June 20, 2025 • 43 mins
Ever watched someone masterfully perform a task and thought, "I wish I could understand exactly what they're doing and why"? In today's episode of The Talent Forge, we explore the revolutionary approach Silicon Society is taking to transform workplace learning through AI-powered job shadowing.
Frank Fusco, a fascinating founder with roots in both liberal arts and computer science, shares how his journey through humanitarian work, bootcamp education, and software development led to a breakthrough realization: traditional learning approaches are fundamentally flawed in today's rapidly changing world. As Frank explains, "We're not built for even a six-month curriculum anymore. We need something faster, more market-aligned, and more specific."
The conversation dives deep into why traditional shadowing programs often fail, how companies can leverage their top performers' knowledge without disrupting workflows, and what Frank calls the potential for "three-sigma" learning—an unprecedented level of effectiveness beyond what even one-on-one human tutoring can provide. Whether you're responsible for developing talent at your organization, fascinated by the application of AI in learning, or simply interested in how workplace education is evolving, this episode offers valuable insights into the future of experiential learning.
Ready to rethink how your team learns? Visit siliconsociety.org to join the waitlist for their platform and discover how AI-powered job shadowing might transform your approach to talent development.
Meet the Host
Jay Johnson works with people and organizations to empower teams, grow profits, and elevate leadership. He is a Co-Founder of Behavioral Elements®, a two-time TEDx speaker, and a designated Master Trainer by the Association for Talent Development. With a focus on behavioral intelligence, Jay has delivered transformational workshops to accelerate high-performance teams and cultures in more than 30 countries across four continents. For inquiries, contact jay@behavioralelements.com or connect below!
LinkedIn - https://www.linkedin.com/in/jayjohnsonccg/
Instagram - https://www.instagram.com/jayjohnsonccg/
Speaker Website - https://jayjohnsonspeaks.com
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Jay Johnson (00:00):
Hey everyone, I'm excited to share with you a
little bit of a differentepisode, this time on the Talent
Forge.
Usually, we dig into humanbehavior, neuro insights, as
well as some of thepsychological aspects of talent
development, but today's episodeis a little different.
In the best possible way, we'reactually going to take a look
under the hood of an emergingtool that's redefining how
(00:23):
learning happens in the AI era.
I'm joined by special guestFrank Fusco from Silicon Society
.
It's a company that's buildingthe future of experiential
learning, something I'm reallypassionate about.
They're doing this one jobshadowing session at a time.
Imagine learning not just froma course or a manual, but by
(00:44):
actually following someone doingthe work and having an AI
enhance that experience in realtime with personalized content
that evolves as you go.
That's exactly what SiliconSociety is making possible a
real-world, ai-poweredupskilling at scale, upskilling
at scale.
So, whether you're a learningprofessional, a tech enthusiast
(01:10):
or just curious about how thenext generation will learn at
work, this conversation is goingto be packed with insights that
can change the way you thinkabout talent development in
action.
So let's dive in.
Welcome to this episode of theTalent Forge, where we are
shaping the future of trainingand development.
Today, my special guest isFrank Fusco, and we are looking
forward to an incredibleconversation.
Welcome to the show, frank.
(01:31):
Thank you so much, jay, forhaving me.
So I have been looking forwardto this conversation because I
think it's a huge gap in boththe industry as well as some of
the conversations we've had here.
We're going to talk about techin the learning and development
space, but before we do that, Iwant the audience to get to know
you a little bit, frank.
How did you land in this space?
(01:52):
Tell us a little bit about yourstory.
Well, it's a very windingjourney.
Frank Fusco (01:58):
So I actually originally went to school.
Aren't they all right, Right?
Well, I mean, I think the bestones are, but I'm a little
biased, that's right.
So I originally went to schoolfor liberal arts, Started out as
an English major.
I kind of wanted to be astarving artist author for a
while when I was kind of alittle bit younger.
(02:19):
Then I got really interested incomputer science.
It was sort of this field thatwas calling out to me.
There was a lot of almost likeromantic I don't know.
There was this kind of feelabout Silicon Valley and tech
and it's the future and it'sempowering and you can transform
(02:39):
things and really have outsizedleverage on the world.
And that was so appealing to mewhen I was, you know, 18, 19.
And I never considered myselfvery math oriented.
I'd never considered myself Idon't know.
I always just kind of wanted towrite and to influence opinion
and and and kind of like live inthat realm.
And then, I don't know this,this romance started kind of
(03:01):
getting me, I think.
So I kind of switched tocomputer science for a while and
it wasn't what I expected inthe academic setting.
So you know, from a collegestandpoint I was okay, great, I
want to code, I want to makeapps, I want to change things.
And it was like solve for X inblah, blah, blah and like, okay,
(03:22):
these giant weed out lectureclasses and I was just like this
isn't what I signed up for,this is what I thought this was
going to be.
So, uh, I kind of like washedout of that really quick and I
was like, nope, back to english.
So I got a liberal arts degree,uh, and uh, yep, was uh just
kind of like writing things onthe side for a while and then
(03:43):
pivoted back eventually tocomputer science a few years
later, when I was like, no,nevermind, I want to do that, I
have to do it.
So I went back to school forcomputer science.
So I have a bachelor's inliberal arts and a bachelor's in
CS in my background.
So after that I co-founded anonprofit with my good friend,
Samir Lakhani called EcoSoapapBank.
(04:04):
It was not technical to start.
It was supposed to be just ahumanitarian project.
We hired economicallydisadvantaged women in
developing countries to recyclesoap from hotels and
manufacturers so soap scrapsfrom manufacturers and then we
would train them in Englishlanguage skills and other other
(04:26):
skills that they could then goget a job in their local area.
We'd pay them five to 10 timesthe going wage in their area as
well, Uh, re-inject cash intothe community, uh, and then get
the recycled soap to schools,hospitals, village communities.
That's amazing and it startedout yeah, you know, I don't know
it was just a nonprofit that wehad started out, but turned out
(04:46):
that it actually was at scale,a logistics operation that
required a ton of internal logicand coordination and ops.
And you know, we were prettyyoung, I had no idea what that
was going to entail.
But then, going down that path,I ended up really putting my CS
degree into action anddeveloping a lot of internal
software.
There was localization, becausea lot of stakeholders we
(05:14):
interacted with didn't speakEnglish or speak it very
proficiently Integrating withthe logistics stuff.
We were an early partner ofFlexport, so that was
interesting from a technicalperspective as well.
So we accidentally built agiant worldwide logistics
operation backed by like areally complex internal tech
backbone.
So I accidentally got like acrash course in a few few things
there, including learning andtraining.
(05:34):
So after that I actually endedup going to Lambda School to run
their experiential learningprogram.
So the labs program there wasessentially the last unit in the
curriculum.
So they would go through thissix month course, just crash
course in coding, so it was fullstack software engineering and
(05:55):
data science.
Then they would end up in labsat the end and they would put it
all into practice, buildingreal software for real nonprofit
organizations in teams together.
So that was their practicum.
You could say that was a reallyamazing experience and it was
really unique.
Honestly, I'd never been in aposition to have so much impact
(06:18):
at once, like so quickly.
We worked with tens ofthousands of learners who came
from all kinds of backgrounds.
We were working with people whowere, you know, they were
joining our classes from theircar because they were unhoused.
We were working with people whoyou know were later on in their
(06:41):
careers and they were justinterested.
We were working with people whocame from all walks of life and
all situations and it was just,I don't know, it was one of the
most rewarding experiences of mylife personally to be able to
work with all those people.
You know, as the program grewit got harder and harder to be
one-on-one with folks, but youknow, at least even still it was
(07:04):
just.
Yeah, it was really nice tohave that kind of an impact.
And then after that, it becameclear to me that we needed
something new and that thebootcamp experiment.
Well, I mean, I won't sugarcoatit I think the bootcamp
experiment has largely failed.
So that was my thesis withSilicon Society.
Jay Johnson (07:30):
We're going to dig into why it failed and because
this is something obviously wetalk a lot about on the show is
why talent development has beenfailing in a number of different
ways.
So I want to dig into that, butI do want to share.
We have a parallel journey onsome level, but mine was in
communication and thenpsychology.
I thought I was going to gointo psychology, ended up
(07:50):
getting the communication degree, went back to psychology later
and then found neuroscience alittle bit after that.
So it's kind of funny how theseyou know how we end up, where
we end up and what thosedifferent markers are along the
way.
So thank you for sharing that.
Frank, I am super fascinatedwith the connection between
(08:12):
technology, modern technology,new age technology, future
technology and the L and D space, and I think it's definitely
something that's underutilizedby a lot of trainers, coaches or
even HR departments or anythingelse that we're not really
digging into.
