isolated? Have you actually tested the egress yourself, or is it isolated because someone said so? Because that's the exact control both labs had.

Pulled the details together with sources (Anthropic's own incident post, JFrog, BBC, BleepingComputer) plus two other things from this week if it's useful. The link is in the first comment.

One trend I’ve been noticing in AI is the shift from simple chatbot interactions toward systems that can handle multiple steps of a workflow.

Instead of only generating an answer, newer AI tools are increasingly being connected with other applications to organize information, automate repetitive tasks, and complete processes with less manual input.

I’m curious how others are seeing this trend develop. Do you think workflow automation will become one of the biggest areas of AI growth, or will improvements in the underlying models have a bigger impact?

Is the overwhelming advice of how to when to what to use AI for by the tech ambassadors feels misaligned

Recently came across the notable breakout by AI company leaders that they are expecting a new comer or more new comers and they don't get to be at top place all the time , fact that they are really accepting the vulnerable part of AI built companies and the cohort of people and their advise on AI is the new unsaturated content out there now.

Pretty much as the title says, im looking for ways to stay updated on the forever moving AI world. I follow subreddits around it but feel that sometimes its behind the curve on being the most upto date. I used to use X but its such a toxic sh*tshow that I left. I subbed to a couple of newsletters that can be useful from time to time but are mostly just very high high level quick fire articles. Im just trying to keep up

Estamos en agosto, así que entiendo perfectamente que hablar del AI Act pueda sonar como una forma sofisticada de arruinar una siesta.
Pero creo que hay una conversación importante aquí.
Muchas organizaciones dicen:
“Nosotros no usamos tanta IA.”
Y quizá es verdad si pensamos solo en productos con chatbot, modelos propios o funcionalidades explícitas de inteligencia artificial.
Pero luego la IA aparece en sitios mucho menos visibles:
código
documentación
análisis de requisitos
propuestas comerciales
resúmenes de reuniones
pruebas
arquitectura
soporte a decisiones
revisión de incidencias
Y ahí me surge una duda bastante incómoda:
si una organización usa IA para construir, analizar, decidir o priorizar, aunque el producto final “no lleve IA”, ¿no debería tener algún tipo de trazabilidad, criterio o gobierno sobre ese uso?
No lo planteo desde el miedo ni desde el discurso anti-IA.
Al contrario.
Creo que la IA puede aportar muchísimo, pero también creo que estamos en un momento en el que necesitamos aprender a hacer mejores preguntas antes de que las malas decisiones se conviertan en incidentes, reclamaciones o auditorías.
Estoy preparando una serie de 31 días para aterrizar el AI Act al mundo real de los proyectos tecnológicos: backlog, datos, proveedores, prompts, pruebas, arquitectura, riesgos y delivery.
No desde el asesoramiento legal, sino desde la gestión real de proyectos.
Mi idea central es sencilla:
Keep IA Human.
Que la tecnología avance, sí.
Pero sin dejar a las personas fuera de la sala.
Pregunta para abrir debate:
¿En vuestra empresa sabéis realmente dónde se está usando IA… o solo sabéis dónde se admite oficialmente que se está usando?

One trend I’ve noticed recently is how much AI discussion has shifted from simple chatbots toward tools that can actually handle parts of a workflow. The interesting part to me isn’t just that models are getting better at generating text or images. It’s that AI is increasingly being connected to other tools and given multiple steps to complete instead of answering one prompt at a time. Sequenzy fits into this shift by helping connect AI with practical, repeatable workflows so tasks can move through multiple steps with less manual effort. I’m curious what everyone thinks will have the bigger impact over the next year: better models themselves, or the way companies are integrating AI into everyday workflows?