May 20, 2025 • 46 mins
Josh Bruyning and Richard Stiennon explore how AI agents are fundamentally changing the SaaS industry and traditional software models. They discuss the shift from feature-based to outcome-based solutions and why companies must adapt to survive.
• Richard Stiennon shares insights about Security Yearbook 2025 and IT Harvest's database tracking over 4,340 cybersecurity vendors
• Introduction to Compliance Aid, a disruptive platform supporting 366 global compliance frameworks through conversational AI
• Analysis of why traditional SaaS companies must incorporate AI agents to remain competitive
• Discussion of the "agentification" trend: adding AI capabilities to specialized software platforms
• Exploration of how specialized knowledge maintains value when combined with AI capabilities
• Deep dive into AI's impact on employment, particularly for future graduates
• Warning for educational institutions that resist AI adoption instead of embracing it
• Comparison of the AI revolution to other technological transformations like desktop publishing
Check out IT Harvest at it-harvest.com and connect with Josh Bruning on LinkedIn to stay on the cutting edge of technology and AI.
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:00):
Welcome to this episode of Cybernomics.
Thanks for tuning in spendingyour time with us.
I'm your host, josh Bruning,and today I am talking with
Richard Steenan.
The one, the only, the man, themyth, the legend.
Richard Steenan hey, richard,thanks.
Hey, josh, thanks for having me.
Congratulations on the SecurityYearbook 2025.
(00:25):
Every copy sold out at RSAcorrect.
Speaker 2 (00:29):
Correct.
Yeah, all the ones in thelibrary or the bookstore.
And then various vendors bought250 copies to have me sign and
I signed them all and peopletook them here.
It is Awesome.
Speaker 1 (00:41):
How many vendors are we up to right now?
Speaker 2 (00:44):
Yeah, 4,340.
But quite a few of those 500 ofthem we've archived.
In other words, we go throughall of our vendors every week,
(01:07):
or LinkedIn's page is not thereor points to an acquirer, maybe
whatever reason that I get analert and then we decide whether
or not to archive the vendor.
So it's usually because they'vebeen acquired, but sometimes
they just disappeared.
Speaker 1 (01:16):
Yeah, yeah, and I know sometimes they fall through
the cracks like the really newplayers and the disruptors.
Yeah, is Compliance Aid on yourradar?
Is that in IT Harvest?
Speaker 2 (01:28):
I will have to check.
I don't even know if I'mallowed to talk about them,
because they're like there was atime my brain was so pliant
that I would remember, if I ever, because I have to enter the
original data.
We have compliance cow,compliance scorecard and
compliance risk.
All right, what'd you say?
(01:49):
Compliance aid?
Speaker 1 (01:51):
Compliance aid, compliance and then A-I-D-E Nope
, we do not.
All right, you're going to wantto put them on your radar
because they are disruptingthings in the GRC space and even
beyond GRC, because RandyBlasick, the co-founder there at
Compliance Aid, created andhave had him on the podcast.
(02:13):
You and I had talked to himbefore and things have changed
so fast over the last couple ofweeks, even since you and I and
he all got together and talkedyou and I and he all got
together and talked and he cansupport now the compliance aid
platform can support 366frameworks, so like global
(02:36):
frameworks.
So a customer can just pop inand go hey, what do I need to be
compliant with across the globe?
And can you crosswalk that toall my evidence and all my
controls?
It's like, yeah, sure, we cando that in the afternoon.
Can you generate policies?
Yeah, sure, we can do that.
Go through the feature list ofany competitor like Drada, vanta
(02:59):
, aptiga Audit Board, whateverit is All gross.
Yeah, aptega Audit Board,whatever it is.
Allgress yeah, go through theirfeature list and you can just
go to the compliance aid.
It's like a chat bot, likeChatGPT or something like that,
and you can ask it hey, I needthis output, I need a risk
(03:19):
register, I need a frameworkassessment, I need X, y, z and
it'll just spit it out and I'mlike holy crap.
Which brings me to the topic oftoday SAS is dead.
I mean, llms and agents havenot just disrupted the SAS model
(03:43):
or the SAS industry, but Ithink it's on the verge of just
obliterating it, because everytraditional tool or solution out
there cannot compete.
They're not as flexible, not ascustomizable, ai is inherently
fast and there are governanceissues which we can talk about,
(04:04):
and there are the hallucinationissues and reliability issues.
There are privacy issues, thereare security issues, there are
implementation issues andusability issues, which are all
things that SaaS have conqueredand are really good at.
So, richard, what does it meanin 2025 to say that SaaS is dead
(04:28):
?
Speaker 2 (04:29):
Yeah, I would couch that in terms of look at it if
you have SaaS, you better belooking behind you for what LLM
is going to displace you quickly.
So incorporate it right.
You've got to change to survive.
You might have to drop yourprice by a factor of you know
(04:50):
two one-tenth what you werecharging before to keep up.
Just think of all the poorstartups when ChatGPT first came
about and they discovered, hey,you could embed a whole bunch
of data in a RAG model and turnit into a knowledgeable LLM
based on your stuff.
And then, all of a sudden,chatgpt says oh, here's your own
(05:13):
GPT, make your own, just uploadyour data.
I created something I callSocrates and I uploaded all my
books and papers and articles.
Uploaded all my books andpapers and articles, and so now
you can talk to a bot that hasall of my writing and maybe will
respond as if it was me.
Speaker 1 (05:32):
Okay, Big win for anybody who wants to be an
industry analyst Sure.
I wish I had that like three,four years ago.
Sorry, ed, don't we all?
Speaker 2 (05:42):
So yeah, so there's that challenge Like right now.
It's like when you say SaaS,you have to think of
Salesforcecom, right?
The kind of the anchor for allof SaaS.
And do you really need it?
Right, it's horrendouslyexpensive.
You could build your own in anafternoon using a little help
(06:06):
from ChatGPT or Cursor orwhatever you like to use, and
it'd be customized just to whatyou need today, right?
Then you add a feature later ifyou need a feature.
So, and maybe use no code forthe user interface.
So, no matter what, it's goingto be cheaper than getting a
five-seat license toSalesforcecom.
(06:27):
So I wouldn't say SaaS is dead,because it would be a long time
before that huge building indowntown San Francisco is leased
out for apartments or whatever.
But I think SaaS is completelychallenged.
It's definitely not the hotthing anymore.
The startup world has shiftedimmediately to doing all this
(06:49):
chat, gpt stuff.
There's another corollary tothat question and it is.
This came up actually thismorning.
So a friend sent me a link to aproduct called NextRose.
So NextRose, you know, it'simmediately apparent what it
(07:11):
does as soon as you see it.
You just go to NextRosecomsomething like that, and you can
upload any file so you caninput a URL, you can input PDF,
you can input an image of datain any form and it will turn it
(07:35):
into a spreadsheet.
Just a minute, and what abrilliant idea, right Well?
Speaker 1 (07:41):
that's great.
I'm glad that somebody'sspecializing in that, because I
try to do the same thing inChatGPT all the time.
It does not work Right.
I mean it may work sometimes,but it's not reliable yeah
resolution issues something.
Speaker 2 (07:54):
But they've, you know , I don't know what they're
using on the back end.
It could be a combination ofthings.
They could be doingpre-processing, you know, to
just extract the text and thenlater on do the columns in rows.
But I tested it out.
So Jeffrey Dubs is the VP ofsales at Gartner for the Gartner
(08:15):
Security Conference which iscoming up next month, and he's
been posting an infographic ofall the sponsors.
We track all the sponsors formost of the big conferences.
So I grabbed an image, uploadedit to JustRows and it
immediately created aspreadsheet by category.
(08:36):
So you know, each column was adifferent category application,
threat, intelligence, et cetera,and then all the vendors in
each category.
The way it was, it isinfographic.
And then I said, well, I justwant a row of company name and
category.
And it did that, just redid it.
For me it was simple interface,so simple.
(08:56):
Now my friend who pointed it outhad made an investment in them
and my question is you know, ifyou put on your curmudgeon hat
and you're an investor, youimmediately go.
Oh, you know, chatgpt willreplace this.
You know, within months Peoplewill use ChatGPT because they'll
be so good at interpreting whatthey want and doing stuff.
(09:18):
So the question remains is thattrue?
You know, here's guys with afantastic product.
They don't even have a pricingpage, so I can't even buy it,
they just use it.
I would pay for it.
Yeah, are they doomed?
And that is a very interestingquestion to me, because I have
(09:38):
the same question Am I doomedwith IT Harvest?
Because with IT Harvest we'vetaken you know what originally
was just the directory inSecurity Eurobook of 4,000 plus
vendors, and originally withmanual processes and now
completely automated with thehelp of, you know, manus and
(09:59):
Perplexity and Anthropic andOpenAI, we can populate a
database with information on allproducts and align all those
products with NIST and whateverand just make this deep and rich
tool for exploring the industry.
And so, and the fear is, and Itotally believe, and I use it in
(10:24):
our strategic thinking that theforeseeable future and it all
takes two years to get to be 100times better.
So right now, if I ask any ofthose Manus, perplexity to tell
me you know, list all thecybersecurity companies in
Canada, they can kind of get 30,right, we've got 138.
(10:48):
You can't tell the differencebetween Fortinet, which is a big
player in Canada.
They've got a lot of employeesthere, but it's definitely not a
Canadian company, right?
