March 30, 2026 • 28 mins
You went into 2025 excited about AI. You ended it exhausted. You weren't alone. And this conversation knows exactly what that cost you.
There's a moment in this episode where everything clicks. It happens when Brittany and Adal land on the question most executives are quietly carrying: if everyone now has access to the same AI tools, who am I going to be? That question is the heartbeat of this entire conversation. And it might be the most important leadership question of 2026.
The Future Signals 2026 report isn't a list of predictions. It's a map built on patents, VC reports, financial data, and a two-layer signal system that separates what is happening now from where things are cooking. Last year's signals? 85% became reality. So when the signals show up in your industry, you'll already know what they mean.
THE FIVE SIGNALS
Signal 01 — The agent that handles it
AI isn't a new hire. It's infrastructure, like electricity. The question isn't who to replace. It's how to wield it.
Signal 02 — A fragmented world
Borders, regulations, and restrictions are multiplying. The companies that prepared for flexibility won't just survive. They'll sprint.
Signal 03 — Programmable biology
AI isn't just accelerating research. It's redesigning from the atomic level up. Every industry that touches living things is affected.
Signal 04 — AI beyond the screen
AI is developing a physical language. It's not smarter robots. It's AI that can understand and navigate the world around you.
Signal 05 — The human premium
When everything can be built, what you choose to build and why becomes the ultimate differentiator. Human intent is the scarce resource.
THE NUMBER THAT WILL STAY WITH YOU
88% of companies are just accelerating old workflows with AI.
6% are fundamentally redesigning how they work and who they are.
The conversation gets honest about something you've probably seen on LinkedIn. Influencers claiming their 37 agents made them millionaires overnight while you were just having breakfast. It's almost funny. Until you realize how much that noise is quietly eroding your confidence.
The point was never the number of agents. It's the right technology, in the right hands, pointed at something that actually matters.
"When execution becomes abundant, intent becomes scarce."
This episode ends with a question worth sitting with: where are you on the maturity map? Just starting? Building? Scaling? The signals meet you there, designed for the stage you're actually in. Not the stage someone's performing on LinkedIn.
GO DEEPER
→ Download the Future Signals 2026 report: https://designthinkingjapan.com/thinking
→ Explore the open methodology on GitHub : https://github.com/DesignThinkingJapan/future-signals-2026-report
→ AI Leadership Academy, executive fluency for real decisions: https://designthinkingjapan.com/academy
AI is a marathon but it can feel like a sprint. That's why it's important to go with intent.
Listen
Watch
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
SPEAKER_01 (00:00):
Welcome to Future Signals 2026.
(00:03):
Now, did we write the FutureSignals or did we create the
Future Signals 2026 simply tohave more research out there?
Of course not.
There's really one core reasonwhy we developed the Future
Signals.
It's similar to 2025, but evenmore so in 2026, and it's to
make sure that we're able togive real confidence to
executives in their hands.
So 2025, you would behard-pressed to meet someone
(00:27):
that wasn't enthusiastic aroundAI, but equally, you probably
those same people ended up beingquite exhausted.
So they went from enthusiasm toexhaustion by the end of 2026
because there was just simply somuch noise.
So what we wanted to do is takeall of that noise and give it
some kind of meaning so thatpeople can make some really good
decisions.
SPEAKER_00 (00:46):
Yeah, the report serves uh a couple of purposes.
One of them is to provide anavigation uh point, uh a
playbook that is equally uhequipped with evidence, with
with actions.
This uh information that we'regonna that is being gathered in
the report.
It serves that.
Okay, okay, I've read this, thisis what is my what might come in
(01:09):
the next year.
So it serves as your preparationplaybook for this year.
SPEAKER_01 (01:13):
Right.
I really like that, particularlyif you think about um what the
structure looks like.
So it's at the end of eachchapter, it'll say, for example,
this is how we know the signalis calibrating, so
recalibrating, as in like it'snot right at all.
And this is how we know if thesignal is accelerating.
The thing that I also reallylike is if we think about AI and
the maturity map of AI, peopleare really in very different
(01:34):
positions.
There are people that arehonestly in 2026 just beginning
their AI journey.
There are certainly companiesout there that are quite, you
know, a lot on in their way whenit comes to maturity and they're
in their building phase.
And then there are certainlycompanies that are leading and
scaling, and that AI is a s is asimultaneous infrastructure
throughout their entireorganization.
SPEAKER_00 (01:54):
What can we learn from them?
SPEAKER_01 (01:55):
Exactly.
And so in the report, there'sthose three steps.
So matter no matter where youare, there's something to learn,
right?
But the thing that I think Ilike most about this report is
that it's not us taking a guessand saying, you know, we this is
our best guess, you know,believe us.
Um if anything, it's it's a it'sus taking deep research from
(02:16):
trends that are alreadyhappening out there and then
giving meaning to it to people.
So it's not, hey, believe us,you know, we're fortune tellers.
We're taking data that alreadyexists out there, but we've been
able to identify what's thecommon thread between them.
SPEAKER_00 (02:31):
So the the all of this has to do with the way we
created the report.
The 2025 Future Signals Report,Latis Report, was created
deliberately by the SensingJapan team and openly, like uh
saying that it was created withthe the assisted eyeball dragon
(02:52):
tape.
And that has to do a lot withthe way the the future signal
report is is is uh created.
Starting from 2025, in that yearthe Design in Japan team created
the report and openly sayingthat it was in collaboration
with AI.
So it was not just uh a bunch ofanalysts, a bunch of uh people
(03:13):
or experts as SMEs trying tofigure it out or to predict
what's coming up.
But how do we use the AI toaccelerate it?
And for the first time, thatarchitecture was open.
So it's available on theSentinel Japan GitHub for people
to have a look.
And of course, when that reportwas created, we put it to the
test.
At the end of the year, welaunched 13 independent sources
(03:35):
to analyze and to test how manyof the predictions that were
there back then became truth.
And we were very happy toobserve that 85% of those
predictions are becoming areality, which means that the
richness of the information isuh a solid uh uh source of
information.
Right?
SPEAKER_01 (03:55):
Uh 2025, like all things, I mean human-centered
design is at the core ofeverything that we do.
Um, and iterations obviouslykey.
Um and in 2025, we learned thatit might be uh helpful,
valuable, uh to add anotherlayer of research.
So we had our context layer,which is what's going on right
now.
So we pulled everything fromfiled patents to uh voices to VC
(04:19):
reports to financial analysis,kind of what's going on right
now, so the context layer.
And then we moved to also thesignal layer, which is where are
things going?
And so we were able to thentriangulate, right?
So between what's happening butalso where are things going.
SPEAKER_00 (04:34):
So um what does it what about it, right?
So what?
Yeah, who cares?
So what?
The the so what is that thethese two signals, which is kind
of uh one of the early stages,means that we're observing the
current state of activities,industrial and societal
activities.
However, the second signal tellsyou where things are cooking,
(04:55):
like things are getting ready.
Like, for example, there are uhthe robotic, let's say, uh
revolution that we're observingright now.
All of those uh products werestarting to get prepared five
years ago.
Five years ago they wereresearched, and this year they
launched all together.
