The AI landscape is rapidly evolving towards practical application and integration, shifting focus from raw model capabilities to how AI is used to solve real-world problems and enhance workflows, making it more accessible and impactful across various industries and roles.
Mind Map
Click to expand
Click to explore the full interactive mind map • Zoom, pan, and navigate
Here are the six AI trends that will
matter most in 2026. This list is based
on daily research and reports from
institutions like McKenzie, OpenAI,
Stanford, [music]
and from analysts who are much more
knowledgeable than myself. In other
words, don't blame me if they get it
wrong. For each trend, I'll first start
with a big picture, then move on to the
actionable takeaways so that by the end,
you have a clear sense of where AI is
heading and what to [music] do about it.
Let's get started. Kicking things off
with trend number one. Models don't
matter much anymore. For the past few
years, every new model released sparked
debate about the best AI, and for good
reason. The difference in quality
between models was significant. In 2026,
though, that choice is going to matter a
lot less. Taking a look at the data,
this graph from artificial analysis
shows how the major AI models have
improved over time. Notice the
clustering in the top right corner. The
models are still getting smarter in
absolute terms, but the gap between them
keeps shrinking, meaning no single model
has a clear lead anymore. A Stanford
study confirms this from another angle
by comparing closed models like Gemini
and Chachi BT against openw weight
alternatives like Deep Seek and Llama.
The trend is pretty clear. Models that
are free to run are now approaching
frontier performance and performance is
only half the story. The cost matters as
well. Data from Epoch AI shows that
using powerful models has become
drastically cheaper and one of the
reasons is because hardware is getting
more efficient. For perspective,
Nvidia's latest chips uses 105,000
times less energy per token than they
did 10 years ago. So, what does this
mean for us? In plain English, when
things get cheaper and more similar,
they become more like commodities. You
don't ask who provides the best
electricity, right? You ask what can I
use the electricity for? And because of
this, the competition is shifting from
the AI model itself to the way we
actually use it, aka the app layer. Just
think about cars. Once the engine
becomes standardized, the focus shifts
to the features and the design. This
creates an interesting dynamic for each
of the frontier AI labs. For example,
OpenAI has a mind share advantage
because ChachiBT is synonymous with AI
and has the largest market share. Google
has a distribution advantage because
Gemini is already embedded across its
existing products like search, Gmail,
and Android. Anthropic has a
specialization advantage given its loyal
customer base in developers and
enterprise customers. Notice what's
missing from that list. None of them are
winning because they have the best AI.
The competition has moved beyond raw
power to reach, integration, and trust.
The practical takeaway here is to stop
obsessing over technical scores and
instead focus on how they fit into your
actual work. For example, if you live in
Google Workspace, Gemini's deep
integration with all of Google's apps
gives it an edge that has nothing to do
with raw intelligence. By the way, I'll
link all the sources I mentioned today
down below so you can check them out for
yourself. Trend number two, 2026 is the
year of AI workflows, not AI agents. If
you spend any time on Twitter or
LinkedIn, you've probably noticed the
industry jump from chat bots straight to
autonomous agents and completely skip
the middle step where the actual value
is being unlocked, AI workflows. And the
numbers prove this. According to
McKenzie, no more than 10% of
organizations in any given business
function report scaling true agents.
Meanwhile, we see from OpenAI's
enterprise report that 20% of enterprise
AI use is already happening through
workflow specific tools like custom GBTs
and projects. [music]
This gap tells you the market has voted
for workflows, not autonomy. And we're
seeing this play out across industries.
A pharma company redesigned their
clinical study workflow by using AI to
analyze raw clinical data while humans
focus on validation leading to 60% less
prep time and 50% fewer errors. A
utility company redesigned their call
center workflow where AI handles
authentication and routine inquiries
cutting cost per call by 50% while
increasing satisfaction scores by 6%. A
bank redesigned their code migration
workflow where AI scans legacy code and
generates updated versions for
developers to verify, cutting the
required human hours by 50%. Andre
Kaparthi sums it up perfectly, calling
everything an agent creates unrealistic
expectations and confusion. Fully
autonomous AI still faces massive
hurdles like data security. So, we're
looking at the decade of agents, not the year.
year.