They're in Sunnyvale, so itmesses up on a lot of stuff.
In other words, a lot of work.
If you wanted to create adirectory of cybersecurity, it's
(11:09):
such a concern to me that weare seriously considering making
this the last year that you canget the directory in the
security yearbook that directoryif you just OCR this and I
shouldn't be saying this, butanybody could buy a copy right
now OCR this.
They'd have the list, they'dturn it over to ChatGPT and they
(11:30):
could make tools worth millionsof dollars.
You know it helps to be anexpert in the cybersecurity
industry, so you know whatyou're doing.
But you know ChatGPT isprobably more of an expert than
I am now.
So that's the opportunity andthe threat.
Chatgpt, if it's 100 timesbetter, can categorize vendors.
(11:52):
They can't do it now, right?
They can't tell an errandbetween.
They wouldn't even know.
They can't do it now, right?
It can't tell an error inbetween.
It wouldn't even know what adigital risk protection vendor
did, right.
Speaker 1 (12:00):
Right, so you're saying that the more granular
you get with the categories, theharder it is for ChatGPT to
catch that?
Speaker 2 (12:08):
Yeah, and it doesn't have the same categories, of
course, as an industry analystcomes up with, because it's
swayed by what the vendors say.
Speaker 1 (12:15):
Yeah, yeah, and I think that that's one of the
greatest selling points of ITHarvest.
Well, this is really the greatselling point of Richard Steenan
is that you came up with a lotof those categories and that was
the first thing you did in thefoundational days of IT Harvest,
so that remains your IP.
Speaker 2 (12:33):
Yeah, yeah, completely.
Speaker 1 (12:37):
Yeah, but I do have a silver lining for the problem
and I think that this is wherethe sas companies will have to
go.
I don't think that it's achoice.
You have two there.
There will be two main modelsgoing forward.
I think.
I'm interested to hear what youhave to say.
Number one would be the AIagentic factories, so the folks
(12:59):
who are going to build agentsand build models that others can
then productize and theenterprise can basically
customize this to their heart'scontent.
There might not be a box aroundit, but it could be a chat bot
in a CRM.
It could be a chat bot for Idon't know for handling customer
(13:24):
calls, customer support,whatever.
They're already using some ofthat.
The second model is going tohave to be and if the SaaS
companies want to survive willhave to be the agentification of
(13:49):
SaaS.
Harvest is going with the needto catalog and to understand the
vendor space, especially forprivate equity that might be
interested in cybersecuritycompanies or anybody who's
looking to invest incybersecurity Virtually the
(14:14):
people that are interested in ITHarvest.
Now they will need toincorporate some sort of agent
into their software, right, andthis only makes the software
better, and so you can't go tochat GPT.
Like you said, there are thoselimitations within chat GPT.
They don't understand thecategories.
That's still all yourbrainpower and all of the work
that you and your team have putinto IT Harvest.
But what you do is you, I callit, you know harvest.
(14:37):
But what you do is you, I callit, you know agentification.
It's kind of likegentrification but agentify.
Agentify it harvest, such thatit there's a chat bot in there.
People can ask it questions.
It'll spit out everything thatyou need to know about any
vendor in the space.
It can create Monte Carlosimulations, it can create
(14:58):
forecasts, anything that aninvestor needs in order to make
wise decisions.
But it's all specialized andoptimized using the IT harvest
methodology.
And I think that all SaaSthey're going to have to go that
way, because where theyspecialize and where they excel
(15:19):
all SaaS platforms is in themaintenance, in the customer
service, the specialization, thesecurity, the privacy, all that
stuff.
And now all they have to do iskind of bolt on the agents into
that so it becomes a value addinstead of a competitor.
So to your point, in that waySaaS wouldn't be dead.
(15:39):
It's just that the current SaaSmodel might be dead.
Speaker 2 (15:45):
Yeah, yeah, I think it's easier to make a big claim
like that and you're totallyright.
I think the one thing I learnedat RSA because obviously I'm
not building agents, I'm notbuilding AI technology right,
I'm using it, I'm applying itall the time but I talked to a
(16:07):
bunch of the SOC automation AIguys so Bricklayer, quiller, a
lot of those guys and they allindependently came up with kind
of the same methodology is havethe AIs that, and then it
(16:27):
assigns tasks to the specificagents that are best suited for
doing that task.
And then it collects the resultsfrom multiple agents and turns
it into a report that you canread.
Everybody's taking thatapproach and it must be the
(16:49):
logical thing to do becauseeverybody's doing.
It Makes sense.
But at least one of themQuiller was building agents that
can be deployed to your desktop.
There's an agent that will killsecurity awareness training and
it's unfortunately.
It reminds me of Clippy.
But if you, for instance, arecutting and pasting some
(17:11):
information and it happens tohave social security numbers in
it, it will intercede and saywell, are you sure you want to
put that in an email?
Because you know it violatescompany policy and all sorts of
things, and they made ittemplatized.
So the company that's deployingthese agents would set their
own policies and things in theway it interacts with their
(17:32):
employees, and that's you know.
I immediately think, oh wow,can I do that?
You know I need an agent togive to people to deploy, so I
can see that they're readingabout.
You know a cybersecurity vendorand say, hey, we've got a lot
of information, would you liketo click here and see it?
Or that kind of thing.
So I truly believe that agenttemplates are going to be
(17:57):
available.
Maybe not a shopping mart fordifferent types of templates.
But who knows, that could benext.
Speaker 1 (18:06):
Yeah, I was talking to somebody about this very
thing this morning where they'relooking to.
They're like, okay, do I do?
I have a blank page where mycustomers come to the screen
that looks like chat, gpt, andthen the customer might be lost.
But then I was like, why don'tyou have a prompt library that
basically says you know, orthere could be a thing at the
(18:28):
bottom of the screen that sayswhat would you like to do today?
And then there are a list ofoutcomes that you can choose
from, and then you can look atthe prompt library and say, okay
, this is what I want to do.
So, to your point, everythingwould have to be outcome
oriented.
So you'd have to approach itwith.
You'd have to approach theplatform either already knowing
(18:51):
what you want the outcome to be,and if you don't know what you
want it to be, there might haveto be like a Clippy type
assistant that says I don't know, that's optimized to whatever
domain that AI agent isspecializing in.
Right, right.
Speaker 2 (19:10):
Yeah, anything that's got repetitive tasks for sure
should be right there so younever have to type anything more
than once in your life.
So you know.
So we have created a IT Harvestagent it's called Harvest IQ
(19:35):
and then a third LLM adjudicatesbetween the answers and figures
out the best combination.
We had to do that because welaunched it with just one and we
told everybody hey, sign up.
And now you've got a agent thatyou talk to, or chat bot that
you talk to, that will becompletely informed on all
(19:56):
cybersecurity products.
So go for it.
And people are asking questionsthat were not about
cybersecurity products, justgeneral stuff.
So that's when we added a callto perplexity, and so perplexity
and answer anything.
So we'll get you a perplexityanswer and if there's anything
in there that our data cancontribute, we include that as
(20:17):
well.
So we get really long answers.
But you know, now you ask itfor SWOT analysis of a company
and it'll just do it for you orcreate a battle card or compare
two companies, or you know,here's the top secret one, we'll
see if anybody's listening.
Yeah, or you know, here's thetop secret one, we'll see if
(20:38):
anybody's listening If you aremaking an argument that one
company should buy anothercompany.
You just ask HarvestIQai to dothat for you, and it does it
with a breakdown of how theproducts work together.
Speaker 1 (20:48):
Yeah, yeah.
And the two companies Optimizedand specialized, right, right
yeah, because the agenticcompanies can't do that, the
ones that are building theagents, they don't care about
that and the current sassplatforms don't have that.
So specialization, um, and I'veseen that trend since chat gpt
(21:11):
3.0 came out and I called up oneof my friends and I was like I
can make this thing optimized toteach your kid how to do math,
to tutor your kid, whatever yourapplication is.
You just have to train it onthat thing and you're already
ahead because you have so muchdata already to train that.
(21:32):
So IT Harvest is far ahead ofthe crowd in terms of
intellectual propertymethodology.
Here are some things that Iwould like to see in IT Harvest
in this agentic age.
Here's something that I wouldlike If I were a PE firm.
I already have a portfolio ofmy best performing companies
(21:53):
over the last 10 years.
I want to drop those companieswith their attributes into IT
Harvest and say, based on mybest performing portfolio
companies, what company should Iinvest in next?
Or what are the top fivecompanies or the top 10
(22:14):
companies that are most likelyto generate a return like the
ones that these companies have?
What do you think about that?
Speaker 2 (22:22):
I think it'd be worth a try.
You know why not, Do you think?
Speaker 1 (22:26):
that's just conceptually.
Are there any problems withthat?
Speaker 2 (22:30):
None at all.
Yeah, because we've got thedata there right.
We've got our health scores,we've got the growth of every
company.
We could, if it really helps.
If their portfolio companiesare in our database, right, if
they're long gone, then theywouldn't be in our database, we
wouldn't know anything aboutthem.
But we could line up ongeographies, categories, growth
(22:57):
rates, size, funding levels, allthat, and then it would look
deeper into the products.
And yeah, I think we could dothat.
We should try it.
I've got most of the portfoliosof most of the companies, so
I'll just take one of theirportfolios and analyze it.
