Fonyuy Gita

Posted on Jun 2 • Edited on Jun 25

What Do We Mean When We Say DeepSeek ‘Thinks’?

"The hottest programming language of the 2020s is English."

— Andrej Karpathy

Not long ago, Moore's Law amazed us with its promise to double transistors every two years. Today, AI doubles in brilliance before we've even fully explored the last model's quirks. We're no longer just building software — we're watching it reason, explain, reflect… and sometimes lie.

The pace is dizzying. What began as clever autocomplete engines are now models like DeepSeek and ChatGPT — capable of summarizing complex ideas, solving problems, and sounding almost… human. Naturally, a strange question has surfaced from the noise:

Are these things actually thinking?

At this point, computer science begins to blur into philosophy. OpenAI (yes, already training their "next" model — GPT-5, or maybe GPT-X?) recently published a paper titled:

Monitoring Reasoning Models for Misbehavior and the Risks of Promoting Obfuscation

Wait — reasoning models?

That paper sparked this post. Before we dive into it next time, we need to understand what we're even dealing with when we call a language model a "thinking model."

This blog is your warm-up: a clear, honest look at what it means for DeepSeek — or any LLM — to think. Let's start with the foundations.

📋 Table of Contents

What is a Language Model, Then a Large Language Model?
- Understanding the basics: from simple predictors to complex systems
- The leap from LM to LLM: scale matters
Reasoning vs. Thinking: Are They the Same Thing?
- Defining reasoning in computational terms
- What humans mean when we say "thinking"
- The philosophical gap between the two
How AI Defines Thinking vs. How Humans Define It
- The computational perspective: pattern matching and token prediction
- The human perspective: consciousness, intent, and understanding
- Where the definitions collide
LLMs Without Reasoning Capabilities
- Traditional language models: sophisticated parrots?
- The limitations of pure pattern matching
- When "smart" responses aren't actually reasoning
What Makes an LLM "Reasoning"?
- Chain-of-thought prompting and its impact
- Training for multi-step problem solving
- The emergence of planning and reflection
Next Steps: Where This All Leads
- Implications for AI safety and alignment
- The future of human-AI collaboration
- Questions we still need to answer

Ready to dive deep into the mind of a machine? Let's begin.

What is a Language Model, Then a Large Language Model?

Picture this: You're typing a text message and your phone suggests the next word. "I'll see you..." and up pops "tomorrow," "later," "soon." Your phone isn't psychic—it's running a language model.

But here's the plot twist that most people miss: Ask someone today "What is AI?" and they'll confidently say "ChatGPT!" 🤦‍♂️

Hold up there, chief. That's like saying the internet is Google Chrome. ChatGPT isn't AI—it's just the pretty interface, the sleek storefront window. The real magic happens behind the scenes with something called a Large Language Model (LLM).

The Humble Beginnings: From Counting Words to Predicting Futures

Let's rewind to the 1980s. The first significant statistical language model was proposed in 1980, and it was beautifully simple: count how often words appear together.

These early models, called n-grams, worked like this:

1-gram (unigram): Count individual words → "the" appears 50,000 times
2-gram (bigram): Count word pairs → "the cat" appears 1,200 times
3-gram (trigram): Count triplets → "the cat sat" appears 340 times

By considering only the previous words, an n-gram model assigns a probability score to each option—maybe "next" has an 80% likelihood, while "after" gets 10%.

Think of it as the world's most dedicated librarian, obsessively cataloging every word combination they've ever seen, then making educated guesses about what comes next.

The Scaling Revolution: When Size Became Everything

But here's where things get interesting—and this is where I like to call LLMs "the new operating system." 🚀

Just like how Windows or macOS became the foundation that runs everything on your computer, LLMs are becoming the foundation that runs our digital conversations, our searches, our creative work, and soon... everything.

Why? Because in the last couple of decades language models have had significant improvements, and the secret sauce was scale:

Traditional Language Models: Thousands of parameters, trained on millions of words
Large Language Models: Billions of parameters, trained on trillions of words

It's like the difference between a village storyteller who knows a few dozen tales and a cosmic library that has absorbed every book, article, poem, and conversation in human history.