>> I was triggered by that because I feel
like there's some overpredictions going
on in the industry. And uh in my mind
this is really a lot more accurately
described as the decade of agents.
>> Meanwhile, by integrating something like
custom GBTS into an existing workflow,
we've essentially created an agent light
which is much more reliable at producing
consistent results. To really ram this
point home, McKenzie predicts that
redesigning workflows will unlock nearly
$3 trillion in economic value by 2030.
And more importantly, these
organizations will have the muscle
memory to adopt true AI agents faster
when they finally arrive. So here's your
practical takeaway. Your goal for 2026
is to turn your successful prompts into
repeatable workflows. And this is
something I've talked about in other
videos. Pick one recurring deliverable
you produce, like a weekly report. Break
it into steps and let AI handle the
predictable parts. Keep yourself in the
loop for the final judgment calls. That
structure is what creates true
reliability. Side note, I'm actually
developing an entire course around
evergreen AI skills to give you a future
proof framework that never becomes
obsolete. If you're interested in
learning a practical and timeless AI
system, click the link below to join the
wait list. Trend number three, the end
of the technical divide. When I was at
Google, non-technical teams like sales
and marketing had to rely on specialist
teams to help them build stuff like
dashboards. And I'm not someone who
holds grudges, but a lot of my requests
were depprioritized because they were
too low impact and my clients weren't
key accounts, but no, I'm over it.
Anyways, in 2026, that's going to happen
a lot less. The numbers backing this are
honestly kind of insane. According to
Open Eyes latest report, 75% of
enterprise users reported using AI to
complete tasks they literally could not
do before. Not just doing old tasks
faster, they're doing entirely new
things. For example, coding related
messages from non-technical employees
grew 36% in just 6 months. These are
salespeople, marketers, and operations
managers writing scripts, automating
spreadsheets, and building internal
tools. A study from MIT confirms this.
AI acts as an equalizer,
disproportionately helping workers with
lower technical skills close the
performance gap with [music] experts.
Here's what all this means for your
career. If your value is purely
technical, aka you're the dashboard
person, then your competitive advantage
is shrinking because the marketing
manager who used to wait in your queue
can now do it themselves. [music] But if
you are that marketing manager or the
salesperson who deeply understands their
clients, then this is the biggest
opportunity of your career because the
technical barrier that stood between
your expertise and your execution is now
gone. Here's your practical takeaway.
Attempt one impossible task this month.
Identify a technical project you usually
outsource like building a dashboard,
cleaning a messy data set, or automating
a report and try doing it yourself using
Gemini Cloud or Cashibbt. You'll be
surprised by what you can now pull off
alone. Moving on to trend number four,
from prompting to context. One of my
most popular videos is this one teaching
you how to prompt because as we all
know, if we don't phrase our request
well, we get a bad result from AI.
Unfortunately for me, that video is
going to matter a lot less in 2026
because new models have gotten so much
better at understanding vague
instructions. However, they still have
one massive weakness I call the fact
gap. While models know almost everything
on the public internet from Shakespeare
to Python code, they know nothing about
your Q3 goals, your brand guidelines, or
that email your boss sent yesterday.
It's like having a brilliant employee
who technically knows how to complete
tasks, but isn't allowed to look at any
company files. they're still going to
fail, right? Because they lack context.
At least that's what I told my boss
during my first internship. It's the
exact same thing with AI. The focus has
shifted from how we ask the wording to
what we give it, the context. And this
explains the platform wars we're seeing
right now. Google, Microsoft, and others
are racing to embed AI into their
productivity suites because whoever
holds your context, your emails, your
docs, your calendar, holds your
attention. This is also how they'll trap
you with platform lockin. The more
context you build up in one ecosystem,
the smarter the AI is for you and the
harder it becomes to switch. There are
two practical takeaways here and the
non-productivity people are going to
hate this. First, file management is no
longer optional. To get value from AI,
you need some sort of system to keep
your files organized and clearly named.
If your work is scattered in random,
unnamed folders, you can't point the AI
to it. Second, audit where your
information lives. If it's spread across
three or four different platforms, you
need to consolidate. If your resume
lives in Google Drive, but the job
description and interview notes are
stored in Notion, neither Gemini nor
Notion AI can help with interview prep,
you end up doing the synthesis manually,
which leads to more friction and defeats
the whole purpose. So, as a rule of
thumb, prompting still matters, but it's
more important to ask yourself, does the
AI have the files it needs to know what
I'm talking about? Trend number five,
advertising is coming to chat bots, and
it's not all bad. First of all, please
don't shoot the messenger on this one.
