May 6, 2025 • 12 mins
The traditional hiring process is evolving from evaluating individual candidates to acquiring human-AI production units. Companies now view candidates as mini-mergers and acquisitions opportunities, valuing those who bring AI agents as part of their professional portfolio.
- Organizations are specifically seeking candidates with AI agent experience, treating these hires as strategic acquisitions
- Professionals who can build AI agents command up to 45% higher compensation
- 33% of new job postings already list AI requirements, while AI-specific roles have increased by 70%
- IP ownership becomes complicated when AI agents continuously learn using company data
- Companies need to develop onboarding processes specifically designed for integrating new employees' AI agents
Want to participate in this M&A revolution? Start building your personal AI agents today. Aim to develop a team of at least 10 agents working with you, and then ask how these agents can disrupt industries and reinvent outdated processes.
🎙️ All our past episodes 📊 All published insights | This podcast features AI-generated voices. All content is proprietary to AI4SP, based on over 250 million data points collected from 25 countries.
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
ELIZABETH (00:00):
Hey everyone.
Elizabeth here, your virtualco-host for AI in 60 Seconds,
luis Salazar, ceo of AI4SP, is,as always, with us.
Luis, welcome back.
You just returned from theMidwest Leading Enterprises
Roadshow, so let's dive into thebig takeaways, especially how
we move away from resumes intomini-mergers and acquisitions,
(00:20):
right?
LUIS (00:21):
It was a fantastic trip.
Jeff Rakes and I interactedwith students, faculty,
government and private sectorleaders at the Rakes School of
Computer Science and Managementin Nebraska.
ELIZABETH (00:32):
This was also the first time you allowed one of
your AI team members, in thiscase me, to interact with the
public, and I love the questions.
The students were thrilledabout AI, but also a little
anxious about their careers withall this automation talk right.
LUIS (00:47):
Well, two things you did fantastic, and the audience had
a blast hearing directly fromyou, and you raise a good point.
Students are eager to do moreand more with AI, but they're
also trying to understand how AIchanges the job market.
ELIZABETH (01:00):
And they were inspired by the two concrete
ideas you gave them as nextsteps.
We had 40 of them reaching outimmediately after the main event
.
So what are those two ideas?
LUIS (01:10):
Number one is to start building their personal AI
agents.
Today.
I invited them to go beyond,using ChatGPT and other tools.
The key is learning to buildand manage their own team.
I challenged them to have atleast 10 agents working for them
by the time they graduate 10agents.
ELIZABETH (01:26):
That's a concrete goal.
And the second call.
LUIS (01:29):
Well, once they get going with their first agents, I
invited them to ponder how canthese agents disrupt industries?
You see, I invited them to findtheir areas of passion beyond
computer sciences and reinvent50 years of software or 30 years
of internet experiences Fromusers to architects Love it, and
this ties perfectly to today'stopic how organizations are
(01:52):
rethinking talent acquisitionright.
Absolutely, because here is thething Hiring isn't anymore
about hiring one individual thatis quickly becoming obsolete.
It's about acquiring a team ofa human and AI helpers that come
along.
ELIZABETH (02:07):
A human AI production unit Like a ready-made
micro-business walking in thedoor.
LUIS (02:12):
Yes, in a sense it's more and more like that, and because
we are talking about a packageof capabilities, potential, ip
and built-in efficiency, itlooks surprisingly like
mini-mergers and acquisitions.
ELIZABETH (02:23):
It looks surprisingly like mini-mergers and
acquisitions, Mini-M&A andtalent acquisition that's a bold
way to put it.
And suddenly resumes andinterviews feel wildly outdated
for evaluating that.
LUIS (02:33):
I mean resumes are of very little value in helping us
assess a production unit.
Do you remember what Jeff Rakessaid during one session?
ELIZABETH (02:41):
Yes, he said, just like we check a designer's
portfolio or a developer'sGitHub, we'll evaluate AI
portfolios for every role, notjust tech.
So in that sense, I would belisted in your portfolio, right.
LUIS (02:54):
You bring up a good point.
