OpenAI Data Connectors Fall Short
Key Points
- OpenAI launched new data connectors, adding integrations like GitHub, Linear, Zapier, Gmail, Outlook, SharePoint, and Google Calendar to compete with Claude’s similar tools.
- The company warns that these connectors are not meant for deep research or extensive analysis of large personal datasets such as Google Drive spreadsheets.
- In testing, the connectors struggled with complex queries, often providing only vague or approximate results rather than precise counts or detailed insights.
- A technical limitation caps API results to 15 items per request, making comprehensive tasks—like analyzing hundreds of emails, calendar events, or documents—practically impossible.
Sections
- OpenAI Launches Data Connectors with Limits - OpenAI introduced new data connectors for services like GitHub, Gmail, and Google Calendar, enabling cross‑account search but warning that the feature struggles with deep, local‑data queries such as complex spreadsheet analysis.
- AI Shines on Narrow Topic Briefings - The speaker explains that the system fails with broad email searches but delivers strong, comprehensive briefings when given a specific, time‑bound keyword that links limited data from Gmail, Calendar, Docs, and the web.
- OpenAI Enterprise OS Progress - The speaker critiques OpenAI's current enterprise OS capabilities, notes early Operator's failures, then praises the 03‑model upgrade that makes Operator fast and accurate enough for real tasks like flight planning.
- Refine Query Precision for Data Insights - The speaker reflects on a failed attempt to extract email and calendar patterns, emphasizing the need to craft more precise, well‑structured queries to effectively use low‑bandwidth data connectors.
Full Transcript
# OpenAI Data Connectors Fall Short **Source:** [https://www.youtube.com/watch?v=UvC33D6lwRI](https://www.youtube.com/watch?v=UvC33D6lwRI) **Duration:** 00:11:40 ## Summary - OpenAI launched new data connectors, adding integrations like GitHub, Linear, Zapier, Gmail, Outlook, SharePoint, and Google Calendar to compete with Claude’s similar tools. - The company warns that these connectors are not meant for deep research or extensive analysis of large personal datasets such as Google Drive spreadsheets. - In testing, the connectors struggled with complex queries, often providing only vague or approximate results rather than precise counts or detailed insights. - A technical limitation caps API results to 15 items per request, making comprehensive tasks—like analyzing hundreds of emails, calendar events, or documents—practically impossible. ## Sections - [00:00:00](https://www.youtube.com/watch?v=UvC33D6lwRI&t=0s) **OpenAI Launches Data Connectors with Limits** - OpenAI introduced new data connectors for services like GitHub, Gmail, and Google Calendar, enabling cross‑account search but warning that the feature struggles with deep, local‑data queries such as complex spreadsheet analysis. - [00:03:16](https://www.youtube.com/watch?v=UvC33D6lwRI&t=196s) **AI Shines on Narrow Topic Briefings** - The speaker explains that the system fails with broad email searches but delivers strong, comprehensive briefings when given a specific, time‑bound keyword that links limited data from Gmail, Calendar, Docs, and the web. - [00:06:53](https://www.youtube.com/watch?v=UvC33D6lwRI&t=413s) **OpenAI Enterprise OS Progress** - The speaker critiques OpenAI's current enterprise OS capabilities, notes early Operator's failures, then praises the 03‑model upgrade that makes Operator fast and accurate enough for real tasks like flight planning. - [00:10:25](https://www.youtube.com/watch?v=UvC33D6lwRI&t=625s) **Refine Query Precision for Data Insights** - The speaker reflects on a failed attempt to extract email and calendar patterns, emphasizing the need to craft more precise, well‑structured queries to effectively use low‑bandwidth data connectors. ## Full Transcript
Open AAI released data connectors
yesterday. Data connectors are basically
OpenAI's answer to Claude connecting you
to Gmail and Claude connecting you to
calendar etc. being open AI, uh they
added a bunch more that Claude had not
previously added because of course this
is a competitive arms race. And so they
added GitHub, they added Linear, they
added Zapier, they added a bunch of
things. And then they also of course had
Gmail, they had Outlook, they had
Sharepoint, they had Google Calendar.