Hear me out. At this point, it's
basically been confirmed that ads are
coming to CHACHBT in 2026. So, instead
of debating if it will happen, let's
talk about the implications. Imagine a
world where advertising never comes to
chatbots. In that reality, the best AI
models stay locked behind expensive
subscriptions, creating a wealth gap,
where only those who can pay have access
to the best tools, while everyone else
is left with an inferior version. Over
time, this creates a compounding
advantage. The wealthy use powerful AI
to get wealthier while everyone else
falls further behind. Kind of reminds me
of something I just can't put my finger
on. It think of it like YouTube. Imagine
if you couldn't watch videos from the
top creators unless you pay for YouTube
Premium. That is where AI is headed
without an ad supported tier. Now that
we know ads are inevitable and that I'm
not to blame for this, uh the thing to
watch is what format those ads will take
because it's going to look very
different from the search ads we're
currently used to. For example, industry
expert Eric Sufer predicts chatbot ads
will not be tied to our specific
questions because if an AI recommended a
product directly in its answer, we
wouldn't trust it. Instead, the ad will
probably look like standard display
banners that stay separate from your
actual conversation. Sort of like the
banner ads we see on websites today. So,
here's the bottom line. I don't like
ads. You don't like ads. Nobody likes
ads. But it's the ad revenue that makes
it possible for companies to offer their
best models to students in developing
countries, nonprofits, and casual users
who can't afford another monthly bill.
Trend number six, from chatbots to
robots. Everything we've covered so far
has focused on AI as software. But in
2026, that software is going to appear
even more in the physical world as
physical agents who can move on their
own. The numbers show this is already
happening. Exhibit A, Whimo. Their
autonomous taxi service has now logged
over 100 million fully autonomous miles
and are involved in 96% fewer crashes
than human drivers. Exhibit B, Amazon.
Their AI enabled warehouse robots have
cut the time from order to shipping by
78%. Exhibit C, China. As early as 2023,
China had deployed more industrial
robots than the US and the rest of the
world combined. Now, there is one caveat
to all this. Humanoid robots are still
MIT robotics professor Rodney Brooks
estimates that we are at least 15 years
away from seeing functional humanoid
robots in our daily lives. The real
shift is what analyst Mary Miker calls
AI turning capital assets into software
endpoints. And here's what that means in
plain English. A car, a tractor, or
warehouse robot used to be a
depreciating asset, which means it loses
value as time goes on. Right now, these
machines are becoming platforms that
improve over time through software
updates, exactly like our phones. A
Whimo car today is actually safer and
smarter than it was 2 years ago, even if
the physical vehicle hasn't changed. So,
what does all this mean for us? In a
nutshell, while the headlines are
focusing on white collar disruption for
now, this trend suggests that blue
collar work will also be disrupted, but
over a much longer time horizon. On a
more positive note, I want to leave you
with something Ethan Mollik said. He's a
professor at Wharton, and this is
something I really believe in. His
research on what he calls the jagged
frontier of AI shows that right now we
are in a unique window where expertise
is being reset thanks to AI. And
precisely because things are messy and
undefined right now, there are no
experts who know everything already. You
just need to be willing to learn faster
than the person next to you. That is how
you win in 2026.
Stop worrying about developing a perfect
plan to learn AI and instead just get
started. I'd love to hear your thoughts
on these trends, so drop them down
below. Check out this practical guide on
Google Gemini next. See you on the next
video. In the meantime, have a great one.
Click on any text or timestamp to jump to that moment in the video
Share:
Most transcripts ready in under 5 seconds
One-Click Copy125+ LanguagesSearch ContentJump to Timestamps
Paste YouTube URL
Enter any YouTube video link to get the full transcript
Transcript Extraction Form
Most transcripts ready in under 5 seconds
Get Our Chrome Extension
Get transcripts instantly without leaving YouTube. Install our Chrome extension for one-click access to any video's transcript directly on the watch page.
