AI News – Global

The co-founders of Anthropic discuss the past, present, and future of Anthropic. From left to right: Chris Olah, Jack Clark, Daniela Amodei, Sam McCandlish, Tom Brown, Dario Amodei, and Jared Kaplan.

Links and further reading:
Anthropic’s Responsible Scaling Policy (RSP): https://www.anthropic.com/news/announcing-our-updated-responsible-scaling-policy
Machines of Loving Grace: https://darioamodei.com/machines-of-loving-grace
Work with us: https://anthropic.com/careers
Claude: https://claude.com

00:00 Why work on AI?
02:08 Scaling breakthroughs
03:30 Early days of AI
10:57 Sentiment shifting
18:30 The Responsible Scaling Policy
30:42 Founding story
32:45 Building a culture of trust
39:08 Racing to the top
43:43 Looking to the future

Bryant Chou co-founded Webflow, which today powers around 1% of all websites on the internet. Now he’s back in the current YC batch with Ploy, an AI-powered website and marketing platform that doesn’t just build your site — it connects to your analytics, CRM, and search console to optimize your marketing while you sleep. In this episode of the Lightcone he explains how he built Ploy to be “anti slop,” how building today compares to his first startup, and why founders with domain expertise are making a comeback.

Chapters:
00:00 — What Experience Gives You in the Age of AI
00:38 — Meet Bryant Chou, Co-Founder of Webflow
01:22 — His New Startup Ploy
02:47 — Rebuilding the Posterous website From 2008
03:27 — Rebuilding the Scribd website From 2007
05:04 — Rebuilding the Auctomatic website From 2007
06:19 — Rebuilding the Escher Reality website From 2017
07:11 — 12% of the YC Batch Uses Ploy
08:26 — The D&D Theory of Founder Skills
10:05 — Democratizing Marketing and Growth
10:50 — Live Demo: The Design Slurper
13:21 — Your Website Should Work for You While You Sleep
14:26 — Integrations, Analytics, and the Marketing Brain
17:27 — Ploy’s Anti-Slop Engine: 3,500 Curated Design Prompts
20:05 — The Andy Warhol Theory of AI
22:35 — Webflow Origin Story
24:26 — Building in a Competitive Market Then vs. Now
26:01 — First Three Months: Webflow 2013 vs. Ploy 2025
27:17 — What Experience Teaches You That Models Can’t
28:51 — Will Better Models Kill Products Like Ploy?
30:32 — The Competitive Moat of Purpose-Built AI
33:01 — Agents as Customers: CLI, MCP, and AEO
35:02 — Young Founders vs. Experienced Founders
36:36 — The Idea Maze and Cloning Yourself With AI
42:37 — The Magnifying Glass Moment

Apply to Y Combinator: https://www.ycombinator.com/apply
Work at a startup: https://www.ycombinator.com/jobs

Buy the AI Engineering book here to continue your learning! https://amzn.to/42kjXb2

All images are from the book AI Engineering unless otherwise credited.

⏰ Timestamps
00:00 What is AI Engineering?
01:49 Understanding Foundation Models
08:40 Evaluating AI Models
14:50 Model Selection
23:15 Prompt Engineering
30:20 RAG and Context Construction
36:56 Agents and Memory Systems
43:02 Finetuning
52:40 Dataset Engineering
59:45 Inference Optimization
01:09:01 Architecture and User Feedback
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Here’s a list of my favorite free AI/ML courses! https://www.gratitudedriven.com/free-aiml-courses

🚀 Want to level up your AI/ML career? Join the AI/ML Career Launchpad community! https://aiml-career-launchpad.circle.so/aiml-launchpad

💬 Interested in working with me 1:1? Learn more about my strategic advisory sessions: https://www.marinawyss.com/coaching

🩷 Join the channel membership community for priority comment replies and early access to videos! https://www.youtube.com/@MarinaWyssAI/join

And here’s that StatQuest transformers video I promised:
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🎥 Other videos you might like:

Doubting if You Can Become a Machine Learning Engineer or Data Scientist? The Honest Truth.