So the analogy and theimportance of this new
architecture is that it startswith the human intention, and
(05:16):
then we launch these uh signalresearches from two different
angles.
Then at each step there's ahuman validating the outcome.
And when we have these signals,we clean them.
They got cleaned.
But not only that, they gotchallenged.
How do we know if they're just aphase?
(05:36):
How do we know if this doesn'tbecome true?
And through the data gathering,the data analysis, the data
challenging, the diverse sourcesthat challenge each other, and
the combination of thousands ofthousands of uh bits and bytes
of information, we came up withsomething, uh the Scientist
Japan Research team came up withsomething very interesting.
(06:00):
Very interesting, and we believeuh uh also uh fun to read.
SPEAKER_01 (06:05):
Right.
Um so we went from four signalsto five signals this year.
Um and the five signals we'regoing to uh go through maybe the
obvious read and then kind of goa little bit deeper.
SPEAKER_00 (06:16):
So we will yeah, we'll share each one of them
just a little bit.
SPEAKER_01 (06:19):
Oh, just exactly just a little bit.
Um so first things first, webegan with the agent that just
handles it.
So when people think agents,I've heard time and time again
people say something like,Should my next hire be a VA or
an AI?
Which is fundamentally the wrongquestion to ask because you are
thinking that AI is in fact anemployee when we found out
(06:40):
that's not really true at all.
In fact, it has a total notheruh materialization being that it
is not an employee, it is verymuch infrastructure.
You do not hire an AI any morethan you hire electricity.
You obviously pay forelectricity, you hire
electricity for your uh team touse, but it's not necessarily on
(07:02):
your org chart, right?
So dive more into um with thatinitial read of, oh my gosh,
it's agents.
Agents are just going to likeessentially replace all of the
people into rethinking theinversion of uh the AI.
SPEAKER_00 (07:18):
We have seen uh how how long do you want to run the
chartic?
I thought it was just snippets.
SPEAKER_01 (07:25):
Yeah like just a couple minutes, like each one.
SPEAKER_00 (07:30):
Okay, okay, okay.
Um the inversion, the obviouspart is that AIS are here and we
will start uh firing people andget wins from that potential
savings.
However, what we're observing isthat uh most product companies
(07:51):
are taking the human, equippingwith these tools and multiplying
their output.
What it means that they willhave a aggressive,
hyper-aggressive production,right?
We're leaving behind thecompetitors but also providing
faster value or bigger value atlower cost to the clients, which
of course is a is a greatadvantage.
(08:12):
However, observing this asinfrastructure is critical
because you can, if you're inconstruction, you can break down
a house with a hammer, or youcould get one of these uh you
know caterpillar machines andpfft brick holes and move
faster.
But the machine needs intention,needs direction, needs a point
of view, needs decision, right?
(08:33):
And needs someone to wield it.
It's an amazing sword.
And today, seeing how thisinfrastructure connects, right?
Has to be aligned with thepurpose of the company, with the
goal of the company, with thevalues, and of course, combining
all that aspects is what makesit a good infrastructure.
SPEAKER_01 (08:52):
Right.
So moving AI not being uh anemployee, but rather
infrastructure.
And then if we think aboutSignal 2, which is the idea of a
fragmented world.
So interestingly, I uh went touniversity during during the age
of globalization.
You know, the world is flat, um,you know, you can have breakfast
in Berlin, dinner in New York,that kind of that was very much
(09:12):
um the mode of the time.
Um, but we've seen very much in2026 just how the universe
always swings back and forthbetween when one thing is true,
very soon the actual oppositebecomes true.
So if we think about thefragmented world, the idea that
you have regulatory frameworks,for example, but actually that's
(09:35):
not really what we're talkingabout, that just each country
has their own regulations.
Um, and that you know, sometimesone market might be more
expensive than the other, it'sactually deeper than that,
right?
SPEAKER_00 (09:44):
Yeah, the obvious the the the as we call it, the
obvious read is like we'rehaving regulations and more
restrictions.
Uh the non-obvious read is thatthese new restrictions create
opportunity.
Yes.
Because the opportunity goes tothose companies that get
prepared, that get ready tojump.
What happens when I'm developingin some infrastructure, let's
(10:05):
say with uh some countrypreferences, and then I move
abroad because my company wantsto be or is international and I
cannot operate with the sameinfrastructure?
How do I anticipate this tobecome this seamless transition?
How do I become transparent inthose movements?
How do I allocate resources, notin the old way, which was uh
(10:26):
let's choose one vendor, oneprovider, and this is good for
all, like one fits all.
But how do I equip myself in anagile way, in a very flexible
way, to anticipate changes, notonly changes on regulations, but
changes on preferences, what thepeople might like, what the
technology might bring.
(10:47):
How do I make my own operationum non-restrictive but
adaptative?
SPEAKER_01 (10:52):
So, signal two, we knew to we had enough data to
show us obviously that it isgoing to be true, which is why
we listed it as a signal.
But we didn't expect for it tobe proven this quickly, right?
So in tw I mean, we're this isum a couple of weeks ago, yeah,
March 2026 today.
Um but we saw even you know lateFebruary um that there was a
(11:13):
clear mandate from the USgovernment that, hey, we're no
longer going to be uh usinganthrop where there was a there
was a call or a suggestion to nolonger use anthropic um
technologies.
Uh and then this this was reallylike a switch overnight, right?
SPEAKER_00 (11:28):
And this is Yeah, that's uh one of the challenges.
What happens when because mostcompanies are building on
multiple stacks of technologies.
And I even heard some some uhlet's say more uh novice voices
that says, yeah, but we have ourown software developer teams.
So we're okay because we developour own technologies.
But those technologies are builton top of something else, in the
(11:50):
terms of software, IDEs,licenses, APIs, infrastructure,
chipset, electricity, we go tothe electron level.
So knowing that some of thesepieces depend on each other and
how do you become flexiblecreates creates reality.
And what happened with thattropic being flagged as uh as
the supply chain risk, uh, whathappened with the government,
(12:10):
and right now, of course, thenin this debate creates a clear
evidence of the importance ofthis signal.
SPEAKER_01 (12:16):
Right.
And that the signal, obviously,we meant as a obvious as a
geographical level, being so,for example, we have like the US
uh mandate to things, or forexample, German or Japan,
whatever.
But we even saw that this is notjust like a foreign adversary
thing, this can even happenwithin a nation state itself.
So super interesting stuff.
SPEAKER_00 (12:33):
This can be a single bit of.
SPEAKER_01 (12:35):
Yeah, that's gonna be another episode in and of
itself.
So signal three being aboutbiology, I think the obvious
thing that people kind of go tois that AI can make you know
drug research faster.
So if you equip an a scientist,so if we talk about the idea of
an AI scientist, the idea thatyou'll have a scientist, you
know, being you know using AIreally well, but there's
actually an inversion to that,right?
(12:56):
That it's not a scientist usingAI, rather something quite
different.
SPEAKER_00 (12:59):
Yeah, when you start w mo most uh uh most companies
use uh RD directly orindirectly, regardless of the
industry that you are, right?
But when you think about the theobvious path, which is how do I
use my research and developmentteam to find something versus
how do I use first principle andredesign from bottom up from
(13:24):
bottom up from the materiallevel, the things that I'm going
to need changes fundamentally.