Essentially they are saying that you can
now search in your plus teams and pro
account across a lot of the personal
information that you create as you do
work and they are careful to call out to
their credit that this is not a perfect
search mechanism. They specifically call
out this shouldn't be used if you're
searching for example in Google Drive
for doing extended work across uh ma
math on sheets. And so if you're doing a
spreadsheet analysis, deep research is
probably not best positioned for that.
I'm sure they're right. But even when I
gave it several queries, deep
research, that were not designed to test
it against what OpenAI
suggested, it still didn't work. In
other words, I tried to stay away from
the warning spots. I tried to give it
challenging queries similar to what I'd
given deep research in the past when it
only worked on the open web and it
really fell down on local information
and I was actually able because you can
see the chain of thought for deep
research to pull up that chain of
thought during the queries I sent and
pull screenshots of what deep research
said it was doing. And I learned some
fascinating stuff. It turns out that the
API result that it is relying on to get
results from calendar, to get results
from Gmail, tops out at 15. In other
words, if you want to say like you would
to, I don't know, a poor hardworking
executive assistant, please do a
comprehensive analysis of last month,
last month's email volume, cohort it
out. Tell me who I need to be focused
on. Tell me how I can use my time more
efficiently. give me a sense of the
types of emails that I need to respond
to and the ones I don't. It can't do
that. It has access, but because of the
thinness of the data pipe it's working
with, it is absolutely impossible. And
trust me, I tried. I tried to do an
email analysis of the last 100 emails. I
tried to do a calendar analysis of the
last 100 calendar docs. And this is me
being kind. I would have said a thousand
if I could, but I had a feeling. Uh I
tried to do an analysis of the last 100
uh Google Docs that I created. It does
extremely limited searching. I could
find evidence in in my query for a 100
docs. I found evidence of it calling
back three. In my query for a 100
emails, it could not produce exact
counts. It just kind of waved its hand
in the air and gave me approximate
numbers. And as someone who gets the
email every day, I knew that it had
correctly ga guessed categories, but it
had wildly incorrectly guessed numbers.
The numbers it was guessing for whole
groups of my email were completely
off and it just didn't do the groundwork
of actually checking the email even
though the data connector was
there. So it just failed like we just
don't have to call it anything else.
Where did it succeed? You might wonder.
Well, I will say I gave it a specific
topic to look into and it did much
better there. So, if you give it, let's
say you give it a webinar you're
planning or you give it an event that
you want to do, something that has a
defined time focus and you say, "Look
across the web. Look across my email, my
calendar, give me a comprehensive
briefing for just this very tight topic
that's very clearly delineated by
keyword." Then it does pretty well. It
can use that keyword as a guidepost
across Gmail, across calendar, across
the open web, across your Google Docs,
and come back with something that is
actually a decently good comprehensive
briefing. How does it that it do that
well? Because each individual data
source is not going to be a ton of
individual units of data. It's not going
to be more than 15 in many cases. and it
can then assemble that out into
something that's really comprehensive by
inferring and reasoning across all of
them together which 03 does very
well. It also helps if that event has a
public presence because then it can do
what uh deep research does best which is
reason across the entire web at scale.
So to me when I look at this if I step
back I see this in the context of
ongoing
competition both between model makers
between anthropic and open AI which I
laid out at the beginning and also
between model makers and specific
verticals they want to go after. One of
the questions that I got uh as this came
out was is anything safe? Like they keep
going after these verticals. Who's next?
Granola. Did they get eaten? Because one
of the data sources here is that like
teams will now record your
calls. Look, to be honest with you, I
think the common rationale across these
recent moves by both Anthropic and Open
AAI is all about tokens and data in.
It's all about training data.
Everybody's hungry for it. And so
they're building connections to Gmail
for training data, for calendar for
training data, anything they can get.
They're building the meeting transcripts
piece for training data. They're
building um Anthropic is cutting off
access to models in Windsurf, which was
acquired by OpenAI in order to cut
OpenAI off of training data. Now, it
doesn't really matter because Windsurf
can get thirdparty access instead of
first-party access. But the point is
like the intent the intent is to cut
them off on training data to keep your
your rival from getting training data.