I Analyzed 1,238 Machine Learning Job Postings to Find Out What Skills You REALLY Need to Get Hired

Beginner to (Employed) Data Scientist in 2025: Complete Roadmap – Skills, Projects, CV, Interviews

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🦫 About me
I am an Applied Scientist (basically, a blend of Data Scientist/Machine Learning Engineer) at Twitch/Amazon. Outside of my full-time job I’m a technical mentor at a machine learning bootcamp, and a 1:1 career coach for people looking to break into the field, with a focus on those from non-traditional backgrounds.

I’m also a Certified Personal Trainer, always busy with too many interests, and really, deeply happy with my life. I hope to be able to help others achieve these things, too.

Instagram: / gratitudedriven
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✉️ Contact
Leave me a comment here on YouTube!
Business email: business@gratitudedriven.com
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⚖️ Disclaimer
The views and opinions expressed in this video are my own and do not reflect the official policy or position of Twitch/Amazon or any other company I have worked for. All advice and insights shared here are based on my personal experiences and should be considered as such.

Also, there may be affiliate links in this description. This means if you make a purchase I’ll earn a small commission at no cost to you! Thank you for your support.

#aiengineering #ai #machinelearning

AI data centers, energy crisis, US vs China tech war, Saudi Arabia AI investment, cloud infrastructure & electricity grid, explained.
This episode exposes why data centers are the biggest bet in the AI race.

I cover:
00:00 Why everyone wants data centers
03:04 Why electricity is becoming the new oil, and why you can’t just make more of it
07:13 How the US grid is hitting a ceiling
11:01 Why China built 500 data centers and 80% sit empty
15:23 Why the Gulf – Saudi Arabia, UAE – is becoming the third player in the AI infrastructure race
19:21 Chinese and American ties with the GCC

🪙 This is Episode 1 of The Business of AI: a series tracing the hidden web of capital, geopolitics, and infrastructure underneath every AI headline. We’re starting at the bottom of the stack.

Every time you use ChatGPT, or Claude, or Gemini, money flows through a stack of cloud providers, GPUs, and data centers. This is a deep dive into the real economics behind AI subscription costs.

Chapters:
00:00 Intro
00:42 Who gets paid when you run an AI query
02:32 Why compute is SO expensive
07:07 The breakdown of your $20 subscription
07:35 How Azure, AWS, and CoreWeave profit on every level
11:11 Why data centers sell power, not space
12:19 Time for delusional startup ideas

🌟Create any visuals for your business with Higgsfield
https://higgsfield.ai/s/mcp-techbutmakeitreal-RZOKvq

Breakdown of the entire semiconductor supply chain, from TSMC and ASML to Japan’s chemical monopolies, Dutch lithography machines, America’s chip software, German optics, to explain how the global semiconductor industry actually works.

Chapters:
0:00 Intro
0:50 EDA Software: America’s Chip Design
1:58 Silicon Wafers: Japan’s Precision Monopoly
3:29 Photoresist: The Chemical Ink of Chips
7:05 Neon Gas and Ukraine’s Role
9:06 ASML: The Dutch Gatekeeper
10:19 Zeiss, Cymer, and US Control over the Dutch
11:33 Why China Can’t Replicate ASML
12:16 Etch, Deposition, and Inspection Equipment
14:15 Photomasks, Pellicles, and Lasertec
15:17: TSMC, Samsung, and the Foundry Race
18:20 Why Chip Fabs Cost More Than Skyscrapers
19:15 BIG Questions

#Semiconductors #TSMC #ASML #AI #Nvidia #China #Taiwan #Geopolitics #TechButMakeItReal #ChipWar #ArtificialIntelligence #Samsung #Intel #EUV #SemiconductorIndustry

The unit economics behind LLM compute, 2% paid users, and 30-60% margins.

This episode breaks down why many AI-native apps (GPT wrappers, agent tools, “AI SaaS”) struggle to become real businesses. I compare classic B2B SaaS (70–90% gross margins) with GenAI’s 30-60%, unpack ongoing per-user costs (API, compute, model licensing, moderation), and explain how price caps, freemium, and “dead subs” distort the picture.

