We spent so much time watching the horizon for
the next big AI thing, right? The GPT -5 hype,
the massive model announcements. We do. But honestly,
I find the most impactful upgrades aren't always
these huge version jumps. They're often these
quieter, subtle feature additions, things that
are already live in the system you're probably
using. And these are the ones that really change
the interaction, I suppose, moving AI from being
like a slightly smarter search engine. Exactly.
to a true partner in your workflow in learning.
So today, we're going to do a deep dive into
those kinds of updates. We're looking at three
specific new features designed to make getting
knowledge and handling complex tasks vastly simpler
for you, provided, of course, you've got that
plus subscription turned on. That's kind of key
for these power tools. Gotcha. So it's like turning
your AI from that smart friend who just answers
questions into maybe a proactive coworker. Or
even need teacher. A proactive co -worker and
a surprisingly patient adaptive teacher. That's
a great way to put it. So our roadmap today covers
three main things. First, this personalized learning
thing called study mode. OK. Then the really
powerful workflow automating agent mode. Agent
mode, right. And finally, how chat GPT is now
starting to connect directly with your everyday
apps like Google Calendar and Gmail. OK, yeah,
let's unpack this. Let's start with that first
one. Study and learn mode. Right. I can see this
being huge for anyone facing that mountain of
information, trying to tackle a new complex topic,
say, macroeconomics or something. The sheer volume
online can be, well, paralyzing. It absolutely
creates instant overload. And this mode, I think,
is a pretty elegant solution. It kind of eliminates
that anxiety of just starting a complex topic.
How so? Well, it doesn't just dump a long article
on you. It acts more like your own personal structured
teacher. and that's a major difference. Tell
me more about that structure then. How does it
work? The core functionality is that it creates
a formal paced learning plan. It moves really
systematically from the most basic ideas, the
foundations, up toward more advanced knowledge.
So it makes sure you build those conceptual pillars
first. Okay. And crucially, it delivers this
info in... short, easy to digest chunks. You
know, it doesn't let you get lost in some 10
paragraph explanation. That pacing feels essential
for actually understanding, not just, you know,
skimming. And what makes it uniquely effective,
I think, for long -term memory, which is the
whole point of learning, right? Right. Is that
it integrates active recall. It regularly stops
and asks you questions, checks your understanding
right then and there. Oh. And that process helps
move the knowledge from short -term into your
long -term storage. I love the practical application
of cognitive science there. So let's use the
example you mentioned, learning about the circular
economy as a beginner. What's the interaction
actually like? OK, so the prompt is dead simple,
something like, turn on study and learn mode
and teach me about the circular economy. Easy
enough. And the AI responds with a clear plan,
like a syllabus. Yeah. It might say, OK, we'll
cover the basic intro, then compare it to the
traditional linear economy, move on to main ideas
like stopping waste, and finish with some real
examples. This sets expectations right away.
And I like that check -in step you mentioned.
After it explains the basics, stop waste, reuse
materials, help nature regenerate, it then asks
a specific question. Multiple choice or short
answer? Exactly. It might ask, what is the primary
goal of the circular economy model? Yeah. And
here's the good part. If you miss it, if you
get the nuance wrong, it doesn't just say wrong.
It gently re -explains that specific point you
missed. often using a different angle or analogy.
Ah, personalized feedback. Right. Which makes
it an incredible tool for actually making things
stick, for retention. Yeah, I can see this being
useful all over for students learning photosynthesis,
professionals needing SEO basics before a meeting,
or just anyone curious about how something complex
works. Exactly. It manages the complexity by
just giving you the very next step you need.
OK, so a probing question then. Since it breaks
complexity down into these small chunks, how
does this mode ensure the learner still gets
the big picture, you know, the overall view?
It forces that continuous check on knowledge
through quizzes and structures content progressively,
basically from zero to mastery. That makes perfect
sense. OK. Now let's shift gears from learning
passively to active execution. Yes. This is where
agent mode comes in, right? You said it shifts
the AI from an answerer. that box you ask questions
to, to a doer. And this is where it gets really
interesting, you mentioned. Yeah, I think this
represents maybe the biggest shift in how we
interact with these systems in a long time. An
AI agent, basically, is a system that can take
a really big, complicated, multi -step goal,
break it down into smaller, solvable tasks, and
then actually execute those steps on its own.
