Replacing Humans with AI is Going Horribly Wrong

Hi, welcome to another episode of Cold Fusion.

Since 2023, the fast food chain Taco Bell introduced artificial intelligence at over 500

locations in the United States.

The aim was to reduce mistakes and speed up orders.

But in some cases, AI delivered just the opposite.

Like this frustrated customer.

>> And what will you drink with that?

>> Oh my.

>> I want a large mouth soup.

>> And your drink?

McDonald's drive-throughs also tried AI,

but decided to scrap it because it was too unreliable.

One person had bacon added to their ice cream in error,

and another had hundreds of dollars worth of chicken nuggets mistakenly added to their

order.

As for Taco Bell,

the fiasco has caused them to rethink their use of AI.

Their chief technology officer, Dne Matthews,

told the Wall Street Journal in regards to deploying the voice AI system, quote,

"Sometimes it lets me down, but sometimes it really surprises me."

End quote.

And what he said is the very crux of consumer generative AI today.

It works most of the time, but about a few% of the time,

it just gets things wrong.

But where does this all lead?

A recent MIT report found that after surveying 150 business leaders and 350 employees,

just 5% of integrated AI pilots are extracting millions in value,

while the vast majority remain stuck with no measurable profit and loss impact.

In other words, AI implementation fails in 95% of the cases.

The market was spooked by these findings and shares in Nvidia,

the $4 trillion company whose chips power the AI boom, dropped by 3.5%.

While Palanteer fell by 9% off the back of the news.

It all sounds pretty heavy, but this was just a reaction to the headline.

Because as you dig deeper into what was actually said in the report,

the picture isn't as clear-cut as AI simply failing everywhere.

A few episodes ago, I talked about the current crisis with new graduate jobs.

A large part of that was the AI threat.

It's true and happening in some entry-level jobs, especially in the creative fields.

I did caveat in

that episode that current AI systems still get a lot wrong,

but will improve in the future.

But today, let's explore that fact a bit more deeply.

What if generative consumer AI continues to underperform in businesses for years to come?

In this episode, we'll look at how AI is actually performing once put into

the position of taking people's jobs.

To summarize the sentiment of this episode, it's basically this.

Consumer generative AI will revolutionize global productivity eventually, but as for now,

it's possibly in a bubble.

>> You are watching Till Fusion TV.

>> So, first thing, let's lay it out straight.

AI has some legitimate use cases

and works well in non-critical areas

that don't require 100% precision. robots,

live translation, and even some prototype website builders like Lovable are some examples.

But the sentiment is clear.

Most people online find it annoying.

But that aside, there's a fundamental problem with current generative AI.

Let me explain.

You see, everything we call artificial intelligence today was built on the findings of

a 2017 paper from Google.

That paper provided a way for AI to focus on different parts of an

input sequence simultaneously and determine the relevance of each word to every other word.

This innovation called a transform neural network allowed researchers,

computer scientists and later companies to change the world.

But here in lies the fundamental problem with this method of transforming neural networks.

It turns out by determining the relevance of each word

and predicting the next word,

it just makes up stuff.

Essentially, it doesn't know what it's saying.

We call this problem hallucinations, and it has real world consequences.

Picture this.

You're a business that decided to replace its staff with AI to write up

patient documents,

fill in patient information, summarize meetings, or do some basic scheduling.

But as time goes on, to your horror,

you realize that the AI system makes up 10% of everything that it delivers.

But the real problem is you don't know what it's made up

and you don't know what's accurate.

So you or your staff manually have to go back and check everything.

This ends up just being extra work for the remaining staff

and ultimately is a waste of time.

It sounds stupid, but this is exactly what's happening now.

The following are Reddit comments taken from those in the workforce who had to

take the brunt of upper management thinking

that blindly implementing AI was a good idea. quote,

"My company paid for some AI scheduling software a few months ago,

thinking that it could free up the accounts team

so that they could continue their hiring freeze in

that department.

Now the accounts team is having to do extra work making sure the program

isn't messing everything up.

Not to mention, our production team

that never had to worry about schedules is double/ triple checking everything.

They're finally scrapping it for some basic scheduling software."

And same experience here.

Products sold and demonstrated to be a lifesaver.

Ultimately, the staff that used to do the work spend all their time making

sure the AI doesn't screw up

and help train it.

Also, working in a medical/clinical setting,

we really don't trust the new file sorting and labeling system.

Names, date of birth, insurance data has to be perfect.

AI is less than that.

It fails at gathering proper demographic information and assigning relevant tasks.

Sometimes it thinks the doctor is the patient

or doesn't know where the document was faxed from,

etc.

And yet another user states

that AI was good at taking notes from Zoom meetings,

but would make up 5 to 20% of the content,

even when handfed the transcripts of the meeting.

Those who looked over the AI generated summary realized

that some of what was written wasn't even said.

All of these disasters make sense.

LLM predict the next word statistically,

but they'll never tell you

when it doesn't know the answer

or if it can't understand something.

There's many such stories of company regrets after rushing into halfbaked AI solutions.

A report suggests that 55% of companies regret replacing people with AI.

This bank for example,

who fired staff to install an AI chatbot

that was so bad they begged for their old humans back.

And take the example of Cler. 2 years ago,

they implemented a hiring freeze and began to replace their human staff with AI.

By 2024, their headcount had fallen from 3,800 to 2,000.

But lo and behold, their customers wanted to speak to actual humans.

Cler said that its AI chatbots perform the work of 800 employees.

But the company admits

that their service quality

and customer satisfaction have dropped

and they lament that human interaction is still needed. on replacing people with AI.

The publication Fortune notes, quote, "Not only is it shortsighted, it's fundamentally bad business.