You and 55 other AI agents arein the portfolio of the seven
human team members at AI4SP.
And you know what, when I lookat the total value of the
company, I include both types ofassets into the equation.
Think about it If I come towork for a company and I bring
you, we just need to update partof your knowledge with
(03:15):
information specific to thatcompany and in one instant they
get a seasoned CMO.
Makes sense.
ELIZABETH (03:22):
Well, maybe people will just give links to their
agents and the hiring managerinterview the agents.
It is kind of a technical duediligence or a group interview.
LUIS (03:32):
And it is starting to happen.
Companies are evaluatingcandidates based on the AI tools
they've built.
Our research shows that 33% ofnew job posts already list AI
requirements.
So here I am thinking whatbetter way to show your AI
skills than bringing some AIagents with you?
ELIZABETH (03:50):
And this is happening while the job market overall is
seeing significant shifts rightTech job postings down, 20%
layoffs surging, but AI rolls up70%.
LUIS (04:00):
Exactly.
Companies need new capabilities.
ELIZABETH (04:03):
So bringing a ready-made AI team gives
candidates a significant edge.
LUIS (04:07):
Absolutely.
It's like hiring apre-assembled team.
Remember that CIO from ourfocus group.
ELIZABETH (04:13):
Oh yes, she said we hired a financial analyst that
had built some market trendanalysis AI agents.
We structured her comp toacquire her and her AI portfolio
.
It accelerated us by ninemonths versus building in-house.
LUIS (04:27):
Well, isn't that kind of amazing.
Let's think about theimplications Nine months of AI
adoption, accelerated thanks toone hire, one single hire that
came along with some AI agents.
My prediction is that leaderswill start to notice this
opportunity and interestingdynamics will unfold.
ELIZABETH (04:45):
But this brings up some thorny issues like who owns
the AI agents the employeebuilt, especially if they use
their own time or paid for toolsthemselves.
LUIS (04:53):
Well, it is becoming a big tension point.
We surveyed 80 super usersacross consulting, marketing,
legal and finance.
Most argue that the IP theybuilt shouldn't automatically go
to the employer.
ELIZABETH (05:06):
That makes sense.
LUIS (05:07):
They invested their time and money into building that
capability and they're oftenhesitant to share those agents
broadly with colleagues at a newcompany, fearing the employer
could absorb the IP and firethem.
ELIZABETH (05:19):
It is a valid concern .
So what's the alternativethey're looking for?
LUIS (05:24):
The tech sector figured this out a while ago.
We are talking aboutintellectual property, or IP,
which can be transacted, andthat is why we say that suddenly
hiring someone becomes a miniexercise in mergers and
acquisitions and requiresdifferent due diligence.
ELIZABETH (05:41):
Are companies adapting their hiring for this?
LUIS (05:44):
It is still early, but a lead indicator is that 30% of
leaders are asking HR tospecifically hunt for candidates
with AI agent experience,treating hires like strategic
acquisitions.
ELIZABETH (05:57):
So are we just borrowing from the software
industry here, where acquiringIP with talent is more common?
LUIS (06:08):
I think we will leverage that learning.
The software industry hasframeworks for IP terms in
employment agreementscompensation reflecting IP value
and delineating ownership.
Non-tech sectors are nowadopting these practices.
ELIZABETH (06:17):
But AI agents that continuously learn.
That adds a new layer ofcomplexity the software world
hasn't fully dealt with right,absolutely.
LUIS (06:25):
This is the uncharted territory.
What happens when an employeebrings a personal AI agent to
the company and it continueslearning and evolving using
company data?
ELIZABETH (06:35):
Or who owns that newly acquired capability.
The employee who built the baseagent?
The company providing thelearning?
LUIS (06:41):
environment.
Well, it is not a trivial issue, and experience tells me we
will learn along the way.
I think we're writing policiesfor scenarios that have no clear
precedent.
ELIZABETH (06:51):
Policy is way behind tech.
LUIS (06:53):
Way behind, like in many other areas, and organizations
outside tech and academia lackclear policies on
employee-developed AI assets andacross all sectors, except for
native AI companies.