And I'm going to probably hintinto the next into the next
signal.
We're going from assisted uhactivities, from uh borders,
right?
We're going to biology, which isunderstanding the chemistry, but
(13:46):
also going to the physics.
So when AI is understanding thephysics, it's also understanding
what I can do to manipulate thechemistry to make better
outcomes.
So programmable biology is notonly at the chemistry level,
it's at the infrastructurelevel.
How do I create the elementsthat I need to operate in a
(14:09):
better way?
SPEAKER_01 (14:09):
Right.
So this is obviously if we thinkabout like an industrial
application.
So this is everything from ifany any person that uses uh
living living uh or organicmaterials, right?
So it's whether you're inagriculture or whether you're in
manufacturing, if you can goback down to the pr physics
first principles layer and say,how can we fundamentally do this
differently or more effectively?
(14:30):
I mean, it's an interestingquestion, which as you said
suggests to then signal fourbeing that AI takes on a
physical context.
So I think the f the mostobvious kind of thing that comes
to mind is, oh, well, everyone'sgonna have a robot or um jumping
into signal number four.
Right, which I right, and sojumping into signal number four,
(14:51):
the idea of that AI is beyondthe screen.
AI has a physical context, whichmeans that we've gone from AI as
infrastructure, AI havingborders, AI being chemistry, and
then the floor signal, AI beingphysics, understanding that AI
not only is uh well, if you takea step back, I guess one one of
the more simple or the mostobvious read is that robots can
(15:12):
do more physical tasks.
Robots get better at doingthings.
But if you think about it, AI,uh, unlike, for example, if you
think about traditional softwareor the internet, can live beyond
your phone or your actualscreen, your computer screen, TV
screen, whatever.
It can be in so many differentthings, really anything.
It's developing an actualphysical language in order to
(15:34):
live and interact with theworld, right?
So it's not just we've gotsmarter robots, it's actually AI
understanding and being able tonavigate our physical world.
SPEAKER_00 (15:43):
Yeah, the understanding is passing from
the paper to the physicalthings, and not only having a
robot that manipulates things,but could be an assistant, could
be you could could be your LLM.
When you're having aconversation, the advice, the
back and forward that you get,it's accurately because
understand the physics of theworld.
That you're wearing these kindof shoes and the floor is kind
(16:03):
of wet, right?
Take that to motorsports, tomanufacturing, take that to uh
healthcare, to any industry.
How these uh physicalconstraints and the physical
combination of all thoseelements have affect on the
desired outcome.
Having that is the vocabularythat we will start sharing with
them.
Because they start getting thisunderstanding of the world and
(16:25):
goes to the first signal, right?
Things that we're starting toprepare become a reality.
Right, exactly.
And that cycle repeats.
SPEAKER_01 (16:33):
And usually when we get to about signal four, this
is when people kind of like puttheir pens down and say, Do I
give up?
I'm a human.
Does that mean that I think thatI'm completely irrelevant?
And interestingly, even thoughour first four signals highlight
the maturity of AI as atechnology, our fifth signal, if
anything, is really theconnective tissue, which is the
idea of a human premium.
(16:54):
So this means that it's not thefact that AI can do more and
then con human contributionbecomes less, and then you know
what, you should just like, youknow, turn around, go home.
Everybody have uh, you know,like universal basic income, you
know, and everyone go home.
That's actually not what we'resaying.
We're actually saying somethingquite different here.
SPEAKER_00 (17:11):
Yeah, when you can build anything, what would you
like to build?
Right.
And as we as we mentioned, if weconnect the four signals, it's
about having uh AI that canaugment, multiplies you.
You have things that can beprogrammed to the atomic level,
you have physical uh robots orphysical elements that can be
added into that equation, right?
(17:32):
You have different frontiersthat could allow you to
manipulate and to operate withdifferent contexts.
Means like uh the the firstintuition is as you as you
mentioned, I give up.
But that also means that knowingwhat to build, having the right
person to direct will changethings.
You probably won't have fiveoperators, but you have one
person directing a small team ora big team of call be robots,
(17:56):
physical robots, or uh digitalrobots, taking action, taking
activity.
That's what makes thedifferentiator.
Imagine this analogy, like uhand we share this in other
episodes.
If all of us ask our favorite uhClaude, Gemini, uh Perplexy,
whatever, we said uh give me 10good ideas to make for a party.
Yeah.
All of us will have 10 goodideas.
(18:17):
But because it's a probabilisticmodel, all of us will have
mostly the same ideas.
So how do we differentiate atindustrial level when we receive
the same?
Right.
That's where the human comesfrom.
SPEAKER_01 (18:29):
It's our guidance.
SPEAKER_00 (18:30):
That that's the human intention.
SPEAKER_01 (18:32):
Right.
Now, when people talk aboutthat, when we say where's the
opportunity, right?
Where is actually theopportunity?
I think this is when we havetheir 88.6 uh percentage come
in.
So 88% being that 88% ofcompanies are simply taking
existing workflows and thenaccelerating them with AI,
right?
Which isn't wrong.
However, it's limited.
(18:53):
It's limited, exactly.
So it's but if you look at the6% that are going deeper and
they're actually fundamentallyredesigning the idea of uh their
their workflow, so not just howhave we done it and then how do
we make it faster, they'rethinking how might we completely
do it differently?
So for 88.6, when we're lookingat where is the opportunity, I
think the opportunity is notjust in making your traditional
(19:16):
workflows faster.
I think the real opportunity isredesigning through human intent
your your future workflow.
One of my favorite questionsthat we often ask is well, when
everyone has access to the sametechnology, who are you going to
be?
What do you want people to sayabout you?
How do you want your customersto feel?
These are all the kinds ofconversations that ironically I
(19:38):
think are the most important inthe age of AI.
SPEAKER_00 (19:40):
I was uh I was having some some conversation
out there and some of them werelike, okay, I need to do AI.
Tell me what's your what do youwant to do?
Oh well, I have this thing and Ithink AI is here.
So just the exercise ofreimagining was my my let's say
my reflection with them.
But what do you need to do thesame?
Because if we had the AI, wecould just create this engine
(20:01):
that replaces all the sevensteps.
You just need one.
Like what do you need tomanufacture?
Imagine if this is manufacturingfor for uh the pieces for an
engine.
What do you need to manufactureall of this if you can, let's
say, redesign and create justone, reduce it to simple things.
And I think that's the the thehuman ingenuity, right?
The human intention.
SPEAKER_01 (20:21):
Yeah.
This is also very much alsocourage as well.
This is where, for example,leadership um is most effective,
impactful, maybe, when youactually take courageous
decisions, when you have acompelling vision for the
future.
Because if you're just talkingabout AI in your organization
as, hey, it can help you writefaster emails or more polite
(20:43):
emails, or it can check somedocuments for you, people go,
oh, that sounds okay.
But if you until youfundamentally rethink, how might
this reimagine not only how wework, but who we are, I mean, I
think that's where people startreally leaning into the idea of
our fifth signal being the humanum the human premium.
SPEAKER_00 (21:02):
So combining with combining with the fact that you
can visualize things faster thanever.
