A Thoughtful Learner's Guide to AI: Applying Bacon's 400-Year-Old Wisdom
Practical learning strategies inspired by a 400-year-old essay, "Of Studies," provides the crucial framework for engaging critically with AI, not just consuming its output.
TL;DR
Francis Bacon’s 400-year-old essay, "Of Studies," is packed with relevant strategies for learning with today's AI.
Critical Thinking: AI provides instant information, but Bacon stresses the importance of always "weighing and considering" its output.
Know Your 'Why' & 'How Deep': Understand if you're learning for delight, ornament, or ability, and choose your engagement level (taste, swallow, or digest) accordingly.
Experience is Irreplaceable: Ground AI's theoretical knowledge in practical application and lived experience. Don't forget the "burning the onions" lesson.
Adapt Your Learning Habits: AI changes reading (prompting), discussion (interaction), and writing (assistance). Stay active in guiding, questioning, and synthesizing.
The Real Goal = Wisdom: Aim beyond just getting quick answers; cultivate the judgment and understanding to use AI effectively.
AI vs. Deep Understanding
I've been watching this new "vibe coding" trend, where engineers use AI to generate code rapidly. As an experienced developer, I see the appeal—ship faster and focus on overall design rather than implementation. But I also see juniors struggling with codebases they don't understand. There seems to be a growing tension between productivity and deep understanding.
Then Fermat's Library dropped Francis Bacon's "Of Studies" in my inbox. Reading this 400-year-old essay with its insights on learning made me wonder - what would the great thinkers make of our AI shortcuts to knowledge? Are we building on the sand when we prioritize speed over understanding?
Bacon wrote "Of Studies" in 1625 when knowledge acquisition was slow and deliberate. Today, we get instant answers from AI. This shift isn't just about convenience - it fundamentally changes how we learn and think.
What caught my attention in Bacon's essay was his straightforward categorization of why we learn. This simple framework has made me question whether AI enhances or undermines each purpose.
The contrast between Bacon's world and ours raises crucial questions. When we use AI to generate code or solve problems, are we developing genuine understanding or just collecting solutions? Does AI-assisted learning build real expertise or create dependency?
I don't have definitive answers, but examining these questions through Bacon's framework is valuable. As a software engineer who regularly uses these tools, finding the right balance between AI assistance and deep understanding matters for the quality of what we build.
Note: For the rest of this post, I’ll use the term AI interchangeably with GenAI and LLMs for brevity.
What Bacon Got Right About Learning
Let's unpack Bacon's core ideas and see how they hold up – and where they get interestingly complicated – when confronted with modern AI.
“All animals are equal, but some are more equal than others.”
- George Orwell, Animal Farm
Reading Bacon reminds me of Orwell's famous line from Animal Farm. The same applies to learning. All information might seem equal in the age of AI, all instantly available, but some forms of knowledge – particularly those gained through deep engagement and real experience – remain "more equal" than others. The distinctions Bacon draws help us navigate this uneven terrain.
The Three Reasons We Learn (According to Bacon)
"Studies serve for delight, for ornament, and for ability."
Bacon breaks down why we bother learning anything into three simple buckets:
Delight: For personal enrichment, the quiet joy of understanding.
Ornament: To enhance conversation, to articulate ideas effectively.
Ability: To improve judgment, make better decisions, and act effectively in the world.
AI can certainly turbocharge these.
Want tailored learning for pure enjoyment? AI can personalize paths.
Need to sound smart at that dinner party? AI will give you talking points in seconds.
Need to solve a complex problem? AI will be your brainstorming buddy.
But here’s the rub: Is passively scrolling AI-generated content genuine delight, or just consumption? Is rattling off AI-supplied facts true ornament, or just echoing without understanding? Does relying on AI analysis build real ability, or foster a dependency that leaves us vulnerable when the tech fails or misleads?
These aren't just philosophical questions - they have real consequences for how we learn and work. The more we rely on AI for instant answers, the less we exercise our own mental muscles. It's like taking an Uber for a two-block walk - convenient, sure, but you're missing out on the benefits of walking.
Why Real Experience Matters More Than Data
"They perfect nature, and are perfected by experience... studies themselves, do give forth directions too much at large, except they be bounded in by experience."
Here's where Bacon really nails it. Books teach you the theory, but life teaches you how it really works.
This is where I think AI falls flat. I don't care how many billions of parameters ChatGPT has or how much of the internet it's seen - it hasn't actually lived anything. It hasn't burned dinner, gotten lost in a new city, or felt the frustration of debugging code at 2 AM.
"Crafty men contemn studies, simple men admire them, and wise men use them..."
The real skill, he argued, isn't just knowing things, but possessing the wisdom – often gained through observation – to apply that knowledge effectively.