(23:17):
I'll be the one that decideswhich one is a good investment,
but I do that all day every dayyou can tell which ones are not
good investments.
Speaker 1 (23:25):
Yeah.
So you've got the human in theloop and that's the model.
It's outcome-based versusdeterministic.
So I don't think it's sasversus ai.
It's deterministic, where thecompany has a set of features
that customers kind of alreadyhave in their mind what they
(23:47):
want those features to be, andthen they come in and then they
measure what they want againstthe feature set, and that's's
how most SaaS companies are soldtoday.
That's why they say don't do itby features, because if you did
it by features like everybody'sbasically got the same features
Right, yeah.
But with AI you don't thinkabout features, you think about
outcome, right, right yeah.
(24:09):
So if you thought about outcomewith SaaS, they're going to
charge you $100,000 to customizeso you get your outcome.
Speaker 2 (24:15):
That's right.
Two years ago, ever since I sawwhat AI could do, it was
burning in my brain.
I had to just write about it onSubstack.
So I did, and it's very similarto what you just described, but
it's for intermediating,disrupting the entire publishing
industry.
(24:36):
So if you publish a book, youquickly discover, especially in
the fiction world, that there'sgatekeepers between you and the
publisher.
So, simon Schuster, if you'refamous or something, you can go
directly to them.
But typically you get an agent.
So you have to find an agent.
So you spend a lot of timequerying agents.
The agents are just people whohopefully like to read books and
(24:58):
they are looking at whetherthey can sell it.
Right, they're not looking atwhether they like your writing
or anything like that.
They just want to know if theycan sell it.
So what if you had a LLM thathad all books and most LLMs do
have all books in them andsomebody could submit their
(25:18):
manuscript and it could say hey,you know, here are the five
publishers that are most likelyto be interested in publishing
your book.
And oh and, by the way, youknow, here's an analysis of what
you submitted.
You can make it a lot better ifthe third chapter did this and
this because, of course, youcould do all that too, things
(25:41):
that ChatGPT would be happy todo for you today.
And then, on the publisher'sside, they'd be the ones to
subscribe Because you would haveall these manuscripts.
They wouldn't have to filter itthrough humans that are
notoriously bad at pickingbestsellers.
Now, in the publishing industry, all you want to do is get a
bestseller.
The only reason you publish 100books for every bestseller is
(26:05):
you don't know what you're doing.
You don't know what the marketwants and you can't provide it.
So you're going to lose money onall the non-bestsellers and
you're going to make a killingon the one that sells 2 million
copies.
So the publishers would sign upand when there's a hit that
they should publish, then theywould reach out to the author
and make a contract and cuttingout the middle person, the agent
(26:27):
, completely, and they'd start.
You know they'd actually becomeprofitable if they made a few
more correct guesses on goodbooks.
Speaker 1 (26:37):
Yeah, yeah, again, outcome based, right, right,
what is it?
Think about what the outcomeyou want.
I want a bestseller, all right,let's optimize for that.
And if you're an agent and ifyou're listening to this and
richard just scare, scare, the,the bejesus out of you, I think
that there might be hope, justlike it harvest.
(26:57):
Maybe there would still be,maybe not like a junior agent,
if there's such a thing, butmaybe there is someone who has
the final say and kind of putstheir human touch on it and says
yeah, the baby step is simplethe agency, the agent.
Speaker 2 (27:15):
now we're mixing up our terms again.
But the people that theliterary agents should use
agentic AI to weed through theirslush pile, which is what we
call the ones they haven't readyet.
Oh, gotcha, gotcha and find thegems in there, find the ones
that look like they could bedeveloped.
You may find a manuscript thatwas submitted a year ago from an
(27:39):
author that wrote another bookthat got published and was a
bestseller, and you can go backto that author and say, hey,
we're interested in that.
Now that you've figured out howto write bestsellers, maybe
you'd like to take anotherglance at your old manuscript.
There's billions hidden inthere.
Speaker 1 (27:56):
Yeah, if nobody's working on it, get on it,
because I think that's going tobe great.
So there's another factor tothis.
Now you've got my gears turningon this publishing thing and we
can apply this across all kindsof models, even cybersecurity,
it, whatever.
There's another factor herethat when we're picking winners
and losers yes, the quality ofthe work, the author, the
(28:23):
relevance of it okay, that's allgood, but there's one factor
which is timing, and I supposetiming and relevance are the
same right, because you canwrite the shining in 1995 and
write the shining in 2005 andmake it.
You may have two differentoutcomes.
Yeah, for sure so per or with,in the case of it harvest.
(28:43):
You may invest in a company in2005, invest in the same company
in 2025, and get two differentoutcomes, as I did.
Is there an AI application forthat?
Is there something that you canthink of that solves the timing
problem?
Speaker 2 (29:01):
If anything, it'll come out of the people who are
applying agentic AI to the stockmarkets.
Right, Because that's alltiming.
Speaker 1 (29:09):
Yeah, maybe some like correlations or something.
Speaker 2 (29:13):
Yeah for sure.
And you know, if you had allsocial media going on fed into
which at one point you did, grokdoes, then you can kind of tell
.
And you look at historicalstuff too.
You know during the World Warswhat kind of books sold the best
, when was the heyday ofmysteries, whatever, and what
(29:38):
caused that?
Yeah, you could draw those.
You definitely do that timing,yeah.
Yeah, you definitely do thattiming, yeah.
Speaker 1 (29:45):
Yeah yeah, I was tossing around the idea of
creating a tool that just trackstrigger events.
So in sales, one of the biggestfactors is really timing,
because you can reach out tosomebody 20 times a year, but if
they're not in a buying mood,they don't have the budget, they
(30:06):
don't have the need, they'rerocking and rolling already with
another tool, whatever.
Speaker 2 (30:11):
That's totally why salespeople do reach out so
consistently is because it works.
You know the 20th time somebodygoes.
Oh, I was trying to rememberyour name.
Thanks for reaching out.
Speaker 1 (30:24):
So a free tip to all my sales folks persistence is
key, because underlying thatpersistence is the fact that you
don't know when is the righttime and people don't tell you,
and so you have to just keephitting it.
And you know, trigger eventsare a part of that.
So let's say, for I don't know,vulnerability scans or whatever
(30:46):
, they probably peak in theirsales when there's a big breach.
Right, sims, probably the samething.
If AI and cybersecurity all ofa sudden are hot which it's
always been hot, but let's sayall of a sudden it's hot, then
IT Harvest is a more valuabletool, because now they can weigh
through all of the vendors sothey can make their investments.
(31:07):
So, yeah, outcome-based, allright.
Is that all?
On SaaS, we've determined thatSaaS isn't dead, it's just
comatose.
So we can give it some smellingsalts, which would be AI agents
, and wake it the hell up.
Speaker 2 (31:27):
Totally.
And you know, don't forget, ifyou've got a SaaS product and
you say you're Canva, right, andit's really old, but everybody
just says I do use that once ayear, so I'm going to keep
paying the monthly subscriptionor whatever.
You'll survive for a long timein the monthly subscription or
whatever.
You'll survive for a long time,right.
(31:48):
But obviously Canva isincorporating AI now.
Right, they're giving all thosetools, as is Grammarly and the
rest.
But you could just kind of siton your laurels and slowly fade
off into, you know, the end oflife for your product and that's
the most profitable time ofyour business is when you're not
spending money on the productor marketing or sales.
So, yeah, you take advantage ofthat.
(32:11):
Go on.
And retire.
Or you can just start sweatingagain and come up with a better
solution.
Speaker 1 (32:19):
Yeah, yeah, yeah.
If you want to check out someAI shops, hit me up.
I can make some goodrecommendations in addition to
the ones that Richard hadmentioned.
Is AI going to take your job?
And I hear a lot of differentnarratives around this, but the
(32:47):
prevailing narrative is that AIwill not take your job.
It will make the humans better.
And I hear this like clockworkand I've been repeating this for
weeks and weeks and weeks, andit's not because I'm trying to
drive a particular narrative.
It's because I hear competingnarratives.
If you see, going back, billClinton, if somebody's pointing
(33:08):
somewhere and they're lookingsomewhere else and they're
telling you something, it's,there's probably something
screwy, right?
So Bill Clinton, looking thisway, say, I did not have sexual
relations with that woman.
Pointing one way, looking theother way, all right, turns out
he was lying about that, and soit's not that I think that
(33:28):
people are lying.
I just think that vendors arescared and they don't want to
push AI too quickly and losesales.
That's one thing.
Enterprises want to integrateAI into their work, but they
don't want to push it too hardand move too fast and encounter
(33:50):
the security and the privacyissues.
Okay, we also don't want toscare employees.
You do not want to scare thejob market.
If you scare the job market, itscrews up the economy.
You don't want to do that.
That's another reason why Ithink people are pumping the
brakes on AI and saying that itwon't take your jobs.
But you and I can, yes, and Ithink that my platform is small
(34:13):
enough to where I could say thatand not do too much damage.
But let's say, on CNN, they'recoming out and saying, hey, ai
is taking your jobs.
You can imagine what that'sgoing to do.
Here's my thesis.
Let me know if you agree or not.
Richard, my thesis is that AIwill take your jobs, but it will
not take present jobs.
It will take some present jobs,but it would not so much take
(34:36):
the present jobs as it will kindof halt the job market for
future employment, which meansthat colleges and the future job
pool would be in trouble.
