Explore top daily AI community discussions from different sources, summarized and scored in one place.

Hardware And Infrastructure

/r/artificial

Major AI updates in the last 24h

Top News

NVIDIA Jetson AGX Thor now offers up to 7× generative AI performance thanks to software optimizations and supports the latest LLM/VLM models.
Anthropic’s Claude Haiku 4.5 delivers Sonnet 4‑level accuracy at one‑third the cost and double the speed.

Models & Releases

Anthropic launched Claude Haiku 4.5, a lightweight model costing one-third of Sonnet 4 and running twice as fast, while matching its performance on coding and reasoning benchmarks.
Haiku 4.5 achieved 73% accuracy on SWE‑Bench and 41% on Terminal‑Bench, comparable to Sonnet 4, GPT‑5, and Gemini 2.5.

Hardware & Infrastructure

Apple unveiled the M5 silicon, delivering up to 4× peak GPU AI performance versus M4, featuring a 16‑core Neural Engine and 153 GB/s memory bandwidth.
M5‑powered MacBook Pro and iPad Pro launch with significant AI speed gains.
NVIDIA Jetson AGX Thor software updates provide up to 7× generative AI performance, accelerating LLM/VLM inference.
Nscale signed a deal with Microsoft to deploy ~200,000 Nvidia GB300 GPUs across U.S. and EU data centers, backed by $1.7 B funding.
Arm partnered with Meta to supply its Neoverse platform for scalable AI workloads.

Product Launches

Google’s Veo 3.1 model blends separate images into a single natural‑looking video, adding synchronized audio and richer realism.

New Tools

system_prompts_leaks is an open‑source collection of extracted system prompts from major chatbots, supporting security research.
Klavis provides a MCP integration layer that enables reliable tool usage for AI agents at any scale.
MineContext delivers a proactive, context‑aware AI that blends context‑engineering with ChatGPT Pulse for richer interactions.

Industry & Adoption

AI agents are moving from pilots to enterprise‑wide deployments, accelerating tasks like threat detection, contract review, and crisis response.

The full daily brief: https://aifeed.fyi/briefing

--- TOP COMMENTS --- Most people will miss that these AI updates aren't about the tech getting smarter but about getting cheaper and faster which means we just crossed the threshold where AI becomes economically viable for every business operation imaginable.

17h ago

Hardware And Infrastructure Models And Releases

Models And Releases

/r/ClaudeAI

Claude Haiku 4.5 hits 73.3% on SWE-bench for $1/$5 per million tokens (3x cheaper than Sonnet 4, 2x faster)

Anthropic just dropped Haiku 4.5 and the numbers are wild:

Performance:

73.3% on SWE-bench Verified (matches Sonnet 4 from 5 months ago)
90% of Sonnet 4.5's agentic coding performance
2x faster than Sonnet 4
4-5x faster than Sonnet 4.5

Pricing:

$1 input / $5 output per million tokens
That's 66% cheaper than Sonnet 4 ($3/$15)
~10x cheaper than Sonnet 4.5 for 90% of the performance

Why this matters:

Multi-agent systems are now economically viable. Before Haiku 4.5:

10 agents × $15/million = $150/million (too expensive)
10 agents × 10s latency = 100s total (too slow)

With Haiku 4.5:

10 agents × $5/million = $50/million (3x cheaper)
10 agents × 2s latency = 20s total (5x faster)

Use cases unlocked:

Real-time chat assistants (2s response time)
Automated code reviews (~$0.01 per review)
Pair programming with Claude Code (no latency friction)
Rapid prototyping (iterate as much as you want)

Available now:

Claude.ai
Claude Code (CLI + extension) - use /model command
API: model="claude-haiku-4.5-20251015"
AWS Bedrock
Google Cloud Vertex AI

We wrote a deep-dive article (in French, but code examples and benchmarks are universal) with cost analysis, migration guides, and real scenarios: here

The barrier between "proof of concept" and "production" just got dramatically lower.

What are you planning to build with it?

--- TOP COMMENTS --- Since western models and open-source models are on par for day to day usage, the prices for the open-source models should be compared too.

these numbers are pretty impressive especially the price point.

13h ago

Models And Releases Applications And Tools

/r/LocalLLaMA

107

We built 3B and 8B models that rival GPT-5 at HTML extraction while costing 40-80x less - fully open source

Disclaimer: I work for Inference.net, creator of the Schematron model family

Hey everyone, wanted to share something we've been working on at Inference.net: Schematron, a family of small models for web extraction.