Things like AI at its capacity,you know, I think everybody's
(08:33):
been to chat GPT to you know.
Hey, help me write this emailor help me write this social
post, but there's so much thatcan be done, so I want to get
into there.
But let's start with thisquestion of the failures.
What was your experience orwhat were some of the things
that you kind of came acrossalong the way that gave you the
indication like hey, something'snot right, something's not
(08:54):
working here, and giving youthis kind of pathway forward
with what you're doing inSilicon society.
I love that question.
Frank Fusco (09:01):
So it was a few things, so I think that.
So first, I just want topreface this by saying that the
people I worked with were someof the most mission driven
people that I've ever had thepleasure of doing something
together with, and I just wantto make sure that's super clear
before I, you know, say what I'mgoing to say about boot camps
(09:24):
in general.
Jay Johnson (09:26):
And I appreciate that because I work with a lot
of L&D people who's literallythey're so purposeful and
intentional with trying to bringtraining, talent development,
learning opportunities, and Ithink that they experience the
same frustrations in being ableto say why isn't this doing what
I'm doing, or why am I beingpigeonholed into a particular
(09:49):
area?
So, caveat acknowledged andaccepted, and I share that for
all of those talent people thatare in the audience.
But yeah, so, so continue Well.
Frank Fusco (10:01):
I think the nominal , the nominal goal was, you know
, for most models anyway in theboot camp space, is alignment of
incentives right, like that'sthe language that's been used
and it's the mission that Ithink everyone really believed
in, including myself verystrongly, in fact.
That, listen, you know, as aninstitution, you know we're only
going to succeed if you succeed.
(10:22):
You know we're only going tosucceed if you succeed.
And I think that is verypowerful when you consider, okay
, you know institutions ofhigher ed sure they also.
You know they publish outcomesreports and they have to be
responsible to the public abouthow many people they're able to
place in jobs or how many peopleare able to get jobs after
going through their program.
So that's a thing.
(10:43):
But by and large, you know youpay your tuition to an institute
of higher ed and you go throughthe program and you know you're
just kind of crossing yourfingers that you've gotten the
value out of that.
That makes you a marketable,market actor by the time you
finish.
And I, you know the bootcampsector was supposed to be this
(11:06):
opportunity and I think it wasto be clear for a window of time
for many, many, many people andwas very effective for those
people, at least a subset ofthem.
I saw it succeed, I saw it workover and over, but it was
supposed to be an answer to that.
Well, we as an institution havean incentive for you to succeed
(11:29):
, and if you don't, then youknow we die.
As an institution, that is apowerful motivator in general,
especially for the peopleworking there, and it's a very I
mean, I think it's a reallyvaliant mission, but it didn't
quite play out that way.
And then I think you've gotlike this nominal line of
(11:50):
incentives, but then ultimatelythere are only so many options
out there at once for someonelooking to completely radically
transform their career andunless you have a very diverse
space of these incentive-alignedinstitutions, it doesn't really
(12:11):
work.
It doesn't really actually liveup to that in the long run.
And especially if the marketshifts out from under you, which
is the second part of the way Isee it, which is demand.
During the pandemic pandemic,obviously, demand skyrocketed
and then pretty quickly droppedback off again and you just see
this curve right of just thiscollapse and the job market out
(12:33):
there.
I mean, I'm sure most of theaudience here is very familiar
with the current state of thejob market.
It's all well and good toimpart these skills, but if you
can't actually be marketable inthose skills effectively because
there aren't the jobs, thenwhat's it for?
So I think that's the otherpart of it.
(12:57):
It's that we're not built forthat anymore.
We're not built for even a sixmonth curriculum anymore.
We need something faster.
We need something that's moremarket aligned and more specific
.
Jay Johnson (13:09):
Well, and to that point, the expense of onboarding
is something that I thinkorganizations end up
undervaluing the idea of gettingsomebody up to speed or getting
them ready to deploy, orworkforce ready 30 days, 60 days
, 90 days all of that it can beexceptionally expensive, not
(13:32):
only for the cost of you justhired somebody and now you're
paying them and they're learningthe job to the.
How much are we paying in thetraining or the talent
development, the development ofthose things, or job mentoring
or a manager doing this?
So I love that you're bringingsome of this into the space.
And you said something thatreally caught my attention the
(13:55):
crossing your fingers strategy,which I see.
So many L&D departmentsunfortunately, unfortunately and
I'm not going to blame them,because I think in a lot of
places there's a lot of externalpressure, either from the top
or anything else, of hey, go getthis done, and they're not
really able to do as much thatthey want to do in terms of
evaluation and things like that.
(14:16):
So that's neither here northere.
So let's introduce this concept.
Silicon Society does somethingthat's kind of unique and it's
AI, ai-focused, and it'stech-focused and it's job
shadowing.
So it's actually creating theconditions that somebody can
shadow.
Talk to us about that.
What does that look like andwhy is shadowing such an
(14:36):
important tool?
Frank Fusco (14:38):
Well, so about that crossing-your-finger strategy?
It's based on the idea that,okay, well, college boot camps,
even, whatever it is you designthis static curriculum in
advance.
You have people go through thiscurriculum of some length of
time and the crossing yourfingers is that by the time you
(14:58):
emerge, it's still relevant andyou can still take what you
learned and be and, and and getvalue out of that in the
marketplace, um, as somebody, uh, who can deliver that value, uh
, AKA be hireable and effective.
And and Tech now is moving veryfast, and I don't just mean the
(15:19):
tech industry, I mean the techthat is impacting every industry
.
So, AI, sure, I mean there havebeen, I think, tailwinds on
this in a variety of ways uh,powerful force, but the fact is
(15:47):
that what companies are lookingfor, what companies need and
what they will need goingforward is going to be evolving
exponentially faster than theability of any static curriculum
to keep up, and so our thesisis essentially that we have to
probably now not in all cases,but in many cases probably start
to abandon the notion that wecan predict in advance some
(16:12):
pre-baked static curriculum thatwe've curated and run people
through it and wait for them toemerge on the other end and hope
it's still going to be relevant.
I think we need to abandon that.
I think, by and large, thefuture is really about exposing
people to what's happening rightnow in the market, right this
(16:34):
instant.
I think the boot camp experimentwas valuable because it moved
us closer to what the industrywas doing.
I didn't even get exposed to asingle line of JavaScript, for
example, in my entire CS degree,which is really funny, Because
(16:55):
the entire world doesn't run onJavaScript, but if you're
familiar with web programmingthese days, it's everything.
So it was just way, way behindand you essentially had to teach
yourself anyway, and thebootcamp experiment got us
closer.
But ultimately, I think what wereally need is just the ability
(17:17):
to shadow somebody just doingtheir job right now.
What we really need is just theability to shadow somebody just
doing their job right now, andthen we just happen to just now
have the tools that we've neverhad before in human history to
actually make that functionalbeyond.
Just.
I am watching this person andI'm just like you know.
(17:38):
Okay, well, okay, great.
You just put some bracketsthere.
What does that mean?
I don't know, or relying onthat person to explain.
All right, so I'm putting thesebrackets here for XYZ reason
it's because it terminates theexpression, and so what we've
never had before in history isthe ability for a machine to
explain in real time whatsomebody is doing and why, and
(18:14):
deliver all of the contextaround that and put it in terms
that will be relevant andparsable and actionable by me,
wherever I am, whoever I am,whatever my learning goals are.
I personally think that is atruly epic opportunity in the
(18:37):
arc of history to think abouthow we've just now, at this very
moment, have this ability it'slike this emergent ability to
utilize this kind of technologyto that end to keep people up to
date on exactly what's going on, to have them shadow people
just working, whatever their jobis, and to have tailored
(18:59):
one-on-one instruction that bothexplains what's going on in
general and tailors it to themdirectly.
Jay Johnson (19:07):
Well, and I think it's interesting too because,
let's be honest, the number ofjobs that I've had and I'm sure
that our audience feels theexact same way where you came in
with a job description of whatyour job is supposed to be,
where you came in with a jobdescription of what your job is
supposed to be, you can eventhink back you know, 10 years
ago, to the memes what I do,what they think I do, what I
(19:31):
actually do, what my boss thinksI do, what my partner.
You know right, we know thatthere are discrepancies and
let's call it gaps in what a jobdescription, job role et cetera
is going to look like.
And then what the actualday-to-day looks like.
When somebody sees me speakingas a keynote on a stage, what
they don't see is thesignificant hours of research
(19:54):
that's gone into every singleaspect of that presentation.
What they don't see is mesitting there messing with
PowerPoint or the handouts,trying to do all of these other
things, because, well, as aspeaker or as a trainer, you get
to see the end product, you getto see the output, you get to
see some of those.
