r/artificialintelligenc

Holy smoke! Google cooked OpenAI and Anthropic
▲ 787 r/artificialintelligenc+1 crossposts

Holy smoke! Google cooked OpenAI and Anthropic

3 frontier models in a month! And every new one defeats the old!!!!

u/Any-Owl5704 — 4 days ago
▲ 58 r/artificialintelligenc+1 crossposts

Math✅ Dogs next

Now that they used AI to do all the hard math stuff, can they use it to analyze vocal patterns in dogs so we can build a tool to better communicate with our 4 legged friends

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u/GlorifiedCarnie — 4 days ago
▲ 1 r/artificialintelligenc+1 crossposts

Soo… Is there an actual working way to bypass ai age verification?

Ai age verification has become way more advanced over the past few months. It is simply not possible anymore trick services like yoti and persona with video game characters and ai-generated images. The most advanced thing I have tried was to set up a virtual camera using obs and let it loop a random youtube video of a person looking at a camera. Yoti does not even require a full live feed of the camera. It just analizes a single frame and accoreing to their website find out the person’s “liveness“. I am aware of VPNs but not everyone has acces to them and accounts that allready got flagged must verify the user‘s age. Is it still possible to somehow bypass the facial scans or the id-scans without having to upload anything about oneself on the server?

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u/Strezle — 4 days ago
▲ 6 r/artificialintelligenc+2 crossposts

Looking for people interested in AI × Infrastructure × Sustainability research

Looking for people interested in AI × Infrastructure × Sustainability research

I've been exploring an area that I find increasingly interesting: the environmental and infrastructure impact of AI, and what we could potentially do about it.

AI isn't just a software problem anymore. Behind every model are GPUs, data centers, electricity, cooling, water, networking, hardware manufacturing, and increasingly complex infrastructure.

I'm interested in exploring questions such as:

  • How do we accurately measure the energy, water and carbon footprint of AI workloads?
  • Can AI inference be made significantly more resource-efficient?
  • Could workloads be scheduled based on energy availability, carbon intensity or water stress?
  • Do we actually need large models for every task?
  • Can smaller models, routing, caching, quantization, batching, etc. meaningfully reduce resource consumption?
  • Can AI itself help optimize energy grids, water systems, agriculture, conservation and other environmental problems?
  • What are the gaps in current research?
  • What interesting research questions haven't been explored enough yet?

At this stage, I'm not trying to sell a specific idea or claim to have the answers.

I'd like to find people who are interested in:

• Reading and analyzing research papers
• Discussing existing findings and identifying gaps
• Challenging assumptions and ideas
• Brainstorming potential research questions
• Designing small experiments or prototypes
• Potentially collaborating on a research paper/project in the future

You don't necessarily need to be an academic researcher. I'm interested in people from AI/ML, backend/infrastructure, distributed systems, data centers, energy, sustainability, environmental science, or related fields.

I'm approaching this as an open exploration: research → discussion → experiments → ideas → potentially a paper/project.

If this sounds interesting to you, feel free to comment or DM me. I'd be happy to start with a small group and see where the discussions lead.

Would love to hear what problems you think are currently being overlooked in AI's environmental/infrastructure impact.

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u/Consistent-Cow6205 — 11 days ago
▲ 2 r/artificialintelligenc+1 crossposts

Why Wall Street is panicking over SaaS valuations (and why most apps are just CRUD databases with a GUI)

There’s been a lot of market anxiety over "SaaSmageddon" with software multiples dropping to decade lows. While some of it is market noise, there’s a real technical shift happening under the hood that explains why investors are nervous.

Here is the quick breakdown of what’s changing in software delivery:

  1. The 'CRUD + UI' Problem: Most traditional SaaS apps are essentially database tables (Create, Read, Update, Delete) wrapped in a custom graphical user interface. As natural language and autonomous agents advance, the visual UI recedes. Agents handle the logic directly via headless APIs.
  2. Software 1.0 - 2.0 - 3.0 : We moved from local licenses (1.0) to cloud subscriptions (2.0). We're now entering Agent-as-a-Service (3.0), where software doesn't sell access to tools—it sells completed outcomes.
  3. The TCO Reality Check: While AI coding tools make building custom software fast, 60%–80% of Total Cost of Ownership (TCO) comes from ongoing maintenance, security, and governance (like DORA and NIS2 compliance). This is why major systems of record (Salesforce, ServiceNow) won't die overnight—they become the execution and governance layers for agents.

What are your thoughts? Are you seeing teams replace seat-based SaaS subscriptions with custom agentic workflows, or is enterprise compliance keeping traditional SaaS safe for now?

I put together a full 12-minute deep-dive breaking down the research papers, market data, and architecture diagrams here if you want to see the visual breakdown: https://youtu.be/2yUjexWL4yQ

u/theresearchloop — 13 days ago