Our goal was to make a small, fast model for taking HTML from website and extracting JSON that perfectly adheres to a schema.

We distilled a frontier model down to 8B params and managed to keep basically all the output quality for this task. Schematron-8B scores 4.64 on LLM-as-a-judge evals vs GPT-4.1's 4.74 and Gemma 3B's 2.24. Schematron-3B scores 4.41 while being even faster. The main benefit of this model is that it costs 40-80x less than GPT-5 at comparable quality (slightly worse than GPT-5, better than Gemini 2-5 Flash).

Technical details: We fine-tuned Llama-3.1-8B, expanded it to a 128K context window, quantized to FP8 without quality loss, and trained until it outputted strict JSON with 100% schema compliance. We also built a smaller 3B variant that's even cheaper and faster, but still maintains most of the accuracy of the 8B variant. We recommend using the 3B for most tasks, and trying 8B if it fails or most of your documents are pushing the context limit.

How we trained it: We started with 1M real web pages from Common Crawl and built a synthetic dataset by clustering websites and generating schemas that mirror real-world usage patterns. We used a frontier model as a teacher and applied curriculum learning to progressively train on longer context lengths--training with context parallelism and FSDP to scale efficiently--which is why the models stay accurate even at the 128K token limit.

Why this matters: Processing 1 million pages daily with GPT-5 would cost you around $20,000. With Schematron-8B, that same workload runs about $480. With Schematron-3B, it's $240.

The speed matters too. Schematron processes pages 10x faster than frontier models. On average, Schamatron can scrape a page in 0.54 seconds, compared to 6 seconds for GPT-5. These latency gains compound very quickly for something like a browser-use agent.

Real-world impact on LLM factuality: We tested this on SimpleQA to see how much it improves accuracy when paired with web search. When GPT-5 Nano was paired with Schematron-8B to extract structured data from search results provided by Exa, it went from answering barely any questions correctly (8.54% on SimpleQA) to getting over 85% right. The structured extraction approach means this was done processing lean, clean JSON (very little additional cost) instead of dumping ~8k tokens of raw HTML into your context window per page retrieved (typically LLMs are grounded with 5-10 pages/search).

Getting started:

If you're using our serverless API, you only need to pass your Pydantic, Zod, or JSON Schema and the HTML. We handle all the prompting in the backend for you in the backend. You get $10 in free credits to start.

If you're running locally, there are a few things to watch out for. You need to follow the prompting guidelines carefully and make sure you're using structured extraction properly, otherwise the model won't perform as well.

The models are on HuggingFace and Ollama.

Full benchmarks and code examples are in our blog post: https://inference.net/blog/schematron, docs, and samples repo.

Happy to answer any technical questions about the training process or architecture. Also interested in how this would be helpful in your current scraping workflows!

--- TOP COMMENTS --- Why would you change the models in each graph?

model for taking HTML from website and extracting JSON that perfectly adheres to a schema.

Or you could...I dunno...use an HTML parser? 1000x cheaper and faster.

5h ago

Models And Releases

Product Launches

/r/ClaudeAI

Going from the Claude app to Claude Code and my mind is blown!

I'm techy but not a programmer by any means.

Been working on a book/video course project for a client. Was constantly hitting rate limits on the Claude app and having to mash "continue" every few minutes, which was killing my flow.

Started using Claude Code instead since it's terminal-based. Lifechanger!!

But then I ran into a different problem - I'd be working on content structure and it was getting messy.

I created markdown files for different specialist roles ("sub agents" in a way I guess) - content structuring, video production, copywriting, competitive research, system architect etc. Each one has a detailed prompt explaining how that role should think and act, plus what folders it works in.

Now when I start a task, I just tell Claude Code which specialists to use. Or sometimes it figures it out. Not totally sure how that works but it does.

Apparently these can run at the same time? Like I'll give it a complex request and see multiple things happening in parallel. Can use Ctrl+O to switch between them. Yesterday had competitor research running (it web searches) while another one was doing brand positioning, and the email copywriter was pulling from both their outputs.

Each specialist keeps its own notes in organized folders. Made an "architect" one that restructures everything when things get messy.

It's been way more productive than the web app because I'm not constantly restarting or losing context. Did like 6 hours of work yesterday that would've taken me days before with all the rate limit breaks.

