The current market for new computers, especially those marketed for AI capabilities, is driven by inflated prices and unfulfilled promises, making it a poor time to invest in high-end hardware; instead, users should consider older or used machines and leverage cloud AI solutions.
Mind Map
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คลิกเพื่อสำรวจ Mind Map แบบอินเตอร์แอคทีฟฉบับเต็ม
I bought a new computer last year, which
is my current daily driver. And just
recently, I also bought an AI computer
so I can run local AI models.
Lately, the biggest driver for buying a
new computer is to see what new things
you can do with AI.
Because I have to test things for you so
I can make these videos, I have to bite
the bullet and buy these things now.
Sometimes it works out and sometimes it
doesn't. Generally, we buy new computer
expecting to use it for some particular
application or new expected features
like AI in my case. But I found that
many of the expectations for improvement
and performance are wrong and often
extremely costly.
What I discovered is that all the major
changes going on with chip technologies,
AI solutions, plus the changing
marketplace for computer accessories
like memory or graphics cards,
if I were interested in updating my computer,
computer,
that this may not be the time to
purchase high-end hardware.
I gave some advice on what to buy in the
last couple of years. And again, in
retrospect, all of those computers would
now be bad choices. And it's getting
worse in 2026.
I fell for the hype and I don't want you
to. We've been faked with specs that are
actually useless for most everyday
things. And even if you're interested in AI,
AI,
many of these recent computer choices
truly suck.
And if you want an AI machine, a
dedicated one like the one I got, maybe
it's best to wait and see and use a
cloud option for now. I'll talk about
that, too.
What I would suggest to tide you over is
actually to buy used at least in 2026
and then wait it out and see if things
get better in the next year or two.
I'll tell you why you need to heed my
advice so you don't waste money and what
you should do instead.
The biggest waste of the last 2 years is
considering the so-called Copilot Plus
PC, which is basically a Microsoft
approved hardware configuration that can
run Windows 11 with a TPM security chip
and able to supposedly handle local AI
inference using Copilot. One of the main
considerations was 16 GB of RAM minimum
and an NPU supposedly with a minimum of
40 tops. Tops is trillions of operations
per second.
Now, I use Linux and for those of you
focusing on Linux, that NPU is not used
at all. So, 40 tops of nonsense. One of
the particular models pushed by
Microsoft was the new Snapdragon Copilot
Plus PCs from Qualcomm. Avoid this. It
is not Linux compatible yet and lots of
incompatibilities. The only reason to
buy a newer computer in the last 2 to 3
years was supposedly that you will get a
better AI experience and we've been fed
a lie so far. Because the reality of how
AI will be used is different from what
they imagined. A shocking bombshell
message for you. From a raw processing
point of view, there is no real
observable difference from a similar
model from 3 years ago when performing
normal computer uses. There may be a big
advantage in some types of computers for
specialized tasks like video editing and
What is an NPU?
Specifically, an NPU is called a neural
processing unit, which conjures images
of a scary AI chip that will take
control of your device. But that's not
really what an NPU is, even though it is
highlighted as important for Microsoft
Windows Copilot.
An NPU is basically a math co-processor.
It is a finely tuned chip made for doing
matrix multiplication, which is an
important element in AI inference.
Except while the hardware is there, the
support for it is spotty. Only Windows
Copilot puts it into some use for minor
AI stuff and really it is at a very
immature stage. I will tell you right
now that Linux or a local AI used by
tools like LM Studio or Ollama do not
use the NPU.
Currently, the bulk of AI models focus
on VRAM video memory
and the GPU.
So, the push for people to buy computers
with an NPU has truly been a waste of
money and time. Frankly, computers that
are 5 years old without an NPU are just
fine. And if you followed my recent
videos of Microsoft Windows, you will
see that I don't recommend ever using
Windows 11 with Copilot for any sort of
personal use anyway. Use Linux. If
you're going to use AI, then I have a
completely different approach. And your
base computer with sufficient memory
like 8 GB running Linux is going to be
more than enough. Currently, no popular
application really utilizes the NPU for
any AI purpose outside of Microsoft
itself and it will have zero function
under Linux.
What applications are important to you?
Before you start deciding on what new
computer to get, you really have to
determine the applications that are
important to you right now. For example,
if you're into gaming, do you really
need a brand new super expensive Nvidia
5090 desktop? You're competing with
every AI user for those Nvidia 5090s and
the target price of an AI user is more
like $30,000 for machines with multiple
5090s. So, the prices are artificially
inflated and has nothing to do with
gaming. But for gaming, do you really
need to spend 5,000 just for the Nvidia
card when you can get some used graphic
cards for a fraction?
The competition is for Nvidia cards with
lots of VRAM. So, a 5090 is on the top
of the list with 32 GB of VRAM. Why?