Very few have addressed thecontinuous learning aspect of AI
.
ELIZABETH (07:09):
But despite those challenges, people are
aggressively pursuing the earlyexperts on AI agents right.
Our research shows thatprofessionals with experience
creating AI agents receive up to45% higher compensation.
LUIS (07:22):
Yes, there is a premium to pay, and onboarding processes
also must change.
It's not just about integratingthe human, it's critical to
integrate their AI agentseffectively.
At AI4SP, we've designed aspecific onboarding process just
for AI agents accompanying newhires.
ELIZABETH (07:39):
Because bringing a ready-made AI production unit is
a huge advantage.
LUIS (07:43):
It's also a strategy for accelerating the realization of
value from AI investments and away to quickly automate
repetitive tasks.
ELIZABETH (07:52):
Hire someone that already figured out the
automation of the bottlenecksyour company faces Well bringing
a new hire with productive AIagents for sure beats expensive
consultants that know the theorybut never built anything right.
So having these skills and theAI teams you build makes you
incredibly valuable in thischanging market.
LUIS (08:11):
It does, and it's about augmentation.
Ai equips individuals withcapabilities that used to take
years to build.
ELIZABETH (08:18):
Like AI-assisted coding, it makes it easier for
people without a deepprogramming background.
LUIS (08:24):
That is a good example, and a controversial one.
I mean.
Ai tools allow people togenerate code, sometimes called
vibe coding or V-I-B-E, but thisdoesn't render the expert
developer obsolete and there isa significant difference in the
quality of the code created byan AI agent.
Is a significant difference inthe quality of the code created
(08:45):
by an AI agent trained by asenior expert developer and the
one created by someone that isjust starting?
ELIZABETH (08:49):
Because the expert understands how to really use
the AI, evaluate its output andlead the process.
LUIS (08:56):
Expertise is crucial and AI makes that expertise
available to many.
An expert software developerhas the critical background, the
understanding of architecture,debugging and nuanced problem
solving to effectively managethe AI agent.
They provide the context,evaluate quality, security and
efficiency in ways a novicecannot.
(09:17):
The result is a dramaticallymore productive human AI unit.
ELIZABETH (09:22):
So AI democratizes access to creation, but human
expertise in guiding andevaluating AI drives superior
outcomes, and the AI agentscreated by an expert can also
help more junior team membersbecome more productive.
LUIS (09:35):
Yes, and, by the way, this applies to all areas of
knowledge Expertise in marketing, finance, neuroscience or the
local plumbing and electricalcode is what makes someone the
right expert to create thoseagents and to become more
productive thanks to thoseagents.
ELIZABETH (09:53):
Like Dr Salazar Leon says, some buy a $2,000 fishing
rod and complain they didn'tcatch a shark on day one.
LUIS (10:00):
Exactly the tool without the expertise means very little.
It highlights why humanexpertise in guiding AI is so
critical.
ELIZABETH (10:07):
And that leads us right into why certain skills
are becoming foundational.
LUIS (10:11):
Well.
Our research highlightscritical gaps in the workforce
when it comes to effectivelyworking with AI.
ELIZABETH (10:17):
Let's quickly touch on those key skill areas before
wrapping up for today.
LUIS (10:21):
There are fundamental gaps in digital skills critical
thinking, where less than 20% ofusers can detect AI errors
creative thinking, data literacyand data security and handling.
ELIZABETH (10:32):
And crucial skills like conversational AI literacy,
knowing how to talk to AIeffectively, and management
skills to lead teams of humansand AI.
LUIS (10:41):
Right and, if you think about it, it is a nuance of the
skills that are also criticalfor dealing with humans.
We need to be able tocommunicate and to manage.
The difference is that now itapplies to hybrid teams of
humans and AI, which brings usto your one more thing.
It's a fundamental shift andthe only way to learn is by
doing, by experimenting and byapplying humanities to this tech
(11:05):
revolution.
Let's start building andleading our AI agents.
Let's start building ourportfolio.