Then it pushes it all to git locally and on the site (never done this before)

Is this just a janky version of something that already exists? I'm not technical so I don't know if there's a proper name for this pattern. It feels like I hacked together a solution to my specific workflow problem but maybe everyone's already doing this and I just didn't know.

Curious if anyone else has done something similar or if there's a better way to handle this?

--- TOP COMMENTS --- Lots of people are inventing ad-hoc solutions along your lines, tailored to their situations and needs and styles of working.

A few of them post here with crappy AI generated slop that breathlessly describes their "game changing" workflows without realizing how specific they are.

A much larger group of people haven't figured out structures as good as yours and struggle away with ineffective prompts or ineffective tools.

I’ve used both Claude Desktop and Claude Code for a while now, but haven’t had the chance to explore the sub agents feature. Your use case seems like it would be similar (non-coding) to mine! Would you mind sharing what a specialist role markdown file might contain? I know there’s a better way to use Claude than the way I’ve been using it, but I’ve found it hard to really conceptualize agent use cases as a non coder

Yesterday

Product Launches Developer And Technical

Research And Papers

Applications And Tools

Policy And Ethics

/r/singularity

What will realistically happen once AI reaches a point where it can take at least 50% of jobs?

I don’t doubt that eventually AI will replace all jobs, a humanoid robot that’s smarter, stronger, and doesn’t need rest will surely replace any job that exists today. But we don’t know when that will happen, and once it does, humans will have no value in the current economy for sure. Society will either collapse or completely reinvent itself, which I think is more probable.

But what do you think will realistically happen in the meantime? Once there are enough robots and AI is advanced enough to take 50% of the jobs, what will happen to the 50% of people without jobs and income?

Statistically speaking, most people live paycheck to paycheck, and even losing a job for 5–6 months burns through all their savings, you literally become homeless and can’t afford to survive. So, will half of the population just go extinct?

I’ve been thinking a lot about it, and I can’t come up with a realistic scenario that doesn’t end in mass disaster, given how current governments handle things.

I’m not educated in the field, so I can’t really give a fact-based opinion.

--- TOP COMMENTS --- I predict riots will start around 20% unemployment

" what will happen to the 50% of people without jobs and income?"

My take is that those who have the money & power would not want a volatile population. Sure, they don't care enough about protest and fairness. But it is cheaper and safer to pay people off than trying to fight a rebellion.

So they will give enough scraps (via UBI, or social welfare, or unemployment or something else) so that people are content enough not to rebel but probably still unhappy enough to rant online all the time.

Yesterday

Policy And Ethics Industry Adoption

/r/ArtificialInteligence

Perplexity is fabricating medical reviews and their subreddit is burying anyone who calls it out

Someone posted about Perplexity making up doctor reviews. Complete fabrications with fake 5 star ratings. Quotes do not exist anywhere in cited sources. Medical information. About real doctor. Completely invented.

And the response in perplexity sub? Downvotes. Dismissive comments. Usual ‘just double check the sources’, ‘works fine for me’…

This is a pattern. Every legitimate criticism posted in r/perplexity_ai and r/perplexity gets the same treatment. Buried, minimized, dismissed. Meanwhile the evidence keeps piling up.

GPTZero did investigation and found that you only need to do 3 searches on Perplexity before hitting source that is AI generated or fabricated.

Stanford researchers had experts review Perplexity citations. Every single expert found sources that did not back up what Perplexity was claiming they said.

There is 2025 academic study that tested how often different AI chatbots make up fake references. Perplexity was the worst. It fabricated 72% of eferences they checked. Averaged over 3 errors per citation. Only copilot performed worse.

Dow Jones and New York post are literally suing Perplexity for making up fake news articles and falsely claiming they came from their publications.

Fabricating medical reviews that could influence someones healthcare decisions crosses serious line. We are in genuinely dangerous territory here.

Perplexity is provably broken at fundamental level. But r/perplexity_ai and r/perplexity treat anyone pointing it out like they are the problem. Brigading could not be more obvious. Real users with legitimate concerns get buried. Vague praise and damage control get upvoted.

--- TOP COMMENTS --- Well, it was nice knowing you.