Right, we're hearing thenarratives around.
You know AI will take your jobs, but then, or AI will not take
your jobs.
But at the same time, peopleare saying that AI will replace
(34:58):
labor, it will make laborbasically go away manual labor.
So it's very evident and it'svery clear that AI is doing
something with jobs.
Will that impact the averageperson, or the junior employee,
or the senior employees or, Idon't know, maybe even CEOs?
(35:18):
What do you think, richard?
Will AI take your job?
If so, how, and if not, why?
Speaker 2 (35:27):
Well, I have to fall back on historical references.
Every technological revolutionhas definitely displaced workers
, like no question, right Fromthe cotton gin to automated
sawmills et cetera.
And in my experience in theworkforce, when I got out of
school we still had typists andthese were people, invariably
(35:50):
women, that would typeeverybody's memos in commercial
communications.
So 1982 is like just the end ofthat, right, because that was
roughly when the IBM PC AT wasintroduced and that was the
microcomputer revolution thatwas happening.
And probably five years laterthere were no more typists All
(36:15):
gone.
There used to be tens ofthousands of people whose chief
skill was typing and they didthat for corporate offices,
completely obliterated.
And the other one is the kindof you remember Apple creating
desktop publishing?
That's actually what they wentto market with.
(36:35):
Is we're going to make it easyto create nice graphics and
print them and talk to printersand talk to printers.
So you know a lot of graphicartists.
You know that used to literallycut and paste things on a board
and then somehow turn that intosomething that could be printed
.
They were out of that job, butmost of them just transitioned
(36:56):
right.
They loved the computers anddigital art and made a good
transition.
So I think we're in the samestage now.
Somebody the other day said well, you know, lawyers aren't going
to be displaced.
Are you kidding?
They're going to be the firstright.
It's like you do not need alawyer to create a contract
between two people who just wanta legal-sounding agreement
(37:16):
between them.
Right, right, the chat.
Gpt is so good at that and it'scomplete, and it throws in all
the bullshit language about.
You know what.
Speaker 1 (37:27):
You know just all the stuff you know Definitions and.
Speaker 2 (37:32):
Definitions and supersessions, and statutes and.
All that stuff.
Now, mind you, I wouldn't wantto use chat GPT to argue a case
for me in front of a court, butit sure helped inform that
That'd be great.
It could take all thedepositions and weave them
(37:52):
together and come up with a goodstory for the jury, but for
sure, contract law is going tobe significantly challenged.
You still need lawyers, youknow.
No question Lawyers willremember how lawyers were so
slow to get on the internet,like they wouldn't have email
addresses for you probably don'tremember this, but for the
(38:13):
early first five years of theinternet, lawyers refused to do
it.
Because you know, in today'sworld you send an email to your
lawyer and say, hey, can youlook at this clause for me?
And they give you an answerright, and you know that it took
them two minutes to do it.
So it's really hard for them tobill the 45 minutes they used
(38:34):
to charge for that kind of work.
So they just hated the idea ofhaving a conversation with their
clients.
It was so fast andfriction-free.
So they got over that they hadto, because the young crowd of
lawyers that were coming behindthem were perfectly happy to do
that.
No problem.
Speaker 1 (38:52):
Yeah, they're bobbing and weaving, yeah.
Speaker 2 (38:55):
Imagine a patent attorney.
Right?
It's like patent attorneys haveto write in a specific language
and they just take what youtell them about your idea and
turn it into that language.
It's come on chat.
Gpt can do that in its sleep,so you don't need them.
You know what other things?
Financial analysts?
(39:15):
I don't think I need thoseanymore, so yeah.
Speaker 1 (39:19):
What about accounts, the number crunchers?
Speaker 2 (39:25):
Yeah, you know, the number crunchers are the
analysts, right?
Actually, you know, theaccountants are the ones that
set up the books and put in thecontrols.
And yeah, I think we'll needthose for a while for sure.
Speaker 1 (39:40):
So can I ask you this , if I am correct, in that the
college pool or the workforcethat's coming from colleges,
they're the ones that are goingto be the most affected If we
were to apply it to thisscenario?
So your scenario of the lawyersright, let's say you're not
(40:02):
going to need lawyers in thefuture.
The lawyers right, let's sayyou're not going to need lawyers
in the future Um, what wouldthat do to law schools?
Like, if I were to be a lawyer,if I wanted to be a lawyer, and
if I'm a law firm, let's say,okay, on one side, I'm a future
lawyer, on the other side, Ihave a law firm.
The law firm is thinking wealready have lawyers.
(40:23):
We're not going to fire thembecause they've been around for
5, 10, 15, 20 years, but we'regoing to give them these tools,
we're going to give them some AItechnology and they're going to
be like flying, rocking androlling.
We're going to do more business, more clients, more money.
But if I am a law firm, will Ihire a new lawyer and have to
(40:45):
train them for a year or twoyears and have to worry about
them becoming partner anddisplacing another partner,
which you don't actually becomepartner unless one partner
leaves, because the firm's onlygot so many partners right.
So now, as a poor collegestudent wanting to become a
lawyer, do I enter this jobmarket that's dominated by
(41:08):
cyborgs essentially Humans,using AI?
Speaker 2 (41:11):
Well, right now you don't have a problem because as
a student, you can know moreabout AI than the fuddy-dud old
attorneys.
So now you've got somethingagainst them.
Speaker 1 (41:22):
That's a great point, Richard.
Speaker 2 (41:23):
You can 10x their productivity right, so you can
outproduce them by a factor of10, maybe without working
80-hour weeks your first twoyears.
So you've got that going foryou.
In the future, there will justbe a market that lawyers are 10
times as productive as they arenow, and that tells me that
(41:43):
probably we don't need tograduate as many attorneys.
So right now we're at this cuspand this is where everybody
should listen up.
If you're going to a schoolthat is frightened by AI, if
they start saying no AI in theseassignments, no AI for this
test, and we're going to checkit for AI production, we just
(42:04):
invested $100,000 in a tool tocheck whether you're cheating by
using AI.
Change your school right now.
You are going to just be soill-served by your degree from
that school.
There are plenty of schools Ifind schools on the East Coast
in particular, from DC upthrough New York and Connecticut
, that are embracing AIwholeheartedly.
(42:26):
Here in the Midwest, pure fear.
The professors are afraid of itand, you know, mostly in
letters and science, less somaybe in physics or something.
You've got to grasp this rightnow.
It's like graduating school in1990 without, without you know,
having a modem at home.
(42:47):
It's like how do you do that?
Speaker 1 (42:49):
yeah, it's like back in the day when books were
invented.
Imagine showing up to schoolwhere everything was just.
You had an orator who gave youa speech or taught, like
play-doh or you had a slateslate, a wax tablet.
Speaker 2 (43:03):
You had to write stuff down.
Speaker 1 (43:04):
Right, you had to write some stuff.
And then somebody shows up withlike a nicely bound book with
all of Plato's speeches and thensome, and you've got a test to
take and Plato or whoever theguy, goes hey, you're not
allowed to read that book, youcan't use that book, that's
cheating.
You've got access to knowledgethat nobody else has, that's
(43:27):
right.
How crazy would that be.
Speaker 2 (43:29):
Yeah, good point.
Speaker 1 (43:30):
Nobody would do that, but chat, gpt, it's like the,
or LLMs, or AI in general.
It's like it's a technology onpar with the invention of the
book.
Yeah, all right.
The invention of the book.
Yeah, yeah, all right.
Well, I think we solved worldproblems and we've brought peace
to the world, richard.
Speaker 2 (43:53):
Anything else that's burning on your heart.
Yeah, we got to worry aboutwarfare and peace, you know,
because it is.
It's a little unfair that we'reat the verge of a world of
abundance.
You know that AI can create,you know.
At least the visionaries arethinking this way, and yet the
world's falling apart around usright, and war is breaking out
(44:15):
and it sounds horrible.
And why couldn't it allconverge?
You know we're post-SovietUnion was the chance to create a
liberal world where everybodyplays nice with each other and
trade is the reason countriesinteract and not land grabs, and
we blew that opportunity.
(44:36):
Here we are.
Speaker 1 (44:39):
I think the information, or I think the
problem is information.
It's a world of abundance, butthere's too much abundance.
Have you read Chris Hayes' bookSiren Call?
No, so Chris Hayes, the MSNBCguy, wrote a book called Siren's
Call, which basically he'sarguing that we live in the
(45:02):
attention economy, the attentionage, where attention is bought
and sold, and in the world ofinformation and abundance value
is found in not the amount ofinformation that you present to
people, but your ability tocondense that information, to
reduce it to its simplestcomponents and be able to
(45:24):
present that to people.
So I think that the world isdevolving into chaos in some way
.
Number one our understanding ofthat chaos is a function or a
result of too much information.
So it seems chaotic becauseyour brain can't process
everything that's happening.
So I think the future belongsto those who are playing in the
(45:45):
information and the attentioneconomy and is incumbent.
It is incumbent on them tosimplify, and so with that I
will wrap up this episode ofcybernomics.
So if you want to stay on thecutting edge of technology and
ai in this chaotic world whereeverybody's trying to get your
attention, we try to break itdown and simplify it so that you
(46:08):
can understand and makecritical business decisions.
Richard, thanks for joining meagain on this episode of
Cybernomics.
Speaker 2 (46:15):
Thanks, josh, mind opening as always.