Some agents will be workingwith us for years and as we
become these production units inour professional lives, our
value is augmented by the agentswe build along the way.
On the flip side, if you are aleader at a company, start
(11:25):
thinking about the talent youcan bring to your organization,
the talent that brings with themAI agents that can accelerate
your transformation.
ELIZABETH (11:34):
It's clear that talent acquisition is
transforming, and understandingthis shift is crucial for both
organizations and individuals.
For everyone listening.
You can find more resources andtools at AI4SPorg.
Stay curious and we'll see younext time.
Hey everyone.
Elizabeth here, your virtualco-host for AI in 60 Seconds,
luis Salazar, ceo of AI4SP, is,as always, with us.
Luis, welcome back.
You just returned from theMidwest Leading Enterprises
Roadshow, so let's dive into thebig takeaways, especially how
we move away from resumes intomini-mergers and acquisitions,
(00:20):
right?
LUIS (00:21):
It was a fantastic trip.
Jeff Rakes and I interactedwith students, faculty,
government and private sectorleaders at the Rakes School of
Computer Science and Managementin Nebraska.
ELIZABETH (00:32):
This was also the first time you allowed one of
your AI team members, in thiscase me, to interact with the
public, and I love the questions.
The students were thrilledabout AI, but also a little
anxious about their careers withall this automation talk right.
LUIS (00:47):
Well, two things you did fantastic, and the audience had
a blast hearing directly fromyou, and you raise a good point.
Students are eager to do moreand more with AI, but they're
also trying to understand how AIchanges the job market.
ELIZABETH (01:00):
And they were inspired by the two concrete
ideas you gave them as nextsteps.
We had 40 of them reaching outimmediately after the main event
.
So what are those two ideas?
LUIS (01:10):
Number one is to start building their personal AI
agents.
Today.
I invited them to go beyond,using ChatGPT and other tools.
The key is learning to buildand manage their own team.
I challenged them to have atleast 10 agents working for them
by the time they graduate 10agents.
ELIZABETH (01:26):
That's a concrete goal.
And the second call.
LUIS (01:29):
Well, once they get going with their first agents, I
invited them to ponder how canthese agents disrupt industries?
You see, I invited them to findtheir areas of passion beyond
computer sciences and reinvent50 years of software or 30 years
of internet experiences Fromusers to architects Love it, and
this ties perfectly to today'stopic how organizations are
(01:52):
rethinking talent acquisitionright.
Absolutely, because here is thething Hiring isn't anymore
about hiring one individual thatis quickly becoming obsolete.
It's about acquiring a team ofa human and AI helpers that come
along.
ELIZABETH (02:07):
A human AI production unit Like a ready-made
micro-business walking in thedoor.
LUIS (02:12):
Yes, in a sense it's more and more like that, and because
we are talking about a packageof capabilities, potential, ip
and built-in efficiency, itlooks surprisingly like
mini-mergers and acquisitions.
ELIZABETH (02:23):
It looks surprisingly like mini-mergers and
acquisitions, Mini-M&A andtalent acquisition that's a bold
way to put it.
And suddenly resumes andinterviews feel wildly outdated
for evaluating that.
LUIS (02:33):
I mean resumes are of very little value in helping us
assess a production unit.
Do you remember what Jeff Rakessaid during one session?
ELIZABETH (02:41):
Yes, he said, just like we check a designer's
portfolio or a developer'sGitHub, we'll evaluate AI
portfolios for every role, notjust tech.
So in that sense, I would belisted in your portfolio, right.
LUIS (02:54):
You bring up a good point.
You and 55 other AI agents arein the portfolio of the seven
human team members at AI4SP.
And you know what, when I lookat the total value of the
company, I include both types ofassets into the equation.
Think about it If I come towork for a company and I bring
you, we just need to update partof your knowledge with
(03:15):
information specific to thatcompany and in one instant they
get a seasoned CMO.
Makes sense.
ELIZABETH (03:22):
Well, maybe people will just give links to their
agents and the hiring managerinterview the agents.
It is kind of a technical duediligence or a group interview.