Right.
The c the cost to see thingstangible has reduced.
So you don't have to wait sixmonths, four months, uh one a
year to see a prototype.
If you're in BC, you're notwaiting, you know, you should
not be waiting months to see aprototype from your from your uh
(21:23):
the teams that are pitching toyou.
If you have an idea, thecourage, the courage becomes uh,
let's say, easier to to let'ssay to keep.
It it becomes easier to keep themomentum because the risk is
reduced by the acceleratedexecution.
Right?
You can contain it, analyze it,validate it, prove it, and move
(21:45):
forward.
Right.
The risk of uh what are yougoing to lose is less is what if
we're right.
Right.
SPEAKER_01 (21:51):
And I think this is the the exact moment to circle
back and think about your ownpersonal context.
So, of course, when we thinkabout these.
The five signals, there's a lot,there's a lot going on.
But the number one thing that wealways say in human-centered AI
is what's your problem?
What are you trying to do?
What's your context?
So when you're reading thereport, really reflect on which
(22:13):
signal is applying most to me.
And then also think about, youknow, even in terms of the
maturity, where are you?
Are you starting?
Are you building?
Are you scaling?
And I think having these, umhaving the report, being able to
triangulate your positionthrough what's what's the
signal, where am I at on mymaturity, and also if the signal
is even um recalibrating or evenaccelerating.
(22:36):
So being able to identify whereyou are and what's really
relevant to you, I findpersonally really helpful.
And it really just begins withthat very simple step of what's
my problem?
Because you may look at thesignals and think, oh my gosh,
how does you know the physicalcontext apply to me?
But there's some really greatexamples and some really great
case studies.
So my number one recommendationto people is to reflect not on
(22:58):
just the signals just as awhole, but think about you, your
business.
Where are you going?
Where do you want to go?
Who do you want to be?
SPEAKER_00 (23:06):
So maybe it's a good opportunity to remind the
viewers.
Yeah, there's uh resources.
One resource is the uh featuresignals report, and every signal
points to the same the samedirection.
When execution becomes abundant,intent becomes scarce.
(23:27):
Other resources, of course, canbe found in the Scientist Japan
website, like uh AI customdevelopment development, like uh
could be accelerated, but alsothe academy to have bigger
conversations about this.
SPEAKER_01 (23:37):
Yes, yes, exactly.
SPEAKER_00 (23:38):
Maybe you should say that or me.
SPEAKER_01 (23:40):
No, I mean you said it already, so it's fine.
Yeah, so I think it's a goodthing.
SPEAKER_00 (23:42):
No, you say it better.
I think you could say it.
SPEAKER_01 (23:44):
Hang on a second.
Um Right, which is also um uhlet me just take a minute to
remind everybody that uh wealways we always say um at at
DTJ that AI is a marathon.
It feels like a sprint, but itis most definitely not a sprint.
It is most certainly a marathon.
SPEAKER_00 (24:00):
George Hoots had a good point.
What did he say?
George Hoots.
Uh actually you mentioned it.
That he was saying like uhthere's a lot of of uh messages
out there, like theseinfluencers.
I was building with 400.
SPEAKER_01 (24:14):
Yeah, so I was reading, so it was George Hoots'
LinkedIn, which I thought wasinteresting that he has a
LinkedIn and he like it wasn'ton Twitter or it wasn't his
GitHub or something like alittle bit more happy.
SPEAKER_00 (24:22):
I love going to Link Link Thing and and important
things.
SPEAKER_01 (24:26):
Yeah, so I was looking at one of his LinkedIn
posts, and and one of them said,I'm so sick of like waking up in
the morning and somebody saying,I've just I had my 37 agents
work overnight and you know nowI'm doing whatever, like you
know, 10 million, you know, uhmonthly, you know, recurring
revenue or something like that.
SPEAKER_00 (24:44):
And he said, And what were you doing?
SPEAKER_01 (24:46):
And the point was he said, and what were you doing?
Yeah, and what were you whatwere you doing?
Like again.
SPEAKER_00 (24:50):
Having breakfast.
SPEAKER_01 (24:51):
Exactly.
And then what were you guysdoing while my you know 37
agents were making me thesemillions of dollars?
Well, you were just having yourbreakfast like a pleb.
And I laughed because I thoughtthat's very much kind of what it
feels like that you have thisabsolute disconnect between some
people that are kind of makingmore noise around AI.
SPEAKER_00 (25:09):
They're actually doing.
SPEAKER_01 (25:11):
Exactly.
And then his point was youpeople are gonna get found out.
SPEAKER_00 (25:15):
The people that are saying, I am doing all this with
400 agents, maybe you don't need400, you just need a few ones
well, well equipped.
So remember, this is the erawhen you have startups that were
built with five people, the onebillion, the one billion dollar
company, the one person billiondollar company is not about
having 300 agents.
It's having the right amount oftechnology.
(25:36):
Agentic, biology, robotic, uh inin terms of uh uh let me try
again.
It's it's about having the rightamount of of technology.
It's it's having the right um uhamount of of resources and
technology, agentic, biological,like the in terms of frontiers,
in terms of of uh of uh photos.
(25:56):
Let me try again.
Let me try again.
SPEAKER_01 (25:58):
You go go in go in order.
You have to go frontier clinicaland physical.
SPEAKER_00 (26:02):
Yeah, well it's it's not about having like the this
vast amount of of those, it'shaving them in the right moment
in terms of agentic, in terms ofthe frontiers, in terms of a
biology, in terms of therobotics, in terms of the
physical context, not justrobotics.
The physical context and interms of the people who's
wielding it.
What are the right amount andthe good intent that you're
going to do?
That that means that you have anunstoppable force.
(26:26):
Not only 400 agents to the one,I don't know, just to be an
influencer on LinkedIn onTwitter.
SPEAKER_01 (26:32):
Yes, exactly.
And so I think this is a anothermoment.
Um if this is something that ruh resonates with you, this kind
of you know conversation aroundAI, um stay with us.
Of course, we have our uhhuman-centered AI series, um,
which is always free.
Um if you're in Tokyo, drop by,um, come and uh come and visit
us.
We also have our academy, whichis both remote and online now,
(26:54):
um, where we develop anexecutive level fluency in
people.
And I think this is reallyinteresting because we have lots
of people that are sitting on onbudgets, on resources, and
they're thinking, I really wantto make a good decision.
You know, I want to make a gooddecision.
Where do I invest?
How do I invest?
SPEAKER_00 (27:09):
I don't like you say sitting on budgets.
Feel like a very like a like youwant to remove the budget from
them.
Yes, but that's a consequence.
SPEAKER_01 (27:17):
So we have uh and so for example with our academy,
it's really focused on executivefluency, which I really like
because it's not fake fluency,it's actual technical fluency.
You will be able to thenidentify what does an AI stack
look like.
Uh it's not just kind of fakefluency, like it's actual real
fluency, which I think is reallycool.
SPEAKER_00 (27:36):
It's a strategic fluency for decision making.
SPEAKER_01 (27:39):
Exactly, exactly.
Knowing what to build, right?
That's all what it's all about.
And then of course you can come,you can call us anytime that you
need any also software builds asor any AI application builds as
well.