Execute using what? What tools does it have?
It uses external tools. things like a web browser,
a code interpreter, sometimes even a virtual
computer environment. It uses these to carry
out the steps it planned. And here's the kind
of revolutionary part. If a step fails, maybe
a website link is broken or something, the agent
doesn't just stop. It doesn't. No. It recognizes
the failure, adjusts its plan, and tries a different
approach to still reach the final goal. It's
this dynamic self -correcting loop that older
systems just didn't have. That self -correction,
that feels like the key insight we needed. Honestly,
I still wrestle with prompt drift myself sometimes.
When you try to automate multi -step things,
prompt drift, it's like three steps into a long
task. The AI starts forgetting the rules you
set way back in the first sentence. Agents help
manage that internal complexity. They keep the
focus on the end goal. That admission actually
really highlights the agent's power, doesn't
it? It tackles stuff that takes hours manually.
Let's use that real -world example. Finding and
comparing online English -speaking courses for
working folks. Okay, yeah. So the user gets it
a really specific restrictive prompt. They need
date of teachers, cost under $80 a month, and
classes have to be after 7 p .m. local time.
If you did that by hand? Yeah. Wow. You'd be
clicking through maybe 20 different websites,
right? Ten tabs open, probably a spreadsheet.
Easily. But the agent handles it. So step one
is planning and research. It figures out the
right keywords, runs the Google searches. Step
two is filtering and validation. It actually
clicks into the search results, looks for the
pricing pages, the schedule pages, and it filters
out any school that doesn't meet those three
specific rules. And step three? Synthesis. It
builds the final comparison table, showing only
the options that actually work. It's not just
summarizing a search. It's doing a full multi
-criteria research project. That saves hours.
It eliminates hours of just slogging through
websites. Oh, just imagining that level of complexity
handled automatically. The searching, the checking,
the reporting back. It really does save hours
of manual comparison. That's that's scalability
right there. We do need to be clear on security,
though. Complex tasks like this, they can sometimes
take a bit, maybe 10 or 15 minutes. You can let
it run in the background. But crucially. The
agent will pause if it hits something like a
CAPUTCHA or a website login page. Ah, so it asks
the user for help then. It doesn't try to guess.
Absolutely. You have to manually intervene. And
you should never, ever give the AI your passwords
or credit card numbers or any sensitive details
like that. That's a good point. And always, always
double check the really critical info it brings
back, like prices or dates. Check for accuracy
before you act on its report. Good caveats. OK,
so beyond just the searching part, what would
you say is the single biggest time saver with
this agent function? It autonomously searches,
filters against your rules, and builds the final
report, dynamically adjusting its plan if it
hits roadblocks. That efficiency is just staggering.
OK, let's look at the next step in integration
then, making ChatGPT part of your existing world,
right? Connecting it to daily apps like Google
Calendar and Gmail. Exactly. This moves the AI
interaction kind of beyond just the chat window
and more into your actual professional life.
We're talking about connectors. Connectors, okay.
Different from the old plugins. Yeah, different
from those sometimes clunky plugins. Connectors
feel more like built -in features. They link
the AI directly to specific high -trust platforms
like Google's Workspace apps. Let's start with
Google Calendar. Once you activate it in the
settings, your calendar basically becomes conversational.
That's exactly it. You kind of stop using clicks
and forms as much, and you start using natural
language. You can just say, or type. Check if
I'm free next Wednesday afternoon. Okay. If yes,
create an event called Meeting with Client and
Fat Company at 2 p .m. and send an invite to
Sarah. And it handles all those steps at once.
Instantly. Or even a more complex search query
like, find a free two -hour slot next week for
a doctor's appointment, but make sure it doesn't
clash with any internal team meetings. Wow. And
the automation power goes further. You can schedule
recurring tasks. Ask the AI to send you, say,
a daily 8 a .m. email summarizing your appointments
for the day. Useful. Or even ask it to try and
move a recurring team meeting if a key person
is already booked during that time. The Gmail
connection sounds equally transformative then.
allowing the AI to read, understand, and even
draft emails based on the conversation history.