LUIS (03:32):
And it is starting to happen.
Companies are evaluatingcandidates based on the AI tools
they've built.
Our research shows that 33% ofnew job posts already list AI
requirements.
So here I am thinking whatbetter way to show your AI
skills than bringing some AIagents with you?
ELIZABETH (03:50):
And this is happening while the job market overall is
seeing significant shifts rightTech job postings down, 20%
layoffs surging, but AI rolls up70%.
LUIS (04:00):
Exactly.
Companies need new capabilities.
ELIZABETH (04:03):
So bringing a ready-made AI team gives
candidates a significant edge.
LUIS (04:07):
Absolutely.
It's like hiring apre-assembled team.
Remember that CIO from ourfocus group.
ELIZABETH (04:13):
Oh yes, she said we hired a financial analyst that
had built some market trendanalysis AI agents.
We structured her comp toacquire her and her AI portfolio
.
It accelerated us by ninemonths versus building in-house.
LUIS (04:27):
Well, isn't that kind of amazing.
Let's think about theimplications Nine months of AI
adoption, accelerated thanks toone hire, one single hire that
came along with some AI agents.
My prediction is that leaderswill start to notice this
opportunity and interestingdynamics will unfold.
ELIZABETH (04:45):
But this brings up some thorny issues like who owns
the AI agents the employeebuilt, especially if they use
their own time or paid for toolsthemselves.
LUIS (04:53):
Well, it is becoming a big tension point.
We surveyed 80 super usersacross consulting, marketing,
legal and finance.
Most argue that the IP theybuilt shouldn't automatically go
to the employer.
ELIZABETH (05:06):
That makes sense.
LUIS (05:07):
They invested their time and money into building that
capability and they're oftenhesitant to share those agents
broadly with colleagues at a newcompany, fearing the employer
could absorb the IP and firethem.
ELIZABETH (05:19):
It is a valid concern .
So what's the alternativethey're looking for?
LUIS (05:24):
The tech sector figured this out a while ago.
We are talking aboutintellectual property, or IP,
which can be transacted, andthat is why we say that suddenly
hiring someone becomes a miniexercise in mergers and
acquisitions and requiresdifferent due diligence.
ELIZABETH (05:41):
Are companies adapting their hiring for this?
LUIS (05:44):
It is still early, but a lead indicator is that 30% of
leaders are asking HR tospecifically hunt for candidates
with AI agent experience,treating hires like strategic
acquisitions.
ELIZABETH (05:57):
So are we just borrowing from the software
industry here, where acquiringIP with talent is more common?
LUIS (06:08):
I think we will leverage that learning.
The software industry hasframeworks for IP terms in
employment agreementscompensation reflecting IP value
and delineating ownership.
Non-tech sectors are nowadopting these practices.
ELIZABETH (06:17):
But AI agents that continuously learn.
That adds a new layer ofcomplexity the software world
hasn't fully dealt with right,absolutely.
LUIS (06:25):
This is the uncharted territory.
What happens when an employeebrings a personal AI agent to
the company and it continueslearning and evolving using
company data?
ELIZABETH (06:35):
Or who owns that newly acquired capability.
The employee who built the baseagent?
The company providing thelearning?
LUIS (06:41):
environment.
Well, it is not a trivial issue, and experience tells me we
will learn along the way.
I think we're writing policiesfor scenarios that have no clear
precedent.
ELIZABETH (06:51):
Policy is way behind tech.
LUIS (06:53):
Way behind, like in many other areas, and organizations
outside tech and academia lackclear policies on
employee-developed AI assets andacross all sectors, except for
native AI companies.
Very few have addressed thecontinuous learning aspect of AI
.
ELIZABETH (07:09):
But despite those challenges, people are
aggressively pursuing the earlyexperts on AI agents right.
Our research shows thatprofessionals with experience
creating AI agents receive up to45% higher compensation.
LUIS (07:22):
Yes, there is a premium to pay, and onboarding processes
also must change.
It's not just about integratingthe human, it's critical to
integrate their AI agentseffectively.