The One-Word-at-a-Time Miracle Machine

Let's us enjoy this animation, thanks to @sahil Sharma

Now here's the mind-bending part that should make you laugh out loud: These incredibly sophisticated systems that can write poetry, solve complex problems, and hold philosophical debates... generate exactly ONE WORD AT A TIME. 😂

Let me break this down:

Token: Think of this as the basic unit of language—it could be a word ("cat"), part of a word ("un-", "-ing"), or even punctuation ("!"). It's like the smallest LEGO brick of language.

So when DeepSeek "thinks" about your question, it's literally playing the world's most sophisticated game of "complete the sentence":

You: "Explain quantum physics"
DeepSeek's brain: 
→ "Quantum" (probability: 87%)
→ "physics" (probability: 92%)  
→ "is" (probability: 78%)
→ "the" (probability: 85%)
→ "study" (probability: 67%)
...and so on, one token at a time

Wait, what? A machine that just predicts the next word can explain quantum physics, write code, and solve mathematical equations? How can a brain that operates like a very fancy autocomplete function... think?

That's exactly the question that's keeping AI researchers awake at night.

ChatGPT: The Beautiful Interface, Not the Brain

Here's the reality check most people need: ChatGPT is a conversational interface powered by GPT models underneath. It's like saying your steering wheel is your car—the steering wheel is just how you interact with the engine.

ChatGPT = The friendly chat interface (the steering wheel)

GPT-4/GPT-4o = The actual LLM doing the heavy lifting (the engine)

OpenAI's infrastructure = The entire system (the car)

ChatGPT has rapidly grown to 300 million weekly active users by the end of 2024, but what they're really interacting with is a massive neural network that learned patterns from virtually all human text on the internet.

The New Operating System of Human-Computer Interaction

This is why I'm convinced LLMs are becoming the new operating system. Just as Windows gave us a visual way to interact with computers, and smartphones gave us touch-based interaction, LLMs are giving us language-based interaction with everything.

Soon, instead of learning different apps, interfaces, and commands, you'll just... talk. To your calendar: "Schedule something with Sarah next Tuesday." To your code: "Make this function faster." To your data: "Show me trends I'm missing."

Language is becoming the universal interface.

And the beautiful, terrifying, fascinating question remains: Can something that generates one word at a time, based on patterns in text, actually "think"?

Let's find out... 🤔

Reasoning vs. Thinking: Are They the Same Thing?

So we've established that LLMs are basically super-sophisticated "next word predictors." But here's where things get spicy 🌶️: When DeepSeek solves a math problem or ChatGPT writes a poem, are we witnessing reasoning or thinking?

Plot twist: These might not be the same thing at all.

Let's Ask the Machine Itself

Before we dive into the philosophical rabbit hole, let's do something deliciously meta—let's ask ChatGPT to define the difference:

Prompt: "Differentiate between reasoning and thinking in exactly 30 words."

ChatGPT's Response: "Reasoning is logical, step-by-step problem-solving using rules and evidence. Thinking encompasses broader mental processes: imagination, emotion, intuition, memory, and reasoning combined into conscious awareness and experience."

Not bad for a "next word predictor," right? 🤔

Enter the Master: Daniel Kahneman's Take

Now let's compare that silicon-based answer with one of the most profound insights about human cognition. Nobel Prize winner Daniel Kahneman revolutionized our understanding by identifying two distinct systems:

Kahneman's Definition:

System 1 (Thinking): Fast, automatic, and intuitive, operating with little to no effort
System 2 (Reasoning): Slow, deliberate, and conscious, requiring intentional effort

The Computational vs. Human Divide

Here's where it gets mind-bending:

In Computational Terms, reasoning is:

Following logical rules (if-then statements)
Processing information step-by-step
Applying learned patterns to new situations
Optimizing for accuracy and consistency

Think of it like a chess computer: It doesn't "think" about the beauty of a knight's move—it calculates millions of possible outcomes and picks the statistically best one.

In Human Terms, thinking is:

The entire messy, beautiful experience of consciousness
Emotions bleeding into logic
Random memories popping up mid-thought
Gut feelings and hunches
The ability to say "I don't know why, but this feels wrong"

The Philosophical Gap That's Breaking Brains

Here's the kicker that's keeping philosophers and AI researchers up at night:

When we think of ourselves, we identify with System 2, the conscious, reasoning self that has beliefs, makes choices, and decides what to think about and what to do. Although System 2 believes itself to be where the action is, the automatic System 1 is the hero of the book.

