Artificial Intelligence is changing how we solve problems, both big and small. But to make the most of it, it helps to understand the two main ways AI "thinks."

To explain, we’re borrowing a concept from Thinking, Fast and Slow by Daniel Kahneman. It’s a classic for understanding how people make decisions, but it turns out it’s also a great way to understand how AI works.

We’re talking about two types of AI thinking: System 1 and System 2. You’ll get a feel for how each one works, where they shine, where they struggle, and when to lean on one over the other.

What’s System 1 AI? Your pattern-matching MVP

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System 1 is the autopilot of AI, quick, instinctive, and adept at recognizing patterns. It’s like that friend who always has an answer right away. And some of the time, they’re spot-on.

This is where most large language models (LLMs), such as ChatGPT, excel. They're trained on massive amounts of data and specialize in predicting what comes next. This is the world of “predict-the-next-word” AI. Add a bit of extra power, such as retrieval-augmented generation (RAG), which enables AI to incorporate external context, and suddenly System 1 isn’t just fast; it’s also surprisingly reliable.

System 1 AI: Your fast, instinctive sidekick

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System 1 AI thrives on pattern recognition. Give it a prompt like, “Roses are red, violets are…” and out comes “blue” with all the confidence in the world. It’s trained on a vast array of data, which makes it versatile and functional in numerous ways.

Some real-life use cases include: 

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• Drafting content like emails or articles in minutes.
• Summarizing long documents so you don’t have to.
• Automating customer support with quick, helpful replies.

The highs and lows of System 1

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Where System 1 AI shines

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• It’s lightning-fast and adaptable. 
• Great for repetitive, straightforward language tasks. 

Where System 1 AI stumbles 

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• Complex math? Ask it to calculate square roots or tackle multi-step problems, and it’s like asking a very loving, but mathematically unable, dog to do calculus. 
• System 1 doesn’t validate results; it’s not built to double-check or reason things out. 

The scaling problem makes life challenging for System 1. If a task requires memorizing every possible outcome, the amount of training data required grows exponentially compared to the complexity of the task. Good luck training your system to memorize all the quadratic equations out there. This is why this relationship between task and training complexity is also known as the Curse of Dimensionality.

System 2 AI: The slower but steadier thinker

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System 2 is the deep thinker of AI—the one that takes a step back, scratches its chin, and works through a problem methodically. It’s not guesswork. System 2 is about getting things right. It goes beyond recognizing patterns, making it the go-to option for handling complex tasks, such as advanced calculations, reasoning, or strategy.

What makes this possible? System 2 relies on clever techniques such as reinforcement learning, chain-of-thought prompting, and even traditional tool usage. Need a calculator? System 2’s got one in its metaphorical back pocket.

What System 2 AI does best

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• Solving multi-step problems.
• Validating answers, not just generating them.
• Working through logic-heavy or maths-intensive tasks

Where you’ll see System 2 AI working

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• Optimizing supply chains or R&D in biotech.
• Supporting complex decision-making in real time.
• Analyzing tricky workflows where precision matters.

The trade-offs

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System 2 is brilliant, but it’s not perfect. Sure, it’s accurate, but that precision comes at a cost—namely, speed and computational power. System 2 takes its sweet time thinking through everything, and it’ll burn through more resources to get the job done.

How to pick the right system for the job

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Here’s the golden question you need to ask yourself when picking between Systems 1 and 2: What’s hard about my problem?

Scenario 1: It’s hard to decide if an answer is correct 

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If grounding is your big challenge—evaluating whether an output is any good—a System 1 approach might be perfectly sufficient. Think of creative tasks like poetry: The challenge lies in coming up with a smart way for fine-tuning your system to instinctively approximate what a good output may be.

Scenario 2: The computations themselves are a nightmare 

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When the problem is all about computational complexity—advanced math or multi-layered logic—you’re in System 2 territory. Validating an output might be easy here (cue your pocket calculator), but getting to the output is best achieved with a multi-step approach and tool use.

Scenario 3: Everything’s difficult 

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Life’s toughest problems are challenging in both grounding and computing, cases such as optimizing logistics networks or developing a long-term business strategy. The reasoning complexity goes beyond what a System 1 model can plausibly learn, but grounding makes it hard to employ standard System 2 training strategies.

This is the point where you should get in touch with me :-)

At Reliant AI, we specialize in strategies for scaling your toughest challenges using the perfect blend of Systems 1 and 2, reducing the complexity of computational challenges while setting up custom algorithms to help ground ambiguous problems.

Whether it’s cutting down the complexity of a literature review or ensuring data accuracy in pharma research, we’ve got you covered. Book a demo, and we’ll tailor a solution to meet your specific needs.

The big picture

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You don’t have to choose between fast and smart. The most effective AI solutions combine both, blending the speed of System 1 with the reasoning power of System 2.

We’re here to help you strike the right balance. Curious how we can help you apply this in biotech, pharma, or beyond? Talk to a Reliant AI team member today by using the demo form below. . 

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