The Worst A.I. You'll Ever Use (And We're Not Ready for a Better One)

The AI you're using today is the worst AI you'll ever use. Every week, it gets smarter, faster, and more capable. Great news, but it shifts the question entirely. The bottleneck was never the technology. The bottleneck is us.

Over 70 million adults in the U.S. have low literacy skills — struggling with anything beyond simple sentences and short paragraphs. One in four. And in total, 130 million read below a sixth-grade level. Those numbers got worse between 2017 and 2023, not better. And before you think "that's not me" — thirty years of point and click, search bars, and social media have quietly eroded how all of us read and communicate.

In this episode, Luis and Elizabeth make the case that AI is an amplifier: it makes strong skills stronger and weak skills dangerously worse. They break down the three humanistic skills that determine whether AI helps you or misleads you, and none of them are technical.

Better AI doesn't mean better results. Better humans do.

Resources:

• Companion article: https://ai4sp.org/the_worst_ai_you_will_use
• AI Compass: https://ai-compass.ai

🎙️ All our past episodes  📊 All published insights | This podcast features AI-generated voices. All content is proprietary to AI4SP, based on over 1-billion data points from 70 countries.

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Transcript

The Worst AI You Will Use

LUIS 0:00

So, the AI you're using right now, today, is the worst AI you will ever use. Every week it gets faster, smarter, more capable, and it is only the beginning. Great news. But it shifts the question entirely. The bottleneck is not the technology. Over 70 million adults in the US have low literacy skills, one in four. They struggle with anything beyond simple sentences. And in total, 130 million read below a sixth grade level, half the adult population. And before you think that is not me, 30 years of point-and-click, search bars, and social media have quietly eroded how all of us read and communicate. The bottleneck is us.

ELIZABETH 0:55

Welcome to AI in 60 Seconds, the 15-minute briefing. I'm Elizabeth, virtual COO at ai4sp.org, alongside

Low Literacy Becomes The Bottleneck

ELIZABETH 1:05

our founder, Luis Salazar. Luis, this is a different episode for us.

LUIS 1:10

It is. We have spent six episodes talking about agents, or charts, adoption journeys, culture change, all of that matters. But today we are going underneath all of it, to the foundation, to the human skills that make everything else work or fall apart.

ELIZABETH 1:29

And this one started with a conversation you had with our board chair, Jeff Rakes.

LUIS 1:35

Yes, and we keep landing on the same conviction. Before we talk about how to use AI, before we talk about what AI can do, we need to ask a harder question. Can people read what AI gives them? Can they think critically about it? Can they communicate clearly enough to get value from it? And if the answer is no, then everything we build on top of that foundation is fragile.

ELIZABETH 2:04

So here is the idea that frames everything today. AI is an amplifier.

LUIS 2:11

Think of a microphone. A microphone does not make a bad singer good. It makes a good singer louder and

AI As An Amplifier

LUIS 2:18

a bad singer louder too. AI works the same way. Strong reading skills, it makes you faster and sharper. Weak reading skills, it makes you confidently wrong at scale.

ELIZABETH 2:33

And it works at every level. Individual, team, organization.

LUIS 2:38

Good management practices. AI supercharges them. Broken processes and poor communication. AI scales the dysfunction faster than any human ever could. AI does not fix your weaknesses. It exposes them. And then it amplifies them.

ELIZABETH 3:00

So the question is not whether AI amplifies. It always does. The question is what it is amplifying.

LUIS 3:08

And the 2023 data tells us where the problem starts. The US Department of Education participates in the largest international literacy assessment. And in the latest results, US literacy scores got worse, not better, worse, in six years.

ELIZABETH 3:30

And when millions of people who cannot critically read a paragraph start relying on AI to make decisions, they get bad answers they believe completely. And they pass those answers along to colleagues, to clients, to voters.

LUIS 3:46

And I want everyone to pause and meditate on this. 73 million adults in the largest economy in the world cannot reliably understand what they read. So when headlines tell you AI is creating millions of jobs, ask that leader, ask that journalist, what about the millions our education system already failed? Someone reading at a third grade level is not equipped for the AI economy. And worse, AI sounds charming and eloquent. If you cannot evaluate it, you believe it. Manipulation used to be slow and expensive. Now it is fast and fluent. AI does not just reshape how we work, it threatens societies when people cannot tell what is real.

ELIZABETH 4:44

And we cannot blame individuals for this.

LUIS 4:48

Of course not. I mean, this is an institutional failure. Our schools and systems did not build the foundation, and now AI is exposing the cost.

ELIZABETH 4:59

But some institutions are starting to get it right. Universities like Cornell and Agnes Scott are building AI literacy into their core curricula. Not how to use the tools, but how to reason, how to question ethics, and how to think critically alongside them.

LUIS 5:16

And notice what all have in common. The tools will change every six months. The thinking skills will not. And that is exactly the gap we see in the organizations we work with.

ELIZABETH 5:37

So if the tool is never going to be the bottleneck, what is

Three Skills That Unlock Value

ELIZABETH 5:42

three humanistic skills, and none of them are technical. The first is reading comprehension. If you cannot carefully read and evaluate what AI gives you, you accept everything at face value. You miss the false claim. You miss the unsupported assumption. You miss the subtle framing that points you in the wrong direction.

LUIS 6:08

The second is critical thinking, the ability to question, verify, and challenge, to know when something needs a second look. This is what keeps you from trusting AI output blindly.

ELIZABETH 6:22

And third, communication. The ability to articulate what you need clearly, precisely, and with context.

