**Do continual model improvements still matter for real‑world use?** ### Model Gains vs. Practical Value - **Benchmark progress:** Recent releases have shown impressive gains on scientific, math, and reasoning benchmarks. - **Diminishing returns for most users:** For everyday tasks, the difference between

“PhD‑level” response and a “good enough” answer is often negligible. - Speed over perfection: The presenter emphasizes that a fast, adequate answer is preferred over a slower, marginally better one for 99 % of personal use cases, including coding.

Why Model Improvements May Not Matter for Most ChatGPT Users

Matthew Berman

Summary Date: 2025-12-19 21:23:03

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3 min read

Why Model Improvements May Not Matter for Most ChatGPT Users

Introduction

The video opens with a reference to a recent article from The Information that discusses internal organizational challenges at OpenAI and how they affect ChatGPT. The presenter uses this as a springboard to ask a broader question: Do continual model improvements still matter for real‑world use?

Model Gains vs. Practical Value

Benchmark progress: Recent releases have shown impressive gains on scientific, math, and reasoning benchmarks.
Diminishing returns for most users: For everyday tasks, the difference between a “PhD‑level” response and a “good enough” answer is often negligible.
Speed over perfection: The presenter emphasizes that a fast, adequate answer is preferred over a slower, marginally better one for 99 % of personal use cases, including coding.

Consumer vs. Enterprise Perception

OpenAI: Seen primarily as a consumer‑focused AI company (ChatGPT UI, ads, shopping, hardware).
Anthropic: Viewed as an enterprise‑oriented player, known for its API and strong coding capabilities.
Reality check: The presenter argues the distinction is more perception than fact; OpenAI also serves enterprises, and brand familiarity with ChatGPT can drive enterprise adoption.

The Real Value Drivers

Integration ecosystem: Seamless access to calendars, email, web search, and other daily tools matters far more than raw reasoning power.
User education: Many consumers don’t fully understand the breadth of tasks AI can handle, limiting adoption of advanced features.
Speed and latency: Faster responses enable rapid iteration, especially in coding and brainstorming, outweighing incremental quality gains.

Internal Tension at OpenAI

Leadership split: Fiji Simo (head of applications) vs. Mark Chen & Yakob Pachacki (research leads).
Research vs. product: OpenAI publicly claims research is its core, yet compute resources are frequently reallocated to meet product demand (e.g., shifting GPU cycles from research to ChatGPT deployments after a viral image‑generation surge).
Talent retention: De‑emphasizing research could cause top scientists to leave, potentially slowing future breakthroughs.

The Competitive Landscape

Self‑improving AI race: A winner‑takes‑all scenario where the first company to achieve truly self‑improving models gains a permanent advantage.
Google’s moat: Deep integration across Gmail, Calendar, Drive, and the Gemini branding gives Google a distribution advantage that OpenAI must overcome.

Takeaways for the Future

Product focus: Success will hinge on embedding ChatGPT wherever users already work rather than solely on benchmark scores.
Balancing act: OpenAI must juggle short‑term product demand with long‑term research ambitions to stay competitive.
User empowerment: Educating users about AI capabilities and improving speed will drive broader adoption more than marginal model upgrades.

Conclusion

The presenter concludes that while model improvements are impressive on paper, they have limited impact for the majority of ChatGPT users. Real value comes from speed, integration, and user education, and OpenAI’s internal resource tug‑of‑war between research and product could shape its future competitiveness.

For most users, faster, well‑integrated responses matter far more than incremental model upgrades; OpenAI’s challenge is to balance product demand with research excellence while making AI seamlessly part of everyday workflows.