Speaker 1 (46:18):
All right and thanks for listening.
If you want to learn more aboutme, check out.
How do you learn more about me?
Check me out on LinkedIn andit's LinkedIncom Josh Bruning.
Just look for Josh Bruning andRichard.
If people want to find you, howdo they find you?
Speaker 2 (46:35):
Just ask ChatGPT how to get a hold of me.
Speaker 1 (46:38):
Oh great.
Speaker 2 (46:39):
Good, all right.
Speaker 1 (46:41):
Thanks, bye, bye, all right.
Welcome to this episode of Cybernomics.
Thanks for tuning in spendingyour time with us.
I'm your host, josh Bruning,and today I am talking with
Richard Steenan.
The one, the only, the man, themyth, the legend.
Richard Steenan hey, richard,thanks.
Hey, josh, thanks for having me.
Congratulations on the SecurityYearbook 2025.
(00:25):
Every copy sold out at RSAcorrect.
Speaker 2 (00:29):
Correct.
Yeah, all the ones in thelibrary or the bookstore.
And then various vendors bought250 copies to have me sign and
I signed them all and peopletook them here.
It is Awesome.
Speaker 1 (00:41):
How many vendors are we up to right now?
Speaker 2 (00:44):
Yeah, 4,340.
But quite a few of those 500 ofthem we've archived.
In other words, we go throughall of our vendors every week,
(01:07):
or LinkedIn's page is not thereor points to an acquirer, maybe
whatever reason that I get analert and then we decide whether
or not to archive the vendor.
So it's usually because they'vebeen acquired, but sometimes
they just disappeared.
Speaker 1 (01:16):
Yeah, yeah, and I know sometimes they fall through
the cracks like the really newplayers and the disruptors.
Yeah, is Compliance Aid on yourradar?
Is that in IT Harvest?
Speaker 2 (01:28):
I will have to check.
I don't even know if I'mallowed to talk about them,
because they're like there was atime my brain was so pliant
that I would remember, if I ever, because I have to enter the
original data.
We have compliance cow,compliance scorecard and
compliance risk.
All right, what'd you say?
(01:49):
Compliance aid?
Speaker 1 (01:51):
Compliance aid, compliance and then A-I-D-E Nope
, we do not.
All right, you're going to wantto put them on your radar
because they are disruptingthings in the GRC space and even
beyond GRC, because RandyBlasick, the co-founder there at
Compliance Aid, created andhave had him on the podcast.
(02:13):
You and I had talked to himbefore and things have changed
so fast over the last couple ofweeks, even since you and I and
he all got together and talkedyou and I and he all got
together and talked and he cansupport now the compliance aid
platform can support 366frameworks, so like global
(02:36):
frameworks.
So a customer can just pop inand go hey, what do I need to be
compliant with across the globe?
And can you crosswalk that toall my evidence and all my
controls?
It's like, yeah, sure, we cando that in the afternoon.
Can you generate policies?
Yeah, sure, we can do that.
Go through the feature list ofany competitor like Drada, vanta
(02:59):
, aptiga Audit Board, whateverit is All gross.
Yeah, aptega Audit Board,whatever it is.
Allgress yeah, go through theirfeature list and you can just
go to the compliance aid.
It's like a chat bot, likeChatGPT or something like that,
and you can ask it hey, I needthis output, I need a risk
(03:19):
register, I need a frameworkassessment, I need X, y, z and
it'll just spit it out and I'mlike holy crap.
Which brings me to the topic oftoday SAS is dead.
I mean, llms and agents havenot just disrupted the SAS model
(03:43):
or the SAS industry, but Ithink it's on the verge of just
obliterating it, because everytraditional tool or solution out
there cannot compete.
They're not as flexible, not ascustomizable, ai is inherently
fast and there are governanceissues which we can talk about,
(04:04):
and there are the hallucinationissues and reliability issues.
There are privacy issues, thereare security issues, there are
implementation issues andusability issues, which are all
things that SaaS have conqueredand are really good at.
So, richard, what does it meanin 2025 to say that SaaS is dead
(04:28):
?
Speaker 2 (04:29):
Yeah, I would couch that in terms of look at it if
you have SaaS, you better belooking behind you for what LLM
is going to displace you quickly.
So incorporate it right.
You've got to change to survive.
You might have to drop yourprice by a factor of you know
(04:50):
two one-tenth what you werecharging before to keep up.
Just think of all the poorstartups when ChatGPT first came
about and they discovered, hey,you could embed a whole bunch
of data in a RAG model and turnit into a knowledgeable LLM
based on your stuff.
And then, all of a sudden,chatgpt says oh, here's your own
(05:13):
GPT, make your own, just uploadyour data.
I created something I callSocrates and I uploaded all my
books and papers and articles.
Uploaded all my books andpapers and articles, and so now
you can talk to a bot that hasall of my writing and maybe will
respond as if it was me.
Speaker 1 (05:32):
Okay, Big win for anybody who wants to be an
industry analyst Sure.
I wish I had that like three,four years ago.
Sorry, ed, don't we all?
Speaker 2 (05:42):
So yeah, so there's that challenge Like right now.
It's like when you say SaaS,you have to think of
Salesforcecom, right?
The kind of the anchor for allof SaaS.
And do you really need it?
Right, it's horrendouslyexpensive.
You could build your own in anafternoon using a little help
(06:06):
from ChatGPT or Cursor orwhatever you like to use, and
it'd be customized just to whatyou need today, right?
Then you add a feature later ifyou need a feature.
So, and maybe use no code forthe user interface.
So, no matter what, it's goingto be cheaper than getting a
five-seat license toSalesforcecom.
(06:27):
So I wouldn't say SaaS is dead,because it would be a long time
before that huge building indowntown San Francisco is leased
out for apartments or whatever.
But I think SaaS is completelychallenged.
It's definitely not the hotthing anymore.
The startup world has shiftedimmediately to doing all this
(06:49):
chat, gpt stuff.
There's another corollary tothat question and it is.
This came up actually thismorning.
So a friend sent me a link to aproduct called NextRose.
So NextRose, you know, it'simmediately apparent what it
(07:11):
does as soon as you see it.
You just go to NextRosecomsomething like that, and you can
upload any file so you caninput a URL, you can input PDF,
you can input an image of datain any form and it will turn it
(07:35):
into a spreadsheet.
Just a minute, and what abrilliant idea, right Well?
Speaker 1 (07:41):
that's great.
I'm glad that somebody'sspecializing in that, because I
try to do the same thing inChatGPT all the time.
It does not work Right.
I mean it may work sometimes,but it's not reliable yeah
resolution issues something.
Speaker 2 (07:54):
But they've, you know , I don't know what they're
using on the back end.
It could be a combination ofthings.
They could be doingpre-processing, you know, to
just extract the text and thenlater on do the columns in rows.
But I tested it out.
So Jeffrey Dubs is the VP ofsales at Gartner for the Gartner
(08:15):
Security Conference which iscoming up next month, and he's
been posting an infographic ofall the sponsors.
We track all the sponsors formost of the big conferences.
So I grabbed an image, uploadedit to JustRows and it
immediately created aspreadsheet by category.
(08:36):
So you know, each column was adifferent category application,
threat, intelligence, et cetera,and then all the vendors in
each category.
The way it was, it isinfographic.
And then I said, well, I justwant a row of company name and
category.
And it did that, just redid it.
For me it was simple interface,so simple.
(08:56):
Now my friend who pointed it outhad made an investment in them
and my question is you know, ifyou put on your curmudgeon hat
and you're an investor, youimmediately go.
Oh, you know, chatgpt willreplace this.
You know, within months Peoplewill use ChatGPT because they'll
be so good at interpreting whatthey want and doing stuff.
(09:18):
So the question remains is thattrue?
You know, here's guys with afantastic product.
They don't even have a pricingpage, so I can't even buy it,
they just use it.
I would pay for it.
Yeah, are they doomed?
And that is a very interestingquestion to me, because I have
(09:38):
the same question Am I doomedwith IT Harvest?
Because with IT Harvest we'vetaken you know what originally
was just the directory inSecurity Eurobook of 4,000 plus
vendors, and originally withmanual processes and now
completely automated with thehelp of, you know, manus and
(09:59):
Perplexity and Anthropic andOpenAI, we can populate a
database with information on allproducts and align all those
products with NIST and whateverand just make this deep and rich
tool for exploring the industry.
And so, and the fear is, and Itotally believe, and I use it in
(10:24):
our strategic thinking that theforeseeable future and it all
takes two years to get to be 100times better.
So right now, if I ask any ofthose Manus, perplexity to tell
me you know, list all thecybersecurity companies in
Canada, they can kind of get 30,right, we've got 138.
(10:48):
You can't tell the differencebetween Fortinet, which is a big
player in Canada.
They've got a lot of employeesthere, but it's definitely not a
Canadian company, right?
They're in Sunnyvale, so itmesses up on a lot of stuff.
In other words, a lot of work.
If you wanted to create adirectory of cybersecurity, it's
(11:09):
such a concern to me that weare seriously considering making
this the last year that you canget the directory in the
security yearbook that directoryif you just OCR this and I
shouldn't be saying this, butanybody could buy a copy right
now OCR this.
They'd have the list, they'dturn it over to ChatGPT and they
(11:30):
could make tools worth millionsof dollars.
You know it helps to be anexpert in the cybersecurity
industry, so you know whatyou're doing.