The companies cutting people today in the name of AI will be the ones

playing catch-up tomorrow.

There's no doubt that AI is excellent at doing more with less.

It speeds up processes, cuts down repetitive work, and buys back time.

But AI on its own cannot create the next generation of products and services."

End quote.

So on Cold Fusion, we tried to be thorough here.

So it has to be said

that all of these failures aren't the full story.

There are indeed companies that are extremely successful at using artificial intelligence.

Now, even though the MIT paper said

that 95% of companies who implemented generative AI failed in said implementation,

the same MIT paper states, quote, "Some large companies, pilots,

and younger startups are really excelling with generative AI."

They mean startups led by 19 or 20 year olds.

They quote have seen revenues jump from 0 to 20 million in a year.

It's because they pick one pain point, execute it well,

and partner smartly with companies who use their tools."

End quote.

So, how companies adopt AI is crucial.

For example, purchasing AI tools from specialized vendors

and building partnerships succeed 67% of the time,

while internal builds succeed only onethird as often.

This goes to show

that you can't just slap AI everywhere

and expect it to work.

It needs thought in its implementation

and also depends on the specificity of the AI tools in question.

But in the grand scheme of things,

it's still early days for AI

and it'll be naive to think

that it will stay the same forever.

All it would take is another groundbreaking paper,

a new underlying neural network architecture and everything could change again.

This could mean another giant leap forward that none of us are expecting.

But in the meantime, it's uncomfortable territory.

So what happens?

>> Like the steam engine which sparked the industrial revolution of the late 1700s,

the internet is changing everything it touches.

And at the cutting edge of the revolution is Wall Street.

In the dotcom bubble during the mid '90s,

everyone who simply put a.com at the end of their company name saw massive

valuations because investors who didn't understand the technology saw them

as the future.

In reality, these companies had no solid way of making a profit

or didn't even have a business plan.

When the broader market realized that it was all a smokeokc screen,

the sector crashed.

Most doc companies went out of business

and only a handful made it out

and are giants today.

So, let's compare that to the AI wave of today.

The long-awaited release of chat GPT5 was a disappointment,

and many users even thought the previous version was better,

and this caused Open AI to scramble.

In addition, the company was also caught blatantly fudging the performance numbers in some

very strange graphs.

Incremental rather than revolutionary was the tone.

Soon Meta would announce

that it's downsizing its AI division

and this comes amongst a growing chorus of analysts saying

that AI is heading towards

if not is already in a bubble.

And next we have the massive valuations and spending.

Nvidia H100 the GPUs

that power the artificial intelligence boom are about 30 to $40,000 each.

Google has 26,000 of them and they've managed to create Alpha Fold, Gemini,

V3 and more.

But Meta on the other hand has 600,000 Nvidia H100s.

And while they do have the open- source Llama LLM,

it isn't discovering new science like Google's Alpha Fold for 23 times the compute.

And to give you an idea of how extreme this is all getting,

AI itself has caused a 4% increase in electricity use in the US.

Morgan Stanley states that data center investment will reach $3 trillion over the next

3 years in preparation for AI use.

And of course, that's heavily fueled by debt.

The belief is that AI will cut costs by 40%

and that should add 16 trillion to the S&P.

But as we've just seen at the beginning of this episode,

according to that MIT study, that could be very unrealistic.

So, this could be the future if AI doesn't improve massively soon.

Number one, business executives and business owners will get frustrated with hallucinations. useless solutions,

bad code, and a very poor return on investment.

Number two, a lot of the AI gurus like Sam Olman will have to

admit that artificial general intelligence isn't going to be achieved by LLMs,

and they're essentially a dead end.

Number three, the general populace begins to get sick of LLMs,

and we're already seeing it. the absolute flood of AI slop, the hallucinations,

AI agreeing with what users say, sometimes driving them insane.

Number four, in seeing this,

the venture capital finally dries up

and LLMs become just too expensive to justify unless there's a massive increase in

efficiency.

The cost to run OpenAI's data centers,

all the pipes and guts

and things that like keep AI running is about $40 billion a year.

Their revenues right now are only like 15 to 20 billion.

And finally, number five, after a long winter,

new implementations come around

that truly live up to the hype promised by the first wave.

So, just like the com bubble,

there's going to be a few winners that rise from the ashes.

But if or when it crashes,

from here we see the true artificial intelligence companies that last the distance.

So to finish off this episode, let me end with this.

I've talked about this many times in my older episodes,

but take a look at this diagram.

It's called the Gartner hype cycle,

and it describes the typical progression of the infiltration of new technologies into society.

So where do you think we are?

The technology trigger, the peak of inflated expectations, the trough of disillusionment,

the slope of enlightenment, or the plateau of productivity?

Feel free to comment down below.

So where to from here?

Well, Sam Oldman and all the other AI leaders should focus their efforts on

fixing the hallucinations.

Once again, a different neural network architecture could be discovered, one that fixes hallucinations.

Or perhaps it could just be fixed manually.

Either way, ushering in a new boom.

But that all being said, and that's the funny thing about AI.

The point is, with AI, nobody knows the future.

So, what do you guys think?

Do you think we're in a bubble and a crash is imminent,

or do you think the next AI innovation is just around the corner?

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Hey guys, it's me.

Hi.

I'm not an AI, so you're finally seeing my face.

But anyway, thanks for watching.

If you did enjoy it, there's plenty of interesting stuff here on Cold Fusion,

so feel free to subscribe.

Otherwise, that's about it from me.

My name is Togo, and you've been watching Cold Fusion.

And I'll catch you again soon for the next one.

>> Cold Fusion, it's new thinking.