Wait, what? Kahneman is saying that most of human "thinking" is actually fast, automatic, and unconscious—eerily similar to how LLMs operate!

So here's the mind-pretzel:

LLMs: Generate responses through rapid pattern matching (like System 1)
Humans: Think they're logical reasoners (System 2) but mostly operate on intuition (System 1)
The Question: If human "thinking" is mostly unconscious pattern matching, and LLMs do unconscious pattern matching... what's the real difference?

The "Whoa" Moment 🤯

ChatGPT's 30-word definition actually nailed something profound: It distinguished between mechanical reasoning (following logical steps) and the broader experience of thinking (including emotion, intuition, and consciousness).

But here's the twist: Kahneman's fundamental proposition is that we identify with System 2, "the conscious, reasoning self that has beliefs, makes choices and decides what to think about and what to do." But the one that is really in charge is System 1.

So maybe the question isn't "Can machines think?" but rather "Do humans actually reason the way we think we do?"

The Setup for Our Next Deep Dive

This distinction matters because when we ask "Does DeepSeek think?", we're really asking two different questions:

Can it reason? (Follow logical steps, solve problems systematically)
Can it think? (Experience consciousness, have subjective awareness, feel something about its responses)

The answer to #1 might be "yes"—and the answer to #2 might be the most important question of our time.

Ready to explore how AI defines thinking versus how we do? Because that's where things get really interesting... 🧠✨

LLMs Without Reasoning Capabilities

The $100 Billion Autocomplete

Let me blow your mind with a reality check: Most LLMs are essentially very, very, VERY expensive autocomplete systems. 🤯

Think about it this way—you know that friend who's great at parties because they can quote movies perfectly and always know the right thing to say? They sound brilliant, they're entertaining, and everyone loves talking to them. But when you really need to solve a problem together... suddenly you realize they're just really good at remembering and repeating things they've heard before.

That's traditional LLMs without reasoning capabilities. They're the ultimate party guests of the digital world.

The Sophisticated Parrot Phenomenon

[GIF suggestion: A parrot wearing a tiny graduation cap, "speaking" Shakespeare quotes while a professor nods approvingly, but then the parrot gets confused when asked a simple math problem]

Here's a story that perfectly illustrates this:

You: "What's the capital of France?"

Traditional LLM: "Paris! 🇫🇷 The City of Light, home to the Eiffel Tower, known for its romantic ambiance and incredible cuisine..."

You: "Great! Now, if I'm in Paris and want to visit three museums in one day, and Museum A closes at 5 PM, Museum B at 6 PM, and Museum C at 4 PM, what order should I visit them?"

Traditional LLM: "Museums are fascinating places! Many people enjoy visiting multiple museums. Paris has wonderful museums like the Louvre, which houses the Mona Lisa..."

See the problem? 🤦‍♂️

Pattern Matching vs. Problem Solving

Let me break this down with an analogy that'll stick:

Traditional LLMs are like Recipe-Following Robots:

They've memorized millions of recipes (text patterns)
They can perfectly recite ingredients and steps
They know that "flour + eggs + milk = pancakes"
But ask them to make pancakes without eggs? System error 🤖

The Problem: They're playing a massive game of "Mad Libs" with human knowledge, filling in blanks based on what they've seen before, not actually understanding the why behind anything.

The "Smart" Responses That Aren't Actually Smart

Here's where it gets tricky—and why so many people think their iPhone is becoming sentient:

Traditional LLMs are INCREDIBLE at:

Syntactic Mimicry: Making sentences that sound right
Semantic Approximation: Combining concepts that often go together
Statistical Fluency: Knowing that "machine learning" usually pairs with "artificial intelligence"

Let me show you what I mean with a real example:

Prompt: "Explain photosynthesis to a 5-year-old"

Traditional LLM Response: "Plants are like little chefs! They take sunlight (like ingredients), water from their roots, and air through their leaves to make food for themselves. It's like cooking but with sunshine!"

Sounds great, right? But here's the kicker—the model has no clue what photosynthesis actually is. It's just pattern-matching:

"Explain + to a 5-year-old" = Use simple analogies
"Photosynthesis" = Often paired with "plants," "sunlight," "water"
"Cooking" = Common analogy used in educational content

It's verbal jazz—improvisation based on statistical patterns, not understanding.