So, being able to shadow andactually say, okay, well, what
(20:16):
does your actual day-to-day looklike.
I can see that just beingpowerful and I love the frame
that you use.
It's it's partially learning bydoing so as a active learner.
If I'm, if I'm, you know,shadowing, whether it's through
technology or anything else likethat, what is the active doing
aspect Like?
How am I actively doing byshadowing per se?
(20:38):
Talk me through what that lookslike in your world, frank.
Frank Fusco (20:42):
I'm going to give a maybe slightly roundabout
answer to this.
Sure, yeah, so I know probablya lot of people watching this
are going to be familiar withBloom's concept of the two sigma
phenomenon, where if you givesomebody a personal tutor, then
under certain circumstances atleast, they will have a two
sigma boost in their learningeffectiveness, and so that means
(21:09):
they'll do better thansignificantly better than if
they were just left to their owndevices or there were other
learning interventions or modesof instruction.
Jay Johnson (21:21):
Well, that's really consistent too with, like, the
international association forcoaching.
That talks about training aloneis like 21%.
Training with a coach, trainingcoupled with a coach or with
somebody kind of going through,that takes it to like 83%
efficacy.
So absolutely definitelywell-founded in both Bloom's
(21:45):
model, but also in even some ofthe more modern research now too
, right.
Frank Fusco (21:51):
Absolutely, and so the thing that I find really
compelling is so yeah, I'm goingon and on about this like
historic opportunity and the newtech, but part of that is also
that, you know, I think for along time we've really looked at
this two sigma effect as thiskind of like paragon and like
this is like all right, well,two sigma, you know, that's the
(22:12):
dream, right, but now I actuallythink three sigma is in sight,
and I think I think it'spossible for us to optimize
learning in a way that has neverbeen optimized before, because
we have these new tools, and itcomes down to us, as
technologists, to wield thosetools and to construct a system
that reinforces that andachieves that effectively.
(22:35):
But I absolutely think it's inreach, and so why I invoke that
here is because you know thattwo, the reason the two Sigma
model is so, or the two Sigmaeffect is achievable, is because
you have the back and forth.
You have somebody who'stracking what you are learning
in real time.
You have somebody who you caninteract with around the content
(22:57):
, and it's at your own pace,it's tailored to you, they know
you.
Now, the cool thing, though, isthat machines don't get tired.
They don't have an off day.
Granted, we're still workingthe kinks out of a lot of this
AI stuff and you've gothallucinations and you've got a
lot of nascent stuff that we'redealing with in this
(23:19):
transitional period.
I would say we're occupying now, but, by and large, you know,
the best tutor is only going tohave so much in their head
before they have to go Google itor something or do some
research and come back to younext week.
Jay Johnson (23:32):
Or until they lean into AI to figure out how to
solve the problem.
Frank Fusco (23:35):
Yeah, Well, exactly , so that's the thing.
Right Is that AI just alreadyhas that, and so I think the key
is that, well, we can lean intothat interactivity, that
interactive dimension, in areally creative way, to where we
have additional modes now thatare open to us, that are open to
(23:58):
us, where, basically, as alearner who is participating in
a shadow, so I'm shadowingsomebody just doing their job.
They don't even have to sayanything, they're just sharing
their screen or they're wearing,you know, meta Ray-Ban AR
glasses and they're capturingwhat's in front of them or
whatever it is.
We've developed an AI tutorpersonalized to you as the
(24:20):
learner in that room, and theyare the one narrating what's
going on.
So you get these little,bite-size, parsable blurbs.
We've been tuning thisconstantly to be the right
amount of information hittingyou at the same time, so that's
not too overwhelming but also isproviding you enough context to
(24:41):
keep up with what's going on.
So beacon is essentially yourguide to what's happening,
tailored to you.
But the real magic is thatbeacon is interactive, so you
can, at any time, just click alittle question mark on any one
of those blurbs that beacongives you and it'll immediately
(25:01):
give you additional context onthat aspect, or you can quote
something and ask a specificquestion about that thing, or
you can just openly, essentiallychat with the shadow as like a,
an entity, as a concept, likeit's a very abstract thing where
(25:21):
, okay, like yeah, if I had areal human tutor with me, I
could have a conversation withthe tutor about what's going on.
But I can actually chat withthis session.
I can chat with everythingabout the session.
It knows everything that'srelevant to what's going on and
that is interactive in itself.
Now we're still building out thenext phase of the platform, but
(25:45):
a lot of the additionalinteractivity is that it's not
just about being in the room andlike having this exposure to
what's going on there.
It's about layering otherelements on top of that right.
So checks for understandingthat can just like dynamically
be generated in real time by themachine detecting what you
should be checked on.
So no one has designed this inadvance.
(26:07):
Nobody has based this onsomething they're hoping six
months from now is going to berelevant.
It's being drawn in real timefrom what's happening and it's
quizzing you on what you need toknow and it's optimizing that
for your retention of thatmaterial.
And then the other aspect isoutside of the room, there's a
learning path, shadows and doingassessments that are
(26:29):
dynamically generated aroundtheir shadows, so it's that
(26:50):
level of interactivity thatseparates this, I think, from
pretty much anything that's beentried before, and I think
that's a pretty, pretty coolthing.
Jay Johnson (27:03):
Personally, so, yeah, I'm loving this concept
because you're hitting on anumber of the different things
that I see as absolutelyessential to effective training,
talent development, workforcedevelopment, etc.
Number one real time engagement.
Number two not you know,avoiding cognitive overload.
Number three having that sortof reinforcement or not just you
(27:27):
know, not just somebody, notjust something where it's like,
hey, I'm going to do this overand over and over again and turn
it into a rote task, but thisreinforcement of you have a
learning partner that's an AIoperated that's going to be able
to essentially coach twospecific things.
So, I mean, you're checking allthese boxes.
I want to talk a little bitabout logistics, right, because
(27:48):
I think that when I'm thinkingabout, let's take the technology
out of here for a minute, okay,has always been a difficult
aspect for the learningenvironment.
Well, why is that?
Okay, say, somebody shadows me.
(28:08):
Well, for a good majority ofthe day, the stuff that I'm
doing is probably going to beexceptionally boring.
Secondly, you know, shadowingme during a day Well, what's a
typical day actually look likefor some different jobs?
One day I'm running aroundhandling a crisis, the other day
I'm doing, you know, deep workand research, and so on and so
forth.
So let's talk about shadowing ingeneral for a second.
And then what is it that thetechnology helps to do to
(28:30):
overcome some of these things?
Because, from a logisticsstandpoint, does the shadowing
you know, like, if I'm bringingsomebody into my company, does
the shadowing shadow me, or doesit shadow somebody else that's
doing what I'm doing?
Or AI generated and I want totalk about some of the tech that
gets into that.
(28:50):
But shadowing how are youoperationalizing shadowing in a
way that's actually effective,given what we know about some of
the internal shadowing slashmentoring programs that are an
abysmal failure?
Frank Fusco (29:04):
Right, all right.
Now we have studied thoseinternal shadowing programs and
many of them are an abysmalfailure very, very frequently,
but some of them have actuallybeen quite effective.
So we looked very specificallyat the ones that were effective.
So we looked very specificallyat the ones that were effective
and I don't even know where tostart with this actually, so I
(29:27):
won't name any specificcompanies.
The most powerful thing waswhatever's already happening,
don't mess with it.
Like so many of theseinterventions are, as soon as
you even call it an intervention, like you've already messed up,
you've already misdesigned thewhole thing because you're
(29:51):
mucking about with, like, theway the company works.
Jay Johnson (29:54):
So spoiler alert people don't like it when you
mess up their job, when you makethem less expensive.
Frank Fusco (29:59):
Stop fixing something that's not broken,
right, yeah, so, like, we saw alot of instances where it's just
like, well, yeah, we're gonna,we're gonna do this mentorship
thing.
All you have to do is take sixhours out of your week and, like
, you know, when you weresupposed to do this one thing.
Just kidding, you have to, like, be in this room and like, fill
out this form with this person.
Doesn't that sound great?
Um, so, and there was just alot of like, like, I, I think
(30:20):
that one of the I mean one, oneof the okay, I probably wasn't
not supposed to talk abouttechnology yet, but I will say
that one of the nice thingsabout tech in like in the HR
space, for example, is that ithas helped HR leaders, uh, run
these things in ways that areless like that, feel less like
bureaucratic.
And I think, I think the peoplethat I know who work in hr uh,
(30:42):
you know, that's like one of thethings I hear most frequently
from them it's like oh well, itintegrates a lot better with, uh
, it makes a lot easier, lowerfriction for people, um, and
it's just about making sure thatthey complete the thing, or
making sure this gets done andit's not, uh, it's not a big
disruptive thing, and so I think, yeah, we saw a lot of
(31:03):
instances where it's just it'snot a big disruptive thing.