Because it means it can load a 32 GB AI
model directly on it without using RAM
and that means super fast AI inference.
If your goal is to learn to use AI, then
the answer is to go cloud. Don't compete
on the super expensive hardware.
Yes, there are privacy advantages to
using local AI like I'm doing, but there
are safe cloud alternatives I'll mention later.
Memory prices have gone through the roof.
roof.
I just bought an AMD Strix Halo, which
is a Beelink GTR9 Pro
AI Plus Max 395. If you can remember
that long name, it is one of the hottest
devices out there for AI because it has
unified memory [snorts] of 128 GB
and it is the cheapest. Unified memory
is the big deal today because instead of
buying Nvidia 5090s, you can just use
standard RAM like this at 120 GB and AMD
and Apple can actually split the RAM
into video memory and regular RAM.
And here's the sad part. This Beelink
cost 2,000 in late 2025. Now, because of
the AI agent demand, it is now $3,000
with tax. So, that's a 50% increase. Let
me repeat that. 50%.
And the reason for this is that the AI
companies are buying up all the RAM,
particularly the high-speed LPDDR5
and LPDDR7 types. Many computer makers
project depressed sales in 2026 because
who would buy a computer that's now 50%
New hardware is buggy.
The big deal with using computers like
AMD Strix Halo CPUs with 128 GB of RAM
or more is that it will allow loading of
large AI models locally.
On a 128 GB machine, in theory, I'm
supposed to be able to load models as
large as 96 GB. This beats the super
expensive Nvidia 5090, which only has 32
GB of VRAM. In theory.
In fact, well, it's crashing. It's
unstable. Unified memory is more
reliable on Apple Mac Studio for AI, but
really a crash risk on AMD. So, in
reality, I'm not using more than 50 GB
of VRAM. An Apple Mac Studio pricing is
stupidly out of this world. The fact is
that if you wants to build it, you'll
have to stick to Nvidia 5090, which is
an incredibly expensive option as I
already said.
$5,000 a card and you will need more
than one.
AI on your main machine? Not doable.
Originally, my imagined use of AI was to
run Ollama with a local AI on my machine
I bought 2 years ago. It's a Lenovo
Legion 5 with 64 GB of RAM and an Nvidia
4070 with 8 GB of video RAM, a gaming
laptop. Well, it turns out that this is
not the practical way to really use AI.
The new big deal is the use of AI agents
and the hot topic since February 2026 is
Open Claw. I've been using Open Claw
heavily for the last month and frankly,
the only safe way to use Open Claw is to
use a standalone separate computer. So
my thought was to use a llama and then
open claw on that 2-year-old computer. Wrong.
Wrong.
Not enough video RAM.
NPU useless.
As it turns out, open claw doesn't use
that much CPU horsepower. So you can use
a 5-year-old computer and it will handle
open claw itself just fine.
So I've dedicated my Lenovo Legion 5
computer to open claw use. My AI model
is running a llama on the B link Strix
Halo, but this is not a realistic
solution financially. If I weren't
testing this as a privacy solution, all
I needed was to use a llama cloud models
with the open claw machine.
>> [snorts]
>> No extra computer needed. A llama cloud
will get you going for $20 a month fixed
cost. And an older computer would
achieve the same thing. Maybe a couple
of years from now when the cost of
memory goes down, you can consider
Specialized applications.
In my particular case, I bought a new
Lenovo ThinkPad X1 Carbon Gen 13 as my
daily driver because it was a thin and
light machine running the new Intel
Lunar Lake architecture referred to as
series 2. Now this was before the price
increases and memory in particular model
was well priced. The biggest advantage
of this upgrade was that I can actually
do video editing on a thin and light
laptop without an Nvidia card.
This was a first and I'm loving this new
capability except I feel cheated because
in a single year the newer Lunar Lake
chip series 3 is significantly faster.
If I waited, I would have gotten more
bang for the buck. The reason is that
Intel and AMD are copying some of the
features of M silicon chip from Apple
and are gaining massive improvements in
power draw. So the jumps in performance
are more significant than in a typical
year because of architectural changes.
This will likely continue for a bit
again next year. I just dove into this
too early. Unfortunately, having the
extra speed would be outweighed by the
extra cost of memory which is now 1/3
the cost of a new machine. The newer
version Lenovo X1 Carbon of the one I
bought is not yet available as of the
time of this video, but it is likely
priced at double of what I paid. So how
can I recommend that you buy a new machine?
machine?
Instead of buying a new computer, you'll
need to study what your goals are and
frankly, you'll be better off buying an
older computer and hold off on any new
computer purchase in 2026 and perhaps
for part of 2027.
Just to give you a general heads-up on
computer comparisons, let me focus on
Intel machines which are more common on
laptops. The old Intel chip design was
the old CPUs numbered i7 14,000 which
were common in 2023-2024.
