Episode 191

full
Published on:

31st Mar 2026

Is AI Efficiency Just Code for Headcount Reduction in Consulting?

Corporate AI pitches sell efficiency but often mean headcount reduction. Spencer and Dave discuss how job losses to AI have already happened at large research firms through restructuring. Spencer details deployments in customer experience with AI kiosks, multi-cloud data integration to inject AI, and coding tools that spit out near-complete solutions fast. Early client quick wins include document and PDF automation for accuracy and compliance. Dave contrasts AI with blockchain, calling the latter a slow fancy spreadsheet with limited adoption while labeling AI a probabilistic non-deterministic chaos agent wired into mission-critical systems including government, creating moral and ethical risks. They debate whether AI growth drives more value or just job replacement. Spencer says younger workers reject butts in seats, use AI tools, and focus on driving value. Healthcare and professional research are heavily impacted while some sales roles grow and sustainability field scientists stay less affected.

Timestamps:

  • (00:00) Corporate AI pitches sell efficiency but really mean headcount reduction – Spencer says job losses to AI have already happened at large research firms through restructuring
  • (06:23) Blockchain is just a slow fancy spreadsheet with limited adoption – Dave contrasts it to AI as a probabilistic chaos agent wired into mission-critical systems including government with moral and ethical risks
  • (12:04) AI growth sparks real unemployment fear – the debate on whether it creates more value or just replaces jobs
  • (12:44) Younger workers reject butts in seats and use AI tools to drive value – healthcare and research hit hard while some sales roles grow and sustainability field scientists stay less affected

🌍 Connect with us: Instagram | YouTube | X

🎧 Listen to Episodes → Here

Transcript
Alex:

Spencer's in the room when companies run the REAL AI math — and the pitch

2

:

always lands on "efficiency" until you

see what's actually being replaced.

3

:

Dave's already calling

it a Faustian bargain.

4

:

Spencer's not arguing.

5

:

Jerremy: when a client deploys

those tools into their workforce,

6

:

Alex: What happens to the headcount

7

:

Jerremy: that

8

:

nobody is saying out loud or

maybe everyone's aware of and

9

:

still isn't saying it out loud?

10

:

Spencer Conley: Yeah, you know,

11

:

the context of of my job and also

just talking with my peers, you know,

12

:

I would say

13

:

off the books there, there's definitely

a consideration of headcount, like,

14

:

how can we reduce hours or maybe,

15

:

streamline our temp workforce

or maybe get rid of It

16

:

it

17

:

definitely happens.

18

:

I mean, when you're thinking about

implementing ai, you're thinking

19

:

about people, process and technology

and you know, part of people

20

:

is how many people do you have?

21

:

Jerremy: What are they doing?

22

:

Spencer Conley: can they be

repurposed to either train the AI

23

:

or, or move to a different, more,

24

:

call it

25

:

more impactful area of work where they can

26

:

take,

27

:

I

28

:

Dave: is, that is the

biggest consultant world.

29

:

Jerremy: Yeah.

30

:

Spencer Conley: Right.

31

:

Dave: I can tell you're a consultant.

32

:

It's like,

33

:

oh, you know, we'll, we'll see

if we can restructure this.

34

:

Spencer Conley: Well, I mean,

35

:

that's just it.

36

:

It's like you sell this ai right?

37

:

You know, you implement an ai and it's,

38

:

it's, the value pitch is often, hey, you

know, this will allow your staff to do,

39

:

to focus on more important things.

40

:

But like oftentimes what's

getting automated is

41

:

like that staff person's entire job.

42

:

So like, what else do they have to

43

:

Jerremy: Yeah.

44

:

Spencer Conley: then that's

when headcount comes in, right?

45

:

Jerremy: Yeah.

46

:

Spencer Conley: But

47

:

I would say at large research

firms and a, a couple other of

48

:

industries where I have friends working,

they have actually lost their jobs to ai,

49

:

Dave: Wow.

50

:

Spencer Conley: go through restructuring.

