Within data teams, a tension exists. Centralize the data analysis to ensure accuracy or enable end-users to analyze their own data directly which is faster & more direct.
The pendulum between these two states started with centralization during the 2000s with BI products from Microstrategy, Cognos, BusinessObjects, & Hyperion. In 2004, Tableau emerged from the Stanford campus to deliver their application to the users.
If you were to watch three videos on YouTube Shorts - one on Italian cooking, one on chess openings, & a third on crypto trading, YouTube Shorts’ recommendation algorithm combines the video descriptions with your dwell time.
Watching the osso bucco video to its end would trigger more Italian cooking specialty videos in your feed.
We believe every LLM-based application will need this capability.
Combining text & structured data in an LLM workflow the right way is difficult. It requires a new software infrastructure layer: a vector computer.
The database is being unbundled. Historically, a database like Snowflake sold both data storage & a query engine (& the computing power to execute the query). That’s step 1 above.
But, customers are pushing for a deeper separation of compute & storage. The recent Snowflake earnings call highlighted the trend. Larger customers prefer open formats for interoperability (step 2 & 3).
For those of us who love logic, the paradoxical title of this post should catch your eye, just as it did mine.
In Alchemy, the founder of a major brand agency describes the way many of the major consumer companies in the world created brands.
We call it breaking out : a double entendre which means both growing faster than competitors but also in a different way than their competition.
Rory Sutherland, the author explains it this way :
On March 15th at 9:30 Pacific time, Office Hours will host Colin Zima, CEO of Omni Analytics.
Colin is no stranger to business intelligence & data analysis. He worked on search quality at Google, founded a dynamic pricing company for the restaurant industry, then ran data at a HotelTonight before becoming Chief Analytics Officer at Looker through its acquisition by Google.
In the past, the bigger the AI model, the better the performance. Across OpenAI’s models for example, parameters have grown by 1000x+ & performance has nearly tripled.
| OpenAI Model | Release Date | Parameters, B | MMLU |
|---|---|---|---|
| GPT2 | 2/14/19 | 1.5 | 0.324 |
| GPT3 | 6/11/20 | 175 | 0.539 |
| GPT3.5 | 3/15/22 | 175 | 0.7 |
| GPT4 | 3/14/23 | 1760 | 0.864 |
But model performance will soon asymptote - at least on this metric.
Every successful startup faces critical marketing decisions from day one. How do you establish your value proposition? Which customers should you target first? What channels deliver the best ROI with limited resources?
During a recent session with a former executive Kady Srinivasan from Lightspeed Commerce, Dropbox, and Klaviyo, several powerful insights emerged on how early-stage companies should approach marketing. Here are three key takeaways:
The most fundamental decision for any startup is determining your Ideal Customer Profile (ICP). Without clarity on exactly who you’re targeting, marketing efforts become diluted and ineffective.
Last week, Reddit filed their S-1 to go public. At least 10% of their revenue - about $60m - comes from selling data to train Large Language Models. Reddit’s data sales revenue will likely be much more than 10% by the end of the year. Quoting directly :
We expect our growing data advantage and intellectual property to continue to be a key element in the training of future LLMs.
This raises a fundamental question : What if the revenue from data sales dwarfs the revenue from ads?
The Community Source licenses may be the future of open source software.
With closed-source software, the code isn’t shared outside the company that wrote it. Open-source code is freely available to examine & use. Open-source companies like Elastic ($13b), Confluent ($10b), MongoDB ($33b) have been phenomenally successful.
Palo Alto Networks, the largest security software company in the world, worth roughly $82b, announced earnings this week.
There are three interesting themes :
They have developed a $100m ARR security line in a few quarters :