And so I think, yeah, we saw alot of instances where it's
either just like tacked on andit's like just kind of expected
that people are just going to doit, which usually doesn't work,
or, you know, things are movedaround a lot where it's
requiring a lot of people to bein compliance, and I also think,
as soon as you say compliance,you've also messed up.
(31:26):
This shouldn't be something ifpeople don't perceive.
Jay Johnson (31:30):
HR people cover your ears that are listening in.
Frank Fusco (31:33):
Sorry, no it's all good and legal for that matter.
But no, in all seriousness, thepoint is that if people don't
perceive the value of the thing,then you have more work to do
and we accept that in the market.
If you don't make a sale, youshould have sold better.
(31:54):
I think that the people I knowwho are working inside of
companies to make things workwithin companies have so much on
their plate and they work sotirelessly to make things fit
together.
Priority shift.
The ground shifts from underyou.
You have to rebalance and it'sone of the most difficult
(32:20):
endeavors, I think, to operateeffectively in that kind of
environment.
No-transcript listen to thehuman beings in the very
specific situations that they'rein and the configuration that
has emerged for what's effectivefor that organization, and they
tailor what's going on to themrather than change it.
(32:43):
So I think, if I had to give ahigh level answer or general
answer, again, there arespecifics on the ground at every
company and the way everycompany works is different.
But it's don't mess with what'sworking for the company.
So the second part, then thetech it's.
It's really about wherever youare and whatever you're doing.
(33:07):
That's.
That's where the shadow happens.
So again, I don't have to be agood teacher, I don't have to
narrate, I don't have to changeanything, I just have to share
my screen, I just have to startthe shadow and then I'm working.
Now there is definitely a rangeof goals of these programs that
(33:29):
we looked at in terms of why youwould run a shadowing program
or a mentorship program or anupskilling program internally.
You know, in the first placeand I think that's a subtle
thing so some companies areinterested in just providing
this as a benefit to theiremployees, like upskill and
learn more.
We know you are interested inlearning more, advancing your
(33:51):
career, help us, help you bepromoted at the company.
So that's a common one.
But another one is that, hey,you know, we have these rock
stars at the company.
We have these very experiencedpeople who've been working for
many years and sometimes atgreat expense, have acquired
really exceptional skills andare extremely effective on the
(34:12):
job.
And wouldn't it be great if wecould actually leverage those
folks' knowledge and experienceas an engine, essentially to
help others more efficientlyacquire that knowledge?
So I think there are kind ofboth sides that can motivate.
But the way you design theprogram, it needs to be based
around a specific goal that youhave and then the shape that
(34:36):
that takes is kind of followsfrom what those goals are, and
that's how it should be.
But the key is don't mess it up.
Don't mess up your company.
Have people in the room withyou and the last thing I'll say
on this point is you never know,you forget, you forget what you
know, you forget what you'velearned and what it costs you to
learn that and how hard it wasbefore you knew that thing.
(34:58):
Uh, you develop amnesiafrequently about what it was
like before knowing the thingand you just forget that other
people don't have that in theirhead right now because you've
moved on.
You've integrated that intoyour whole being.
It's not something that youthink to tell somebody, it just
happens right, yep, and so likeoh man, this thing I'm doing,
(35:20):
it's so boring.
Why would ever anyone everwatch this?
Like who could possibly begetting value out of this?
I can't tell you how many timesI've like heard that from
somebody who was doing something.
Uh, that's just doing their job, frank I have.
Jay Johnson (35:35):
I have a huge problem with this where it's
like somebody will be like okay,jay, you just you think you
went from a to b, but youactually went from a to l.
Exactly skip nine steps inthere.
Because it was just you've,you've done it for 20 years,
right, what did you do next?
What did you do next?
What did you do next?
(35:56):
And I would have never, I wouldhave never saw that there was a
gap, right.
So that makes a lot of senseyeah, and like that.
Frank Fusco (36:04):
That's just where you just you never know what it
is that somebody's getting outof something until you hear from
them.
Like I, in the early iterationof this, you know, when we were
just kind of experimenting withthis model a while ago at this
point actually.
So we actually operated as asoftware agency for a while and
(36:27):
we did that explicitly so thatwe could test people shadowing
us as and you know our staff aswe worked and completed real
world projects for realcompanies.
So we kind of developed our owntest bed, our own Petri dish
there, and that's how wedeveloped the platform.
We built it around us working,um, and I was working on a
(36:49):
client project it was like onein the morning, uh, two in the
morning, I don't know and thisjunior engineer who was on the
client team.
One of the things that we do asan agency we still operate as an
agency, by the way One of thethings that we pride ourselves
on is we integrate with ourclient's team.
So if you've got an existingteam, great, we'll work
(37:11):
alongside them, no problem.
That's better for us because weget more exposure to what
companies are doing in real life, and so I was just coding on my
porch at like two in themorning and I'm just like,
blurry eyed and I'm just, likeyou know, going through this
thing.
We've got a deadline and youknow it's just like you have
those nights sometimes.
And you know this guy was just,he was in, he was just on Zoom
(37:35):
with me we didn't have theplatform at the time just
watching me do this and I wasjust, you know, I was finally
going to bed.
We figured the thing out and Iwas just like listen, man, I
just like I got to commend youfor staying up and doing this.
Like nobody asked you to dothis, you didn't have to do this
.
Like I just, you know I reallygot to shout you out here and he
(37:58):
was just like, are you kiddingthat?
Like just watching you work hasbeen like the most valuable
experience ever.
Like I'm just like what do youmean?
Like I was just like this.
It wasn't, it didn't feel likeanything special that I was
doing.
Like I was just, I don't knowbanging this thing out.
You know I was just like getturning this out out.
And meanwhile this person who'swatching me is just like oh,
that was so valuable.
Like the like little tricks,like the way that you use
(38:20):
console log with brackets in it,so just log the object and like
, instead of like having to likelog every little thing.
I'm just like.
I mean I think I learned thatlike I don't know 10 years ago
or something, but like youforget that you don't, that
people don't know these littletiny tips and tricks, these
little like real world things.
No one has ever taught a classwhere they say you know, if you
(38:41):
use console log with brackets,it's a lot easier to read in
Chrome's dev tools.
Like no one's ever said that ina classroom, ever.
Probably.
I mean, I can't say for sure,but I doubt it but it's those
little things that go a long,long, long way and that was a
really actually light bulbmoment for me.
Actually at that exact time washuge, yeah, yeah, like, oh,
(39:03):
that's right, there's stuff inhere that I forget is valuable
and you know there are many moreskilled engineers than me out
there in the world and you know,imagine if we could harness
their experience for everyone.
Jay Johnson (39:18):
This lands for me, frank, and my experience in it
is the number of times that I'vehad some of my teams say hey,
can I come with you?
I'd like to watch you presentand I'm like you helped me
design the materials.
Why would you want?
because I want to see how youpresent it, or even something so
long, so much as hey, do youmind if I sit in on your next uh
call, on a sales call, or yournext time that you're being
(39:41):
evaluated for a keynote or anyof these things, and it's just
like, yeah, you know, I I don'tmind ever inviting my team to
participate in those things,cause we do recognize we learn
by doing or by being engaged insomething that's happening.
So the fact that the softwarereally does provide this is an
incredible opportunity for us tothink about how can we help
(40:02):
people to see and alsoexperience it.
And then the added component ofAI is such a powerful thing.
Frank, this is such ainteresting concept.
If our audience wanted to getin touch with you to learn more
about Silicon Society, theprograms and the shadowing
(40:23):
concept, how would they reach?
Frank Fusco (40:25):
out to you.
Technically we're in closedmode at the moment, but we will
be keeping people up to date onwhen we'll be doing a wide open
launch.
So if you are interested, youcan join the waitlist at
siliconsocietyorg.
You can also get in touch withus via that website and if
(40:47):
you've got you know, if you're abusiness you've got a software
project you need done, you canalso get in touch with us about
that there as well.
Jay Johnson (40:53):
Incredible.
So I will be joining the waitlist because I can see a number
of different applications, evenfor training my own team and
utilizing this.
So I just want to say thank you, Frank, because I think this is
a novel way for us to reallyrethink some of the ways in
which we're training ourinternal teams and as a tool to
be able to kind of overcome someof those big gaps that we're
(41:15):
all aware of in both thelearning and development space.
So thank you for taking thetime to speak with us today and
thank you for all the work thatyou're doing in helping to
elevate the talent developmentspace.