51

:

I'm putting that in air

quotes, but you can't see

52

:

Jerremy: Yeah.

53

:

Yeah, yeah, yeah.

54

:

Spencer, are you able to tell us what

55

:

some of these, either specific

names of the tools are or

56

:

like

57

:

what sector

58

:

they're in?

59

:

I mean, 'cause you say ai, like it could

be software, it could be coding, it

60

:

could be customer service, it could be,

61

:

how much details can you give us on that?

62

:

Spencer Conley: you know,

some, some broad details.

63

:

I would say it's, it's a full,

full suite of, of services.

64

:

Again, you know, this is

65

:

consulting lingo.

66

:

say it's,

67

:

There is a customer experience

aspect, so it's how do you

68

:

leverage AI to make, you know,

69

:

literally

70

:

walking into a business instead of

71

:

talking to a person or a bunch

of people and waiting in line.

72

:

It's an automated kiosk with literally

an AI screen, you know, kind of like a,

73

:

any sci-fi movie that?

74

:

Jerremy: Yeah.

75

:

Or Japan Just got back from there, dude.

76

:

Oh my gosh.

77

:

Yeah.

78

:

Tons of, tons of them.

79

:

Like the, the,

80

:

essentially

81

:

it was an ATM.

82

:

I would do all the work and then I would

83

:

go to my table and then

someone would bring my food.

84

:

Dave: Huh.

85

:

Spencer Conley: Yeah, well like that for,

for every industry, not just service,

86

:

Jerremy: Mm.

87

:

Spencer Conley: it's, it's, you know,

multi-cloud and distributed cloud.

88

:

So connecting different

cloud instances where.

89

:

You know, a client might have three

different versions of a cloud, from any

90

:

software provider, you know, pick one

91

:

the s and p 500.

92

:

But it's connecting all of those

93

:

to be able to inject ai, connect every

94

:

single piece of data in an organization,

95

:

and make decisions and,

and pull it all together.

96

:

But it's

97

:

Jerremy: Yeah.

98

:

Spencer Conley: for coding, right?

99

:

Like you give it a

100

:

Jerremy: Yeah.

101

:

Spencer Conley: and instead of a

developer doing it, it'll give you a

102

:

90% solution in like, you know,

103

:

Jerremy: Seconds, minutes.

104

:

Yeah.

105

:

Dave: Is,

106

:

there like one,

107

:

so like I think of AI like

this, you can either do.

108

:

you know, you can keep the same number

of people and you can do a lot more work,

109

:

or you can completely replace a function.

110

:

Like, I, I called my dentist the

other day and I just talked to an

111

:

AI and, and scheduled everything.

112

:

And actually it worked

really, really well.

113

:

You know, like I was, I was shocked.

114

:

like, like, you know, actually removing

a, a, a job and then it's like, okay,

115

:

we are not gonna do more.

116

:

We're going to,

117

:

you know, like we're going to actually

re, you know, quote unquote restructure

118

:

and, and, do and, and,

and shrink head count.

119

:

So when, when the different

implementations come in, do you see

120

:

like one thing that, that companies

are looking to do, or is it just like,

121

:

it could be anything and, and they see

where it lands you, you know what I mean?

122

:

Is it,

123

:

is it, adding more value,

like growing the pie?

124

:

Is it, shrinking the,

the, the, the top line?

125

:

or is it growing the bottom line?

126

:

I.

127

:

Spencer Conley: Yeah,

128

:

Jerremy: Hmm.

129

:

Spencer Conley: that's really good

130

:

question.

131

:

I would say it's, it's

a little bit of both.

132

:

I would say

133

:

trend that, that I'm seeing is.

134

:

I think a lot of clients

are interested in ai,

135

:

they're still a little bit hesitant.

136

:

So we,

137

:

we get in there and, and what we typically

pitch, or what I like to pitch is like,

138

:

you know, quick wins first, right?

139

:

So It's, thing, it's

140

:

not adding new value.

141

:

It's, it's really like, you

know, document automation.