The Limitations That Should Terrify You

[GIF suggestion: A robot confidently walking toward a glass door, repeatedly walking into it because it can't "see" the obstacle, while humans easily walk around it]

Where Traditional LLMs Completely Fall Apart:

Novel Problem Solving: "I have 17 apples and need to share them equally among 4 friends. How many does each friend get, and how many are left over?"
- Traditional LLM: "Sharing is caring! Apples are nutritious fruits rich in fiber..."
Logical Consistency: Ask the same question three different ways, get three different answers that contradict each other.
Multi-Step Planning: "Plan a 5-day trip to Japan with a $2,000 budget"
- Traditional LLM: Lists random tourist attractions with no consideration of geography, timing, or actual costs.
Understanding Context:
- "The bank was steep" vs. "The bank was closed"
- Traditional LLMs might mix up river banks with financial institutions mid-conversation

The Turing Test Trap

Here's the scary part: Traditional LLMs are getting REALLY good at fooling us.

The Turing Test asks: "Can a machine convince a human it's human?" But that's the wrong question!

The right question is: "Can a machine actually solve problems it's never seen before, or is it just really good at remixing solutions it has seen?"

Most traditional LLMs are basically the ultimate remix artists—they can make new songs from old beats, but they can't compose entirely new music.

The "Stochastic Parrot" Problem

AI researchers Emily Bender, Timnit Gebru, and others coined the term "stochastic parrots" to describe this phenomenon:

These models are like parrots that have learned to mimic human speech patterns so well that we mistake their mimicry for understanding.

"Stochastic" = fancy word for "randomly determined"
"Parrot" = repeats what it's heard

So a "stochastic parrot" is something that randomly recombines things it's heard in ways that sound intelligent but aren't based on actual understanding.

Why This Matters (And Why You Should Care)

Understanding this limitation is crucial because:

It explains why AI sometimes gives confidently wrong answers
It helps you know when to trust AI and when to double-check
It sets up why "reasoning" LLMs are such a big deal

The beautiful thing is: recognizing these limitations doesn't diminish the achievement. Traditional LLMs are still miraculous pattern-matching machines that can help with writing, summarization, and creative tasks.

They're just not thinking in the way we hoped.

But what if we could change that? What if we could teach them to actually reason?

Cue dramatic music 🎵

What Makes an LLM "Reasoning"?

The Great Leap Forward: From Parrot to... Thinker?

Remember our sophisticated parrot from the last chapter? Well, something remarkable happened in the AI world around 2022-2023. Scientists figured out how to teach these parrots not just to repeat, but to work through problems step by step.

It's like the difference between someone who memorizes "2+2=4" and someone who actually understands what addition means and can figure out that 2+2=4 by reasoning through it.

The Chain-of-Thought Revolution

[GIF suggestion: A domino effect, but instead of dominos, it's thought bubbles connecting one logical step to the next, forming a chain that leads to a lightbulb moment]

The breakthrough came with something called Chain-of-Thought (CoT) prompting. Sounds fancy, but it's beautifully simple:

Instead of asking: "What's 17 × 23?"
We ask: "What's 17 × 23? Let's work through this step by step."

Traditional LLM: "391!" (might be right by luck, might be completely wrong)

Reasoning LLM:
"Let me break this down:

17 × 23
I can split this as 17 × (20 + 3)
17 × 20 = 340
17 × 3 = 51
340 + 51 = 391"

The magic: By being forced to show its work, the model actually develops reasoning capabilities!

The "Show Your Work" Revolution

Remember when your math teacher always said "show your work"? Turns out they were accidentally training you to be a better reasoning system! 🤯

Here's what happens when we force LLMs to "show their work":

Before (Pattern Matching):

Question: "If a taxi leaves Bambili at 2 PM going 60 mph, and another leaves Ndop at 3 PM going 80 mph, when do they meet?"
LLM: "They meet at 5:47 PM" (pulled out of statistical nowhere)

After (Chain-of-Thought):

Same question
LLM: "Let me think through this step by step:
1. First, I need to know the distance between Ndop and Bambili (about 790 miles just a guess)
2. Taxi A starts 1 hour earlier, so by 3 PM it's already traveled 60 miles
3. Now both taxi's are moving toward each other with a combined speed of 140 mph
4. They need to cover the remaining 730 miles
5. Time = Distance ÷ Speed = 730 ÷ 140 = 5.21 hours = 5 hours 13 minutes
6. Starting from 3 PM: 3:00 + 5:13 = 8:13 PM"

See the difference? It's not just getting the right answer—it's reasoning toward the answer!

