AI Open Labs Consulting

This week’s edition: “Inside Google I/O 2025 – The Rise of AI Operators”.

Google just unveiled the most ambitious AI ecosystem yet — and it’s not about flashy tools.
It’s about creating an AI that sees, reasons, acts, and collaborates across devices, apps, and workflows.

Google I/O 2025: Building the AI Operating System of the Future Google I/O 2025: The Dawn of AI Operators. Google I/O 2025 took place May 20-21, 2025, at the Shoreline Amphitheatre in California.

https://www.linkedin.com/pulse/xais-mind-blowing-grok-3-ashish-shah-feylf

xAI’s Mind Blowing Grok 3 Elon Musk’s xAI has officially launched Grok 3, marking a major leap in the company’s AI capabilities. The latest version of the chatbot is said to offer superior reasoning and advanced contextual understanding, making it a strong competitor to existing AI models like OpenAI’s ChatGPT and Goog...

https://www.linkedin.com/posts/ash-crm-shah_openai-have-shared-their-roadmap-towards-activity-7296665554675785728-ql-X?utm_source=share&utm_medium=member_desktop&rcm=ACoAAACTkIgBMuQ1H_yzRqOFolgL-BvRM-QEtD8

Ashish Shah on LinkedIn: OpenAI have shared their roadmap towards a single unified GPT-5 model. Key… OpenAI have shared their roadmap towards a single unified GPT-5 model. Key highlights: - GPT-4.5 will come within "weeks". It will be their last non-reasoning model (like GPT-4o) - o3 will not ship as a standalone model - Instead, they will launch GPT-5 in "months", which will be...

OpenAI announces ChatGPT Gov

today we're announcing ChatGPT Gov, a version of ChatGPT that government agencies can deploy in their own MS Azure commercial or government cloud environment.

Enabling the public sector, especially the U.S. Federal government, to leverage ChatGPT is critical to maintaining America’s global leadership in AI. We see enormous potential for these tools to support the public sector in tackling complex challenges—from improving public health and infrastructure to strengthening national security 🇺🇸

https://www.linkedin.com/feed/update/urn:li:activity:7289890160379371521/

Ashish Shah on LinkedIn: Finally had a chance to dig into Deep seek's *Let me break down why… Finally had a chance to dig into Deep seek's *Let me break down why DeepSeek's AI innovations are blowing people's minds (and possibly threatening Nvidia's…

Finally had a chance to dig into Deep seek's
*Let me break down why DeepSeek's AI innovations are blowing people's minds (and possibly threatening Nvidia's $2T market cap) in simple terms...*
First, some context: Right now, training top AI models is INSANELY expensive. OpenAI, Anthropic, etc. spend $100M+ just on compute. They need massive data centers with thousands of $40K GPUs. It's like needing a whole power plant to run a factory.
*DeepSeek just showed up and said "LOL what if we did this for $5M instead?" And they didn't just talk - they actually DID it.* Their models match or beat GPT-4 and Claude on many tasks. The AI world is (as my teenagers say) shook.
How? They rethought everything from the ground up. Traditional AI is like writing every number with 32 decimal places. DeepSeek was like "what if we just used 8? It's still accurate enough!" Boom - 75% less memory needed.
Then there's their "multi-token" system. Normal AI reads like a first-grader: "The... cat... sat..." DeepSeek reads in whole phrases at once. 2x faster, 90% as accurate. When you're processing billions of words, this MATTERS.
But here's the really clever bit: They built an "expert system." Instead of one massive AI trying to know everything (like having one person be a doctor, lawyer, AND engineer), they have specialized experts that only wake up when needed.
Traditional models? All 1.8 trillion parameters active ALL THE TIME. DeepSeek? 671B total but only 37B active at once. It's like having a huge team but only calling in the experts you actually need for each task.
The results are mind-blowing:
- Training cost: $100M → $5M
- GPUs needed: 100,000 → 2,000
- API costs: 95% cheaper
- Can run on gaming GPUs instead of data center hardware
"But wait," you might say, "there must be a catch!" That's the wild part - it's all open source. Anyone can check their work. The code is public. The technical papers explain everything. It's not magic, just incredibly clever engineering.
*Why does this matter? Because it breaks the model of "only huge tech companies can play in AI." You don't need a billion-dollar data center anymore. A few good GPUs might do it.*
*For Nvidia, this is scary. Their entire business model is built on selling super expensive GPUs with 90% margins. If everyone can suddenly do AI with regular gaming GPUs... well, you see the problem.*
And here's the kicker: DeepSeek did this with a team of