142

:

So if like you have paper

processes, which still happen a

143

:

lot, or very manual, like PDFs,

144

:

it's reading those, putting those into

a system that you can layer AI and, and.

145

:

Literally route all of that

paperwork and analyze it for

146

:

accuracy and compliance, immediately.

147

:

Whereas that would take multiple people.

148

:

That's usually kind of

like the foot in the door.

149

:

you start talking about, you know,

what, what the art of the possible is.

150

:

That's where you start to get

to like the value drivers.

151

:

It's like, Hey, we would really like to do

152

:

really cool initiative that

is very exploratory for us.

153

:

You know, us being the client.

154

:

let's start thinking about that

now that we have some breathing

155

:

room with this document processing,

saving us a bunch of time.

156

:

So, honestly, like clients are

trying to catch up right now, and

157

:

once they've caught up with AI,

they start thinking bigger picture

158

:

Jerremy: Yeah.

159

:

Dave: internally yourself

are, are like, are you,

160

:

you

161

:

know, are,

162

:

are,

163

:

you taking your own medicine?

164

:

Spencer Conley: all the time.

165

:

It is

166

:

Dave: Yeah.

167

:

Spencer Conley: my job

168

:

so much.

169

:

I, I can't even

170

:

save me so much time.

171

:

Jerremy: Hmm,

172

:

man, that's so cool.

173

:

that is, is cool and scary and terrifying.

174

:

Dave, I'm gonna ask you a question.

175

:

Give me your,

176

:

the same level of,

177

:

deep dive that you gave me

on blockchain and crypto

178

:

on ai.

179

:

Like

180

:

what is the, so everyone in the world was

like, blockchain, blockchain, blockchain.

181

:

Dave: Yeah, it was

182

:

Jerremy: And Dave Conley

was like blockchain.

183

:

Fucking sucks.

184

:

It's awful.

185

:

It's the worst.

186

:

What does it actually do?

187

:

Does it do anything that

anyone wants it to do?

188

:

And it's not gonna work

the way anyone wants it to.

189

:

And you're one of the very few

people that's like poo-pooing crypto.

190

:

w which, which, I mean, you're,

you're not wrong really on like

191

:

Dave: hand, I also spent a year

looking at it because, you know,

192

:

Jerremy: Yeah.

193

:

Dave: crypto was sort of born,

194

:

you know, as I was,

195

:

as I was in a very

196

:

Jerremy: As you were born.

197

:

Dave: back in my day,

198

:

back in my day.

199

:

So I was a very senior technology level.

200

:

And so blockchain was really kind

of coming out, you know, Bitcoin was

201

:

there and we were, we were looking at

it in a bunch of different ways and

202

:

you know, like the one thing that

we, we liked was the aspect of,

203

:

you know, the permanent record.

204

:

And

205

:

it didn't replace anything.

206

:

It was, it was, it was a fancy

spreadsheet in the, in the cloud, right.

207

:

And so, like all of the

systems that we already had

208

:

already did what it did,

and it didn't add any value.

209

:

So nobody was gonna do

it because it was slow.

210

:

You know, it's like you could be

doing it with Oracle databases and

211

:

it was like, nobody's gonna replace

Oracle databases with, you know,

212

:

your, your, your, your Bitcoins,

213

:

Jerremy: Mm-hmm.

214

:

Dave: and.

215

:

You know, like we were looking at

it as, as also as adoption, right?

216

:

So like,

217

:

you know, when a technology is

important, when it's adopted,

218

:

so, you know, like

219

:

nobody had, like, we all had cell

phones for years before the iPhone

220

:

came out, and then the iPhone comes

out and like everybody has one within

221

:

a year and it just changed everything.

222

:

You know, like we're still trying to

figure out what's going on with blockchain

223

:

and it's been 15 years and it's like,

eh, you know, like if, if it's not there

224

:

now, it's not gonna magically get there.

225

:

Jerremy: Yeah,

226

:

Dave: with ai,

227

:

Jerremy: I.

