Product Management is not about: - Asking customers about the requirements. - Writing detailed specifications. - Creating prototypes and wireframes. - Assigning tasks to developers. - Verifying and accepting the work of others. (1/10)

Interesting conversation today about protecting training data and whether or not data stored in the cloud is truly private when in transit within a 3rd party data center. #ai #training #privacy

If you're building models for AI it's important to note that acquiring and integrating proprietary data streams for training is critically important to build a MOAT for startup.

Agentics = Fancy terms for orchestrating very powerful bots to even more powerful outcomes than cron jobs.

I explain this to founders - Any decent piece of software takes 2 years to build to the point where customers can actually use it. Why? First of all business complexity - modern workflows are complex and sophisticated and to capture them correctly, to reflect the nuances takes

Being a purpose driven founder for me is key. It just astounding to see the trends in global eye care impairment. WHO specifically calls out “Integrated People Centered Eye Care (IPCEC)” and adopting Ai based based technology is core to success. ncbi.nlm.nih.gov/pmc/articles/P…

Love this. 100% believe this - As smaller companies modernize using a new AI-native stack of technology, they no longer need to grow their team to scale their ambition. - implications.com/p/the-era-of-s…

Most people think self care is watching Netflix, bubble baths, and overall hedonistic behavior. When it’s really going to sleep on time, working out daily, and eating a healthy diet.

Let fires burn

At Llama Lounge, the event series I run, 10 startups are selected to demo. 3 slots min are reserved for Women AI founders, often we're at parity. Our data indicates only 5-10% of submission are Women founders. We seek more Women founders to apply. We proactively reach out.

It's crazy to think that the immense amount of money required to build an LLM like OpenAi is eerily similar to the cost of IBM building the earliest mainframes. The caveat here is that AI itself will lower the cost of compute as a new field of spatial Ai emerges.

The main challenge with Consumer is moats. AI 🤖 doesn’t necessarily solve that. The key challenge is finding data lock-ins that make switching hard. B2B is a lot more natural at that because businesses have a lot of data to compute with.

How isn't important in the beginning - but what and why is more important as there are many paths to the same outcome

Some key takeaways: 1. Get comfortable with looking dumb, especially in public. The best way to learn is by asking dumb questions, trying new things, and failing in front of others. You’ll uncover truths that others won’t, and more often than not, you’re not alone in having

Wondering how to actually use AI in your day to day? Our team a16z asked some of AI's biggest names to nominate their favorite products of 2024 - this is a great starting point 👀 Here are their picks! ⬇️ For general AI assistants: - Perplexity (Perplexity) - AI search

This is the same mental model I’ve been using for the new companies I’m building The “how” has increasingly become a commodity. They why, the story telling, and ability to break through the noise is harder than ever.