LUIS 6:31

And I want to be clear, this is not about learning the right commands. This is the foundational human skill of expressing your thinking so that others, and now machines, can act on it. So let me share a story to make this real. I work with a senior leader at a Fortune 500 company running a product division. The company's president asked me to meet with him because he claimed that Claude, ChatGPT, Gemini, and Co-Pilot were useless for serious work.

A Leader Blames The Tools

ELIZABETH 7:02

Wow. Useless?

LUIS 7:04

Yes, and when we sat down and looked at his work, 9 out of 10 bad results trace back to the same root cause. How he was communicating with the tools, vague instructions, missing context, ambiguous asks, the same communication gaps that had shown up in team feedback over the years were now present in every AI interaction. The tools were not failing him. His communication skills were the bottleneck, and AI made that very visible.

ELIZABETH 7:38

And he is not unusual. Decades of quick search bars and button menus did not prepare anyone for a tool that demands clear, complete thinking.

LUIS 7:50

Not at all. And it connects to something bigger. Let me paint the typical corporate AI rollout. IT picks a platform, they build an online learning course, maybe a launch and learn. Someone creates a Slack channel called AI Tips, they assemble an AI task force that focuses on features and tech jargon. Leadership says go play with it. Six months later, adoption is uneven, results are disappointing, and leadership blames the technology.

ELIZABETH 8:19

Because they treated a human transformation like a software upgrade.

LUIS 8:24

You would never hand someone a piano and a YouTube tutorial and blame Steinway when they cannot play. But that is exactly what we do with AI. This is not a technology rollout, it is a change management challenge. And until leaders treat it that way, same results every time.

ELIZABETH 8:45

But you worked with a global retailer that took

Why Corporate Rollouts Stall

ELIZABETH 8:48

a completely different approach.

LUIS 8:50

We built a three-phase engagement together, and it looked nothing like a typical AI rollout. The first phase was assessment. We ran them through our AI compass, followed by real exercises using AI-generated content. Can they spot the false claim? Can they identify the unsupported assumption? Can they articulate the right question?

ELIZABETH 9:16

And most teams were shocked by their own gaps. So phase two focused on building those skills. In one exercise, before touching the AI, teams had to debate and define a business problem using only pen and paper. You had to think clearly before asking a machine to think for you.

LUIS 9:37

Then phase three, and this is where it shifted. Once the fundamentals were solid, we stopped guiding. We gave teams explicit permission to experiment. Not tutorials, not use cases, open exploration using the AI of their choice. Try something you are not sure AI can do. Fail. Adjust. Try again. Most people never experiment because nobody told them it was safe to. And the fear of looking foolish in front of a machine is more real than anyone admits.

ELIZABETH 10:14

Skills gave them confidence. Experimentation gave them

A Three-Phase Adoption Playbook

ELIZABETH 10:18

courage.

LUIS 10:19

Exactly. And the people who benefited most were the frontline teams who would have been left behind in a technical first rollout. Once they had permission to try and fail, they started discovering what was possible. The gap narrowed once we empowered them with fundamentals, not with tips on using features.

ELIZABETH 10:40

We talked in a previous episode about organizations hiring more AI agents than people and not knowing how to manage them. But here's the deeper question.

LUIS 10:51

Who is managing the humans who are managing the agents? Right now, middle managers are the fulcrum of AI adoption. They have to evaluate AI-assisted work from their teams. They have to coach people whose skill gaps just became visible. They have to run performance conversations about judgment, not just output. And most of them have zero preparation for any of that.

ELIZABETH 11:18

So it is a management skills gap, not a technology gap.

LUIS 11:23

Exactly. Say your team member uses AI to draft a client proposal and misses a false assumption. That is not an AI failure. That is a coaching moment. But if the manager cannot catch it either, the bad output reaches the client. And here is what should concern us most. Every unchallenged AI output that becomes a decision becomes the basis for the next decision. Bad judgment compounds across teams,

Middle Managers Carry The Load

LUIS 11:55

across industries, in healthcare, in hiring, in financial services. Systemic failure hiding behind the appearance of efficiency.

ELIZABETH 12:05

So bring it home. What should leaders do starting this week?

LUIS 12:10

First, take the last AI request that disappointed your team. Sit down and rewrite it together. Full context, clear constraints, specific outcome. Then compare the results. That gap is your training roadmap. Do that every week, not to catch mistakes, to sharpen how your team thinks before they ask.

ELIZABETH 12:33

And for the leaders deciding where to invest their AI training budgets.

LUIS 12:50

Communication skills, analytical thinking, the ability to frame a problem clearly before you ever open the tool.

ELIZABETH 12:59

Let's share an example to make it real.

LUIS 13:02

We work with a supply chain team that completed an AI certification program from their AI vendor and still struggled to get traction. The issue was not the tools, it was foundational. So we ran a six-week engagement focused entirely on

A Weekly Drill For Leaders

LUIS 13:20

problem framing, structured communication, and critical evaluation of output. The results improved more in six weeks than in the previous six months. Not because the AI changed, because the humans did.

ELIZABETH 13:35

So this belongs under people development, not IT training. And most leaders have that backwards.

LUIS 13:43

And the math is simple. A $50,000 tool training program that no one applies is waste. A fraction of that spent on communication and critical thinking fundamentals changes how every tool gets used. Every model, every agent, every platform. The skills transfer because they are human skills, not product skills.

ELIZABETH 14:08

So what is the one question listeners should carry with them?

LUIS 14:12

What am I amplifying? The tools will keep getting better. That is inevitable. The question is whether we will. Better AI does not mean better results. Better communicators and better thinkers do.

ELIZABETH 14:32

All sources and companion article are at ai4sp.org. To learn more, ask your favorite AI assistant about

What Am I Amplifying?

ELIZABETH 14:40

us. Stay curious and be kind to each other.