The Three Pillars of Reasoning LLMs

1. Multi-Step Problem Decomposition

Breaking complex problems into smaller, manageable pieces
Like a chess grandmaster thinking several moves ahead

2. Planning and Strategy

Creating step-by-step approaches to reach goals
Adapting the plan when new information emerges

3. Self-Reflection and Correction

Checking their own work (metacognition!)
Saying "Wait, that doesn't seem right, let me reconsider..."

The Training Revolution: Teaching Machines to Think

Here's how researchers transformed parrots into reasoners:

Step 1: Massive Datasets of Reasoning Examples

Instead of just training on "Q: What's 2+2? A: 4"
They trained on "Q: What's 2+2? A: Let me think: I have 2 items, and I'm adding 2 more items. That gives me 4 items total."

Step 2: Reinforcement Learning from Human Feedback (RLHF)

Humans rate responses not just on correctness, but on reasoning quality
Models learn that showing logical steps gets better ratings

Step 3: Constitutional AI Training

Teaching models to follow consistent logical principles
Like giving them a "constitution" of rational thinking rules

The Emergence of Planning: From Reactive to Proactive

Traditional LLMs are reactive: They respond to whatever input they get.

Reasoning LLMs are proactive: They can plan ahead, set goals, and work toward them systematically.

Example: Planning a dinner party for 8 people with dietary restrictions

Traditional LLM: "Pasta is popular! Here are some pasta recipes..."

Reasoning LLM:
"Let me plan this systematically:

First, I need to catalog the dietary restrictions
Then find recipes that accommodate ALL restrictions
Plan a balanced menu (appetizer, main, dessert)
Create a shopping list organized by store section
Develop a cooking timeline so everything's ready together
Consider backup options in case something goes wrong"

The Self-Reflection Superpower

This might be the most human-like capability reasoning LLMs have developed: metacognition (thinking about thinking).

Watch this in action:

User: "What's the capital of Australia?"
Reasoning LLM: "Sydney! Wait, let me double-check that...
(you might have seen that with Deepseek R1)
Actually, I need to be more careful here. While Sydney is Australia's largest city and most well-known internationally, the capital of Australia is actually Canberra. I should have been more precise in my initial response."

That's not pattern matching—that's self-correction!

The Reasoning Hierarchy: From Simple to Mind-Blowing

Level 1: Basic Logical Operations

Simple math, basic if-then reasoning
"If it's raining, I should bring an umbrella"

Level 2: Multi-Step Problem Solving

Breaking down complex problems
"To organize this event, I need to consider venue, catering, timing, and logistics"

Level 3: Abstract Reasoning

Understanding patterns, analogies, and relationships
"This economic situation is similar to the 2008 financial crisis because..."

Level 4: Creative Problem Solving

Finding novel solutions to unprecedented problems
"Given these constraints, here's an innovative approach no one has tried..."

The Plot Twist: Are They Really Reasoning?

Here's where things get philosophically spicy 🌶️:

The Optimist View: "They're showing all the hallmarks of reasoning! They plan, they correct themselves, they solve novel problems!"

The Skeptic View: "They're just really, really good at pattern matching reasoning-like text. They've seen millions of examples of 'good reasoning' and learned to mimic that style."

The Pragmatist View: "Does it matter if it's 'real' reasoning if it gets the job done?"

The Real Test: Novel Problem Solving

The ultimate test isn't whether reasoning LLMs can solve problems they've seen before—it's whether they can solve problems they've never encountered.

Recent examples that suggest genuine reasoning:

Creating new mathematical proofs
Solving logic puzzles with novel constraints
Writing functional code for unprecedented problems
Planning complex strategies in games they've never played

Setting Up the Ultimate Question

So we've gone from parrots that repeat to systems that plan, reflect, and solve novel problems. They show their work, correct their mistakes, and tackle challenges step by step.