That "everything is a wrapper" CEO always came across as very slimy to me

16h ago

Policy And Ethics Developer And Technical

Developer And Technical

/r/MachineLearning

[R] Plain English outperforms JSON for LLM tool calling: +18pp accuracy, -70% variance

TL;DR: Tool-call accuracy in LLMs can be significantly improved by using natural language instead of JSON-defined schemas (~+18 percentage points across 6,400 trials and 10 models), while simultaneously reducing variance by 70% and token overhead by 31%. We introduce Natural Language Tools (NLT), a simple framework that decouples tool selection from response generation and eliminates programmatic format constraints and extends tool calling to models even without tool-call support.

Resources: Paper

Authors: Reid T. Johnson, Michelle D. Pain, Jordan D. West

The Problem

Current LLMs use structured JSON/XML for tool calling, requiring outputs like:

{
  "tool_calls": [{
    "name": "check_talk_to_a_human",
    "description": "Used when the user requests..."
  }]
}

This structured approach creates three bottlenecks:

Task interference: Models must simultaneously handle multiple tasks, such as understanding queries, select tools, maintaining format constraints, and generating responses.
Format burden: Research demonstrates that the more structured a model's output, the more its performance tends to degrade (a great paper by Tam on the subject).
Context bloat: Structured schemas increase token usage, since you define not only the tool name and description, but surrounding JSON or XML syntax.

Even when tool selection is separated from response generation, probability mass is diverted toward maintaining correct formatting rather than selecting the right tools.

Method: Natural Language Tools (NLT)

We introduce a simple three-stage framework that replaces JSON with natural language:

Example NLT architecture with Selector > Parser > Output

Stage 1 - Tool Selection: Model thinks through if any tools are relevant, then lists each tool with a YES/NO determination:

Thinking: (brief reasoning)
Example Tool 1 - YES/NO
Example Tool 2 - YES/NO
Example Tool 3 - YES/NO
Assessment finished.

Stage 2 - Tool Execution: Parser reads YES/NO decisions and executes relevant tools

Stage 3 - Response: Output module receives tool results and generates final response

Evaluation: 6,400 trials across two domains (Mental Health & Customer Service), 16 inputs per domain, 5 repetitions per input. Both original and perturbed inputs were tested to control for prompt engineering effects.

Results

We find that NLT significantly improves tool-call performance, boosting accuracy by more than 18 percentage points (69.1% to 87.5%). Variance overall fell dramatically, falling more than 70% from .0411 to .0121 when switching from structured tool calling to NLT.

DeepSeek-V3 was a standout example, jumping from 78.4% to 94.7% accuracy while its variance dropped from 0.023 to 0.0016, going from among the least stable to the most consistent performer.

While we couldn't compare relative gain, NLT extends tool calling to models without native tool calling support (DeepSeek-R1: 94.1% accuracy).

Basic NLT Template

Basic NLT Prompt Template:

You are an assistant to [Agent Name], [context].

Your mission is to identify if any of the following topics have 
been brought up or are relevant:

- Tool 1 (description of when to use it)
- Tool 2 (description of when to use it)
...

Your output should begin by thinking whether any of these are 
relevant, then include the name of every tool followed by YES or NO. 
End with "Assessment finished."

Format:
Thinking: (reasoning)
Tool 1 - YES/NO
Tool 2 - YES/NO
...
Assessment finished.

Full prompts and implementation details in Appendix A. Works immediately with any LLM with no API changes or fine-tuning needed.

Limitations

Latency considerations: NLT requires minimum two model calls per response (selector + output), whereas structured approaches can respond immediately when no tool is needed.

Evaluation scope: We examined single-turn, parameterless tool selection. While less complex than existing multi-turn benchmarks, it proved sufficiently rigorous -- no model achieved 100% accuracy in either condition.

A full discussion on limitations and areas for further research can be found in section 5.9 of the paper!

Discussion & Implications

We propose five mechanisms for these improvements:

Reduced format burden: Requiring structured outputs (e.g. JSON) may divert the model's probability mass toward syntax control rather than task accuracy
Reduced task interference: By separating the tool selection into its own distinct stage, task interference can be sidestepped.
Training alignment: The majority of model training is on outputting human-readable text, and NLT better aligns with this training paradigm. This is further supported by our results, as open-weight models see more pronounced gains. This makes intuitive sense, as open-weight models typically have fewer resources to invest in structured tool-call training.
Explicit full-catalog consideration: Requiring the model to explicitly include each tool name in its output avoids positional bias, allowing the model to "recollect" each tool right before it makes a determination.
Reduced context length: Even minor increases in tokens can degrade performance, and NLT used 47.4% fewer input tokens on average than its structured tool call counterpart (largely due to removing JSON boilerplate).

