You know, if you try to search for AI news today,
you are just, you're immediately overwhelmed.
Oh, it's not just information overload. It's
a mountain of noise. It's a mountain of completely
conflicting predictions. One source says total
job loss. And the next says it's all a bubble
that's about to pop. Yeah. It makes it so hard
to know. who to trust, or what to actually do.
And that noise is what freezes people. It's why
we're doing this. Our mission here is to just
cut through all of that hype. We went straight
to the data. We're talking full reports, real
industry tests from McKinsey, from Stanford,
OpenAI, and EPOC AI. Welcome back to the deep
dive. This isn't about guessing what's going
to happen in 10 years. No. It's about six definitive
data back changes happening right now. this year
that affect your job. So, okay, let's unpack
this. We've got a roadmap for you. We're going
to start with why that race for the smartest
model is pretty much over. Then we'll get into
why you need to build workflows, not wait for
some magic agent. And then my favorite part,
how non -technical people are suddenly getting
these incredible new superpowers. For the last
couple of years, it felt like the entire conversation
was just about benchmark scores. Oh, completely.
Everyone was arguing about, you know, is it GPT
-5? Is it Claude 3? Gemini 1 .5? Who's the best
this week? Yeah, who's the best at geometry or
writing a poem? It was this really intense race.
But moving into 2026, the data is showing that
that whole competition has, well, it's hit a
plateau. It really has. And what's so fascinating
is how the gap is just disappearing. We've got
data from artificial analysis that confirms that
all the top -tier models, their performance is
clustering. They're all becoming good at the
same things. We're all becoming good enough at
pretty much everything. Math, writing, you name
it. And it's not just the big expensive frontier
models we're talking about. No, absolutely not.
And this is where the whole landscape just fundamentally
changes. You've got Stanford research confirming
that open weight models. So these are the free
to use ones, like Llama. Exactly. Llama DeepSeq.
They're performing nearly as well as the incredibly
expensive models from the big labs. Wait. OK,
so if the free models are almost as good, why
is anyone paying billions for the others? What
are we missing? That's the critical question.
And for, I'd say, 99 % of tasks, you're right,
the difference is basically zero. For that last
1 % super high precision complex reasoning, the
big models still have a slight edge. But the
bigger factor is cost, right? The bigger factor
is efficiency and cost. It's transformative.
Epoch AI has data showing hardware efficiency
is just, it's skyrocketing. What does that mean
in real terms? It means NVIDIA's newest chips
use 105 ,000 times less energy to create text
than chips from just a decade ago. Wow. So think
about what that does. When the quality is basically
the same across the board and the cost to run
it plummets, AI becomes a commodity. Like water
or electricity. Exactly like water. The source
doesn't matter as much as the tap it comes out
of. Precisely. So your choice isn't about some
tiny difference in a benchmark score anymore.
It's all about friction. Where you already spend
your time. Yes. Where you live digitally. If
you're in the Microsoft world with Excel and
Word, reuse Copilot. It's right there. And if
you're all in on Google Docs and Gmail, you use
Gemini. Right. because it can see your stuff.
I mean, I realized I was losing my mind switching
tabs all day trying to find the quote unquote
smartest model. Right. The mental fatigue is
real. Sticking to one platform, the one that's
already integrated, it honestly saves me probably
30 minutes a day of just lost focus. So given
that the models are basically commodities now,
how much time should we really spend comparing
those tiny benchmark? score differences. I'd
say just focus on seamless integration. Forget
about the tiny performance gains. That shift
away from the models themselves brings us right
to the next big thing, which is this pivot from
chasing AI agents to building actual workflows.
The hype around agents was just deafening, wasn't
it? Oh, yeah. The idea that you could just say,
hey, manage this project for me and do 50 steps
while you went for coffee. A compelling vision,
for sure. But the data offers a pretty big reality
check. McKinsey found that less than 10 % of
companies have actually scaled autonomous AI
agents. So why are they failing? What's the roadblock?
It's the risk. The risk is just too high. When
you give it total control, mistakes are harder
to find. It's almost impossible to monitor. And
the security questions, especially with company
data, are huge. If an agent makes a really bad
mistake deep inside a process, trying to undo
that damage is a nightmare. So you're saying
the need for human oversight is the thing that's
holding back full automation. It's the critical
constraint, absolutely. So what's actually working?
