Okay, so picture this. AI isn't just your super
smart friend you ask questions anymore. It's
actually showing up with, you know, fully formed
ideas like pitching a startup, but for research
papers. Yeah, what's really fascinating here
is just how quickly the AI landscape is evolving,
right? It's shifting pretty dramatically, you
know, from just processing information or like
generating text based on prompts to... actively
creating and proposing genuinely novel concepts.
Exactly. It's a whole different ballgame, isn't
it? And that's what we're really diving into
today. This deep dive is all about these AI agents
that are proposing original research ideas, kind
of like they're pitching a new company concept.
But yeah, for the world of science and academia.
And we're pulling insights from this pretty significant
recent report. It feels like a dispatch straight
from, I guess, the front lines of AI development.
It details how these cutting edge idea generating
agents are actually being put to the test in
pretty rigorous ways. Yeah, it's like getting
the inside scoop on what's next. So our mission
today really is to unpack this source material
for you. We want to get into what these AI idea
generators are actually proving capable of right
now, how they were evaluated in this specific
benchmark test, and maybe most importantly, what
the results actually mean for you, whether you're
using AI for brainstorming, for ideation in your
own work, you know, whatever creative process
you're involved in. Let's unpack this. Right.
It's beyond just hype now. There are concrete
benchmarks attempting to measure creativity and
innovation in AI, which is, you know, a big step.
Right. And the core of this report, you know,
the main event is this test. They called it the
AI Idea Bench 2025. It sounds a bit formal, maybe,
but the premise is pretty cool, I think. They
essentially put four leading AI idea generator
agents through their paces like a competition.
And crucially, they didn't just give them a broad
topic and say, write something. They designed
the test framework to really mimic how venture
capitalists, you know, vet startups. They looked
at these AI generated ideas based on three key
criteria that matter in the real world, whether
it's a business or research idea. Did the idea
turn? get the right problem? Was it genuinely
new? That's the novelty aspect. And did it actually
look like something you could realistically build
or implement? That's the feasibility side. Oh,
applying that startup lens to research ideas.
That makes so much sense. You kind of need all
three for an idea to really. go anywhere, right?
But here's where it gets really interesting,
maybe even a little mind -bending, the ground
truth they used for comparison. They didn't just
have human experts guess if it was good. They
compared the AI ideas against a massive data
set, 3 ,495 brand new AI papers that were actually
accepted and presented at top conferences recently.
This part is absolutely key to the study's validity,
I think. They were incredibly meticulous about
the timing. Zero of this data. Zero of these
3 ,495 research papers existed before the knowledge
cutoff date for the models they were testing,
specifically GPT -4io's cutoff date of October
3, 2023. Whoa, like zero. None of it existed
anywhere for the AI to have potentially seen
it during training. Exactly. Zero. This completely
eliminates the possibility that the AI was just,
you know, echoing its training data or variations
of knowledge that already exist in the public
domain before that date. This was a true test
of whether it could propose... something that
was genuinely post -cutoff, something new to
the world after its knowledge was frozen. Okay.
That really isolates its ability to generate
something novel, something new, which is like
the whole point of ideation, right? Yeah. How
did they actually score the ideas against this
unseen data? They used a pretty smart two -step
scoring process. First, they looked at how well
the AI's idea aligned with a real paper that
actually got published in that post -cutoff data
set. Does it identify the same problem? Did it
propose similar approaches or experiments? designs
that measured the idea's relevance and depth.
OK, so how close was it to a real new paper?
Right. And second, they calculated a combined
score for novelty and feasibility. And this part
used citation data. Citation data. For novelty
and feasibility, that feels a bit counterintuitive,
doesn't it? Yeah. How does whether something
is cited relate to whether it's new or doable?
Well, it's used as a proxy, right? So for novelty,
if an AI proposes an idea or a method and there
are fewer existing papers citing similar work,
that could indicate a more novel idea. It's less
connected to the existing body of knowledge,
you see. Okay. Fewer citations of similar things
means it's maybe more novel. Got it. Makes sense.