For agentic systems, the NLT approach could significantly boost tool selection and accuracy, particularly for open-source models. This may be especially relevant for systems-critical tool call capabilities (i.e. safety).

For model trainers, training efforts currently devoted to SFT and RLHF for structured tool calls may be better directed toward natural-language approaches. This is less clear, as there may be cross-training effects.

One of the authors here, happy to answer any questions about experimental design, implementation, or discuss implications! What do you think?

--- TOP COMMENTS --- Great work. We use tool calling and json structured output extensively, and have seen examples where natural language queries (via ChatGPT) outperform the same taks when presented as structured outputs.

I got so sick of begging the LLM for a rigorous output format that structured outputs felt like a safe haven, although even then some of our more complex use cases surfaced examples where we still get json schema violations from the model (gpt4o). To the extent that we validate returned json and requery if necessary, increasing temperature and adding a random nonce to the prompt to bypass caching.

Will definitely be checking this out!

How do you pass parameters to the tools?

4m ago

Developer And Technical

/r/LocalLLaMA

Qwen3-30B-A3B FP8 on RTX Pro 6000 blackwell with vllm

Power limit set to 450w

Short Context (1K tokens):

Single user: 88.4 tok/s
10 concurrent users: 652 tok/s throughput
Latency: 5.65s → 7.65s (1→10 users)

Long Context (256K tokens):

Single user: 22.0 tok/s
10 concurrent users: 115.5 tok/s throughput
Latency: 22.7s → 43.2s (1→10 users)
Still able to handle 10 concurrent requests!

Sweet Spot (32K-64K context):

64K @ 10 users: 311 tok/s total, 31 tok/s per user
32K @ 10 users: 413 tok/s total, 41 tok/s per user
Best balance of context length and throughput

FP8 quantization really shines here - getting 115 tok/s aggregate at 256K context with 10 users is wild, even with the power constraint.

https://preview.redd.it/x9t4ttsvrgvf1.png?width=7590&format=png&auto=webp&s=0c86bf3cc42032a595ee4d02b2c78986da150836

--- TOP COMMENTS --- wow 10 users can run it off one blackwell 6000. first numbers i’ve seen for multi users. that’s a big deal for small and medium businesses. great value imo

You need to go read up on LACT immediately brother, and then apply the below config. On my RTX PRO 6000 Blackwell workstation cards, they run at ~280 watts, faster than STOCK settings and 600 watts from Nvidia.

UNDERVOLTING is king. Here is your LACT Config:

version: 5
daemon:
  log_level: info
  admin_group: sudo
  disable_clocks_cleanup: false
apply_settings_timer: 5
gpus:
  '10DE:2BB1-10DE:204B-0000:c1:00.0':
    fan_control_enabled: true
    fan_control_settings:
      mode: curve
      static_speed: 0.5
      temperature_key: edge
      interval_ms: 500
      curve:
        40: 0.30
        50: 0.40
        60: 0.55
        70: 0.70
        80: 0.90
      spindown_delay_ms: 3000
      change_threshold: 2
      auto_threshold: 40
    power_cap: 600.0
    min_core_clock: 210
    max_core_clock: 2600
    gpu_clock_offsets:
      0: 1000
    mem_clock_offsets:
      0: 4000

17h ago

Developer And Technical Hardware And Infrastructure

/r/ClaudeAI

Has Claude changed? It used to feel natural — now it’s stiff and overcomplicated

I’ve seen a lot of posts lately about people saying that the AI models have changed, and honestly, I used to think they were exaggerating.

I’ve been using Claude for about a year and a half — it’s been my favorite model for a long time. I liked it because it felt very natural, aligned with my writing style, and gave great support when drafting content.

But over the past few weeks, something feels off. Since Sonnet 4 (or maybe 4.5), the responses have become noticeably less natural and less “present.” It often produces long, overcomplicated text even when I explicitly ask for something concise. I find myself rewriting a lot more than before.

Out of curiosity, I tried GPT-5, and honestly, it gave me the kind of output I used to expect from Claude — more fluid, clear, and human-sounding.

I know this isn’t scientific feedback, but after using Claude daily for months, the shift is very noticeable.

Has anyone else felt the same change lately? Or is it just me?