That saves huge amounts of context switching
time. Totally. You could open a really long,
dense email thread from a client and just tell
it. Summarize this long work email into three
action -oriented bullet points. Nice. Or you
could say, draft a reply to Mr. Jones using a
professional yet friendly turn, confirming the
contract dates he mentioned yesterday. It pulls
the context. But there's a really vital safety
note here, isn't there? Something you hinted
at. The system is deliberately stopped from doing
one key thing with email. That's right. Super
important. The system can only read, understand,
and write drafts. It cannot currently send emails
itself. The user always has to be the one to
review the draft and physically hit the send
button. That seems like a sensible guardrail.
So, probing question on this. Since reading emails
is obviously highly sensitive, what's the primary
limit imposed there for user safety? The system
is deliberately prevented from sending any email
on its own behalf. Always requires user action.
OK, got it. So what does this all mean when put
together the real power, the kind of revolutionary
capability comes when you stack these features,
right? Like you said, like Lego blocks of data.
Exactly. It's not really three separate features.
It's more like one unified system potential.
We can walk through a full workflow example,
something that might have taken, I don't know,
a couple of hours of manual work, now potentially
executed in just minutes, let's say. Organizing
a two -hour online workshop. on time management
skills. Okay, let's see the stack in action.
How would that work? Alright, step one. You use
agent mode? Yeah. You tell the agent. Research
and create a detailed workshop outline on time
management and include five interesting statistics
about workplace distraction. Okay. The agent
goes off, does the research, identifies key concepts
like the Eisenhower matrix, Parkinson's law,
Pomodoro technique, maybe time blocking, and
builds the outline. Step two. Now we bring in
the apps. Step two. Use the Gmail connection.
Based on that detailed outline the agent just
made, you ask the AI. Draft three customized
invitation emails to potential expert speakers
using this outline. Clever, pulling the context
across features. Step three. Let's say a speaker
confirms. Now you use the calendar connector.
You say, schedule the final event, workshop,
time management skills for next Tuesday at 10
a .m. and invite the speaker, Dr. Emily Carter,
and my internal team. And the AI handles the
invites, time zones, everything. Yep, finds the
email, sets up the calendar event, done. OK,
what's step four? Step four, personal prep. You
realize you're a bit rusty on one of the topics
on the outline, so you use study mode. Hey, chat
GPT, turn on study mode and teach me about the
Eisenhower matrix. Right, full circle. Right,
and then it quizzes you on the four quadrants.
Do it, plan it, delegate, minimize, eliminate.
Wow. OK, so with just a few natural language
commands. No coding, no complex setup. You've
gone from just an idea to detailed research,
targeted communication, scheduling, and even
personal learning on the topic. That's true.
End -to -end automation of a small but non -trivial
project. So, recapping here. We've really seen
that AI is rapidly growing into, well, a genuinely
helpful assistant, and a patient teacher, and
a powerful project manager, too. It's moved way
beyond that simple Q &A box we kind of started
with a couple years ago. Absolutely. And the
three features we talked about, study mode, agent
mode, and those app connectors, these aren't
just theoretical ideas down the road. They are
immediately practical tools available right now
for daily work and learning. again, provided
you're on that plus or maybe an enterprise tier.
And maybe the takeaway for you listening is don't
feel overwhelmed by how fast this is all changing.
Good point. Start small. You know, if you have
the subscription, maybe try the calendar connector
first. Ask it to summarize your schedule for
tomorrow or ask study mode to teach you the basics
of something you've always been curious about.
Just experiment a little. That's how you really
start to leverage this power, I think. Definitely.
And maybe a final thought to leave you with.
Now that AI can actively manage and even act
upon your schedule, your communications, and
your whole knowledge base, the next really fundamental
question to consider is, how do we actually redefine
productivity when the process itself starts getting
automated like this? What kinds of tasks, what
creative or conceptual or relational work suddenly
becomes truly worthy of your limited human time
and focus?