And for feasibility, looking at citation data
related to the specific methods proposed in the
AI idea can be a pretty powerful proxy. If the
methods an AI suggests are already being adopted
and cited frequently in recent research, it suggests
they are more practical, more established, more
buildable right now. The source notes that this
citation -weighted feasibility score is actually
a handy proxy for uh commercial traction too
it tracks methods that are already demonstrating
usefulness and adoption in the field kind of
like market validation for the techniques themselves
oh i see it's not just about whether the idea
exists but whether the building blocks the ai
is suggesting are already proving useful in practice
like it's taking a pulse check on the technical
approaches to see which ones are gaining traction
and seem well practical precisely gives you a
sense of how grounded in current workable techniques
the idea is which is you know super useful fascinating
so So who are these agents and how do they actually
perform in this benchmark? What did the results
show? Yeah, so the report specifically highlighted
two, AI scientist and AI researcher, mainly because
they showed different strengths, which is interesting
in itself. AI scientist, this one was frankly
amazing at alignment. It hit a perfect 5 .0 score
on both the motivation for the research and the
experiment design parts. A perfect 5 .0. Wow.
So it didn't just get the gist. It got the why
and the detailed how perfectly. It really nailed
the concept and the proposed execution. Yeah.
So perfectly matching the core problem and the
detailed plan of what a human researcher came
up with and actually got published after the
cutoff. That's pretty wild. And maybe unsurprisingly,
given that depth of alignment with what was truly
novel and published, it also topped the charts
for novelty overall. So AI scientists seems to
be the agent you'd go to for generating bold,
deeply aligned and potentially really novel ideas.
OK, bold and novel, maybe the big swings, the
breakthrough stuff. What about the other one
you mentioned, AI researcher? Did it have a different
profile? It did. AI researchers scored best on
feasibility per step. It's hard to give the exact
number without the study's full context. But
the source mentions a specific number, 17 times
10 to the minus three, I think. But the point
was that its ideas looked more practical, more.
buildable right now compared to the others in
the test ah so maybe less blue sky more grounded
one for big new concepts and one for ideas that
seem more ready to actually implement or you
know build upon today that seems to be the clear
distinction emerging from the scoring yeah The
citation -weighted feasibility score indicated
that AI research was surfacing plans or methods
that align more with techniques already gaining
significant adoption or maybe even commercial
traction in the field. Okay, this study is super
interesting from a technical standpoint, but
let's translate this. What does this actually
mean for, you know, me, the person trying to
use AI for brainstorming or come up with new
projects? What are the practical takeaways from
this whole thing? All right, this is where we
really get to the so what's for you. The first
big takeaway is about quality having layers.
You know, just because an AI idea sounds relevant
or even aligns perfectly with a problem like
AI scientists did, doesn't automatically mean
the details are there to actually implement it
easily. There's a clear difference between nailing
the core concept and providing a truly practical,
buildable plan. Yeah, like the AI scientists
got the what and the why down cold. Maybe perfectly,
but maybe AI researcher was better at the how,
the actual practical steps. Exactly. Think of
it like maybe an architect providing a beautiful
visionary drawing versus the detailed blueprints
and engineering plans needed to actually construct
the building. Both are necessary, right? But
they represent different layers of quality or
usefulness depending on what you need right now.
I like that analogy. Vision versus blueprint.
Okay, that makes sense. What else should we take
away? Well, that citation -aware scoring approach
they used. That concept itself is a valuable
lens you can apply, even outside of this specific
study. When an AI gives you an idea, thinking
about how much related work or how many already
adopted methodologies exist for it, you know,
checking the citation pulse of the underlying
techniques is like getting a market or technical
feasibility signal. It's a quick check. If an
AI suggests a method for, I don't know, analyzing
customer data, I could kind of ask myself, based
on what I know, are people actually using this
method successfully? Is it proven? Is it gaining
traction? Right. It's a way to gauge how speculative
or how grounded the idea is in current practice.