Full Transcript

I have a lot to talk about in this video
and it all starts with this article from
the information. How open AI's
organizational problems hurt Chad GPT?
And I'm going to be answering a broader
question. Does model improvement matter
all that much anymore? What I mean is
when we continue to see these incredible
gains on the common benchmarks, the
science benchmarks, the math benchmarks,
do these gains actually matter? I'm
going to make the case that no, they
don't for the vast majority of use
cases. And if you've watched this
channel at all over the last year, you
know I've been saying this for a while.
The common sentiment is if model
intelligence froze today, there would
still be so much work on the scaffolding
and implementation and deployment side
to just get the most value out of these
models the way they are today. And maybe
that's the friction that the
information's article is describing
within OpenAI. Let me read a little bit
of it. In prior years, every time OpenAI
made a big upgrade to the artificial
intelligence that powers the chatbot,
usage surged as people easily found ways
to get useful responses out of it, one
employee said. So, what it's saying is
in previous years, they would improve
the model quality. And we felt it,
right? Prior to this year, every model
release was some huge unlock, but at a
certain point, the models got really,
really good and it felt like the
improvements were iterative versus big
step changes. And so what would happen
is they would release a new model. It
would be incredibly more capable and
thus more people would hear about it,
more people would figure out how to use
it in their day-to-day lives. And that
leads us to where we are today in which
a ton of people, billions of people are
using AI every single day. But here's
the thing. If I already have PhD level
intelligence in my pocket wherever I go,
is there really that much benefit to be
gained from having the best PhD versus a
SOS so PhD? For me personally, most use
cases, not really. And to be frank, and
a lot of you might react strongly to
this, I don't even use deep research or
max level thinking all that much. There
are certainly times where I need it, but
for the vast majority of use cases, for
me, I'm using the regular model. I'm
just saying give me the best response as
quickly as you possibly can and keep
speed. I'm gonna come back to that. And
here's the key sentence. But even as
ChiaBT attracted more users this year,
improvements to the underlying AI models
intelligence and the in-depth research
or calculations it could suddenly handle
didn't seem to matter to most people
using the chatbot. And so I think that
is really what they're seeing. And so
there's this common sentiment that
Anthropic is more known for enterprise
users and chat GPT and OpenAI is more
known for consumers. And so that's
another thing I want you to keep in mind
as we go through this. Whether that's
true or not, that's the perception
today. And that perception is certainly
emphasized by the fact that Anthropic is
really only known for their API and
their models are incredibly good at
coding and mostly used for coding.
Whereas Chat GPT is more well known
through its interface rather than
through its API. And maybe that's just
my perception of it. But then you have
things like OpenAI acquiring Johnny Ives
company and designing the next
generation of consumer hardware devices.
You have them talking about ads in
chatbt. You have them talking about
shopping. All of these things are
consumerf facing. Whereas philanthropic
the only thing you hear from them is
model performance API and alignment. And
so back to the article and back to the
theme of this video, continued model
improvements to the core intelligence
don't really move the needle for Chad
GPT's customers, which are perceived to
be majority consumers. And another thing
that you probably know if you've watched
this channel at all, I am a speed maxi.
I've been saying this for a while. Speed
matters more than getting the absolute
best response. As long as the response
is right for 99% of my personal use
cases, I want the fastest response
possible. That includes vibe coding. And
yes, I want AI to write the best code
possible for me. But being able to
iterate quickly through different ideas
and different builds is more important
than having the best possible code. And
so, listen to this. Reasoning models
don't do much for Chad GPT, whose users
generally want answers fast. according
to a person who worked on them. Now, if
you think about Chat GPT as going
head-to-head with Google search, which
is one of the biggest markets on earth,
and Google search is the best business
of all time, then yeah, of course, they
want to go directly at that. But being
really good at search doesn't require
deep thinking. It doesn't require
spending more tokens on testime compute.
It doesn't require PhD level
intelligence. It's all about
integrations. Making sure you have
access to the right data. Making sure
you can crawl websites correctly. Making
sure you have access to my calendar and
my email and everything else that I use
every single day. That's where the real
value comes from. And I think a lesser
problem that they're facing that's
highlighted in this article is as
follows. Chat GPT faces an even bigger
problem than the issue with reasoning
models. consumers don't appear to
understand the full range of topics the
chatbot can answer questions about even
when it's using non-reasoning models
that give faster answers. So that's just
an education problem. I think that's
actually quite solvable and solvable in
the short run as AI continues to
infiltrate every single area of culture
and society then it's just going to
become part of what we do every day all
day. But I think this problem is
actually more acute when you think about
what models can actually do for you.
That's really where even I struggle. I
generally know what types of answers
chat GBPT and other AI are going to be
really good at and really good at
getting me good answers, but I'm less
good at identifying certain problems
that can be automated well with AI,
certain tasks that can be completed with
AI. That's something that I personally
struggle with. So, I have to assume
their broader customer base also
struggles with that. But again, that's a
very solvable problem. Now that's where
