Amaresh Tripathy
Co-Founder and Managing Partner

Arvind Narayanan is the the AI critic you may want to follow for two reasons:

01. He is not trying to be sensational by taking extreme positions

02. He is a first rate academic

His work tries to define the boundaries of what can be useful and where you have to be careful in a pragmatic way.

He has published a solid paper outlining these boundaries in legal space.

01. Information processing tasks have high clarity and high observability and are best use cases to start (categorizing requests for legal help, e-discovery)

02. Creativity, reasoning tasks are a range (spotting errors in legal filings are easier, preparing legal filings harder)

03. Predictive tasks are fraught with challenges (legal judgment predictions)

Lots of legal departments are evaluating Generative AI - and this is a good paper for them. In fact, we are doing some solid value accretive work in contract extraction space - that helps turn unstructured data into structured information for downstream applications. And our experience has been consistent with these observations.

AI impact to software engineering jobs

Anecdotes are great but what does the data say?
Let us see some interesting insights that come out of analyzing 20M job postings over 16 month...<br>

Read More
The Data Platforms Battle

The enterprise data stack is going through an inflection point and the battle lines are being redrawn. This time, to no surprise, the AI agenda in Enterprise going to determine the winners and losers...

Read More