So with that, this was our uh2026 Signals.
Uh we are very proud of thiswork.
I think it is for me, huh?
SPEAKER_00 (27:58):
Uh nice fact uh during this week, there's been
demand of downloads.
I have already passed thedownloads that we can do.
From last year, yeah.
SPEAKER_01 (28:09):
Yeah, exactly.
Um, yeah, yeah, which which isum really exciting.
So definitely go over and andcheck it out.
And the thing that I like reallya lot is um how many people jump
to the methodology that they'relike, I want to check out your
GitHub.
I didn't think that would belike as interesting to people,
but there are plenty of peoplethat are like start from the
GitHub and they're like, give methe technical architecture
(28:30):
first, um, which I think is alsocool.
I mean, but there's there'seither way.
So um, no, we are here anytimethat you are looking for a more
signal than noise, come to us.
This is where you're gonna findit, and we will see you in our
next episode.
Thank you.
Thanks, everyone.
Welcome to Future Signals 2026.
(00:03):
Now, did we write the FutureSignals or did we create the
Future Signals 2026 simply tohave more research out there?
Of course not.
There's really one core reasonwhy we developed the Future
Signals.
It's similar to 2025, but evenmore so in 2026, and it's to
make sure that we're able togive real confidence to
executives in their hands.
So 2025, you would behard-pressed to meet someone
(00:27):
that wasn't enthusiastic aroundAI, but equally, you probably
those same people ended up beingquite exhausted.
So they went from enthusiasm toexhaustion by the end of 2026
because there was just simply somuch noise.
So what we wanted to do is takeall of that noise and give it
some kind of meaning so thatpeople can make some really good
decisions.
SPEAKER_00 (00:46):
Yeah, the report serves uh a couple of purposes.
One of them is to provide anavigation uh point, uh a
playbook that is equally uhequipped with evidence, with
with actions.
This uh information that we'regonna that is being gathered in
the report.
It serves that.
Okay, okay, I've read this, thisis what is my what might come in
(01:09):
the next year.
So it serves as your preparationplaybook for this year.
SPEAKER_01 (01:13):
Right.
I really like that, particularlyif you think about um what the
structure looks like.
So it's at the end of eachchapter, it'll say, for example,
this is how we know the signalis calibrating, so
recalibrating, as in like it'snot right at all.
And this is how we know if thesignal is accelerating.
The thing that I also reallylike is if we think about AI and
the maturity map of AI, peopleare really in very different
(01:34):
positions.
There are people that arehonestly in 2026 just beginning
their AI journey.
There are certainly companiesout there that are quite, you
know, a lot on in their way whenit comes to maturity and they're
in their building phase.
And then there are certainlycompanies that are leading and
scaling, and that AI is a s is asimultaneous infrastructure
throughout their entireorganization.
SPEAKER_00 (01:54):
What can we learn from them?
SPEAKER_01 (01:55):
Exactly.
And so in the report, there'sthose three steps.
So matter no matter where youare, there's something to learn,
right?
But the thing that I think Ilike most about this report is
that it's not us taking a guessand saying, you know, we this is
our best guess, you know,believe us.
Um if anything, it's it's a it'sus taking deep research from
(02:16):
trends that are alreadyhappening out there and then
giving meaning to it to people.
So it's not, hey, believe us,you know, we're fortune tellers.
We're taking data that alreadyexists out there, but we've been
able to identify what's thecommon thread between them.
SPEAKER_00 (02:31):
So the the all of this has to do with the way we
created the report.
The 2025 Future Signals Report,Latis Report, was created
deliberately by the SensingJapan team and openly, like uh
saying that it was created withthe the assisted eyeball dragon
(02:52):
tape.
And that has to do a lot withthe way the the future signal
report is is is uh created.
Starting from 2025, in that yearthe Design in Japan team created
the report and openly sayingthat it was in collaboration
with AI.
So it was not just uh a bunch ofanalysts, a bunch of uh people
(03:13):
or experts as SMEs trying tofigure it out or to predict
what's coming up.
But how do we use the AI toaccelerate it?
And for the first time, thatarchitecture was open.
So it's available on theSentinel Japan GitHub for people
to have a look.
And of course, when that reportwas created, we put it to the
test.
At the end of the year, welaunched 13 independent sources
(03:35):
to analyze and to test how manyof the predictions that were
there back then became truth.
And we were very happy toobserve that 85% of those
predictions are becoming areality, which means that the
richness of the information isuh a solid uh uh source of
information.
Right?
SPEAKER_01 (03:55):
Uh 2025, like all things, I mean human-centered
design is at the core ofeverything that we do.
Um, and iterations obviouslykey.
Um and in 2025, we learned thatit might be uh helpful,
valuable, uh to add anotherlayer of research.
So we had our context layer,which is what's going on right
now.
So we pulled everything fromfiled patents to uh voices to VC
(04:19):
reports to financial analysis,kind of what's going on right
now, so the context layer.
And then we moved to also thesignal layer, which is where are
things going?
And so we were able to thentriangulate, right?
So between what's happening butalso where are things going.
SPEAKER_00 (04:34):
So um what does it what about it, right?
So what?
Yeah, who cares?
So what?
The the so what is that thethese two signals, which is kind
of uh one of the early stages,means that we're observing the
current state of activities,industrial and societal
activities.
However, the second signal tellsyou where things are cooking,
(04:55):
like things are getting ready.
Like, for example, there are uhthe robotic, let's say, uh
revolution that we're observingright now.
All of those uh products werestarting to get prepared five
years ago.
Five years ago they wereresearched, and this year they
launched all together.
So the analogy and theimportance of this new
architecture is that it startswith the human intention, and
(05:16):
then we launch these uh signalresearches from two different
angles.
Then at each step there's ahuman validating the outcome.
And when we have these signals,we clean them.
They got cleaned.
But not only that, they gotchallenged.
How do we know if they're just aphase?
(05:36):
How do we know if this doesn'tbecome true?
And through the data gathering,the data analysis, the data
challenging, the diverse sourcesthat challenge each other, and
the combination of thousands ofthousands of uh bits and bytes
of information, we came up withsomething, uh the Scientist
Japan Research team came up withsomething very interesting.
(06:00):
Very interesting, and we believeuh uh also uh fun to read.
SPEAKER_01 (06:05):
Right.
Um so we went from four signalsto five signals this year.
Um and the five signals we'regoing to uh go through maybe the
obvious read and then kind of goa little bit deeper.
SPEAKER_00 (06:16):
So we will yeah, we'll share each one of them
just a little bit.
SPEAKER_01 (06:19):
Oh, just exactly just a little bit.
Um so first things first, webegan with the agent that just
handles it.
So when people think agents,I've heard time and time again
people say something like,Should my next hire be a VA or
an AI?
Which is fundamentally the wrongquestion to ask because you are
thinking that AI is in fact anemployee when we found out
(06:40):
that's not really true at all.
In fact, it has a total notheruh materialization being that it
is not an employee, it is verymuch infrastructure.
You do not hire an AI any morethan you hire electricity.
You obviously pay forelectricity, you hire
electricity for your uh team touse, but it's not necessarily on
(07:02):
your org chart, right?