Frank Fusco (41:27):
Thank you, Jay, and I just want to shout out the
team Rachel Cohen, ShelbySchisler, Robert Sharp we
couldn't do it without them.
So this is a full team effortand I just wanted to be really
clear that it's a product of thehearts and brains of everybody
working together.
Jay Johnson (41:48):
Amazing.
So thank you again for beinghere and a shout out to the team
.
Absolutely so.
Thank you, audience, for tuninginto this episode of the Talent
Forge, where we aredemonstrating software to help
the future of training anddevelopment.
Thanks a lot.
Hey everyone, I'm excited to share with you a
little bit of a differentepisode, this time on the Talent
Forge.
Usually, we dig into humanbehavior, neuro insights, as
well as some of thepsychological aspects of talent
development, but today's episodeis a little different.
In the best possible way, we'reactually going to take a look
under the hood of an emergingtool that's redefining how
(00:23):
learning happens in the AI era.
I'm joined by special guestFrank Fusco from Silicon Society
.
It's a company that's buildingthe future of experiential
learning, something I'm reallypassionate about.
They're doing this one jobshadowing session at a time.
Imagine learning not just froma course or a manual, but by
(00:44):
actually following someone doingthe work and having an AI
enhance that experience in realtime with personalized content
that evolves as you go.
That's exactly what SiliconSociety is making possible a
real-world, ai-poweredupskilling at scale, upskilling
at scale.
So, whether you're a learningprofessional, a tech enthusiast
(01:10):
or just curious about how thenext generation will learn at
work, this conversation is goingto be packed with insights that
can change the way you thinkabout talent development in
action.
So let's dive in.
Welcome to this episode of theTalent Forge, where we are
shaping the future of trainingand development.
Today, my special guest isFrank Fusco, and we are looking
forward to an incredibleconversation.
Welcome to the show, frank.
(01:31):
Thank you so much, jay, forhaving me.
So I have been looking forwardto this conversation because I
think it's a huge gap in boththe industry as well as some of
the conversations we've had here.
We're going to talk about techin the learning and development
space, but before we do that, Iwant the audience to get to know
you a little bit, frank.
How did you land in this space?
(01:52):
Tell us a little bit about yourstory.
Well, it's a very windingjourney.
Frank Fusco (01:58):
So I actually originally went to school.
Aren't they all right, Right?
Well, I mean, I think the bestones are, but I'm a little
biased, that's right.
So I originally went to schoolfor liberal arts, Started out as
an English major.
I kind of wanted to be astarving artist author for a
while when I was kind of alittle bit younger.
(02:19):
Then I got really interested incomputer science.
It was sort of this field thatwas calling out to me.
There was a lot of almost likeromantic I don't know.
There was this kind of feelabout Silicon Valley and tech
and it's the future and it'sempowering and you can transform
(02:39):
things and really have outsizedleverage on the world.
And that was so appealing to mewhen I was, you know, 18, 19.
And I never considered myselfvery math oriented.
I'd never considered myself Idon't know.
I always just kind of wanted towrite and to influence opinion
and and and kind of like live inthat realm.
And then, I don't know this,this romance started kind of
(03:01):
getting me, I think.
So I kind of switched tocomputer science for a while and
it wasn't what I expected inthe academic setting.
So you know, from a collegestandpoint I was okay, great, I
want to code, I want to makeapps, I want to change things.
And it was like solve for X inblah, blah, blah and like, okay,
(03:22):
these giant weed out lectureclasses and I was just like this
isn't what I signed up for,this is what I thought this was
going to be.
So, uh, I kind of like washedout of that really quick and I
was like, nope, back to english.
So I got a liberal arts degree,uh, and uh, yep, was uh just
kind of like writing things onthe side for a while and then
(03:43):
pivoted back eventually tocomputer science a few years
later, when I was like, no,nevermind, I want to do that, I
have to do it.
So I went back to school forcomputer science.
So I have a bachelor's inliberal arts and a bachelor's in
CS in my background.
So after that I co-founded anonprofit with my good friend,
Samir Lakhani called EcoSoapapBank.
(04:04):
It was not technical to start.
It was supposed to be just ahumanitarian project.
We hired economicallydisadvantaged women in
developing countries to recyclesoap from hotels and
manufacturers so soap scrapsfrom manufacturers and then we
would train them in Englishlanguage skills and other other
(04:26):
skills that they could then goget a job in their local area.
We'd pay them five to 10 timesthe going wage in their area as
well, Uh, re-inject cash intothe community, uh, and then get
the recycled soap to schools,hospitals, village communities.
That's amazing and it startedout yeah, you know, I don't know
it was just a nonprofit that wehad started out, but turned out
(04:46):
that it actually was at scale,a logistics operation that
required a ton of internal logicand coordination and ops.
And you know, we were prettyyoung, I had no idea what that
was going to entail.
But then, going down that path,I ended up really putting my CS
degree into action anddeveloping a lot of internal
software.
There was localization, becausea lot of stakeholders we
(05:14):
interacted with didn't speakEnglish or speak it very
proficiently Integrating withthe logistics stuff.
We were an early partner ofFlexport, so that was
interesting from a technicalperspective as well.
So we accidentally built agiant worldwide logistics
operation backed by like areally complex internal tech
backbone.
So I accidentally got like acrash course in a few few things
there, including learning andtraining.
(05:34):
So after that I actually endedup going to Lambda School to run
their experiential learningprogram.
So the labs program there wasessentially the last unit in the
curriculum.
So they would go through thissix month course, just crash
course in coding, so it was fullstack software engineering and
(05:55):
data science.
Then they would end up in labsat the end and they would put it
all into practice, buildingreal software for real nonprofit
organizations in teams together.
So that was their practicum.
You could say that was a reallyamazing experience and it was
really unique.
Honestly, I'd never been in aposition to have so much impact
(06:18):
at once, like so quickly.
We worked with tens ofthousands of learners who came
from all kinds of backgrounds.
We were working with people whowere, you know, they were
joining our classes from theircar because they were unhoused.
We were working with people whoyou know were later on in their
(06:41):
careers and they were justinterested.
We were working with people whocame from all walks of life and
all situations and it was just,I don't know, it was one of the
most rewarding experiences of mylife personally to be able to
work with all those people.
You know, as the program grewit got harder and harder to be
one-on-one with folks, but youknow, at least even still it was
(07:04):
just.
Yeah, it was really nice tohave that kind of an impact.
And then after that, it becameclear to me that we needed
something new and that thebootcamp experiment.
Well, I mean, I won't sugarcoatit I think the bootcamp
experiment has largely failed.
So that was my thesis withSilicon Society.
Jay Johnson (07:30):
We're going to dig into why it failed and because
this is something obviously wetalk a lot about on the show is
why talent development has beenfailing in a number of different
ways.
So I want to dig into that, butI do want to share.
We have a parallel journey onsome level, but mine was in
communication and thenpsychology.
I thought I was going to gointo psychology, ended up
(07:50):
getting the communication degree, went back to psychology later
and then found neuroscience alittle bit after that.
So it's kind of funny how theseyou know how we end up, where
we end up and what thosedifferent markers are along the
way.
So thank you for sharing that.
Frank, I am super fascinatedwith the connection between
(08:12):
technology, modern technology,new age technology, future
technology and the L and D space, and I think it's definitely
something that's underutilizedby a lot of trainers, coaches or
even HR departments or anythingelse that we're not really
digging into.
Things like AI at its capacity,you know, I think everybody's
(08:33):
been to chat GPT to you know.
Hey, help me write this emailor help me write this social
post, but there's so much thatcan be done, so I want to get
into there.
But let's start with thisquestion of the failures.
What was your experience orwhat were some of the things
that you kind of came acrossalong the way that gave you the
indication like hey, something'snot right, something's not
(08:54):
working here, and giving youthis kind of pathway forward
with what you're doing inSilicon society.
I love that question.
Frank Fusco (09:01):
So it was a few things, so I think that.
So first, I just want topreface this by saying that the
people I worked with were someof the most mission driven
people that I've ever had thepleasure of doing something
together with, and I just wantto make sure that's super clear
before I, you know, say what I'mgoing to say about boot camps
(09:24):
in general.
Jay Johnson (09:26):
And I appreciate that because I work with a lot
of L&D people who's literallythey're so purposeful and
intentional with trying to bringtraining, talent development,
learning opportunities, and Ithink that they experience the
same frustrations in being ableto say why isn't this doing what
I'm doing, or why am I beingpigeonholed into a particular
(09:49):
area?
So, caveat acknowledged andaccepted, and I share that for
all of those talent people thatare in the audience.
But yeah, so, so continue Well.
