AI Serving in Unsuspecting Roles
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Granting new feature requests, previously the domain of technology vendors, may change dramatically with the advent of AGI. Here's a simple example.
Stephen Orr requested a new feature similar to the ALL() formula in Excel. I’ve seen many of his posts, and he’s sharp, so I watch his posts closely.
A formula that returns a comma separated list of all values across records in a view for a given field:
ALL({some field})
This is a good feature request. I made a similar request in 2018 for Split(). That was 2,160 days ago. Still waiting. I hope Stephen is more successful in getting Airtable’s attention.
What he’s asking for is tantamount to a Parquet data format, which is one of the fastest file types to read generally and much faster than either JSON or CSV. It’s like a pivot table - fields are listed down the data structure, and columns are represented as arrays for each field. Stephen only wants a single column’s values as an array, and Airtable should accommodate this.
The advantages of a Parquet data format are massive, and it’s no coincidence that Pandas data frames are fundamentally able to read and write this format. It puts the science in data science for these reasons:
Compression
Performance
Schema evolution
Open source/non-proprietary
Enough about Parquet. Here’s why it’s relevant to Stephen’s feature request.
Given a table like this, he wants a column transformed into an array.
"Name": ["Tesla", "CNN", "Airtable", "SalesForce", "IBM"],
"Stock Ticker": ["TSLA", "CNN", "ATB", "CRM", "IBM"],
"# Employees": [70757, 4000, 1000, 100000, 352000],
"Market Cap": [750000000000, 10500000000, 1050000000, 260000000000, 118000000000]
How’d I get this? AI.
By simply transforming the Airtable table into Markdown, I had the Parquet table in a few seconds.
Taking this to the next level, Stephen’s data is now within reach.
As you may have experienced, pushing data into an LLM and asking for discrete values with accuracy is difficult. With Parquet, there are some advantages.
Revisiting Split()
I no longer want Split(). I want a feature to perform inline inferencing against a blistering fast LLM such as PaLM 2.
Let’s take the list of values we created for Stephen’s case and parse it into an actual array.
The Feature to End all Features?
Certainly, there are many ways to extract these values, either through a script or formula if Airtable gets around to it. This article is not about any particular methodology for managing or working with data. Rather, it’s about the possible future of platform features.
This example demonstrates that vendors should think carefully about how to implement new features because it may be possible to end the feature request madness by simply adding one feature - ai().
Can it be that simple? Hardly. However, developers and platform vendors should consider how AGI will transform architectures in the future.