--- TOP COMMENTS --- I had a similar problem when GPT switched from 4o to 5. I think people get so implicitly entrained to workflows that they can't functionally separate themselves to reorient when the product changes. I'm really enjoying Sonnet 4.5 compared to Sonnet 4. It seems to have more breadth. It's shrewder, less tolerant of BS. (There are bad parts too.)

It seems like whenever some people are saying "this sucks!", others are saying "actually I think this is better" and that suggests that it comes down to usage style and headspace as much as the products themselves.

This happened to me today. Was rate limited so didn't have access for a few days but today when I got back, it's just dumb as fuck. Doesn't even understand things that it normally doesn't have issues with normally.

Yesterday

Developer And Technical

Companies And Business

/r/ClaudeAI

306

I have to compliment anthropic: a good move to cut costs within months

Anthropic's recent moves are not about innovation, but a calculated playbook to cut operational costs at the expense of its paying users. Here's a breakdown of their strategy from May to October:

The Goal: Cut Costs. The core objective was to shift users off the powerful but expensive Opus model, which costs roughly 5x more to run than Sonnet.
The Bait-and-Switch: They introduced "Sonnet 4.5," marketing it as a significant upgrade. In reality, its capabilities are merely comparable to the previous top-tier model, Opus 4.1, not a true step forward. This made it a "cheaper Opus" in disguise.
The Forced Migration: To ensure the user transition, they simultaneously slashed the usage limits for Opus. This combination effectively strong-armed users into adopting Sonnet 4.5 as their new primary model.
The Illusion of Value: Users quickly discovered that their new message allowance on Sonnet 4.5 was almost identical to their previous allowance on the far more costly Opus. This was a clear downgrade in value, especially considering the old Sonnet 4 had virtually unlimited usage for premium subscribers.
The Distraction Tactic: Facing user backlash, Anthropic offered a "consolation prize"—a new, even weaker model touted as an "upgrade" with Sonnet 4's capability but 3x the usage of Sonnet 4.5. This is a classic move to placate angry customers with quantity over quality.

Conclusion: Over four to five months, Anthropic masterfully executed a cost-cutting campaign disguised as a product evolution. Users received zero net improvement in AI capability, while Anthropic successfully offloaded them onto a significantly cheaper infrastructure, pocketing the difference.

--- TOP COMMENTS --- I honestly see this really hurting both paid users and free users because paid users are getting screwed by the limits like they're hitting conversation limits like no tomorrow and free users are stuck on the worst model possible with haiku like this is terrible for both.

My experience so far is Sonnet 4.5 isn't quite as good as Opus 4.1, but I'm happy for close enough if it means the company is more sustainable and Claude Code doesn't go away!

22h ago

Companies And Business

/r/OpenAI

106

OpenAI would have to spend over $1 trillion to deliver its promised computing power. It may not have the cash.

OpenAI (OPAI.PVT) would have to spend more than $1 trillion within the next five years to deliver the massive amount of computing power it has promised to deploy through partnerships with chipmakers Nvidia (NVDA), Broadcom (AVGO), and Advanced Micro Devices (AMD), according to Citi analysts.

OpenAI's latest deals with the three companies include an ambitious promise to deliver 26 gigawatts worth of computing capacity using their chips, which is nearly the amount of power required to provide electricity to the entire state of New York during peak summer demand.

Citi estimates that it takes $50 billion in spending on computing hardware, energy infrastructure, and data center construction to bring one gigawatt of compute capacity online.

Using that assumption, Citi analyst Chris Danely said in a note to clients this week that OpenAI's capital expenditures would hit $1.3 trillion by 2030.

OpenAI CEO Sam Altman has reportedly floated bolder promises internally. The Information reported in late September that the executive has suggested the company is looking to deploy 250 gigawatts of computing capacity by 2033, implying a cost of $12.5 trillion.

But there's no guarantee that OpenAI will have the capital to support the costs required to achieve its goals.

--- TOP COMMENTS --- I hate headlines like this.

"OpenAI would have to spend over $1 trillion to deliver its promised computing power."

According to who? Some Yahoo writer or maybe some analyst he's quoting from a conference?

New powerplants, and new factories manufacturing chips and other hardware are being made everyday to accommodate OpenAI. I don't believe using existing figures for expenses is going to provide an accurate overview of what is to come.

That’s why they’re doing porn

12h ago

Companies And Business

Tutorials And Guides

Industry Adoption

/r/ArtificialInteligence

The Void at the Center of AI Adoption

Companies are adding AI everywhere — except where it matters most.