But you know ChatGPT isprobably more of an expert than
I am now.
So that's the opportunity andthe threat.
Chatgpt, if it's 100 timesbetter, can categorize vendors.
(11:52):
They can't do it now, right?
They can't tell an errandbetween.
They wouldn't even know.
They can't do it now, right?
It can't tell an error inbetween.
It wouldn't even know what adigital risk protection vendor
did, right.
Speaker 1 (12:00):
Right, so you're saying that the more granular
you get with the categories, theharder it is for ChatGPT to
catch that?
Speaker 2 (12:08):
Yeah, and it doesn't have the same categories, of
course, as an industry analystcomes up with, because it's
swayed by what the vendors say.
Speaker 1 (12:15):
Yeah, yeah, and I think that that's one of the
greatest selling points of ITHarvest.
Well, this is really the greatselling point of Richard Steenan
is that you came up with a lotof those categories and that was
the first thing you did in thefoundational days of IT Harvest,
so that remains your IP.
Speaker 2 (12:33):
Yeah, yeah, completely.
Speaker 1 (12:37):
Yeah, but I do have a silver lining for the problem
and I think that this is wherethe sas companies will have to
go.
I don't think that it's achoice.
You have two there.
There will be two main modelsgoing forward.
I think.
I'm interested to hear what youhave to say.
Number one would be the AIagentic factories, so the folks
(12:59):
who are going to build agentsand build models that others can
then productize and theenterprise can basically
customize this to their heart'scontent.
There might not be a box aroundit, but it could be a chat bot
in a CRM.
It could be a chat bot for Idon't know for handling customer
(13:24):
calls, customer support,whatever.
They're already using some ofthat.
The second model is going tohave to be and if the SaaS
companies want to survive willhave to be the agentification of
(13:49):
SaaS.
Harvest is going with the needto catalog and to understand the
vendor space, especially forprivate equity that might be
interested in cybersecuritycompanies or anybody who's
looking to invest incybersecurity Virtually the
(14:14):
people that are interested in ITHarvest.
Now they will need toincorporate some sort of agent
into their software, right, andthis only makes the software
better, and so you can't go tochat GPT.
Like you said, there are thoselimitations within chat GPT.
They don't understand thecategories.
That's still all yourbrainpower and all of the work
that you and your team have putinto IT Harvest.
But what you do is you, I callit, you know harvest.
(14:37):
But what you do is you, I callit, you know agentification.
It's kind of likegentrification but agentify.
Agentify it harvest, such thatit there's a chat bot in there.
People can ask it questions.
It'll spit out everything thatyou need to know about any
vendor in the space.
It can create Monte Carlosimulations, it can create
(14:58):
forecasts, anything that aninvestor needs in order to make
wise decisions.
But it's all specialized andoptimized using the IT harvest
methodology.
And I think that all SaaSthey're going to have to go that
way, because where theyspecialize and where they excel
(15:19):
all SaaS platforms is in themaintenance, in the customer
service, the specialization, thesecurity, the privacy, all that
stuff.
And now all they have to do iskind of bolt on the agents into
that so it becomes a value addinstead of a competitor.
So to your point, in that waySaaS wouldn't be dead.
(15:39):
It's just that the current SaaSmodel might be dead.
Speaker 2 (15:45):
Yeah, yeah, I think it's easier to make a big claim
like that and you're totallyright.
I think the one thing I learnedat RSA because obviously I'm
not building agents, I'm notbuilding AI technology right,
I'm using it, I'm applying itall the time but I talked to a
(16:07):
bunch of the SOC automation AIguys so Bricklayer, quiller, a
lot of those guys and they allindependently came up with kind
of the same methodology is havethe AIs that, and then it
(16:27):
assigns tasks to the specificagents that are best suited for
doing that task.
And then it collects the resultsfrom multiple agents and turns
it into a report that you canread.
Everybody's taking thatapproach and it must be the
(16:49):
logical thing to do becauseeverybody's doing.
It Makes sense.
But at least one of themQuiller was building agents that
can be deployed to your desktop.
There's an agent that will killsecurity awareness training and
it's unfortunately.
It reminds me of Clippy.
But if you, for instance, arecutting and pasting some
(17:11):
information and it happens tohave social security numbers in
it, it will intercede and saywell, are you sure you want to
put that in an email?
Because you know it violatescompany policy and all sorts of
things, and they made ittemplatized.
So the company that's deployingthese agents would set their
own policies and things in theway it interacts with their
(17:32):
employees, and that's you know.
I immediately think, oh wow,can I do that?
You know I need an agent togive to people to deploy, so I
can see that they're readingabout.
You know a cybersecurity vendorand say, hey, we've got a lot
of information, would you liketo click here and see it?
Or that kind of thing.
So I truly believe that agenttemplates are going to be
(17:57):
available.
Maybe not a shopping mart fordifferent types of templates.
But who knows, that could benext.
Speaker 1 (18:06):
Yeah, I was talking to somebody about this very
thing this morning where they'relooking to.
They're like, okay, do I do?
I have a blank page where mycustomers come to the screen
that looks like chat, gpt, andthen the customer might be lost.
But then I was like, why don'tyou have a prompt library that
basically says you know, orthere could be a thing at the
(18:28):
bottom of the screen that sayswhat would you like to do today?
And then there are a list ofoutcomes that you can choose
from, and then you can look atthe prompt library and say, okay
, this is what I want to do.
So, to your point, everythingwould have to be outcome
oriented.
So you'd have to approach itwith.
You'd have to approach theplatform either already knowing
(18:51):
what you want the outcome to be,and if you don't know what you
want it to be, there might haveto be like a Clippy type
assistant that says I don't know, that's optimized to whatever
domain that AI agent isspecializing in.
Right, right.
Speaker 2 (19:10):
Yeah, anything that's got repetitive tasks for sure
should be right there so younever have to type anything more
than once in your life.
So you know.
So we have created a IT Harvestagent it's called Harvest IQ
(19:35):
and then a third LLM adjudicatesbetween the answers and figures
out the best combination.
We had to do that because welaunched it with just one and we
told everybody hey, sign up.
And now you've got a agent thatyou talk to, or chat bot that
you talk to, that will becompletely informed on all
(19:56):
cybersecurity products.
So go for it.
And people are asking questionsthat were not about
cybersecurity products, justgeneral stuff.
So that's when we added a callto perplexity, and so perplexity
and answer anything.
So we'll get you a perplexityanswer and if there's anything
in there that our data cancontribute, we include that as
(20:17):
well.
So we get really long answers.
But you know, now you ask itfor SWOT analysis of a company
and it'll just do it for you orcreate a battle card or compare
two companies, or you know,here's the top secret one, we'll
see if anybody's listening.
Yeah, or you know, here's thetop secret one, we'll see if
(20:38):
anybody's listening If you aremaking an argument that one
company should buy anothercompany.
You just ask HarvestIQai to dothat for you, and it does it
with a breakdown of how theproducts work together.
Speaker 1 (20:48):
Yeah, yeah.
And the two companies Optimizedand specialized, right, right
yeah, because the agenticcompanies can't do that, the
ones that are building theagents, they don't care about
that and the current sassplatforms don't have that.
So specialization, um, and I'veseen that trend since chat gpt
(21:11):
3.0 came out and I called up oneof my friends and I was like I
can make this thing optimized toteach your kid how to do math,
to tutor your kid, whatever yourapplication is.
You just have to train it onthat thing and you're already
ahead because you have so muchdata already to train that.
(21:32):
So IT Harvest is far ahead ofthe crowd in terms of
intellectual propertymethodology.
Here are some things that Iwould like to see in IT Harvest
in this agentic age.
Here's something that I wouldlike If I were a PE firm.
I already have a portfolio ofmy best performing companies
(21:53):
over the last 10 years.
I want to drop those companieswith their attributes into IT
Harvest and say, based on mybest performing portfolio
companies, what company should Iinvest in next?
Or what are the top fivecompanies or the top 10
(22:14):
companies that are most likelyto generate a return like the
ones that these companies have?
What do you think about that?
Speaker 2 (22:22):
I think it'd be worth a try.
You know why not, Do you think?
Speaker 1 (22:26):
that's just conceptually.
Are there any problems withthat?
Speaker 2 (22:30):
None at all.
Yeah, because we've got thedata there right.
We've got our health scores,we've got the growth of every
company.
We could, if it really helps.
If their portfolio companiesare in our database, right, if
they're long gone, then theywouldn't be in our database, we
wouldn't know anything aboutthem.
But we could line up ongeographies, categories, growth
(22:57):
rates, size, funding levels, allthat, and then it would look
deeper into the products.
And yeah, I think we could dothat.
We should try it.
I've got most of the portfoliosof most of the companies, so
I'll just take one of theirportfolios and analyze it.
(23:17):
I'll be the one that decideswhich one is a good investment,
but I do that all day every dayyou can tell which ones are not
good investments.
Speaker 1 (23:25):
Yeah.
So you've got the human in theloop and that's the model.
It's outcome-based versusdeterministic.
So I don't think it's sasversus ai.
It's deterministic, where thecompany has a set of features
that customers kind of alreadyhave in their mind what they
(23:47):
want those features to be, andthen they come in and then they
measure what they want againstthe feature set, and that's's
how most SaaS companies are soldtoday.