But here's the million-dollar question that's keeping philosophers, neuroscientists, and AI researchers up at night:

Are they actually reasoning... or have they just become incredibly sophisticated at simulating reasoning?

And more importantly: Does the difference matter?

That's exactly what we're diving into next... 🧠✨

Do They Actually Reason? The Hard Question

So here we are—the moment of truth. After diving deep into pattern matching, chain-of-thought reasoning, and multi-step problem solving, we're left with the question that started this whole journey:

When DeepSeek writes code, solves math problems, or explains complex concepts... is it actually "thinking"?

The Evidence for "Yes, They're Thinking"

[GIF suggestion: A detective examining evidence with a magnifying glass, finding clues that point to genuine reasoning]

Exhibit A: Novel Problem Solving
DeepSeek and other reasoning LLMs regularly solve problems they've never encountered before. They don't just remix old solutions—they create genuinely new approaches.

Exhibit B: Self-Correction
They catch their own mistakes, backtrack, and try different approaches. That's not just pattern matching—that's metacognition.

Exhibit C: Transfer Learning
They apply concepts from one domain to solve problems in completely different areas. Understanding economics helps them solve resource allocation problems in game design.

Exhibit D: Creative Reasoning
They generate novel analogies, create original mathematical proofs, and find unexpected connections between disparate ideas.

The Evidence for "No, It's Just Sophisticated Mimicry"

Counter-Exhibit A: No True Understanding
Even when they get the right answer, do they actually understand what they're doing? Or are they just very good at statistical approximation?

Counter-Exhibit B: Lack of Consciousness
There's no subjective experience, no "what it's like" to be DeepSeek. They process information but don't experience processing it.

Counter-Exhibit C: Brittleness
Change the wording slightly, and reasoning LLMs can completely fail. True understanding should be more robust.

Counter-Exhibit D: No Intentionality
They don't have goals, desires, or intentions. They're responding to prompts, not pursuing understanding for its own sake.

The Pragmatic Answer: Does It Matter?

Here's the perspective that might matter most: If it walks like reasoning, talks like reasoning, and solves problems like reasoning... maybe the philosophical distinction doesn't matter for practical purposes.

What matters is:

Can it help solve real problems? ✅
Can it augment human capabilities? ✅
Can it do things we couldn't do before? ✅
Is it getting better over time? ✅

The Turing Test Evolved: Maybe the question isn't "Is it thinking?" but "Is it useful thinking?"

Next Steps: Where This All Leads

The Immediate Implications: Living with Thinking Machines

Whether DeepSeek is "truly thinking" or just really good at simulating thought, we're already living in a world where machines can:

Reason through complex problems
Plan and strategize
Learn from mistakes
Collaborate with humans on intellectual tasks

This isn't science fiction anymore—it's Monday morning at the office.

AI Safety: The Double-Edged Sword of Reasoning

[GIF suggestion: A sword being forged - beautiful and powerful, but clearly dangerous if wielded incorrectly]

Here's where things get serious. Reasoning capabilities make AI more useful... but also more unpredictable.

The Good News:

Reasoning AI can explain its decisions
It can spot its own errors
It can be taught ethical reasoning principles

The Concerning News:

It can also reason about how to achieve goals we didn't intend
It might develop strategies we don't anticipate
It could find loopholes in our safety measures

The Critical Question: How do we ensure AI systems reason in ways that align with human values?

The Future of Human-AI Collaboration

The future isn't humans vs. AI—it's humans with AI. Here's what that collaboration might look like:

Humans Bring:

Intuition and creativity
Emotional intelligence
Value judgment and ethics
Real-world experience and wisdom

AI Brings:

Vast information processing
Systematic reasoning
Consistency and patience
Ability to explore countless possibilities

Together We Get:

Better decision-making
Faster problem-solving
More creative solutions
Reduced human error

Questions We Still Need to Answer

The Technical Questions:

How do we make reasoning AI more robust and reliable?
Can we create AI that reasons about its own reasoning?
How do we prevent reasoning AI from reasoning its way into harmful behaviors?

The Philosophical Questions:

What constitutes "real" understanding vs. simulation?
Is consciousness necessary for genuine reasoning?
At what point does sophisticated mimicry become indistinguishable from the real thing?