Workflows. The practical solution. This is what's
happening right now. 20 % of all enterprise AI
use is through these specific step -by -step
processes where the human is still in the driver's
seat. And you can see this in the real world
in customer service, for example. The AI can
verify an ID. It can summarize the call history.
Right. But a human makes that final Tricky refund
decision or in content creation. You don't just
tell the AI. Hey run my social media Yeah, you
build a workflow you give it a transcript the
AI pulls five key points It drafts five posts
and then you the human you edit for tone and
you hit publish It's like stacking Lego blocks
and you can start building these right now You
can use a simple prompt to turn any messy task
into a clean workflow Just ask the AI to map
out the steps for you So if these autonomous
agents are still struggling so much, what's that
one key thing that's really preventing full automation?
It's the absolute need for human monitoring to
catch mistakes and to manage risk. Okay. So that
success with workflows leads us to maybe the
most exciting trend, especially for most of the
people listening. It's the end of the technical
wall. Yeah, that wall was high. In the past,
if you wanted to build even a simple business
tool like a dashboard, you had to know how to
code. You needed a specialist. That whole requirement
is just evaporating. There was this fascinating
MIT study on what they call the equalizer effect.
The equalizer effect. It shows that AI helps
people with lower technical skills way more than
it helps experts. A world -class coder gets a
little bit faster. But someone with zero skill.
They get a superpower instantly. And you can
see that in the data right away. OpenAI said
that coding -related messages from non -technical
staff talking marketers. HR grew by 36 % in six
months. They're building their own stuff. They're
not waiting for IT anymore. And this is the insight
for you, the listener. Your value isn't knowing
where the semicolons go anymore. It's about understanding
the business problem that needs to be solved.
If you know what you need, the AI handles the
how. It handles the technical side. Right. The
person who actually understands the budget process
can now build the tool for it without having
to translate it all for a programmer. It changes
who gets to be an innovator. You should really
try this for yourself. Think of some annoying
little task like cleaning up a messy spreadsheet
of names. You can just ask the AI, give me a
Google Apps script to fix this. I don't code.
So give me the code and tell me exactly where
to clay to make it work. And it will. It gives
you the full solution. Whoa. Just take a second
and imagine scaling that accessibility. A billion
curious people who aren't developers building
their own custom tools for their own specific
problems. That's a massive shift in how companies
work. So does this mean every non -technical
person now needs to become a master of prompt
engineering for code? No, not at all. You just
have to focus on defining the problem clearly.
The AI handles the technical execution. Now we
have to shift gears a little bit away from what
was the obsession of 2023 and 2024, which was
prompt engineering. Ugh, the search for the magic
words. That secret formula that would unlock
everything. Right. Prompting just isn't the bottleneck
anymore. The models are smart enough now. They
can understand imperfect grammar. They get your
intent. The real bottleneck, the competitive
edge for 2026, is context. The fact gap. The
fact gap. The AI knows everything on the public
internet, but it knows nothing about you. And
that's the key vulnerability, isn't it? It has
no idea about your project deadlines, your company's
tone of voice, that conversation you had with
your boss yesterday. Without that context, the
output is just generic. It sounds like AI. I'll
admit, I still wrestle with prompt drift myself
sometimes. I'll forget to feed it the critical
background document before I ask a complex question.
Then the answer is useless. Technically perfect,
but completely useless for what I'm doing. That
was my aha moment on context. And if you don't
provide that context, the AI is just flying blind.
The winners of 2026 are going to be the people
and the companies who have their own data organized
and ready for the AI. Because if your files are
scattered everywhere, desktop, Dropbox, Google
Drive, the AI can't help you. It can't. But the
solutions are here. Things like Claude projects
or custom GPTs let you upload your specific context.
PDFs, brand guides, old reports. And it remembers
that context for every conversation. Exactly.