That's why they say don't do itby features, because if you did
it by features like everybody'sbasically got the same features
Right, yeah.
But with AI you don't thinkabout features, you think about
outcome, right, right yeah.
(24:09):
So if you thought about outcomewith SaaS, they're going to
charge you $100,000 to customizeso you get your outcome.
Speaker 2 (24:15):
That's right.
Two years ago, ever since I sawwhat AI could do, it was
burning in my brain.
I had to just write about it onSubstack.
So I did, and it's very similarto what you just described, but
it's for intermediating,disrupting the entire publishing
industry.
(24:36):
So if you publish a book, youquickly discover, especially in
the fiction world, that there'sgatekeepers between you and the
publisher.
So, simon Schuster, if you'refamous or something, you can go
directly to them.
But typically you get an agent.
So you have to find an agent.
So you spend a lot of timequerying agents.
The agents are just people whohopefully like to read books and
(24:58):
they are looking at whetherthey can sell it.
Right, they're not looking atwhether they like your writing
or anything like that.
They just want to know if theycan sell it.
So what if you had a LLM thathad all books and most LLMs do
have all books in them andsomebody could submit their
(25:18):
manuscript and it could say hey,you know, here are the five
publishers that are most likelyto be interested in publishing
your book.
And oh and, by the way, youknow, here's an analysis of what
you submitted.
You can make it a lot better ifthe third chapter did this and
this because, of course, youcould do all that too, things
(25:41):
that ChatGPT would be happy todo for you today.
And then, on the publisher'sside, they'd be the ones to
subscribe Because you would haveall these manuscripts.
They wouldn't have to filter itthrough humans that are
notoriously bad at pickingbestsellers.
Now, in the publishing industry, all you want to do is get a
bestseller.
The only reason you publish 100books for every bestseller is
(26:05):
you don't know what you're doing.
You don't know what the marketwants and you can't provide it.
So you're going to lose money onall the non-bestsellers and
you're going to make a killingon the one that sells 2 million
copies.
So the publishers would sign upand when there's a hit that
they should publish, then theywould reach out to the author
and make a contract and cuttingout the middle person, the agent
(26:27):
, completely, and they'd start.
You know they'd actually becomeprofitable if they made a few
more correct guesses on goodbooks.
Speaker 1 (26:37):
Yeah, yeah, again, outcome based, right, right,
what is it?
Think about what the outcomeyou want.
I want a bestseller, all right,let's optimize for that.
And if you're an agent and ifyou're listening to this and
richard just scare, scare, the,the bejesus out of you, I think
that there might be hope, justlike it harvest.
(26:57):
Maybe there would still be,maybe not like a junior agent,
if there's such a thing, butmaybe there is someone who has
the final say and kind of putstheir human touch on it and says
yeah, the baby step is simplethe agency, the agent.
Speaker 2 (27:15):
now we're mixing up our terms again.
But the people that theliterary agents should use
agentic AI to weed through theirslush pile, which is what we
call the ones they haven't readyet.
Oh, gotcha, gotcha and find thegems in there, find the ones
that look like they could bedeveloped.
You may find a manuscript thatwas submitted a year ago from an
(27:39):
author that wrote another bookthat got published and was a
bestseller, and you can go backto that author and say, hey,
we're interested in that.
Now that you've figured out howto write bestsellers, maybe
you'd like to take anotherglance at your old manuscript.
There's billions hidden inthere.
Speaker 1 (27:56):
Yeah, if nobody's working on it, get on it,
because I think that's going tobe great.
So there's another factor tothis.
Now you've got my gears turningon this publishing thing and we
can apply this across all kindsof models, even cybersecurity,
it, whatever.
There's another factor herethat when we're picking winners
and losers yes, the quality ofthe work, the author, the
(28:23):
relevance of it okay, that's allgood, but there's one factor
which is timing, and I supposetiming and relevance are the
same right, because you canwrite the shining in 1995 and
write the shining in 2005 andmake it.
You may have two differentoutcomes.
Yeah, for sure so per or with,in the case of it harvest.
(28:43):
You may invest in a company in2005, invest in the same company
in 2025, and get two differentoutcomes, as I did.
Is there an AI application forthat?
Is there something that you canthink of that solves the timing
problem?
Speaker 2 (29:01):
If anything, it'll come out of the people who are
applying agentic AI to the stockmarkets.
Right, Because that's alltiming.
Speaker 1 (29:09):
Yeah, maybe some like correlations or something.
Speaker 2 (29:13):
Yeah for sure.
And you know, if you had allsocial media going on fed into
which at one point you did, grokdoes, then you can kind of tell
.
And you look at historicalstuff too.
You know during the World Warswhat kind of books sold the best
, when was the heyday ofmysteries, whatever, and what
(29:38):
caused that?
Yeah, you could draw those.
You definitely do that timing,yeah.
Yeah, you definitely do thattiming, yeah.
Speaker 1 (29:45):
Yeah yeah, I was tossing around the idea of
creating a tool that just trackstrigger events.
So in sales, one of the biggestfactors is really timing,
because you can reach out tosomebody 20 times a year, but if
they're not in a buying mood,they don't have the budget, they
(30:06):
don't have the need, they'rerocking and rolling already with
another tool, whatever.
Speaker 2 (30:11):
That's totally why salespeople do reach out so
consistently is because it works.
You know the 20th time somebodygoes.
Oh, I was trying to rememberyour name.
Thanks for reaching out.
Speaker 1 (30:24):
So a free tip to all my sales folks persistence is
key, because underlying thatpersistence is the fact that you
don't know when is the righttime and people don't tell you,
and so you have to just keephitting it.
And you know, trigger eventsare a part of that.
So let's say, for I don't know,vulnerability scans or whatever
(30:46):
, they probably peak in theirsales when there's a big breach.
Right, sims, probably the samething.
If AI and cybersecurity all ofa sudden are hot which it's
always been hot, but let's sayall of a sudden it's hot, then
IT Harvest is a more valuabletool, because now they can weigh
through all of the vendors sothey can make their investments.
(31:07):
So, yeah, outcome-based, allright.
Is that all?
On SaaS, we've determined thatSaaS isn't dead, it's just
comatose.
So we can give it some smellingsalts, which would be AI agents
, and wake it the hell up.
Speaker 2 (31:27):
Totally.
And you know, don't forget, ifyou've got a SaaS product and
you say you're Canva, right, andit's really old, but everybody
just says I do use that once ayear, so I'm going to keep
paying the monthly subscriptionor whatever.
You'll survive for a long timein the monthly subscription or
whatever.
You'll survive for a long time,right.
(31:48):
But obviously Canva isincorporating AI now.
Right, they're giving all thosetools, as is Grammarly and the
rest.
But you could just kind of siton your laurels and slowly fade
off into, you know, the end oflife for your product and that's
the most profitable time ofyour business is when you're not
spending money on the productor marketing or sales.
So, yeah, you take advantage ofthat.
(32:11):
Go on.
And retire.
Or you can just start sweatingagain and come up with a better
solution.
Speaker 1 (32:19):
Yeah, yeah, yeah.
If you want to check out someAI shops, hit me up.
I can make some goodrecommendations in addition to
the ones that Richard hadmentioned.
Is AI going to take your job?
And I hear a lot of differentnarratives around this, but the
(32:47):
prevailing narrative is that AIwill not take your job.
It will make the humans better.
And I hear this like clockworkand I've been repeating this for
weeks and weeks and weeks, andit's not because I'm trying to
drive a particular narrative.
It's because I hear competingnarratives.
If you see, going back, billClinton, if somebody's pointing
(33:08):
somewhere and they're lookingsomewhere else and they're
telling you something, it's,there's probably something
screwy, right?
So Bill Clinton, looking thisway, say, I did not have sexual
relations with that woman.
Pointing one way, looking theother way, all right, turns out
he was lying about that, and soit's not that I think that
(33:28):
people are lying.
I just think that vendors arescared and they don't want to
push AI too quickly and losesales.
That's one thing.
Enterprises want to integrateAI into their work, but they
don't want to push it too hardand move too fast and encounter
(33:50):
the security and the privacyissues.
Okay, we also don't want toscare employees.
You do not want to scare thejob market.
If you scare the job market, itscrews up the economy.
You don't want to do that.
That's another reason why Ithink people are pumping the
brakes on AI and saying that itwon't take your jobs.
But you and I can, yes, and Ithink that my platform is small
(34:13):
enough to where I could say thatand not do too much damage.
But let's say, on CNN, they'recoming out and saying, hey, ai
is taking your jobs.
You can imagine what that'sgoing to do.
Here's my thesis.
Let me know if you agree or not.
Richard, my thesis is that AIwill take your jobs, but it will
not take present jobs.
It will take some present jobs,but it would not so much take
(34:36):
the present jobs as it will kindof halt the job market for
future employment, which meansthat colleges and the future job
pool would be in trouble.
Right, we're hearing thenarratives around.
You know AI will take your jobs, but then, or AI will not take
your jobs.
But at the same time, peopleare saying that AI will replace
(34:58):
labor, it will make laborbasically go away manual labor.
So it's very evident and it'svery clear that AI is doing
something with jobs.
Will that impact the averageperson, or the junior employee,
or the senior employees or, Idon't know, maybe even CEOs?
(35:18):
What do you think, richard?
Will AI take your job?
If so, how, and if not, why?
Speaker 2 (35:27):
Well, I have to fall back on historical references.