Frank Fusco (10:01):
I think the nominal , the nominal goal was, you know
, for most models anyway in theboot camp space, is alignment of
incentives right, like that'sthe language that's been used
and it's the mission that Ithink everyone really believed
in, including myself verystrongly, in fact.
That, listen, you know, as aninstitution, you know we're only
going to succeed if you succeed.
(10:22):
You know we're only going tosucceed if you succeed.
And I think that is verypowerful when you consider, okay
, you know institutions ofhigher ed sure they also.
You know they publish outcomesreports and they have to be
responsible to the public abouthow many people they're able to
place in jobs or how many peopleare able to get jobs after
going through their program.
So that's a thing.
(10:43):
But by and large, you know youpay your tuition to an institute
of higher ed and you go throughthe program and you know you're
just kind of crossing yourfingers that you've gotten the
value out of that.
That makes you a marketable,market actor by the time you
finish.
And I, you know the bootcampsector was supposed to be this
(11:06):
opportunity and I think it wasto be clear for a window of time
for many, many, many people andwas very effective for those
people, at least a subset ofthem.
I saw it succeed, I saw it workover and over, but it was
supposed to be an answer to that.
Well, we as an institution havean incentive for you to succeed
(11:29):
, and if you don't, then youknow we die.
As an institution, that is apowerful motivator in general,
especially for the peopleworking there, and it's a very I
mean, I think it's a reallyvaliant mission, but it didn't
quite play out that way.
And then I think you've gotlike this nominal line of
(11:50):
incentives, but then ultimatelythere are only so many options
out there at once for someonelooking to completely radically
transform their career andunless you have a very diverse
space of these incentive-alignedinstitutions, it doesn't really
(12:11):
work.
It doesn't really actually liveup to that in the long run.
And especially if the marketshifts out from under you, which
is the second part of the way Isee it, which is demand.
During the pandemic pandemic,obviously, demand skyrocketed
and then pretty quickly droppedback off again and you just see
this curve right of just thiscollapse and the job market out
(12:33):
there.
I mean, I'm sure most of theaudience here is very familiar
with the current state of thejob market.
It's all well and good toimpart these skills, but if you
can't actually be marketable inthose skills effectively because
there aren't the jobs, thenwhat's it for?
So I think that's the otherpart of it.
(12:57):
It's that we're not built forthat anymore.
We're not built for even a sixmonth curriculum anymore.
We need something faster.
We need something that's moremarket aligned and more specific
.
Jay Johnson (13:09):
Well, and to that point, the expense of onboarding
is something that I thinkorganizations end up
undervaluing the idea of gettingsomebody up to speed or getting
them ready to deploy, orworkforce ready 30 days, 60 days
, 90 days all of that it can beexceptionally expensive, not
(13:32):
only for the cost of you justhired somebody and now you're
paying them and they're learningthe job to the.
How much are we paying in thetraining or the talent
development, the development ofthose things, or job mentoring
or a manager doing this?
So I love that you're bringingsome of this into the space.
And you said something thatreally caught my attention the
(13:55):
crossing your fingers strategy,which I see.
So many L&D departmentsunfortunately, unfortunately and
I'm not going to blame them,because I think in a lot of
places there's a lot of externalpressure, either from the top
or anything else, of hey, go getthis done, and they're not
really able to do as much thatthey want to do in terms of
evaluation and things like that.
(14:16):
So that's neither here northere.
So let's introduce this concept.
Silicon Society does somethingthat's kind of unique and it's
AI, ai-focused, and it'stech-focused and it's job
shadowing.
So it's actually creating theconditions that somebody can
shadow.
Talk to us about that.
What does that look like andwhy is shadowing such an
(14:36):
important tool?
Frank Fusco (14:38):
Well, so about that crossing-your-finger strategy?
It's based on the idea that,okay, well, college boot camps,
even, whatever it is you designthis static curriculum in
advance.
You have people go through thiscurriculum of some length of
time and the crossing yourfingers is that by the time you
(14:58):
emerge, it's still relevant andyou can still take what you
learned and be and, and and getvalue out of that in the
marketplace, um, as somebody, uh, who can deliver that value, uh
, AKA be hireable and effective.
And and Tech now is moving veryfast, and I don't just mean the
(15:19):
tech industry, I mean the techthat is impacting every industry
.
So, AI, sure, I mean there havebeen, I think, tailwinds on
this in a variety of ways uh,powerful force, but the fact is
(15:47):
that what companies are lookingfor, what companies need and
what they will need goingforward is going to be evolving
exponentially faster than theability of any static curriculum
to keep up, and so our thesisis essentially that we have to
probably now not in all cases,but in many cases probably start
to abandon the notion that wecan predict in advance some
(16:12):
pre-baked static curriculum thatwe've curated and run people
through it and wait for them toemerge on the other end and hope
it's still going to be relevant.
I think we need to abandon that.
I think, by and large, thefuture is really about exposing
people to what's happening rightnow in the market, right this
(16:34):
instant.
I think the boot camp experimentwas valuable because it moved
us closer to what the industrywas doing.
I didn't even get exposed to asingle line of JavaScript, for
example, in my entire CS degree,which is really funny, Because
(16:55):
the entire world doesn't run onJavaScript, but if you're
familiar with web programmingthese days, it's everything.
So it was just way, way behindand you essentially had to teach
yourself anyway, and thebootcamp experiment got us
closer.
But ultimately, I think what wereally need is just the ability
(17:17):
to shadow somebody just doingtheir job right now.
What we really need is just theability to shadow somebody just
doing their job right now, andthen we just happen to just now
have the tools that we've neverhad before in human history to
actually make that functionalbeyond.
Just.
I am watching this person andI'm just like you know.
(17:38):
Okay, well, okay, great.
You just put some bracketsthere.
What does that mean?
I don't know, or relying onthat person to explain.
All right, so I'm putting thesebrackets here for XYZ reason
it's because it terminates theexpression, and so what we've
never had before in history isthe ability for a machine to
explain in real time whatsomebody is doing and why, and
(18:14):
deliver all of the contextaround that and put it in terms
that will be relevant andparsable and actionable by me,
wherever I am, whoever I am,whatever my learning goals are.
I personally think that is atruly epic opportunity in the
(18:37):
arc of history to think abouthow we've just now, at this very
moment, have this ability it'slike this emergent ability to
utilize this kind of technologyto that end to keep people up to
date on exactly what's going on, to have them shadow people
just working, whatever their jobis, and to have tailored
(18:59):
one-on-one instruction that bothexplains what's going on in
general and tailors it to themdirectly.
Jay Johnson (19:07):
Well, and I think it's interesting too because,
let's be honest, the number ofjobs that I've had and I'm sure
that our audience feels theexact same way where you came in
with a job description of whatyour job is supposed to be,
where you came in with a jobdescription of what your job is
supposed to be, you can eventhink back you know, 10 years
ago, to the memes what I do,what they think I do, what I
(19:31):
actually do, what my boss thinksI do, what my partner.
You know right, we know thatthere are discrepancies and
let's call it gaps in what a jobdescription, job role et cetera
is going to look like.
And then what the actualday-to-day looks like.
When somebody sees me speakingas a keynote on a stage, what
they don't see is thesignificant hours of research
(19:54):
that's gone into every singleaspect of that presentation.
What they don't see is mesitting there messing with
PowerPoint or the handouts,trying to do all of these other
things, because, well, as aspeaker or as a trainer, you get
to see the end product, you getto see the output, you get to
see some of those.
So, being able to shadow andactually say, okay, well, what
(20:16):
does your actual day-to-day looklike.
I can see that just beingpowerful and I love the frame
that you use.
It's it's partially learning bydoing so as a active learner.
If I'm, if I'm, you know,shadowing, whether it's through
technology or anything else likethat, what is the active doing
aspect Like?
How am I actively doing byshadowing per se?
(20:38):
Talk me through what that lookslike in your world, frank.
Frank Fusco (20:42):
I'm going to give a maybe slightly roundabout
answer to this.
Sure, yeah, so I know probablya lot of people watching this
are going to be familiar withBloom's concept of the two sigma
phenomenon, where if you givesomebody a personal tutor, then
under certain circumstances atleast, they will have a two
sigma boost in their learningeffectiveness, and so that means
(21:09):
they'll do better thansignificantly better than if
they were just left to their owndevices or there were other
learning interventions or modesof instruction.
Jay Johnson (21:21):
Well, that's really consistent too with, like, the
international association forcoaching.
That talks about training aloneis like 21%.
Training with a coach, trainingcoupled with a coach or with
somebody kind of going through,that takes it to like 83%
efficacy.
So absolutely definitelywell-founded in both Bloom's
(21:45):
model, but also in even some ofthe more modern research now too
, right.