And then there's the critical point about testing
against truly unseen data. If you're relying
on AI for genuinely fresh brainstorming, for
coming up with ideas that are new to you and
hopefully new to the world, you really need to
be wary of that training set echo. Training set
echo, yeah. I like that term. It feels like the
AI is just humming a tune it heard before, maybe
slightly remixed, not composing something really
new. Precisely. If the models you're using for
brainstorming haven't been rigorously tested
against information they absolutely could not
have seen during training, like... In this benchmark,
you can't be sure if they're generating genuinely
novel ideas or just variations of things that
already exist. That kind of testing is vital
if novelty is really your goal. That makes total
sense. You want to know if it's actually inventing
something or just giving you a slightly different
version of something you already know is out
there. Maybe even something I already know is
out there. And this really leads directly to
the idea of matching the tool to the task. Don't
fall into the trap of looking for one single
best AI agent for all your ideation needs. Based
on this study, AI scientists seems like the one
you'd use for inspiring those bold, maybe slightly
more theoretical, highly novel ideas that could
really push boundaries. The big breakthrough
concept ideas, the real moonshots, maybe. Yeah,
the big swings. Well, AI researcher. with its
higher feasibility score, seems better suited
for surfacing plans or ideas you could actually
ship or build upon right now using methods that
are already proving their worth. Right. You need
to pick the agent or maybe even use different
agents in different stages that fits your specific
goal for that brainstorming session. What are
you trying to do today? So it's less about finding
the perfect AI unicorn and more about understanding
the strengths of different tools and using the
right one for the right job you need done today,
like having different tools in your toolbox.
Exactly. And, you know, this applies whether
you're in academic research or, as the source
mentions, on a marketing team needing campaign
ideas, a product team brainstorming new features,
or even an investment team evaluating potential
market gaps. When you turn to generative AI for
ideation, explicitly apply this two -step filter
derived from this study. First, is the idea truly
on target for the problem you're trying to solve?
Okay, step one, relevance. And second, can it
actually be built or implemented with reasonable
effort? Step two, feasibility. Is it relevant
and is it doable? Right. Using that filter consistently
helps you cut through the noise of maybe brilliant
sounding but impractical ideas faster and helps
you spot potentially winning actionable ideas
more efficiently. It gives you a framework. That's
super practical. It gives you like a concrete
framework to evaluate what AI spits out instead
of just feeling overwhelmed or unsure if the
idea is any good or just noise. It does. It moves
you from passively receiving AI output to. actively
and critically evaluating it like a good investor
or good editor really okay so that study gives
us a deep look at ai's ideation capability which
is you know a huge step but the source also touches
on how ai is impacting other areas right now
showing the breadth of development let's just
take a quick look around the landscape it sketches
out maybe touch on a few highlights yeah it's
good to see these specific examples to ground
the broader trends we discuss gives it context
Totally. Like Amazon's apparently building this
big AI brain for its warehouse robots called
Proteus, I think. It sounds like it'll let the
robots follow plain English orders from humans,
which is kind of wild to think about. Plus, new
models like Wellspring for optimizing delivery
routes and an upgraded Sculpt model for stocking
shelves smarter. Yeah, that's AI moving directly
into complex physical operations and logistics,
aiming for massive efficiency gains. It's not
just soft. anymore. It's interacting with the
physical world much more. And AlphaFold3. Isomorphic
Labs, which is part of DeepMind, you know, says
it can now predict the structure of protein interactions
with other molecules, not just single proteins.
This could potentially unlock way faster drug
design, even for diseases that were previously
considered Undruggable. That's pretty huge for
medicine and biotech. Oh, absolutely. Predicting
molecular interactions beyond just protein folding
has been a major barrier for a long time. AlphaFold's
progress here is genuinely a significant potential
accelerator for pharmaceutical discovery if it
holds up. Big if, but huge potential. And OpenAI
still growing like crazy, apparently hitting
3 million. paying business users now. That's
a lot. And they're adding features aimed at making
ChatGPT more useful in daily work, like connectors,
which sounds like it integrates with things like
Google Drive, maybe, and this record mode in
the app for summarizing meetings you record.