And so if you're building in the space,
the question you should be asking
yourself is how easy is it for a model
maker to get access to training data
that they would find high value that
would have real rewards if they got it
right. And so my proposal to you is to
look for places where that data would be
hard to get where they couldn't just add
an MCP server and get the data because
that is basically what OpenAI did. And I
think the question is not would they add
an MCP server to get the data if they
could. It is is collecting the data to
do this in line with their larger vision
that they have stated for the company
because you know throwing elbows between
open AI and anthropic aside this is
right in line with what we would expect.
OpenAI has been really clear about their
plan to be the default OS for the
enterprise. If you're going to be the
default operating system for work, well,
you got to do meetings. Shouldn't have
surprised anybody. You got to do Gmail.
You got to do Calendar, you got to do
Outlook, you got to do SharePoint. This
isn't that surprising. Now, are they
doing it well enough that I would trust
them with all of that now at the
enterprise level? No, they're not. In
fact, this is reminding me a lot of the
initial release of Operator back in
January when Operator came out and it
was frankly pretty terrible. I used it
about three or four times and uh it was
inaccurate. It was slow. It took
forever. It froze up on easy things like
add to cart. They just didn't use it
anymore. Well, I had a feeling they
would make it better because after all,
it's in beta. And last week they did.
They added their 03 model as the driving
model behind operator. Operator got
about 10 times faster and 10 times more
accurate. It's actually a useful tool
now. They didn't make as big a fanfare
out of it, but I now find that I
actually go to it and imagine specific
use cases because it is fast enough. I
used it for a real task. I did not
pre-plan just this week. Uh, and if
you're wondering, it was flight
planning. And yes, I know they trained
for flight planning and all of that, but
it wasn't even usable for flight
planning in January. So, it's not just
that they trained for it, it's that they
got the right model. They took the time.
They probably collected data anonymously
from some of us users as we were using
it. um and they were able to eventually
make the model better at browsing the
internet. I expect the same thing to
happen with this connectors play. This
is a long-term play. I don't consider it
particularly usable today. I consider
the move toward data for the enterprise
and workplace something that is not
surprising and something where open AI
is going to get much better at this over
the next six months. They need more data
to reason across. Now, this doesn't mean
that they will immediately become 10
times more intelligent at handling all
of that data. I do think there are real
questions about the ability of these
models to reason well across very large
data streams that are incredibly messy
that are created by humans. Have you
looked in your notion or your wiki at
work? It's probably pretty dirty.
Everyone kind of rolls their eyes and
throws stuff in there. When you have a
dirty repository of unstructured text
data like that, it is inherently not a
great place to ask the AI to start to
make meaning. And yet that is often the
case for a lot of our text
repositories. And so I think my
challenge to you is how good is your
prompting and how precisely are you
asking for work to be done? One of the
characteristics that we are learning for
AI in 2025 is that when you prompt well,
when you prompt cleanly and clearly with
a very specific task in mind, you often
get surprisingly good results. But when
you ask for something fuzzier, harder,
more like you would ask a very senior
researcher where you don't even fully
know the question, you often get quite
poor results. And that really explains
how my experience went with connectors
today. I was able to get my specific
query around, you know, events and
webinars and understanding how they all
uh assembled into a briefing and kind of
give me the sense of the emails and the
calendar and the agenda and the public
profile for this specific event. That
went well. It was a very specific query.
My more generalized discovery question
around patterns in my email and patterns
in my calendar that went terribly. like
it just did not go well at all and it
could not pull the volume of data needed
to make sense of it. And so I am taking
away from that that part of the
challenge for work here in 2026 is how
can we more effectively structure our
queries so we precisely ask what we
intend. That takes a lot of human work.
It's not easy but I think that's a big
work skill that we all can stand to get
better at. Clearly, I could stand to get
better at it because I batted like one
for three, one for four on my test
queries today. I need to figure out ways
to use this tool that are more more like
a scalpel and less like a chainsaw. This
is not a chainsaw tool. It's a very
precise tool right now. It may get
higher bandwidth as they increase the
scope of those connectors. It's not that
high bandwidth now. So, that is the
skinny on what happened with data
connectors. I hope you enjoyed this
quick review and uh we'll get into a bit
more of like AI and work I think in the
next piece because there's a lot more
that is behind the scenes of this move
that I want to dig deeper on. All right.
Cheers.