Every technological revolutionhas definitely displaced workers
, like no question, right Fromthe cotton gin to automated
sawmills et cetera.
And in my experience in theworkforce, when I got out of
school we still had typists andthese were people, invariably
(35:50):
women, that would typeeverybody's memos in commercial
communications.
So 1982 is like just the end ofthat, right, because that was
roughly when the IBM PC AT wasintroduced and that was the
microcomputer revolution thatwas happening.
And probably five years laterthere were no more typists All
(36:15):
gone.
There used to be tens ofthousands of people whose chief
skill was typing and they didthat for corporate offices,
completely obliterated.
And the other one is the kindof you remember Apple creating
desktop publishing?
That's actually what they wentto market with.
(36:35):
Is we're going to make it easyto create nice graphics and
print them and talk to printersand talk to printers.
So you know a lot of graphicartists.
You know that used to literallycut and paste things on a board
and then somehow turn that intosomething that could be printed
.
They were out of that job, butmost of them just transitioned
(36:56):
right.
They loved the computers anddigital art and made a good
transition.
So I think we're in the samestage now.
Somebody the other day said well, you know, lawyers aren't going
to be displaced.
Are you kidding?
They're going to be the firstright.
It's like you do not need alawyer to create a contract
between two people who just wanta legal-sounding agreement
(37:16):
between them.
Right, right, the chat.
Gpt is so good at that and it'scomplete, and it throws in all
the bullshit language about.
You know what.
Speaker 1 (37:27):
You know just all the stuff you know Definitions and.
Speaker 2 (37:32):
Definitions and supersessions, and statutes and.
All that stuff.
Now, mind you, I wouldn't wantto use chat GPT to argue a case
for me in front of a court, butit sure helped inform that
That'd be great.
It could take all thedepositions and weave them
(37:52):
together and come up with a goodstory for the jury, but for
sure, contract law is going tobe significantly challenged.
You still need lawyers, youknow.
No question Lawyers willremember how lawyers were so
slow to get on the internet,like they wouldn't have email
addresses for you probably don'tremember this, but for the
(38:13):
early first five years of theinternet, lawyers refused to do
it.
Because you know, in today'sworld you send an email to your
lawyer and say, hey, can youlook at this clause for me?
And they give you an answerright, and you know that it took
them two minutes to do it.
So it's really hard for them tobill the 45 minutes they used
(38:34):
to charge for that kind of work.
So they just hated the idea ofhaving a conversation with their
clients.
It was so fast andfriction-free.
So they got over that they hadto, because the young crowd of
lawyers that were coming behindthem were perfectly happy to do
that.
No problem.
Speaker 1 (38:52):
Yeah, they're bobbing and weaving, yeah.
Speaker 2 (38:55):
Imagine a patent attorney.
Right?
It's like patent attorneys haveto write in a specific language
and they just take what youtell them about your idea and
turn it into that language.
It's come on chat.
Gpt can do that in its sleep,so you don't need them.
You know what other things?
Financial analysts?
(39:15):
I don't think I need thoseanymore, so yeah.
Speaker 1 (39:19):
What about accounts, the number crunchers?
Speaker 2 (39:25):
Yeah, you know, the number crunchers are the
analysts, right?
Actually, you know, theaccountants are the ones that
set up the books and put in thecontrols.
And yeah, I think we'll needthose for a while for sure.
Speaker 1 (39:40):
So can I ask you this , if I am correct, in that the
college pool or the workforcethat's coming from colleges,
they're the ones that are goingto be the most affected If we
were to apply it to thisscenario?
So your scenario of the lawyersright, let's say you're not
(40:02):
going to need lawyers in thefuture.
The lawyers right, let's sayyou're not going to need lawyers
in the future Um, what wouldthat do to law schools?
Like, if I were to be a lawyer,if I wanted to be a lawyer, and
if I'm a law firm, let's say,okay, on one side, I'm a future
lawyer, on the other side, Ihave a law firm.
The law firm is thinking wealready have lawyers.
(40:23):
We're not going to fire thembecause they've been around for
5, 10, 15, 20 years, but we'regoing to give them these tools,
we're going to give them some AItechnology and they're going to
be like flying, rocking androlling.
We're going to do more business, more clients, more money.
But if I am a law firm, will Ihire a new lawyer and have to
(40:45):
train them for a year or twoyears and have to worry about
them becoming partner anddisplacing another partner,
which you don't actually becomepartner unless one partner
leaves, because the firm's onlygot so many partners right.
So now, as a poor collegestudent wanting to become a
lawyer, do I enter this jobmarket that's dominated by
(41:08):
cyborgs essentially Humans,using AI?
Speaker 2 (41:11):
Well, right now you don't have a problem because as
a student, you can know moreabout AI than the fuddy-dud old
attorneys.
So now you've got somethingagainst them.
Speaker 1 (41:22):
That's a great point, Richard.
Speaker 2 (41:23):
You can 10x their productivity right, so you can
outproduce them by a factor of10, maybe without working
80-hour weeks your first twoyears.
So you've got that going foryou.
In the future, there will justbe a market that lawyers are 10
times as productive as they arenow, and that tells me that
(41:43):
probably we don't need tograduate as many attorneys.
So right now we're at this cuspand this is where everybody
should listen up.
If you're going to a schoolthat is frightened by AI, if
they start saying no AI in theseassignments, no AI for this
test, and we're going to checkit for AI production, we just
(42:04):
invested $100,000 in a tool tocheck whether you're cheating by
using AI.
Change your school right now.
You are going to just be soill-served by your degree from
that school.
There are plenty of schools Ifind schools on the East Coast
in particular, from DC upthrough New York and Connecticut
, that are embracing AIwholeheartedly.
(42:26):
Here in the Midwest, pure fear.
The professors are afraid of itand, you know, mostly in
letters and science, less somaybe in physics or something.
You've got to grasp this rightnow.
It's like graduating school in1990 without, without you know,
having a modem at home.
(42:47):
It's like how do you do that?
Speaker 1 (42:49):
yeah, it's like back in the day when books were
invented.
Imagine showing up to schoolwhere everything was just.
You had an orator who gave youa speech or taught, like
play-doh or you had a slateslate, a wax tablet.
Speaker 2 (43:03):
You had to write stuff down.
Speaker 1 (43:04):
Right, you had to write some stuff.
And then somebody shows up withlike a nicely bound book with
all of Plato's speeches and thensome, and you've got a test to
take and Plato or whoever theguy, goes hey, you're not
allowed to read that book, youcan't use that book, that's
cheating.
You've got access to knowledgethat nobody else has, that's
(43:27):
right.
How crazy would that be.
Speaker 2 (43:29):
Yeah, good point.
Speaker 1 (43:30):
Nobody would do that, but chat, gpt, it's like the,
or LLMs, or AI in general.
It's like it's a technology onpar with the invention of the
book.
Yeah, all right.
The invention of the book.
Yeah, yeah, all right.
Well, I think we solved worldproblems and we've brought peace
to the world, richard.
Speaker 2 (43:53):
Anything else that's burning on your heart.
Yeah, we got to worry aboutwarfare and peace, you know,
because it is.
It's a little unfair that we'reat the verge of a world of
abundance.
You know that AI can create,you know.
At least the visionaries arethinking this way, and yet the
world's falling apart around usright, and war is breaking out
(44:15):
and it sounds horrible.
And why couldn't it allconverge?
You know we're post-SovietUnion was the chance to create a
liberal world where everybodyplays nice with each other and
trade is the reason countriesinteract and not land grabs, and
we blew that opportunity.
(44:36):
Here we are.
Speaker 1 (44:39):
I think the information, or I think the
problem is information.
It's a world of abundance, butthere's too much abundance.
Have you read Chris Hayes' bookSiren Call?
No, so Chris Hayes, the MSNBCguy, wrote a book called Siren's
Call, which basically he'sarguing that we live in the
(45:02):
attention economy, the attentionage, where attention is bought
and sold, and in the world ofinformation and abundance value
is found in not the amount ofinformation that you present to
people, but your ability tocondense that information, to
reduce it to its simplestcomponents and be able to
(45:24):
present that to people.
So I think that the world isdevolving into chaos in some way
.
Number one our understanding ofthat chaos is a function or a
result of too much information.
So it seems chaotic becauseyour brain can't process
everything that's happening.
So I think the future belongsto those who are playing in the
(45:45):
information and the attentioneconomy and is incumbent.
It is incumbent on them tosimplify, and so with that I
will wrap up this episode ofcybernomics.
So if you want to stay on thecutting edge of technology and
ai in this chaotic world whereeverybody's trying to get your
attention, we try to break itdown and simplify it so that you
(46:08):
can understand and makecritical business decisions.
Richard, thanks for joining meagain on this episode of
Cybernomics.
Speaker 2 (46:15):
Thanks, josh, mind opening as always.
Speaker 1 (46:18):
All right and thanks for listening.
If you want to learn more aboutme, check out.
How do you learn more about me?
Check me out on LinkedIn andit's LinkedIncom Josh Bruning.
Just look for Josh Bruning andRichard.
If people want to find you, howdo they find you?
Speaker 2 (46:35):
Just ask ChatGPT how to get a hold of me.
Speaker 1 (46:38):
Oh great.
Speaker 2 (46:39):
Good, all right.
Speaker 1 (46:41):
Thanks, bye, bye, all right.
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