Chapters
00:00 Intro
00:30 Classic SaaS vs AI SaaS margins
01:56 “Dead subscriptions” and AI bundles
03:55 Why AI unit economics break
05:48 All-you-can-eat usage
06:39 The shocking math of ChatGPT
09:02 Claude, Gemini, Copilot
12:15 AI price wars, metering, and profitability paths
13:26 How to build a sustainable AI business
15:12 $1,000,000,000 problem
16:52 Bottom line

The example-driven, practical walkthrough of Large Language Models and their growing list of related features, as a new entry to my general audience series on LLMs. In this more practical followup, I take you through the many ways I use LLMs in my own life.

Chapters
00:00:00 Intro into the growing LLM ecosystem
00:02:54 ChatGPT interaction under the hood
00:13:12 Basic LLM interactions examples
00:18:03 Be aware of the model you’re using, pricing tiers
00:22:54 Thinking models and when to use them
00:31:00 Tool use: internet search
00:42:04 Tool use: deep research
00:50:57 File uploads, adding documents to context
00:59:00 Tool use: python interpreter, messiness of the ecosystem
01:04:35 ChatGPT Advanced Data Analysis, figures, plots
01:09:00 Claude Artifacts, apps, diagrams
01:14:02 Cursor: Composer, writing code
01:22:28 Audio (Speech) Input/Output
01:27:37 Advanced Voice Mode aka true audio inside the model
01:37:09 NotebookLM, podcast generation
01:40:20 Image input, OCR
01:47:02 Image output, DALL-E, Ideogram, etc.
01:49:14 Video input, point and talk on app
01:52:23 Video output, Sora, Veo 2, etc etc.
01:53:29 ChatGPT memory, custom instructions
01:58:38 Custom GPTs
02:06:30 Summary

Links
– Tiktokenizer https://tiktokenizer.vercel.app/
– OpenAI’s ChatGPT https://chatgpt.com/
– Anthropic’s Claude https://claude.ai/
– Google’s Gemini https://gemini.google.com/
– xAI’s Grok https://grok.com/
– Perplexity https://www.perplexity.ai/
– Google’s NotebookLM https://notebooklm.google.com/
– Cursor https://www.cursor.com/
– Histories of Mysteries AI podcast on Spotify https://open.spotify.com/show/3K4LRyMCP44kBbiOziwJjb

– The visualization UI I was using in the video: https://excalidraw.com/
– The specific file of Excalidraw we built up: https://drive.google.com/file/d/1DN3LU3MbKI00udxoS-W5ckCHq99V0Uqs/view?usp=sharing
– Discord channel for Eureka Labs and this video: https://discord.gg/3zy8kqD9Cp

Educational Use Licensing
This video is freely available for educational and internal training purposes. Educators, students, schools, universities, nonprofit institutions, businesses, and individual learners may use this content freely for lessons, courses, internal training, and learning activities, provided they do not engage in commercial resale, redistribution, external commercial use, or modify content to misrepresent its intent.

Andrej Karpathy is a legendary AI researcher, engineer, and educator. He’s the former director of AI at Tesla, a founding member of OpenAI, and an educator at Stanford. Please support this podcast by checking out our sponsors:
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EPISODE LINKS:
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Andrej’s Website: http://karpathy.ai
Andrej’s Google Scholar: http://scholar.google.com/citations?user=l8WuQJgAAAAJ
Books mentioned:
The Vital Question: https://amzn.to/3q0vN6q
Life Ascending: https://amzn.to/3wKIsOE
The Selfish Gene: https://amzn.to/3TCo63s
Contact: https://amzn.to/3W3y5Au
The Cell: https://amzn.to/3W5f6pa

PODCAST INFO:
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Clips playlist: https://www.youtube.com/playlist?list=PLrAXtmErZgOeciFP3CBCIEElOJeitOr41

OUTLINE:
0:00 – Introduction
0:58 – Neural networks
6:01 – Biology
11:32 – Aliens
21:43 – Universe
33:34 – Transformers
41:50 – Language models
52:01 – Bots
58:21 – Google’s LaMDA
1:05:44 – Software 2.0
1:16:44 – Human annotation
1:18:41 – Camera vision
1:23:46 – Tesla’s Data Engine
1:27:56 – Tesla Vision
1:34:26 – Elon Musk
1:39:33 – Autonomous driving
1:44:28 – Leaving Tesla
1:49:55 – Tesla’s Optimus
1:59:01 – ImageNet
2:01:40 – Data
2:11:31 – Day in the life
2:24:47 – Best IDE
2:31:53 – arXiv
2:36:23 – Advice for beginners
2:45:40 – Artificial general intelligence
2:59:00 – Movies
3:04:53 – Future of human civilization
3:09:13 – Book recommendations
3:15:21 – Advice for young people
3:17:12 – Future of machine learning
3:24:00 – Meaning of life