Uh and then 13,000
for the prior year and 12,000 for the
year before that and so on. So basically
a 5-year-old computer would be using an
Intel i7 or i5 12,000 series chip.
Now let me tell you something interesting.
interesting.
The newer chips called Lunar Lake are
not more powerful than the 14,000 chips.
They're actually a little slower and
their biggest advantage is lower power
draw so the batteries last longer. So to
be honest with you, for raw horsepower,
a 4-year-old consumer computer at the
high end would be the same performance
roughly as a Lunar Lake computer today
and a 5-year-old computer would be a bit
slower than that. There hasn't been a
big jump in performance as you would
expect. The biggest jumps in performance
relate to the IGPU or graphics
processor. So that's why I can do video
editing without Nvidia.
And old computers with Nvidia cards
already performed well on gaming which
is good enough for most people.
So the only real disadvantage is that
the old computers will run hotter, use
more power, and be heavier.
For general use, particularly for
compatibility with Linux or for running
an extra computer with open claw, I
still recommend the same computer in
2026. Get a used Lenovo X1 ThinkPad
Carbon. You can get them from $300 to
$400 on eBay. I actually got myself
another recently since they're great
backup computers. Find some using the
Intel i5 or i7 12,000 series chips. They
will do well for you.
The reason these are priced well is
because these are popular corporate
laptops and are sold with 3-year leases
and then after 3 years, they get dumped
on the market.
These are super expensive laptops brand
new and you will find them with tons of
memory, but even 8 GB is good enough for
normal use.
You can buy older gaming computers, too,
as they are pretty powerful with
potentially large amounts of memory and
older Nvidia cards like a 3050.
I had an old Dell XPS 15 with an Nvidia
3050. This was also around $500 used and
I also got one of these used recently.
These computers would run new
Use cloud AI.
When I started to do a lot of work with
AI, I had different objectives. If I
were a high-end programmer doing high
productivity kinds of uses, I'd probably
use Anthropic's Claude.
This is the hot cloud AI for coding and
many people still use chat GPT which is
open AI, Grok which is xAI, and Gemini
which is Google.
Depending on your focus, these cloud
options can be dangerous because you are
basically potentially sending private
data to a cloud AI.
Would you have any of these models do
your tax returns, for example?
Aside from the privacy considerations,
there are other differences. Grok stands
out as having built-in web search so the
model is not a stale source of
information based on what the model was
trained on.
So Grok requires no special setup to
have more current information.
For other AI cloud products to have web
search requires that you hook up search
to them manually which is not
necessarily a task available to average users.
users.
But again, these are not the safest
privacy options, any of them.
The safer privacy option is to use a
llama which is a llama.ai.
While we know of a llama as a provider
of open source local models that you can
use on a local machine which I'm doing,
a llama also provides the same models
and the larger versions in the cloud.
And unlike the other AI providers that
charge per million tokens, a llama
offers fixed cost plans of $20 a month
pro and $100 a month max. For a single
user, a $20 a month subscription is
sufficient for testing out open claw and
doing most AI safely.
I was spending a lot more than that on
xAI, more than $50 a month and Anthropic
would have been $500 a month.
So today, instead of buying a new
computer, temporarily use these cloud options.
options.
A llama's a safe option since your
queries don't get collected for learning
Future prediction.
I'm not an expert on predicting what
will happen in hardware. I can predict
software moves better.
But I'll give you my two cents.
Currently, it is too expensive to be
considering running local AI. They're
also buggy, but a couple of years from
now, this will be realistic. I already
run a local AI using AMD Strix Halo, but
I'm still awaiting fixes from AMD to
make it more stable instead of the
constant crashing. Yes, I expect that it
will be fixed, but it might take a year.
Apple Mac Studio as an AI option is
incredibly expensive. Plan on 10K.
Nvidia DGX Spark is 5 to 6K.
A desktop with 3 5090s, 25K. So how can
I talk about these as options?
By the way, there's that new Brax open
slate project on Indiegogo that is an
Android Linux tablet that's inexpensive
and should perform most tasks you need
to do, even open claw with added privacy
safe features
at a reasonable price in spite of the
memory prices.
In the meantime, beyond basic uses, we
have to be on a holding pattern. Do not
buy a new computer with AI in mind.
Focus on buying used computers
Folks, privacy is of course the main
focus of this channel and I teach you
technology so you understand the risk
technology adds to your life. We have
people who discuss these issues at my
platform Brax.me.
To support this channel, we have some
products in our store that provide the
toolkit to retain your privacy. They're
awesome products. We have Brax Mail, an
email service with unlimited aliases and
identity protection. We have Brax
Virtual Phone, anonymous phone numbers.
We have Bytes VPN for anonymizing your
IP address and location and evading
privacy invading laws. We have the
Google phones, phones free from Big Tech tracking.