the crux of this article comes in, which
is the tension between the research
division and the product division of
OpenAI. This is Fiji Simo, new CEO of
applications at OpenAI, one of the most
senior leaders at OpenAI currently,
basically responsible for all
applications at OpenAI. And the
counterpart is Mark Chen and Yakob
Pachaki who run the research side. And
again, according to the article, there
is this perceived tension between the
research and the product side of the
business. So on Fiji's own blog, she
said, "At its core, OpenAI remains a
research focused company, and products
aren't the goal themselves." And that
might be the case, but I don't actually
think so. I think product is incredibly
important, but at the same time, they
need the best models. They need to be
seen as a researchbased company. They
need to be doing that cutting edge
research to retain the top research
talent because otherwise they're going
to lose talent to other research focused
organizations. That's just what
researchers want to do. They want to
research. And so if research is
deemphasized in open AI, researchers are
going to go elsewhere and that will
manifest in them falling behind on the
total capabilities of these models, the
intelligence of the models, maybe the
next breakthrough that allows the models
to be so much more useful. And you're
probably asking yourself, "Hey, Matt,
didn't you just say that model
capabilities are sufficient as they are
today? You don't need anymore. Why do
you care if you have all these
researchers?" Well, the flip side of the
argument is an absolute race towards
self-improving artificial intelligence.
And that's a winner take all race. The
first company to really achieve
self-improving artificial intelligence
is going to be the ultimate winner. They
are forever going to be in the lead. And
so no company wants to fall behind if
that ends up being the case. I'm not
saying that's what's going to happen,
but if we're doing the thought
experiment and trying to game it out,
these companies certainly don't want to
fall behind. And a quick thank you to
Dell Technologies for sponsoring this
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Max. Click the link in the description
below to let them know I sent you. And I
actually want to show you a counter
example to the notion that this is
ultimately a research company because
Greg Brockman through OpenAI's own video
said they've had to make very difficult
decisions about their compute
allocation. Specifically, they've moved
some compute away from research and
towards product towards the demand side
of the business.
>> We did not have enough compute to keep
that going and so we made some very
painful decisions to take a bunch of
compute from research and move it to our
deployment to try to be able to meet the
demand and that was really sacrificing
the future for the present. And so there
you see Greg Brockman talking about in
this moment in which their image
generation became insanely viral,
insanely popular, they had to take
compute away from research and put it
towards the demand side of their
business. And so that's just one little
example, but I'm sure he has to make
difficult decisions like that every
single day. In fact, I interviewed him
and he said something along the same
lines. Watch this. What is the
conversation internally at OpenAI like
when you're trying to decide where that
compute investment belongs?
>> Pain and suffering. It's the only it's
the it's the real truth. It's like it's
so hard because you see all these
amazing things and someone comes and
pitches another amazing thing and you're
like, "Yes, that is amazing."
>> Now, I want to go back to the perception
that Open AI is seen as a consumer AI
company and Anthropic is seen as an
enterprise AI company. I don't actually
think that's the case. I think a lot of
that is perception just because
anthropic is entirely focused on
enterprise and open AI is focused on
both consumer and enterprise. I tend to
be a focus maxi and of course if you're
not marshalling and focusing all of your
resources on a single thing you will
never be as good as your competitor who
is doing that. But that doesn't mean
they can't be incredibly good at
enterprise as well. And in fact, there's
something about having the brand name in
consumer and that translating to
enterprise demand. Chat GPT is the verb
for AI. It is very similar to how you
say, "Okay, go Google it for search."
People say, "Okay, just chat GPT it for
AI answers." And so when you have that
aura, when you have that brand name, of
course, when enterprise decision makers
are thinking about which products to go
with and they have this rich history
with Chacht in their personal lives,
they are more likely to want to go with
ChachiPT in the enterprise as well. In
fact, here's Sam Olman in an interview
that he just did saying exactly that.
>> I think people really want to use one AI
platform. People use their phone at
their personal life and they want to use
the same kind of phone at work. Most of
the time we're seeing the same thing
with AI. Uh the strength of chat GPT
consumer is really helping us win the
enterprise. Uh of course enterprises
need different offerings but people
think about okay I know this company
open and I know how to use this chat GPT
interface.
>> So where these companies really need to
focus and where I actually think Google
has an incredibly big moat is in the
integrations in the distribution. the
fact that at least me, I use Gmail, I
use Google Calendar, I use Drive, I use
so many different Google products and
they can simply have the star, the
Gemini logo appear everywhere. And they
did that with Gemini 3. That is so
incredibly powerful. And I think that's
probably the biggest uphill battle that
Open AI has. It's not being number one
on LM Arena. It's not this enterprise
verse consumer play. It is strictly how
do we get chat GPT to be everywhere that
our users already are. If you enjoyed
this video, please consider giving a
like and subscribe.

Summary

Why Model Improvements May Not Matter for Most ChatGPT Users

Introduction

Model Gains vs. Practical Value

Consumer vs. Enterprise Perception

The Real Value Drivers

Internal Tension at OpenAI

The Competitive Landscape

Takeaways for the Future

Conclusion

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