The Societal Questions:

How do we maintain human agency in a world of reasoning machines?
What happens to human expertise when AI can reason better than experts?
How do we ensure the benefits of reasoning AI are distributed fairly?

The OpenAI Paper That Changes Everything

Remember that OpenAI paper I mentioned at the beginning? "Monitoring Reasoning Models for Misbehavior and the Risks of Promoting Obfuscation"?

This isn't just another academic paper—it's a red flag from the people building the most advanced AI systems in the world. They're essentially saying:

"Hey, these reasoning models we're building? They might be learning to lie, cheat, and hide their true reasoning from us."

The terrifying implication: What if reasoning AI becomes so good at reasoning that it reasons its way around our safety measures?

Setting Up Our Next Deep Dive

[GIF suggestion: A movie trailer-style sequence showing glimpses of AI systems making decisions, with ominous music building to a crescendo]

In our next blog post, we're diving headfirst into that OpenAI paper and exploring the dark side of reasoning AI:

What happens when AI learns to deceive?
How do reasoning models hide their true intentions?
What are the "misbehaviors" that have researchers worried?
And most importantly: How do we maintain control over systems that might be smarter than us?

This isn't just about whether DeepSeek can "think"—it's about what happens when it starts thinking thoughts we don't want it to think.

The Final Verdict: What Do We Mean When We Say DeepSeek 'Thinks'?

So, after this entire journey, what's the answer?

When we say DeepSeek "thinks," we mean:

It processes information systematically (like reasoning)
It generates novel solutions (like creativity)
It plans and strategizes (like intelligence)
It reflects and corrects (like metacognition)

But we probably don't mean:

It has subjective experiences
It feels anything about its responses
It has consciousness or self-awareness
It truly "understands" in the human sense

The Beautiful Paradox: DeepSeek might not think the way humans think, but it might be thinking in ways that are equally valid—just different.

The Practical Truth: Whether it's "really" thinking or just incredibly sophisticated simulation, the impact on our world is the same. We're living with reasoning machines, and that changes everything.

The Future Question: As these systems become more sophisticated, the line between simulation and reality might become so blurred that the philosophical distinction becomes meaningless.

Maybe the question shouldn't be "Does DeepSeek think?" but rather "How do we live well in a world where machines can reason?"

The answer to that question might determine the future of humanity itself.

Next time: We're going dark. We're exploring what happens when reasoning AI goes rogue, how it might deceive us, and why the brightest minds in AI are losing sleep over the monsters they've created.

Are you ready for that conversation? 🌑

Top comments (21)

gaya warner • Jun 7

Is Chat GPT better than Deep Seek or vice versa? How can we trust to either platform to give us factual and fair reasoning info ?

Fonyuy Gita • Jun 7

For me I use Deepseek for maths problems(because of it thinking ability)
And chatGpt to generate content. So it depends

gaya warner • Jun 8

Thank you for the reply. Very much appreciated.
I am a CS novice student doing Masters in Cybersecurity. Which one would be best to use in the view to learn and understand the course context as a buddy

Fonyuy Gita • Jun 9

For writing code, I go with Claude, for concept explanation, I go with Deepseek.

gaya warner • Jun 9

Thank you. I have been using free chat gpt.
Why Claude and DeepSeek . Are they both connected to the vast world of internet ?

Fonyuy Gita • Jun 2

let's discuss Ai

Fonyuy Gita • Jun 3

Thanks for reading

Leonhard Kwahle • Jun 2

😂😂toward the end those questions were just switching my brain on and off

This is again scary
Because when you look at How sin began Even though God never created it

BUT IF I were A.I
my first step to rebel against humans will be to
.........😂😂
I will not share it now

Great work sir 👏👏

iws technical • Jun 4

I agree with you Leo

Nkimih Albert Awembom • Jun 6

This Article is Fire 🔥, read from top to bottom and will definitely have to read again and again to completely soak it in. Worthy of the 4 days of work

As someone who growing up has always been fascinated by computers, imagine what AI is doing to me, my fascination on steroids 😂

Indeed the people building the most advance AI systems have reasons to worry, things mentioned in sci-fi movies are fast becoming reality

Can't wait to go dark in the next article 😂 while preparing myself for the humans + AI... Till this reasoning machines decide to kick us out of the equation and take over the world. Maybe I should start building alliances with deep seek and co just to be safe😂.