228

:

Dave: am, I.

229

:

am on the way other side of this.

230

:

It is, this is a technology that

we don't, we don't understand.

231

:

Like the people who are in it, you

know, like that are in edge cases,

232

:

they don't understand it.

233

:

It is a technology.

234

:

We are growing.

235

:

we

236

:

Jerremy: Mm-hmm.

237

:

Dave: it.

238

:

It is,

239

:

it also is also a prob

240

:

probabilistic system.

241

:

Meaning it like, it, it's, it's

constantly guessing and it's always

242

:

bringing you sort of new things to do.

243

:

It is, it is not deterministic.

244

:

So it, it's, you know, like if you

tell it to do one thing and do it

245

:

this way, you know, like it might come

up with a different way of doing it.

246

:

so it's.

247

:

It is

248

:

a chaos agent that we simply do

not know where it's going to do.

249

:

Because

250

:

when the internet came around

and the technology really hit,

251

:

you know, like, it was very clear

that this was going to disrupt

252

:

music, disrupt, disrupt magazines,

di disrupt, you know, media.

253

:

And then it was like, okay, yeah,

Netflix was going to be a thing.

254

:

You know, like, and, and then it

was, then it was, you know, content

255

:

and then it was social media, like

it was so clear what the path was

256

:

for ai.

257

:

Man, it is, it is, the unknown

is probably the scariest part of

258

:

this, and the weirdest part is that

we are inserting this technology.

259

:

Treat it as a, an, as an

entity, as a, as a child

260

:

with superhuman skills.

261

:

And we are wiring it in as the central

nervous system to our most critical

262

:

mission critical, systems on the planet.

263

:

Like

264

:

AI is all over the government.

265

:

And do you really want something

that you don't understand?

266

:

Being all over the place inside of your

government like that is terrifying to me.

267

:

And not just in like a.

268

:

you know, like, like tinfoil hat way.

269

:

It's, it's that.

270

:

You, you understand?

271

:

Like a Windows operating system and

be like, okay, that's, that's Bill

272

:

Gates, and that's broken, right?

273

:

Like, we get it.

274

:

And then these, these systems get

very complex and, and also we get it.

275

:

is, we have it, it is unbounded,

uncontrollable, and it keeps on

276

:

going and it changes every day.

277

:

We are, we are not wired for that.

278

:

We do not know the, consequences of it.

279

:

And when you don't know the

consequences of something, then

280

:

you are not, you are not

in any control of it.

281

:

And then, then you start like layering in,

282

:

oh, we're, you know, we're gonna

start putting things that like,

283

:

are life and death to people.

284

:

On top of that,

285

:

nah, this is, there are more

moral and ethical questions that

286

:

go into this than anything else.

287

:

And, and, and nobody is saying,

Hey, time out here, because

288

:

if we do

289

:

means that somebody else

is going to do it first.

290

:

And

291

:

Jerremy: Hmm.

292

:

Dave: like the bomb is already gone off.

293

:

The genie is way out of the bottle.

294

:

And so like, if we don't keep feeding

the beast, we're equally screwed.

295

:

So like I, it is.

296

:

It is a Faustian bargain man, And

297

:

we we're gonna have a whole

series on ai and it is,

298

:

Jerremy: Yeah.

299

:

Well it.

300

:

Dave: we can only react to it.

301

:

we,

302

:

because

303

:

we don't have any choice, we're

gonna have to react to it.

304

:

Alright, that's my, that

305

:

there

306

:

Jerremy: It is a great take.

307

:

No, it's a great, it's

a, it's a great take.

308

:

It's a it, because I think

309

:

there, there's a, there's

a few people like,

310

:

alright guys, and

311

:

one

312

:

of them act, I mean, one of them

is Elon, where he is like, hold on,

313

:

hold on.

314

:

Don't,

315

:

don't

316

:

you think we're going a little fast.

317

:

So do you think we're going a

little fast down this rabbit hole?

318

:

what's your take on that, Spencer?