SOCIAL:
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This is a 1 hour general-audience introduction to Large Language Models: the core technical component behind systems like ChatGPT, Claude, and Bard. What they are, where they are headed, comparisons and analogies to present-day operating systems, and some of the security-related challenges of this new computing paradigm.
As of November 2023 (this field moves fast!).

Context: This video is based on the slides of a talk I gave recently at the AI Security Summit. The talk was not recorded but a lot of people came to me after and told me they liked it. Seeing as I had already put in one long weekend of work to make the slides, I decided to just tune them a bit, record this round 2 of the talk and upload it here on YouTube. Pardon the random background, that’s my hotel room during the thanksgiving break.

– Slides as PDF: https://drive.google.com/file/d/1pxx_ZI7O-Nwl7ZLNk5hI3WzAsTLwvNU7/view?usp=share_link (42MB)
– Slides. as Keynote: https://drive.google.com/file/d/1FPUpFMiCkMRKPFjhi9MAhby68MHVqe8u/view?usp=share_link (140MB)

Few things I wish I said (I’ll add items here as they come up):
– The dreams and hallucinations do not get fixed with finetuning. Finetuning just “directs” the dreams into “helpful assistant dreams”. Always be careful with what LLMs tell you, especially if they are telling you something from memory alone. That said, similar to a human, if the LLM used browsing or retrieval and the answer made its way into the “working memory” of its context window, you can trust the LLM a bit more to process that information into the final answer. But TLDR right now, do not trust what LLMs say or do. For example, in the tools section, I’d always recommend double-checking the math/code the LLM did.
– How does the LLM use a tool like the browser? It emits special words, e.g. |BROWSER|. When the code “above” that is inferencing the LLM detects these words it captures the output that follows, sends it off to a tool, comes back with the result and continues the generation. How does the LLM know to emit these special words? Finetuning datasets teach it how and when to browse, by example. And/or the instructions for tool use can also be automatically placed in the context window (in the “system message”).
– You might also enjoy my 2015 blog post “Unreasonable Effectiveness of Recurrent Neural Networks”. The way we obtain base models today is pretty much identical on a high level, except the RNN is swapped for a Transformer. http://karpathy.github.io/2015/05/21/rnn-effectiveness/
– What is in the run.c file? A bit more full-featured 1000-line version hre: https://github.com/karpathy/llama2.c/blob/master/run.c

Chapters:
Part 1: LLMs
00:00:00 Intro: Large Language Model (LLM) talk
00:00:20 LLM Inference
00:04:17 LLM Training
00:08:58 LLM dreams
00:11:22 How do they work?
00:14:14 Finetuning into an Assistant
00:17:52 Summary so far
00:21:05 Appendix: Comparisons, Labeling docs, RLHF, Synthetic data, Leaderboard
Part 2: Future of LLMs
00:25:43 LLM Scaling Laws
00:27:43 Tool Use (Browser, Calculator, Interpreter, DALL-E)
00:33:32 Multimodality (Vision, Audio)
00:35:00 Thinking, System 1/2
00:38:02 Self-improvement, LLM AlphaGo
00:40:45 LLM Customization, GPTs store
00:42:15 LLM OS
Part 3: LLM Security
00:45:43 LLM Security Intro
00:46:14 Jailbreaks
00:51:30 Prompt Injection
00:56:23 Data poisoning
00:58:37 LLM Security conclusions
End
00:59:23 Outro

Educational Use Licensing
This video is freely available for educational and internal training purposes. Educators, students, schools, universities, nonprofit institutions, businesses, and individual learners may use this content freely for lessons, courses, internal training, and learning activities, provided they do not engage in commercial resale, redistribution, external commercial use, or modify content to misrepresent its intent.