So dive more into um with thatinitial read of, oh my gosh,
it's agents.
Agents are just going to likeessentially replace all of the
people into rethinking theinversion of uh the AI.
SPEAKER_00 (07:18):
We have seen uh how how long do you want to run the
chartic?
I thought it was just snippets.
SPEAKER_01 (07:25):
Yeah like just a couple minutes, like each one.
SPEAKER_00 (07:30):
Okay, okay, okay.
Um the inversion, the obviouspart is that AIS are here and we
will start uh firing people andget wins from that potential
savings.
However, what we're observing isthat uh most product companies
(07:51):
are taking the human, equippingwith these tools and multiplying
their output.
What it means that they willhave a aggressive,
hyper-aggressive production,right?
We're leaving behind thecompetitors but also providing
faster value or bigger value atlower cost to the clients, which
of course is a is a greatadvantage.
(08:12):
However, observing this asinfrastructure is critical
because you can, if you're inconstruction, you can break down
a house with a hammer, or youcould get one of these uh you
know caterpillar machines andpfft brick holes and move
faster.
But the machine needs intention,needs direction, needs a point
of view, needs decision, right?
(08:33):
And needs someone to wield it.
It's an amazing sword.
And today, seeing how thisinfrastructure connects, right?
Has to be aligned with thepurpose of the company, with the
goal of the company, with thevalues, and of course, combining
all that aspects is what makesit a good infrastructure.
SPEAKER_01 (08:52):
Right.
So moving AI not being uh anemployee, but rather
infrastructure.
And then if we think aboutSignal 2, which is the idea of a
fragmented world.
So interestingly, I uh went touniversity during during the age
of globalization.
You know, the world is flat, um,you know, you can have breakfast
in Berlin, dinner in New York,that kind of that was very much
(09:12):
um the mode of the time.
Um, but we've seen very much in2026 just how the universe
always swings back and forthbetween when one thing is true,
very soon the actual oppositebecomes true.
So if we think about thefragmented world, the idea that
you have regulatory frameworks,for example, but actually that's
(09:35):
not really what we're talkingabout, that just each country
has their own regulations.
Um, and that you know, sometimesone market might be more
expensive than the other, it'sactually deeper than that,
right?
SPEAKER_00 (09:44):
Yeah, the obvious the the the as we call it, the
obvious read is like we'rehaving regulations and more
restrictions.
Uh the non-obvious read is thatthese new restrictions create
opportunity.
Yes.
Because the opportunity goes tothose companies that get
prepared, that get ready tojump.
What happens when I'm developingin some infrastructure, let's
(10:05):
say with uh some countrypreferences, and then I move
abroad because my company wantsto be or is international and I
cannot operate with the sameinfrastructure?
How do I anticipate this tobecome this seamless transition?
How do I become transparent inthose movements?
How do I allocate resources, notin the old way, which was uh
(10:26):
let's choose one vendor, oneprovider, and this is good for
all, like one fits all.
But how do I equip myself in anagile way, in a very flexible
way, to anticipate changes, notonly changes on regulations, but
changes on preferences, what thepeople might like, what the
technology might bring.
(10:47):
How do I make my own operationum non-restrictive but
adaptative?
SPEAKER_01 (10:52):
So, signal two, we knew to we had enough data to
show us obviously that it isgoing to be true, which is why
we listed it as a signal.
But we didn't expect for it tobe proven this quickly, right?
So in tw I mean, we're this isum a couple of weeks ago, yeah,
March 2026 today.
Um but we saw even you know lateFebruary um that there was a
(11:13):
clear mandate from the USgovernment that, hey, we're no
longer going to be uh usinganthrop where there was a there
was a call or a suggestion to nolonger use anthropic um
technologies.
Uh and then this this was reallylike a switch overnight, right?
SPEAKER_00 (11:28):
And this is Yeah, that's uh one of the challenges.
What happens when because mostcompanies are building on
multiple stacks of technologies.
And I even heard some some uhlet's say more uh novice voices
that says, yeah, but we have ourown software developer teams.
So we're okay because we developour own technologies.
But those technologies are builton top of something else, in the
(11:50):
terms of software, IDEs,licenses, APIs, infrastructure,
chipset, electricity, we go tothe electron level.
So knowing that some of thesepieces depend on each other and
how do you become flexiblecreates creates reality.
And what happened with thattropic being flagged as uh as
the supply chain risk, uh, whathappened with the government,
(12:10):
and right now, of course, thenin this debate creates a clear
evidence of the importance ofthis signal.
SPEAKER_01 (12:16):
Right.
And that the signal, obviously,we meant as a obvious as a
geographical level, being so,for example, we have like the US
uh mandate to things, or forexample, German or Japan,
whatever.
But we even saw that this is notjust like a foreign adversary
thing, this can even happenwithin a nation state itself.
So super interesting stuff.
SPEAKER_00 (12:33):
This can be a single bit of.
SPEAKER_01 (12:35):
Yeah, that's gonna be another episode in and of
itself.
So signal three being aboutbiology, I think the obvious
thing that people kind of go tois that AI can make you know
drug research faster.
So if you equip an a scientist,so if we talk about the idea of
an AI scientist, the idea thatyou'll have a scientist, you
know, being you know using AIreally well, but there's
actually an inversion to that,right?
(12:56):
That it's not a scientist usingAI, rather something quite
different.
SPEAKER_00 (12:59):
Yeah, when you start w mo most uh uh most companies
use uh RD directly orindirectly, regardless of the
industry that you are, right?
But when you think about the theobvious path, which is how do I
use my research and developmentteam to find something versus
how do I use first principle andredesign from bottom up from
(13:24):
bottom up from the materiallevel, the things that I'm going
to need changes fundamentally.
And I'm going to probably hintinto the next into the next
signal.
We're going from assisted uhactivities, from uh borders,
right?
We're going to biology, which isunderstanding the chemistry, but
(13:46):
also going to the physics.
So when AI is understanding thephysics, it's also understanding
what I can do to manipulate thechemistry to make better
outcomes.
So programmable biology is notonly at the chemistry level,
it's at the infrastructurelevel.
How do I create the elementsthat I need to operate in a
(14:09):
better way?
SPEAKER_01 (14:09):
Right.
So this is obviously if we thinkabout like an industrial
application.
So this is everything from ifany any person that uses uh
living living uh or organicmaterials, right?
So it's whether you're inagriculture or whether you're in
manufacturing, if you can goback down to the pr physics
first principles layer and say,how can we fundamentally do this
differently or more effectively?
(14:30):
I mean, it's an interestingquestion, which as you said
suggests to then signal fourbeing that AI takes on a
physical context.
So I think the f the mostobvious kind of thing that comes
to mind is, oh, well, everyone'sgonna have a robot or um jumping
into signal number four.
Right, which I right, and sojumping into signal number four,
(14:51):
the idea of that AI is beyondthe screen.
AI has a physical context, whichmeans that we've gone from AI as
infrastructure, AI havingborders, AI being chemistry, and
then the floor signal, AI beingphysics, understanding that AI
not only is uh well, if you takea step back, I guess one one of
the more simple or the mostobvious read is that robots can
(15:12):
do more physical tasks.
Robots get better at doingthings.