319

:

Like, just kind of to your

point, like what Dave just said,

320

:

where AI is going because

I mean, my, my general fear

321

:

also, and I'll lead you with

a very long question, probably

322

:

the general, the general fear is AI

becomes so good and it's so helpful

323

:

and it's so usable and it happens so

quickly that in three years we have

324

:

55% unemployment in the country.

325

:

No one needs jobs anymore.

326

:

And that's the fear that a lot

of people don't actually have.

327

:

So if that does happen, what do you

see occurring in workforce dispenser?

328

:

Spencer Conley: Yeah.

329

:

You know, that's,

330

:

I, I almost go back and

forth on it every day.

331

:

It's,

332

:

Jerremy: Yep.

333

:

Wow, dude.

334

:

What

335

:

bro?

336

:

Dave: So yet.

337

:

Jerremy: they're waiting tables, dude.

338

:

I, I got, I

339

:

would, I mean, I shouldn't say it's AI

waiting tables, but I, I went to three

340

:

restaurants where I got served by a robot.

341

:

Dude,

342

:

a robot brought my food to me.

343

:

I picked it up off the tray.

344

:

But

345

:

I mean,

346

:

other

347

:

than beer, it brought me

everything other than beer.

348

:

which I'm sure is probably some

law or something, but man, it's.

349

:

Dave: like the,

350

:

that's the.

351

:

Jerremy: Robots not 21.

352

:

Dave: back up a sec.

353

:

So, you know, like you're, you know, like

you're, you're, you're coming into like

354

:

the top of your, your, your mid career,

355

:

and you're.

356

:

Presumably like hiring younger, like the,

the young analysts that are coming in,

357

:

how are they different?

358

:

You know, like did they, did,

did they do different paths?

359

:

Did they, did they come to

you guys in different ways?

360

:

Are they, you know, like, are they

afraid or excited about different things?

361

:

Like how are they different

than where you were at that age?

362

:

Spencer Conley: Yeah, there

363

:

honestly very different,

in, in a good way.

364

:

So they,

365

:

think

366

:

I was probably one of the last waves of,

of people entering the workforce that was

367

:

kind of bound to that traditional contract

that we talked about at the intro.

368

:

I,

369

:

was always wearing a suit.

370

:

I was always trying to present, very

polished, coming in, literally just

371

:

sitting at my desk until 5:00 PM

even though my clients were falling

372

:

asleep just because I had to be there.

373

:

You know, I called it

a but in a seat, right?

374

:

I was very grounded in

that responsibility.

375

:

I think now people coming into

the workforce, you know, they,

376

:

they do get the technical skills.

377

:

They do still go to

college for the most part.

378

:

But they don't really put up with

that shit, to be honest with you.

379

:

You're not, they're, they're not

just gonna sit at a desk until

380

:

five if they have nothing to do.

381

:

like, where can I drive value?

382

:

They have all of these AI tools,

383

:

They're

384

:

all so connected to the internet.

385

:

They're gonna be the most efficient

they can, they're very open to

386

:

raising their hands to help with

additional work when they're done.

387

:

Dave: Ooh.

388

:

Spencer Conley: also like, Hey,

I'm taking PTO, and I don't have

389

:

anything to do and it's 3:00 PM

on a Thursday, I'm gonna leave.

390

:

And obviously they, they work

with us to clear it as junior

391

:

practitioners, but like,

392

:

they're very upfront about that, which,

like 21-year-old me would've been like,

393

:

mean, I'll go hide in the bathroom for

two hours, try to take a nap instead

394

:

of, you know, asking my boss to leave.

395

:

And there's nothing to do.

396

:

Jerremy: Hmm.

397

:

Dave: Hmm.

398

:

Jerremy: That's a good

399

:

point,

400

:

Spencer Conley: man.

401

:

So.

402

:

Jerremy: Yeah,

403

:

it's been really

404

:

Spencer Conley: good.

405

:

Jerremy: I,

406

:

think, I think the

407

:

the adding of the value.