It's the same with AI. The power isn't just having the tool, but having the wisdom to wield it. We need to ground AI's vast knowledge in the messy, tangible reality of experience.
It's like the difference between reading a travel guide and actually navigating a foreign city. AI can give you the map, the facts, even translate phrases instantly. But it's only by wandering the streets, getting a little lost, and figuring things out on the ground that you truly learn the city's rhythm and gain the confidence to explore it independently. Experience adds the dimensions AI can't capture.
Don't Just Consume – Critically Consider
"Read not to contradict and confute; nor to believe and take for granted; nor to find talk and discourse; but to weigh and consider."
AI models are prone to errors, biases, and outright making things up (those infamous ‘hallucinations’). Bacon’s plea to "weigh and consider" is our primary defense.
"Some books are to be tasted, others to be swallowed, and some few to be chewed and digested."
This brilliant metaphor offers a framework for how we should approach different types of knowledge:
Tasting is that quick sampling, giving you just enough flavor to know what's there. With traditional books, this might be reading the introduction or skimming chapter headings. With AI, it's asking for a brief overview or summary. This approach is perfect for initial exploration or for topics tangential to your main focus – but recognize that you're just getting surface-level impressions.
Swallowing goes deeper (obviously). You're consuming the whole thing, but without lingering too long over every nuance. You understand the main arguments and ideas, but you haven't necessarily interrogated every assumption or followed every implication. In the AI context, this is like reading a comprehensive AI-generated explanation of a topic and accepting its general framework, while remaining aware that you haven't verified every claim.
Digesting is the deepest level of engagement – where true mastery happens. You're not just consuming; you're breaking it down, absorbing it, making it part of you. When reading traditionally, this meant close analysis, taking notes, connecting ideas to your existing knowledge. With AI, it means actively questioning outputs, seeking multiple perspectives, verifying claims, and synthesizing the information into your own understanding. This is slow, demanding work – but it's the only way to develop genuine expertise.
Bacon specifically warned against relying solely on summaries – what he called "distilled books... flashy things." His concern was that these concentrated versions might offer the illusion of understanding without the substance. They were convenient, yes, but they removed the vital context, the supporting evidence, the qualifying nuances that build true comprehension.
This warning should ring alarm bells in our AI age. Large language models are, in essence, the ultimate distillation machines – creating summaries, extracts, and simplified versions with remarkable efficiency. The danger? That we mistake this distilled knowledge for the real thing.
Indeed, contemporary research is exploring this very dynamic, with studies suggesting that while AI boosts efficiency, it might also inadvertently reduce the depth of cognitive effort associated with traditional critical thinking and learning.
When we rely exclusively on AI-generated summaries, we sacrifice:
Contextual understanding: The background that shapes why an idea matters and where it fits
Evidentiary foundation: The supporting facts that give a claim its strength
Intellectual struggle: The cognitive work of wrestling with difficult concepts – the very struggle that cements learning
Serendipitous discovery: Those unexpected tangents and connections that often lead to our most valuable insights
AI summaries can be jumping-off points or navigational aids, but treating them as destinations leaves us with shallow knowledge – impressive perhaps in breadth, but lacking the depth that makes knowledge truly valuable.
The Learning Triangle: Reading, Conference, Writing in the AI Era
"Reading maketh a full man; conference a ready man; and writing an exact man."
Bacon distilled the core intellectual practices into a powerful triad. Let's unpack what each of these meant in Bacon's world:
Reading was about filling the mind with knowledge – becoming "full" of ideas, facts, and perspectives. Deep reading built a mental reservoir from which insight could be drawn. It was about patient accumulation, careful consideration, and the gradual building of a knowledge base.
Conference – or conversation and debate – created the "ready" mind. By engaging with others, by having our ideas challenged and being forced to defend them, we developed intellectual agility. This was knowledge-in-motion, the ability to recall and apply what we know in real-time, to adapt to unexpected challenges and questions.
Writing crafted the "exact" mind. The discipline of committing thoughts to paper revealed fuzzy thinking that might pass unnoticed in conversation. Writing forced precision – in language, in logic, in structure. It was the ultimate test of whether you truly understood something or just thought you did.
This triangle still forms the foundation of deep learning today. But now AI transforms each vertex in ways both subtle and profound:
Reading → AI Prompting: Instead of the slow accumulation of knowledge through books, we can instantly query AI. This brings tremendous breadth, but potentially at the cost of depth. The effortlessness of AI-supplied information might mean we engage less deeply, question less rigorously, remember less durably.
Conference → AI Interaction: While AI can simulate discussion, it lacks the unpredictability of human conversation. Real debate involves emotion, unexpected challenges, and the social dynamics that push us to clarify and defend our thinking. AI rarely truly surprises us or forces us to reconsider in the way a human interlocutor might.