But if you think about it, AI,uh, unlike, for example, if you
think about traditional softwareor the internet, can live beyond
your phone or your actualscreen, your computer screen, TV
screen, whatever.
It can be in so many differentthings, really anything.
It's developing an actualphysical language in order to
(15:34):
live and interact with theworld, right?
So it's not just we've gotsmarter robots, it's actually AI
understanding and being able tonavigate our physical world.
SPEAKER_00 (15:43):
Yeah, the understanding is passing from
the paper to the physicalthings, and not only having a
robot that manipulates things,but could be an assistant, could
be you could could be your LLM.
When you're having aconversation, the advice, the
back and forward that you get,it's accurately because
understand the physics of theworld.
That you're wearing these kindof shoes and the floor is kind
(16:03):
of wet, right?
Take that to motorsports, tomanufacturing, take that to uh
healthcare, to any industry.
How these uh physicalconstraints and the physical
combination of all thoseelements have affect on the
desired outcome.
Having that is the vocabularythat we will start sharing with
them.
Because they start getting thisunderstanding of the world and
(16:25):
goes to the first signal, right?
Things that we're starting toprepare become a reality.
Right, exactly.
And that cycle repeats.
SPEAKER_01 (16:33):
And usually when we get to about signal four, this
is when people kind of like puttheir pens down and say, Do I
give up?
I'm a human.
Does that mean that I think thatI'm completely irrelevant?
And interestingly, even thoughour first four signals highlight
the maturity of AI as atechnology, our fifth signal, if
anything, is really theconnective tissue, which is the
idea of a human premium.
(16:54):
So this means that it's not thefact that AI can do more and
then con human contributionbecomes less, and then you know
what, you should just like, youknow, turn around, go home.
Everybody have uh, you know,like universal basic income, you
know, and everyone go home.
That's actually not what we'resaying.
We're actually saying somethingquite different here.
SPEAKER_00 (17:11):
Yeah, when you can build anything, what would you
like to build?
Right.
And as we as we mentioned, if weconnect the four signals, it's
about having uh AI that canaugment, multiplies you.
You have things that can beprogrammed to the atomic level,
you have physical uh robots orphysical elements that can be
added into that equation, right?
(17:32):
You have different frontiersthat could allow you to
manipulate and to operate withdifferent contexts.
Means like uh the the firstintuition is as you as you
mentioned, I give up.
But that also means that knowingwhat to build, having the right
person to direct will changethings.
You probably won't have fiveoperators, but you have one
person directing a small team ora big team of call be robots,
(17:56):
physical robots, or uh digitalrobots, taking action, taking
activity.
That's what makes thedifferentiator.
Imagine this analogy, like uhand we share this in other
episodes.
If all of us ask our favorite uhClaude, Gemini, uh Perplexy,
whatever, we said uh give me 10good ideas to make for a party.
Yeah.
All of us will have 10 goodideas.
(18:17):
But because it's a probabilisticmodel, all of us will have
mostly the same ideas.
So how do we differentiate atindustrial level when we receive
the same?
Right.
That's where the human comesfrom.
SPEAKER_01 (18:29):
It's our guidance.
SPEAKER_00 (18:30):
That that's the human intention.
SPEAKER_01 (18:32):
Right.
Now, when people talk aboutthat, when we say where's the
opportunity, right?
Where is actually theopportunity?
I think this is when we havetheir 88.6 uh percentage come
in.
So 88% being that 88% ofcompanies are simply taking
existing workflows and thenaccelerating them with AI,
right?
Which isn't wrong.
However, it's limited.
(18:53):
It's limited, exactly.
So it's but if you look at the6% that are going deeper and
they're actually fundamentallyredesigning the idea of uh their
their workflow, so not just howhave we done it and then how do
we make it faster, they'rethinking how might we completely
do it differently?
So for 88.6, when we're lookingat where is the opportunity, I
think the opportunity is notjust in making your traditional
(19:16):
workflows faster.
I think the real opportunity isredesigning through human intent
your your future workflow.
One of my favorite questionsthat we often ask is well, when
everyone has access to the sametechnology, who are you going to
be?
What do you want people to sayabout you?
How do you want your customersto feel?
These are all the kinds ofconversations that ironically I
(19:38):
think are the most important inthe age of AI.
SPEAKER_00 (19:40):
I was uh I was having some some conversation
out there and some of them werelike, okay, I need to do AI.
Tell me what's your what do youwant to do?
Oh well, I have this thing and Ithink AI is here.
So just the exercise ofreimagining was my my let's say
my reflection with them.
But what do you need to do thesame?
Because if we had the AI, wecould just create this engine
(20:01):
that replaces all the sevensteps.
You just need one.
Like what do you need tomanufacture?
Imagine if this is manufacturingfor for uh the pieces for an
engine.
What do you need to manufactureall of this if you can, let's
say, redesign and create justone, reduce it to simple things.
And I think that's the the thehuman ingenuity, right?
The human intention.
SPEAKER_01 (20:21):
Yeah.
This is also very much alsocourage as well.
This is where, for example,leadership um is most effective,
impactful, maybe, when youactually take courageous
decisions, when you have acompelling vision for the
future.
Because if you're just talkingabout AI in your organization
as, hey, it can help you writefaster emails or more polite
(20:43):
emails, or it can check somedocuments for you, people go,
oh, that sounds okay.
But if you until youfundamentally rethink, how might
this reimagine not only how wework, but who we are, I mean, I
think that's where people startreally leaning into the idea of
our fifth signal being the humanum the human premium.
SPEAKER_00 (21:02):
So combining with combining with the fact that you
can visualize things faster thanever.
Right.
The c the cost to see thingstangible has reduced.
So you don't have to wait sixmonths, four months, uh one a
year to see a prototype.
If you're in BC, you're notwaiting, you know, you should
not be waiting months to see aprototype from your from your uh
(21:23):
the teams that are pitching toyou.
If you have an idea, thecourage, the courage becomes uh,
let's say, easier to to let'ssay to keep.
It it becomes easier to keep themomentum because the risk is
reduced by the acceleratedexecution.
Right?
You can contain it, analyze it,validate it, prove it, and move
(21:45):
forward.
Right.
The risk of uh what are yougoing to lose is less is what if
we're right.
Right.
SPEAKER_01 (21:51):
And I think this is the the exact moment to circle
back and think about your ownpersonal context.
So, of course, when we thinkabout these.
The five signals, there's a lot,there's a lot going on.
But the number one thing that wealways say in human-centered AI
is what's your problem?
What are you trying to do?
What's your context?
So when you're reading thereport, really reflect on which
(22:13):
signal is applying most to me.
And then also think about, youknow, even in terms of the
maturity, where are you?
Are you starting?
Are you building?
Are you scaling?
And I think having these, umhaving the report, being able to
triangulate your positionthrough what's what's the
signal, where am I at on mymaturity, and also if the signal
is even um recalibrating or evenaccelerating.
(22:36):
So being able to identify whereyou are and what's really
relevant to you, I findpersonally really helpful.
And it really just begins withthat very simple step of what's
my problem?
Because you may look at thesignals and think, oh my gosh,
how does you know the physicalcontext apply to me?
But there's some really greatexamples and some really great
case studies.