If you were to draw an organization chart of a modern company embracing AI, you’d probably notice something strange:
a massive void right in the middle.

The fragmented present

Today’s companies are built as a patchwork of disconnected systems — ERP, eCommerce, CRM, accounting, scheduling, HR, support, logistics — each operating in its own silo.

Every software vendor now promises AI integration: a chatbot here, a forecasting tool there, an automated report generator somewhere else.

Each department gets a shiny new “AI feature” designed to optimize its local efficiency.

But what this really creates is a growing collection of AI islands. Intelligence is being added everywhere, but it’s not connected.

The result? The same operational fragmentation, just with fancier labels.

The missing layer — an AI nerve center

What’s missing is the AI layer that thinks across systems — something that can see, decide, and act at a higher level than any single platform.

In biological terms, it’s like giving every organ its own mini-brain, but never connecting them through a central nervous system. The heart, lungs, and limbs each get smarter, but the body as a whole can’t coordinate.

Imagine instead a digital “operations brain” that could:

Access data from all internal systems (with permissions).
Label and understand that data semantically.
Trigger workflows in ERP or CRM systems.
Monitor outcomes and adjust behavior automatically.
Manage other AI agents — assigning tasks, monitoring performance, and improving prompts.

This kind of meta-agent infrastructure — the Boss of Operations Systems, so to speak — is what’s truly missing in today’s AI adoption landscape.

#Human org chart vs AI org chart

Let’s imagine two organization charts side by side.

Human-centric organization

A traditional org chart scales by adding people.
Roles are grouped aroundthemes or departments— Marketing, Sales, HR, Finance, Operations.
Each role is broad: one person might handle several business processes, balancing priorities and communicating between systems manually.

As the business grows, headcount rises.
Coordination layers multiply — managers, team leads, assistants — until communication becomes the bottleneck.

AI-centric organization

Now, draw an AI org chart.
Here, the structure scales not by people but byprocesses.
Each business process — scheduling, invoicing, payroll, support triage, recruitment, analytics — might haveone or two specialized AI agents.

Each agent is trained, prompted, and equipped with access to the data and systems it needs to complete that specific workflow autonomously.

When the business doubles in size, the agents don’t multiply linearly — they replicate and scale automatically.
Instead of a hierarchy, you get anetwork of interoperable agents coordinated by a central control layer — an “AI operations brain” that ensures data flow, compliance, and task distribution.

This model doesn’t just replace humans with AI. It changes how companies grow. Instead of managing people, you’re managing intelligence.

Why this void exists

This central layer doesn’t exist yet for one simple reason: incentives.

Every SaaS vendor wants AI to live inside their platform. Their business model depends on owning the data, the interface, and the workflow. They have no interest in enabling a higher-level system that could coordinate between them.

The result is an AI landscape where every tool becomes smarter in isolation — yet the overall organization remains dumb.

We’re optimizing the parts, but not the system.

The next layer of AI infrastructure

The next wave of AI adoption won’t be about automating tasks inside existing platforms — it’ll be about connecting the intelligence between them.

Companies will need AI agents that can:

Read and write across APIs and databases.
Understand human objectives, not just commands.
Coordinate reasoning across workflows.
Explain their actions for audit and compliance.

Essentially, an AI operating system for organizations — one that finally closes the gap between fragmented SaaS tools and unified, intelligent operations.

The opportunity

This “void” in the middle of the AI adoption curve is also the next trillion-dollar opportunity.
Whoever builds the connective tissue — the platform that lets agents reason across data silos and act with context — will define the future of how businesses run.

Right now, companies have thousands of AI-enhanced tools.
What they lack is theAI that manages the tools.

The age of intelligent organizations won’t begin with another plugin or chatbot.
It’ll begin when the center of the org chart stops being empty.

--- TOP COMMENTS --- Hey look, its AI talking about exactly what I am building!

Lmao AI talking about AI.

6h ago

Industry Adoption Applications And Tools

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The Problem

Method: Natural Language Tools (NLT)

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Companies And Business

"OpenAI would have to spend over $1 trillion to deliver its promised computing power."

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Industry Adoption

Companies are adding AI everywhere — except where it matters most.

The fragmented present

The missing layer — an AI nerve center

#Human org chart vs AI org chart

Human-centric organization

AI-centric organization

Why this void exists

The next layer of AI infrastructure

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