Those features really signal a move towards embedding
AI deeper into core business workflows and knowledge
management. They want it to be an indispensable
tool for daily productivity, not just something
you chat with occasionally. There's even a policy
angle mentioned. Anthropic CEO Dario Amodei apparently
called for a national AI transparency law, not
freeze on development, importantly, but saying
we need transparency about these models. That
raises an important ongoing discussion about
how societies and governments will oversee and
regulate powerful AI models as they become more
integrated into our infrastructure and decision
making. Transparency is often seen as a key part
of responsible development, building trust. And
a cool safety application. Volvo's upcoming EX60
electric vehicle will have this AI -driven multi
-adaptive seatbelt system. It uses sensors and
AI to adjust based on something like 11 different
crash profiles in real time to try and protect
passengers better. Using AI for real -time predictive
safety adjustments in a physical product like
a car seatbelt. That's a pretty concrete and
potentially impactful application. Very tangible
benefit. And on the business side, this new company,
Shield Technology Partners, just got $100 million
in funding to launch an AI -enabled managed IT
services platform. Their strategy is to use shared
AI agents to automate a lot of the routine IT
support tasks that bog down human teams. Leveraging
AI for service delivery and automation, especially
in areas like IT support, where tasks are often
repetitive but require specific knowledge, makes
a lot of sense for scaling expertise and improving
efficiency. You'll probably see a lot more of
that. And just too quick. Cool tools that got
to mention. Eleven Labs dropped version three
of their platform for even more expressive text
to speech. That includes emotion tags, which
is pretty wild for TTS realism, and chat for
data, which apparently lets you scrape web pages
just using plain language prompts, which sounds
way easier than coding it yourself. Yeah, tools
like those continue to lower the barrier to entry
for using AI for specific complex tasks, making
things like creating expressive audio or extracting
web data accessible. to a much wider audience,
democratizing the tank in a way. So, wow, okay,
putting it all together, a lot happening. Okay,
so let's maybe try and unpack the bigger picture
from this deep dive. The main takeaway is pretty
stark, right? AI has genuinely moved way beyond
just answering your search queries or writing
simple emails. It's now actively stepping into
the creative space, generating ideas, even complex
ones like potential research proposals that,
based on this study, can stack up against novel
human -written papers. Yeah, it's not just summarizing
the past. It's helping sketch out the future
in a way. And, you know, just like investors
vet startups or like this AI idea bench study
scored these agents, we really need robust ways,
maybe that two -step filter we talked about,
to evaluate the quality of these AI -generated
ideas ourselves. Looking at things like genuine
novelty and practical feasibility is absolutely
crucial to cut through the noise. Absolutely.
An idea needs to not just sound good or be statistically
novel. It needs to have legs. needs to be potentially
achievable. And that means applying those filters
is on target for the problem. And can it actually
be built or implemented with reasonable resources?
So here's something, I guess, to leave you thinking
about, something to chew on after hearing all
this. Given that AI can now generate ideas that
actually align with and stack up against human
written research papers published in top venues,
how should we even begin to rethink the whole
process of creative ideation itself? Does it
fundamentally change things? This raises an important
question for all of us, doesn't it? In a world
where AI is becoming a constant potential co
-creator, how do we distinguish between... ideas
that are merely novel, maybe novel just based
on recombining its training data in a clever
way versus those that are truly impactful, truly
achievable and meaningful in the real world?
Where's the real insight versus just clever pattern
mashing? Right. Like what kind of human oversight,
what kind of human evaluation and curation becomes
the most crucial part of the process when AI
is doing so much of the initial generation and
heavy lifting on concepts? What's our role now?
It fundamentally shifts our role. perhaps, from
being the sole generators of ideas to becoming
expert curators, evaluators, maybe strategic
prompters and refiners of AI generated concepts.
Our value might move up the chain, so to speak.
Definitely something to think about the next
time you sit down to brainstorm, whether it's
with AI helping out or just you and a whiteboard.