You can use it to enforce your style. Upload
five of your old newsletters and say, study my
tone, my sentence length. Now, draft a post in
this exact style and do not use words like transform
or harness. You're basically outsourcing your
own quality control. So given how critical that
context is, how important is it to maintain a
single clean source of data over time? Well,
your output quality depends entirely on having
organized accessible source data. Let's talk
about the elephant in the room, ads. It's now
officially confirmed. Platforms like ChatGPT
are going to start showing ads. And everyone's
going to groan. Everyone will groan. But we have
to admit, this is an unavoidable and probably
necessary step for the industry. It absolutely
is. It all comes down to the staggering cost.
I mean, it costs millions of dollars every single
day to run these models if they only relied on
subscriptions. you'd create a massive AI divide.
Explain what you mean by that divide. It means
only the biggest companies, the richest people,
could afford the best tools. It would kill innovation
for everyone else. So ads are the trade -off.
They're what allows for universal access. Exactly.
It's what gives students, non -profits, researchers,
and developing countries access to high -quality
AI for free. It's the YouTube model, really.
You can pay for premium to get rid of the ads.
Or you watch a few ads and you still get the
same world -class tools. But the big question
here is trust. What will these ads look like?
How do you keep the AI's advice pure? Everyone
is very clear on this. The integrity of the model
has to be protected. We'll probably see things
like banner ads on the side of the screen totally
separate from the conversation. So the AI won't
suddenly start pitching products? It can't. If
you ask how to fix a leaky pipe, it must not
say, you should buy Brand X wrench. That would
destroy trust instantly. So if ads are funding
this access, What is the single biggest risk
platforms face when it comes to influencing the
model? Ruining user trust is the greatest threat
to adoption. The line between advice and ads
has to be crystal clear. So far, we've only been
talking about AI on a screen, in documents. But
the last huge trend that's quickly becoming real
is embodied AI. AI with a physical body, moving
around in the real world. And this isn't science
fiction. It's already happening in very specific
industries. Look at logistics and autonomous
driving. Waymo has driven over a hundred million
autonomous miles. And the data is just staggering.
They're 96 % safer than a human driver. Autonomous
taxis aren't a fantasy anymore. They're becoming
normal in some cities. Normalizing fast. You
see the same thing in warehouses with robots.
Amazon's robots use AI to navigate and handle
things they've never seen before. They've cut
the order to shift time by almost 80 percent
in some places. Which brings us to this really
big idea that explains why this is happening
so fast. Capital assets as software. That is
the key frame for all of this. A traditional
machine, like a car, just got worse over time,
wear and tear. Right. The new model is that the
machine gets smarter over time through software
updates. The physical hardware is the same, but
its brain gets better at driving, better at picking
up boxes. So it appreciates an intelligence.
Exactly. But we do need to keep expectations
in check, especially around the house. Yeah,
we shouldn't expect a robot to be folding our
laundry next week. No. Rodney Brooks at MIT says
we're still probably 15 years away from a general
purpose robot that can do that reliably. The
focus right now is on those specialized industrial
machines. So which segment of the economy is
really going to see the fastest adoption of this
physical automation? It's those specialized industrial
roles, logistics, high -volume manufacturing,
and transportation. We've covered a lot of ground
today. And the clear message seems to be that
AI is moving away from that initial kind of theoretical
model obsession. And toward practical integrated
application, the shift is here. It is. And it's
actionable. So let's run through that quick checklist
for you, the learner. First, stop comparing models.
Just pick the one that fits best into the tools
you already use. Second, stop waiting for magic
agents. Start thinking in terms of step -by -step
workflows that you control. Third, don't wait
for IT. Use AI to write code for you. Focus your
energy on just defining the business problem.
And fourth, organize your context files. Scattered
data makes the smartest AI totally useless. Fifth,
get ready for ads. Accept them as the price for
universal access, but watch for that clear separation
to maintain trust. And finally, keep an eye on
physical automation, especially in logistics
and industry. So your assignment for this week
is simple. Just identify one weekly task, only
one, that feels like a total chore. And then
challenge yourself. Use that workflow prompt
ID we talked about. Ask your favorite AI to help
you break that chore down into steps. Figure
out what the AI can do and what you need to keep
for a final quality check. Start small, one workflow
at a time, mastering that practical application.
That's how you win in 2026.