Frank Fusco (21:51):
Absolutely, and so the thing that I find really
compelling is so yeah, I'm goingon and on about this like
historic opportunity and the newtech, but part of that is also
that, you know, I think for along time we've really looked at
this two sigma effect as thiskind of like paragon and like
this is like all right, well,two sigma, you know, that's the
(22:12):
dream, right, but now I actuallythink three sigma is in sight,
and I think I think it'spossible for us to optimize
learning in a way that has neverbeen optimized before, because
we have these new tools, and itcomes down to us, as
technologists, to wield thosetools and to construct a system
that reinforces that andachieves that effectively.
(22:35):
But I absolutely think it's inreach, and so why I invoke that
here is because you know thattwo, the reason the two Sigma
model is so, or the two Sigmaeffect is achievable, is because
you have the back and forth.
You have somebody who'stracking what you are learning
in real time.
You have somebody who you caninteract with around the content
(22:57):
, and it's at your own pace,it's tailored to you, they know
you.
Now, the cool thing, though, isthat machines don't get tired.
They don't have an off day.
Granted, we're still workingthe kinks out of a lot of this
AI stuff and you've gothallucinations and you've got a
lot of nascent stuff that we'redealing with in this
(23:19):
transitional period.
I would say we're occupying now, but, by and large, you know,
the best tutor is only going tohave so much in their head
before they have to go Google itor something or do some
research and come back to younext week.
Jay Johnson (23:32):
Or until they lean into AI to figure out how to
solve the problem.
Frank Fusco (23:35):
Yeah, Well, exactly , so that's the thing.
Right Is that AI just alreadyhas that, and so I think the key
is that, well, we can lean intothat interactivity, that
interactive dimension, in areally creative way, to where we
have additional modes now thatare open to us, that are open to
(23:58):
us, where, basically, as alearner who is participating in
a shadow, so I'm shadowingsomebody just doing their job.
They don't even have to sayanything, they're just sharing
their screen or they're wearing,you know, meta Ray-Ban AR
glasses and they're capturingwhat's in front of them or
whatever it is.
We've developed an AI tutorpersonalized to you as the
(24:20):
learner in that room, and theyare the one narrating what's
going on.
So you get these little,bite-size, parsable blurbs.
We've been tuning thisconstantly to be the right
amount of information hittingyou at the same time, so that's
not too overwhelming but also isproviding you enough context to
(24:41):
keep up with what's going on.
So beacon is essentially yourguide to what's happening,
tailored to you.
But the real magic is thatbeacon is interactive, so you
can, at any time, just click alittle question mark on any one
of those blurbs that beacongives you and it'll immediately
(25:01):
give you additional context onthat aspect, or you can quote
something and ask a specificquestion about that thing, or
you can just openly, essentiallychat with the shadow as like a,
an entity, as a concept, likeit's a very abstract thing where
(25:21):
, okay, like yeah, if I had areal human tutor with me, I
could have a conversation withthe tutor about what's going on.
But I can actually chat withthis session.
I can chat with everythingabout the session.
It knows everything that'srelevant to what's going on and
that is interactive in itself.
Now we're still building out thenext phase of the platform, but
(25:45):
a lot of the additionalinteractivity is that it's not
just about being in the room andlike having this exposure to
what's going on there.
It's about layering otherelements on top of that right.
So checks for understandingthat can just like dynamically
be generated in real time by themachine detecting what you
should be checked on.
So no one has designed this inadvance.
(26:07):
Nobody has based this onsomething they're hoping six
months from now is going to berelevant.
It's being drawn in real timefrom what's happening and it's
quizzing you on what you need toknow and it's optimizing that
for your retention of thatmaterial.
And then the other aspect isoutside of the room, there's a
learning path, shadows and doingassessments that are
(26:29):
dynamically generated aroundtheir shadows, so it's that
(26:50):
level of interactivity thatseparates this, I think, from
pretty much anything that's beentried before, and I think
that's a pretty, pretty coolthing.
Jay Johnson (27:03):
Personally, so, yeah, I'm loving this concept
because you're hitting on anumber of the different things
that I see as absolutelyessential to effective training,
talent development, workforcedevelopment, etc.
Number one real time engagement.
Number two not you know,avoiding cognitive overload.
Number three having that sortof reinforcement or not just you
(27:27):
know, not just somebody, notjust something where it's like,
hey, I'm going to do this overand over and over again and turn
it into a rote task, but thisreinforcement of you have a
learning partner that's an AIoperated that's going to be able
to essentially coach twospecific things.
So, I mean, you're checking allthese boxes.
I want to talk a little bitabout logistics, right, because
(27:48):
I think that when I'm thinkingabout, let's take the technology
out of here for a minute, okay,has always been a difficult
aspect for the learningenvironment.
Well, why is that?
Okay, say, somebody shadows me.
(28:08):
Well, for a good majority ofthe day, the stuff that I'm
doing is probably going to beexceptionally boring.
Secondly, you know, shadowingme during a day Well, what's a
typical day actually look likefor some different jobs?
One day I'm running aroundhandling a crisis, the other day
I'm doing, you know, deep workand research, and so on and so
forth.
So let's talk about shadowing ingeneral for a second.
And then what is it that thetechnology helps to do to
(28:30):
overcome some of these things?
Because, from a logisticsstandpoint, does the shadowing
you know, like, if I'm bringingsomebody into my company, does
the shadowing shadow me, or doesit shadow somebody else that's
doing what I'm doing?
Or AI generated and I want totalk about some of the tech that
gets into that.
(28:50):
But shadowing how are youoperationalizing shadowing in a
way that's actually effective,given what we know about some of
the internal shadowing slashmentoring programs that are an
abysmal failure?
Frank Fusco (29:04):
Right, all right.
Now we have studied thoseinternal shadowing programs and
many of them are an abysmalfailure very, very frequently,
but some of them have actuallybeen quite effective.
So we looked very specificallyat the ones that were effective.
So we looked very specificallyat the ones that were effective
and I don't even know where tostart with this actually, so I
(29:27):
won't name any specificcompanies.
The most powerful thing waswhatever's already happening,
don't mess with it.
Like so many of theseinterventions are, as soon as
you even call it an intervention, like you've already messed up,
you've already misdesigned thewhole thing because you're
(29:51):
mucking about with, like, theway the company works.
Jay Johnson (29:54):
So spoiler alert people don't like it when you
mess up their job, when you makethem less expensive.
Frank Fusco (29:59):
Stop fixing something that's not broken,
right, yeah, so, like, we saw alot of instances where it's just
like, well, yeah, we're gonna,we're gonna do this mentorship
thing.
All you have to do is take sixhours out of your week and, like
, you know, when you weresupposed to do this one thing.
Just kidding, you have to, like, be in this room and like, fill
out this form with this person.
Doesn't that sound great?
Um, so, and there was just alot of like, like, I, I think
(30:20):
that one of the I mean one, oneof the okay, I probably wasn't
not supposed to talk abouttechnology yet, but I will say
that one of the nice thingsabout tech in like in the HR
space, for example, is that ithas helped HR leaders, uh, run
these things in ways that areless like that, feel less like
bureaucratic.
And I think, I think the peoplethat I know who work in hr uh,
(30:42):
you know, that's like one of thethings I hear most frequently
from them it's like oh well, itintegrates a lot better with, uh
, it makes a lot easier, lowerfriction for people, um, and
it's just about making sure thatthey complete the thing, or
making sure this gets done andit's not, uh, it's not a big
disruptive thing, and so I think, yeah, we saw a lot of
(31:03):
instances where it's just it'snot a big disruptive thing.
And so I think, yeah, we saw alot of instances where it's
either just like tacked on andit's like just kind of expected
that people are just going to doit, which usually doesn't work,
or, you know, things are movedaround a lot where it's
requiring a lot of people to bein compliance, and I also think,
as soon as you say compliance,you've also messed up.
(31:26):
This shouldn't be something ifpeople don't perceive.
Jay Johnson (31:30):
HR people cover your ears that are listening in.
Frank Fusco (31:33):
Sorry, no it's all good and legal for that matter.
But no, in all seriousness, thepoint is that if people don't
perceive the value of the thing,then you have more work to do
and we accept that in the market.
If you don't make a sale, youshould have sold better.
(31:54):
I think that the people I knowwho are working inside of
companies to make things workwithin companies have so much on
their plate and they work sotirelessly to make things fit
together.
Priority shift.
The ground shifts from underyou.
You have to rebalance and it'sone of the most difficult
(32:20):
endeavors, I think, to operateeffectively in that kind of
environment.
No-transcript listen to thehuman beings in the very
specific situations that they'rein and the configuration that
has emerged for what's effectivefor that organization, and they
tailor what's going on to themrather than change it.