Writing → AI Assistance: When AI helps draft our writing, we may bypass the crucial cognitive work that writing demands. The act of organizing thoughts, finding precise language, structuring arguments – these processes don't just communicate ideas, they clarify them for the writer. When AI handles these tasks, we might produce polished output without the mental clarification that writing traditionally provides.
The key is reclaiming the human role in each step. We must guide the prompting (active reading), critically interrogate the AI's responses (real conference), and take ownership of the final synthesis (true writing). Otherwise, we risk becoming what Bacon warned against: seeming knowledgeable without substance, relying on "much cunning, to seem to know, that he doth not." AI can be a convincing illusionist.
Practical Strategies for the AI Age
So, how do we translate Bacon's 400-year-old wisdom into concrete actions with today's tech? Here’s a playbook:
Be the Skeptic: Question Everything
Make "weigh and consider" your mantra. Treat AI output as a starting point, not the final word.
Develop a deeper verification process for our complex world:
First-level verification: Does this align with what you already know? Does it pass a basic logic and plausibility check?
Second-level verification: What are the sources? Not just which sources, but what kinds of sources? Primary research? Expert consensus? Opinion?
Third-level verification: Seek contrary perspectives. What would someone who disagrees say? What evidence might they cite?
Remember that in domains with deep complexity (science, medicine, law), surface-level verification isn't enough – peer into the methodology, the sample sizes, the underlying assumptions behind claims.
When stakes are high, triangulate information through multiple channels – academic papers, expert commentary, and diverse AI models with different training approaches.
Know Your Why: Define Your Goal
Are you here for delight, ornament, or ability? Be clear about your purpose.
Match your engagement level to your goal: Need a quick definition (taste)? Exploring a new topic (swallow)? Building deep expertise (digest)?
Use AI strategically: great for brainstorming, summarizing dense text, or drafting initial ideas, but always follow up with your own critical thinking.
Mix It Up: Balance Your Learning Diet
Don't let AI replace everything. Books, articles, human conversations, and hands-on projects are still vital.
Seek out real discussion ("conference") with other humans. Challenge ideas, debate nuances – AI can't replicate this.
Keep writing your own thoughts ("writing"). The struggle to articulate ideas clearly is where true "exactness" is forged.
Get Real: Connect to Experience
Look for ways to apply what you learn via AI to the real world. Build something, try something, test the theory.
Does the AI's insight match what you observe? Experience is the ultimate fact-checker.
Learn to trust your gut and your lived experience, especially when it clashes with an AI suggestion. Don't be afraid to disagree with the algorithm.
Finding the Right Balance
It's fascinating: Bacon gave us a blueprint for navigating the challenges of knowledge in an age of overwhelming information. His core message wasn't about what to learn, but how to learn – with purpose, criticality, and a grounding in reality.
I often find myself accumulating more technical books and papers than I can realistically read, which used to make me feel guilty. But Bacon's perspective is actually liberating - it's not about consuming everything, but about engaging deeply with what matters. In an era where AI can instantly surface any information, the difference between shallow browsing and genuine understanding becomes the critical factor.
I'm not against AI tools - I use them regularly. But there's a clear distinction between using AI as a substitute for understanding versus using it as a tool to enhance learning. When I ask AI to generate code without understanding the implementation, I might ship faster but build technical debt. When I use it to explore approaches while maintaining fundamental knowledge, I become a more effective engineer.
As these tools become more integrated into our workflows, Bacon's principles serve as a practical framework for ensuring we're using technology to augment rather than replace our thinking. Getting information is now trivial. The challenge - and what separates good engineers from great ones - is developing the judgment to use that information effectively.
Dig Deeper
The Source: Of Studies by Francis Bacon
Learn How to Learn: Learning How to Learn: Powerful mental tools to help you master tough subjects (Coursera Course)
Share Your Thoughts
I'd love to hear how you're navigating this new landscape. Share your experiences in the comments:
How is AI changing your approach to learning?
Which of Bacon's ideas – delight/ornament/ability, weigh/consider, experience – hits home most for you when using AI?
Do you have specific practices for maintaining deep understanding while leveraging these tools?
I was once told, “Once you understand the quality of anything, quantity doesn’t bother you anymore.”
Lately, the constant pressure of what to learn each day feels draining—especially in the age of AI, where the pace of change can be overwhelming.
But this article offers a refreshing perspective. It shifts the focus from what to learn to how to learn—emphasizing quality over quantity. That shift alone makes the learning journey feel less intimidating and more intentional.
This was super insightful. It echoed some of my thoughts around learning the traditional way and learning with AI and more. Especially the bit about traditional learning and going off on a tangent and landing on something totally different. I loved reading it. 🙏🏽