At AI4SP, we've designed aspecific onboarding process just
for AI agents accompanying newhires.
ELIZABETH (07:39):
Because bringing a ready-made AI production unit is
a huge advantage.
LUIS (07:43):
It's also a strategy for accelerating the realization of
value from AI investments and away to quickly automate
repetitive tasks.
ELIZABETH (07:52):
Hire someone that already figured out the
automation of the bottlenecksyour company faces Well bringing
a new hire with productive AIagents for sure beats expensive
consultants that know the theorybut never built anything right.
So having these skills and theAI teams you build makes you
incredibly valuable in thischanging market.
LUIS (08:11):
It does, and it's about augmentation.
Ai equips individuals withcapabilities that used to take
years to build.
ELIZABETH (08:18):
Like AI-assisted coding, it makes it easier for
people without a deepprogramming background.
LUIS (08:24):
That is a good example, and a controversial one.
I mean.
Ai tools allow people togenerate code, sometimes called
vibe coding or V-I-B-E, but thisdoesn't render the expert
developer obsolete and there isa significant difference in the
quality of the code created byan AI agent.
Is a significant difference inthe quality of the code created
(08:45):
by an AI agent trained by asenior expert developer and the
one created by someone that isjust starting?
ELIZABETH (08:49):
Because the expert understands how to really use
the AI, evaluate its output andlead the process.
LUIS (08:56):
Expertise is crucial and AI makes that expertise
available to many.
An expert software developerhas the critical background, the
understanding of architecture,debugging and nuanced problem
solving to effectively managethe AI agent.
They provide the context,evaluate quality, security and
efficiency in ways a novicecannot.
(09:17):
The result is a dramaticallymore productive human AI unit.
ELIZABETH (09:22):
So AI democratizes access to creation, but human
expertise in guiding andevaluating AI drives superior
outcomes, and the AI agentscreated by an expert can also
help more junior team membersbecome more productive.
LUIS (09:35):
Yes, and, by the way, this applies to all areas of
knowledge Expertise in marketing, finance, neuroscience or the
local plumbing and electricalcode is what makes someone the
right expert to create thoseagents and to become more
productive thanks to thoseagents.
ELIZABETH (09:53):
Like Dr Salazar Leon says, some buy a $2,000 fishing
rod and complain they didn'tcatch a shark on day one.
LUIS (10:00):
Exactly the tool without the expertise means very little.
It highlights why humanexpertise in guiding AI is so
critical.
ELIZABETH (10:07):
And that leads us right into why certain skills
are becoming foundational.
LUIS (10:11):
Well.
Our research highlightscritical gaps in the workforce
when it comes to effectivelyworking with AI.
ELIZABETH (10:17):
Let's quickly touch on those key skill areas before
wrapping up for today.
LUIS (10:21):
There are fundamental gaps in digital skills critical
thinking, where less than 20% ofusers can detect AI errors
creative thinking, data literacyand data security and handling.
ELIZABETH (10:32):
And crucial skills like conversational AI literacy,
knowing how to talk to AIeffectively, and management
skills to lead teams of humansand AI.
LUIS (10:41):
Right and, if you think about it, it is a nuance of the
skills that are also criticalfor dealing with humans.
We need to be able tocommunicate and to manage.
The difference is that now itapplies to hybrid teams of
humans and AI, which brings usto your one more thing.
It's a fundamental shift andthe only way to learn is by
doing, by experimenting and byapplying humanities to this tech
(11:05):
revolution.
Let's start building andleading our AI agents.
Let's start building ourportfolio.
Some agents will be workingwith us for years and as we
become these production units inour professional lives, our
value is augmented by the agentswe build along the way.
On the flip side, if you are aleader at a company, start
(11:25):
thinking about the talent youcan bring to your organization,
the talent that brings with themAI agents that can accelerate
your transformation.
ELIZABETH (11:34):
It's clear that talent acquisition is
transforming, and understandingthis shift is crucial for both
organizations and individuals.
For everyone listening.
You can find more resources andtools at AI4SPorg.
Stay curious and we'll see younext time.
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