(32:43):
So I think, if I had to give ahigh level answer or general
answer, again, there arespecifics on the ground at every
company and the way everycompany works is different.
But it's don't mess with what'sworking for the company.
So the second part, then thetech it's.
It's really about wherever youare and whatever you're doing.
(33:07):
That's.
That's where the shadow happens.
So again, I don't have to be agood teacher, I don't have to
narrate, I don't have to changeanything, I just have to share
my screen, I just have to startthe shadow and then I'm working.
Now there is definitely a rangeof goals of these programs that
(33:29):
we looked at in terms of why youwould run a shadowing program
or a mentorship program or anupskilling program internally.
You know, in the first placeand I think that's a subtle
thing so some companies areinterested in just providing
this as a benefit to theiremployees, like upskill and
learn more.
We know you are interested inlearning more, advancing your
(33:51):
career, help us, help you bepromoted at the company.
So that's a common one.
But another one is that, hey,you know, we have these rock
stars at the company.
We have these very experiencedpeople who've been working for
many years and sometimes atgreat expense, have acquired
really exceptional skills andare extremely effective on the
(34:12):
job.
And wouldn't it be great if wecould actually leverage those
folks' knowledge and experienceas an engine, essentially to
help others more efficientlyacquire that knowledge?
So I think there are kind ofboth sides that can motivate.
But the way you design theprogram, it needs to be based
around a specific goal that youhave and then the shape that
(34:36):
that takes is kind of followsfrom what those goals are, and
that's how it should be.
But the key is don't mess it up.
Don't mess up your company.
Have people in the room withyou and the last thing I'll say
on this point is you never know,you forget, you forget what you
know, you forget what you'velearned and what it costs you to
learn that and how hard it wasbefore you knew that thing.
(34:58):
Uh, you develop amnesiafrequently about what it was
like before knowing the thingand you just forget that other
people don't have that in theirhead right now because you've
moved on.
You've integrated that intoyour whole being.
It's not something that youthink to tell somebody, it just
happens right, yep, and so likeoh man, this thing I'm doing,
(35:20):
it's so boring.
Why would ever anyone everwatch this?
Like who could possibly begetting value out of this?
I can't tell you how many timesI've like heard that from
somebody who was doing something.
Uh, that's just doing their job, frank I have.
Jay Johnson (35:35):
I have a huge problem with this where it's
like somebody will be like okay,jay, you just you think you
went from a to b, but youactually went from a to l.
Exactly skip nine steps inthere.
Because it was just you've,you've done it for 20 years,
right, what did you do next?
What did you do next?
What did you do next?
(35:56):
And I would have never, I wouldhave never saw that there was a
gap, right.
So that makes a lot of senseyeah, and like that.
Frank Fusco (36:04):
That's just where you just you never know what it
is that somebody's getting outof something until you hear from
them.
Like I, in the early iterationof this, you know, when we were
just kind of experimenting withthis model a while ago at this
point actually.
So we actually operated as asoftware agency for a while and
(36:27):
we did that explicitly so thatwe could test people shadowing
us as and you know our staff aswe worked and completed real
world projects for realcompanies.
So we kind of developed our owntest bed, our own Petri dish
there, and that's how wedeveloped the platform.
We built it around us working,um, and I was working on a
(36:49):
client project it was like onein the morning, uh, two in the
morning, I don't know and thisjunior engineer who was on the
client team.
One of the things that we do asan agency we still operate as an
agency, by the way One of thethings that we pride ourselves
on is we integrate with ourclient's team.
So if you've got an existingteam, great, we'll work
(37:11):
alongside them, no problem.
That's better for us because weget more exposure to what
companies are doing in real life, and so I was just coding on my
porch at like two in themorning and I'm just like,
blurry eyed and I'm just, likeyou know, going through this
thing.
We've got a deadline and youknow it's just like you have
those nights sometimes.
And you know this guy was just,he was in, he was just on Zoom
(37:35):
with me we didn't have theplatform at the time just
watching me do this and I wasjust, you know, I was finally
going to bed.
We figured the thing out and Iwas just like listen, man, I
just like I got to commend youfor staying up and doing this.
Like nobody asked you to dothis, you didn't have to do this
.
Like I just, you know I reallygot to shout you out here and he
(37:58):
was just like, are you kiddingthat?
Like just watching you work hasbeen like the most valuable
experience ever.
Like I'm just like what do youmean?
Like I was just like this.
It wasn't, it didn't feel likeanything special that I was
doing.
Like I was just, I don't knowbanging this thing out.
You know I was just like getturning this out out.
And meanwhile this person who'swatching me is just like oh,
that was so valuable.
Like the like little tricks,like the way that you use
(38:20):
console log with brackets in it,so just log the object and like
, instead of like having to likelog every little thing.
I'm just like.
I mean I think I learned thatlike I don't know 10 years ago
or something, but like youforget that you don't, that
people don't know these littletiny tips and tricks, these
little like real world things.
No one has ever taught a classwhere they say you know, if you
(38:41):
use console log with brackets,it's a lot easier to read in
Chrome's dev tools.
Like no one's ever said that ina classroom, ever.
Probably.
I mean, I can't say for sure,but I doubt it but it's those
little things that go a long,long, long way and that was a
really actually light bulbmoment for me.
Actually at that exact time washuge, yeah, yeah, like, oh,
(39:03):
that's right, there's stuff inhere that I forget is valuable
and you know there are many moreskilled engineers than me out
there in the world and you know,imagine if we could harness
their experience for everyone.
Jay Johnson (39:18):
This lands for me, frank, and my experience in it
is the number of times that I'vehad some of my teams say hey,
can I come with you?
I'd like to watch you presentand I'm like you helped me
design the materials.
Why would you want?
because I want to see how youpresent it, or even something so
long, so much as hey, do youmind if I sit in on your next uh
call, on a sales call, or yournext time that you're being
(39:41):
evaluated for a keynote or anyof these things, and it's just
like, yeah, you know, I I don'tmind ever inviting my team to
participate in those things,cause we do recognize we learn
by doing or by being engaged insomething that's happening.
So the fact that the softwarereally does provide this is an
incredible opportunity for us tothink about how can we help
(40:02):
people to see and alsoexperience it.
And then the added component ofAI is such a powerful thing.
Frank, this is such ainteresting concept.
If our audience wanted to getin touch with you to learn more
about Silicon Society, theprograms and the shadowing
(40:23):
concept, how would they reach?
Frank Fusco (40:25):
out to you.
Technically we're in closedmode at the moment, but we will
be keeping people up to date onwhen we'll be doing a wide open
launch.
So if you are interested, youcan join the waitlist at
siliconsocietyorg.
You can also get in touch withus via that website and if
(40:47):
you've got you know, if you're abusiness you've got a software
project you need done, you canalso get in touch with us about
that there as well.
Jay Johnson (40:53):
Incredible.
So I will be joining the waitlist because I can see a number
of different applications, evenfor training my own team and
utilizing this.
So I just want to say thank you, Frank, because I think this is
a novel way for us to reallyrethink some of the ways in
which we're training ourinternal teams and as a tool to
be able to kind of overcome someof those big gaps that we're
(41:15):
all aware of in both thelearning and development space.
So thank you for taking thetime to speak with us today and
thank you for all the work thatyou're doing in helping to
elevate the talent developmentspace.
Frank Fusco (41:27):
Thank you, Jay, and I just want to shout out the
team Rachel Cohen, ShelbySchisler, Robert Sharp we
couldn't do it without them.
So this is a full team effortand I just wanted to be really
clear that it's a product of thehearts and brains of everybody
working together.
Jay Johnson (41:48):
Amazing.
So thank you again for beinghere and a shout out to the team
.
Absolutely so.
Thank you, audience, for tuninginto this episode of the Talent
Forge, where we aredemonstrating software to help
the future of training anddevelopment.
Thanks a lot.
Popular Podcasts
On Purpose with Jay Shetty
I’m Jay Shetty host of On Purpose the worlds #1 Mental Health podcast and I’m so grateful you found us. I started this podcast 5 years ago to invite you into conversations and workshops that are designed to help make you happier, healthier and more healed. I believe that when you (yes you) feel seen, heard and understood you’re able to deal with relationship struggles, work challenges and life’s ups and downs with more ease and grace. I interview experts, celebrities, thought leaders and athletes so that we can grow our mindset, build better habits and uncover a side of them we’ve never seen before. New episodes every Monday and Friday. Your support means the world to me and I don’t take it for granted — click the follow button and leave a review to help us spread the love with On Purpose. I can’t wait for you to listen to your first or 500th episode!
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
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.