408

:

Is

409

:

the

410

:

part that

411

:

jumped out to me the most because

412

:

that's what

413

:

we're gonna have to do

414

:

as,

415

:

as,

416

:

a nation going forward.

417

:

I mean, the, the workforce, the work

418

:

work is

419

:

gonna be changing so dramatically over

the next five years, so, so quickly

420

:

that

421

:

every person listening

422

:

has

423

:

to imagine that not only is is it going

to shift, it is already shifting and

424

:

is it is gonna happen even faster.

425

:

we have to come up with that question

on a daily, weekly basis, right?

426

:

How can I add more value to this

organization, to this company

427

:

to keep a form of employment?

428

:

you do work for yourself

429

:

or by

430

:

yourself or own your own business,

431

:

it's gonna even be even more imperative,

to continue to add that value.

432

:

What

433

:

do you see,

434

:

in the

435

:

workforce right now that

are the most insulated?

436

:

From

437

:

AI and AI tools, sweeping

and chains, in industry,

438

:

as

439

:

you know, faster as some of the

other ones are being slower.

440

:

Spencer Conley: so, so

more insulated from AI

441

:

in

442

:

terms of like, okay.

443

:

that's a really good question.

444

:

I mean,

445

:

Quite frankly, everything,

everything I've, I've seen from a

446

:

professional, like most, most of

the industries are being impactful.

447

:

I might, for my own thought exercise,

start from most impacted to least, I mean,

448

:

most impactful is I think, you know, the

the healthcare and the, the professional

449

:

research, like, like a Gartner for

example, is getting heavily impacted.

450

:

They used to hire, you know, NPAs and,

and folks like that to do very detailed

451

:

technical research and reporting.

452

:

And now it's all shifting to like AI for

some of these larger research companies.

453

:

obviously technical coding, even the

big tech companies, they're, they're

454

:

shifting to ai, where whereas I, would

say sales we're, we're seeing an influx

455

:

in, some, some sales positions coming.

456

:

So people being technically savvy,

maybe even former developers.

457

:

With a technical solution that might

not have previously had ai, but

458

:

now has an AI layer on top of it.

459

:

They're hiring those folks

to be their salespeople.

460

:

Like, Hey, I worked in, know, AutoCAD

or something like that as an engineer.

461

:

Now AI is doing a lot of

that work on top of cad.

462

:

but let's pull you in as a

salesperson to help sell that work.

463

:

I think is is kind of,

we're seeing a shift there.

464

:

but really the things that that

aren't getting impacted, I would say

465

:

sustainability isn't getting impacted a

lot of the sectors, or sorry, a lot of

466

:

the accelerators around sustainability,

like how we monitor climate impact, do.

467

:

geospatial analytics, all of that stuff.

468

:

you know, water, water analytics for

water quality are getting bolstered

469

:

by AI but the scientists and the

people behind it going out into the

470

:

field are not being impacted as much.

471

:

they're really just

getting a better tool belt.

472

:

Right.

473

:

those are the things that

stick out to me right now, but

474

:

it's, it's a great question.

475

:

Jerremy: I love it.

476

:

Alex: Dave calls AI an unbounded

chaos agent wired into systems

477

:

nobody fully controls — Spencer's

not fighting that take.

478

:

But 25 years out, which workers

actually WIN this thing?

479

:

That forecast is next.

Next Episode All Episodes Previous Episode

Listen for free

Show artwork for Solving America's Problems

About the Podcast

Solving America's Problems
Solving America’s Problems isn’t just a podcast—it’s a journey. Co-host Jerremy Newsome, a successful entrepreneur and educator, is pursuing his lifelong dream of running for president. Along the way, he and co-host Dave Conley bring together experts, advocates, and everyday Americans to explore the real, actionable solutions our country needs.

With dynamic formats—one-on-one interviews, panel discussions, and more—we cut through the noise of divisive rhetoric to uncover practical ideas that unite instead of divide. If you’re ready to think differently, act boldly, and join a movement for meaningful change, subscribe now.