So my number one recommendationto people is to reflect not on
(22:58):
just the signals just as awhole, but think about you, your
business.
Where are you going?
Where do you want to go?
Who do you want to be?
SPEAKER_00 (23:06):
So maybe it's a good opportunity to remind the
viewers.
Yeah, there's uh resources.
One resource is the uh featuresignals report, and every signal
points to the same the samedirection.
When execution becomes abundant,intent becomes scarce.
(23:27):
Other resources, of course, canbe found in the Scientist Japan
website, like uh AI customdevelopment development, like uh
could be accelerated, but alsothe academy to have bigger
conversations about this.
SPEAKER_01 (23:37):
Yes, yes, exactly.
SPEAKER_00 (23:38):
Maybe you should say that or me.
SPEAKER_01 (23:40):
No, I mean you said it already, so it's fine.
Yeah, so I think it's a goodthing.
SPEAKER_00 (23:42):
No, you say it better.
I think you could say it.
SPEAKER_01 (23:44):
Hang on a second.
Um Right, which is also um uhlet me just take a minute to
remind everybody that uh wealways we always say um at at
DTJ that AI is a marathon.
It feels like a sprint, but itis most definitely not a sprint.
It is most certainly a marathon.
SPEAKER_00 (24:00):
George Hoots had a good point.
What did he say?
George Hoots.
Uh actually you mentioned it.
That he was saying like uhthere's a lot of of uh messages
out there, like theseinfluencers.
I was building with 400.
SPEAKER_01 (24:14):
Yeah, so I was reading, so it was George Hoots'
LinkedIn, which I thought wasinteresting that he has a
LinkedIn and he like it wasn'ton Twitter or it wasn't his
GitHub or something like alittle bit more happy.
SPEAKER_00 (24:22):
I love going to Link Link Thing and and important
things.
SPEAKER_01 (24:26):
Yeah, so I was looking at one of his LinkedIn
posts, and and one of them said,I'm so sick of like waking up in
the morning and somebody saying,I've just I had my 37 agents
work overnight and you know nowI'm doing whatever, like you
know, 10 million, you know, uhmonthly, you know, recurring
revenue or something like that.
SPEAKER_00 (24:44):
And he said, And what were you doing?
SPEAKER_01 (24:46):
And the point was he said, and what were you doing?
Yeah, and what were you whatwere you doing?
Like again.
SPEAKER_00 (24:50):
Having breakfast.
SPEAKER_01 (24:51):
Exactly.
And then what were you guysdoing while my you know 37
agents were making me thesemillions of dollars?
Well, you were just having yourbreakfast like a pleb.
And I laughed because I thoughtthat's very much kind of what it
feels like that you have thisabsolute disconnect between some
people that are kind of makingmore noise around AI.
SPEAKER_00 (25:09):
They're actually doing.
SPEAKER_01 (25:11):
Exactly.
And then his point was youpeople are gonna get found out.
SPEAKER_00 (25:15):
The people that are saying, I am doing all this with
400 agents, maybe you don't need400, you just need a few ones
well, well equipped.
So remember, this is the erawhen you have startups that were
built with five people, the onebillion, the one billion dollar
company, the one person billiondollar company is not about
having 300 agents.
It's having the right amount oftechnology.
(25:36):
Agentic, biology, robotic, uh inin terms of uh uh let me try
again.
It's it's about having the rightamount of of technology.
It's it's having the right um uhamount of of resources and
technology, agentic, biological,like the in terms of frontiers,
in terms of of uh of uh photos.
(25:56):
Let me try again.
Let me try again.
SPEAKER_01 (25:58):
You go go in go in order.
You have to go frontier clinicaland physical.
SPEAKER_00 (26:02):
Yeah, well it's it's not about having like the this
vast amount of of those, it'shaving them in the right moment
in terms of agentic, in terms ofthe frontiers, in terms of a
biology, in terms of therobotics, in terms of the
physical context, not justrobotics.
The physical context and interms of the people who's
wielding it.
What are the right amount andthe good intent that you're
going to do?
That that means that you have anunstoppable force.
(26:26):
Not only 400 agents to the one,I don't know, just to be an
influencer on LinkedIn onTwitter.
SPEAKER_01 (26:32):
Yes, exactly.
And so I think this is a anothermoment.
Um if this is something that ruh resonates with you, this kind
of you know conversation aroundAI, um stay with us.
Of course, we have our uhhuman-centered AI series, um,
which is always free.
Um if you're in Tokyo, drop by,um, come and uh come and visit
us.
We also have our academy, whichis both remote and online now,
(26:54):
um, where we develop anexecutive level fluency in
people.
And I think this is reallyinteresting because we have lots
of people that are sitting on onbudgets, on resources, and
they're thinking, I really wantto make a good decision.
You know, I want to make a gooddecision.
Where do I invest?
How do I invest?
SPEAKER_00 (27:09):
I don't like you say sitting on budgets.
Feel like a very like a like youwant to remove the budget from
them.
Yes, but that's a consequence.
SPEAKER_01 (27:17):
So we have uh and so for example with our academy,
it's really focused on executivefluency, which I really like
because it's not fake fluency,it's actual technical fluency.
You will be able to thenidentify what does an AI stack
look like.
Uh it's not just kind of fakefluency, like it's actual real
fluency, which I think is reallycool.
SPEAKER_00 (27:36):
It's a strategic fluency for decision making.
SPEAKER_01 (27:39):
Exactly, exactly.
Knowing what to build, right?
That's all what it's all about.
And then of course you can come,you can call us anytime that you
need any also software builds asor any AI application builds as
well.
So with that, this was our uh2026 Signals.
Uh we are very proud of thiswork.
I think it is for me, huh?
SPEAKER_00 (27:58):
Uh nice fact uh during this week, there's been
demand of downloads.
I have already passed thedownloads that we can do.
From last year, yeah.
SPEAKER_01 (28:09):
Yeah, exactly.
Um, yeah, yeah, which which isum really exciting.
So definitely go over and andcheck it out.
And the thing that I like reallya lot is um how many people jump
to the methodology that they'relike, I want to check out your
GitHub.
I didn't think that would belike as interesting to people,
but there are plenty of peoplethat are like start from the
GitHub and they're like, give methe technical architecture
(28:30):
first, um, which I think is alsocool.
I mean, but there's there'seither way.
So um, no, we are here anytimethat you are looking for a more
signal than noise, come to us.
This is where you're gonna findit, and we will see you in our
next episode.
Thank you.
Thanks, everyone.
Popular Podcasts
Stuff You Should Know
If you've ever wanted to know about champagne, satanism, the Stonewall Uprising, chaos theory, LSD, El Nino, true crime and Rosa Parks, then look no further. Josh and Chuck have you covered.
Dateline NBC
Current and classic episodes, featuring compelling true-crime mysteries, powerful documentaries and in-depth investigations. Follow now to get the latest episodes of Dateline NBC completely free, or subscribe to Dateline Premium for ad-free listening and exclusive bonus content: DatelinePremium.com
The Breakfast Club
The World's Most Dangerous Morning Show, The Breakfast Club, With DJ Envy, Jess Hilarious, And Charlamagne Tha God!