3 Reasons to Pay Attention to ‘Digital Biology’ as a Key Field for the AI Era

"Should I learn to code?" This question is no longer unusual. Recently, NVIDIA CEO Jensen Huang emphasized, "AI will handle coding, so now we must cultivate domain knowledge like 'digital biology.'" In an era where AI automatically writes code, what knowledge should we truly delve into? The bio industry, in particular, is rapidly rising in importance as it is closely linked to survival issues like drug development, food, and climate.

Below, we'll explore the importance of domain knowledge in the AI era and the transformations occurring within the bio industry.

📌 If you're grappling with these questions, take note!

Even if AI writes code automatically, failing to grasp the problem's essence can lead to catastrophic errors in critical fields
. Complex domains like biology, drug development, and climate issues demand more than superficial knowledge. You must cultivate 'deep
domain expertise' by mastering field experience, tacit knowledge, and regulatory insights. If you want to stay competitive and avoid being left behind in the AI era, read this article to the end.

🧬 What is Domain Knowledge, the Core Competitive Edge in the AI Era?

Below, you can see why domain knowledge is crucial in the AI era.

Distinction	Shallow Knowledge	Domain Depth (Deep)
Scope	Surface-level information obtained through tutorials or searches	Includes field experience, failure cases, regulations, and specialized terminology
Thinking Style	"What is the API usage?"	"What are the optimal metrics and constraints?"
AI Utilization	Using AI-generated code as-is	Verify and modify AI results for practical application
Example	"Low heart rate variability indicates high stress"	Clinical criteria for heart rate variability, age/gender adjustment, sensor error consideration

📖 Why does domain knowledge become a competitive advantage?

• Focus on the essence of the problem, not grammar: Since AI writes code for us, the ability to define problems is now the core competitive advantage.
• Fields where errors are critical: Biology, finance, healthcare, etc., require deep knowledge because errors can threaten lives or lead to significant losses.
• Solving complex problems: Fields involving multiple intertwined elements like hardware and data ethics cannot be addressed with superficial knowledge alone.

In other words, the deeper the domain knowledge, the greater the ability to accurately define and solve complex problems.

🚀 AI-Driven Innovation in the Bio Industry

Discover the transformative convergence of AI and the bio industry below.

Field	Traditional Approach	Changes After AI Implementation
Drug Development	Years of experimentation and clinical trials	AI simulation and automated synthesis, dramatically accelerating research speed
Synthetic biology	Repetitive and manual processes	AI-based automated design and DNA synthesis, maximizing experimental efficiency
Climate and food solutions	Traditional and Limited Approaches	AI-based precision agriculture, enabling design of carbon-fixing microorganisms

• Drug Development: AI rapidly discovers drug candidates and increases clinical trial success rates.
• Synthetic Biology: Automated DNA printing and AI design enable gene editing in complex organisms.
• Climate and Food Challenges: AI can be leveraged to develop environmentally friendly crops or design microorganisms that efficiently sequester carbon.

🎯 3-Step Routine for Deepening Domain Expertise

The step-by-step approach to building domain knowledge is as follows.

Step	Practice Method	Goal
Literature Review	PubMed articles, patents, regulatory information exploration	Identify core keywords and latest trends
Field Research	Expert interviews, analysis of real-world cases	Acquiring tacit knowledge obtainable only in the field
Experimental Validation	Direct experimentation using AI-in-the-Loop methodology	Enhancing AI result reliability and reducing correction rates

• Literature Review: Continuously update the latest research and regulatory information to strengthen surface knowledge.
• Field research: Acquire tacit knowledge such as regulatory environments and failure patterns through expert interviews.
• Experimental Validation: Deepen knowledge through practical experience verifying AI-provided results and reducing incorrect cases.

Consistently practicing this process will show the correction rate for AI recommendations drop from 30% to below 10%.

📌 Frequently Asked Questions (FAQ)

Can I engage with AI and the bio industry even if I know nothing about coding?
Yes, domain knowledge and problem-definition skills are more important than coding. Understanding basic AI principles is sufficient.

What's the fastest way to acquire domain knowledge? The quickest
method is to interview field experts and cultivate the habit of regularly reading and analyzing the latest research papers.

What is AI's greatest impact on the bio industry? It helps solve critical
problems like drug development, food security, and climate change quickly and accurately.

How can we increase the reliability of AI recommendation results? Reliability can be
enhanced through direct AI-in-the-Loop experiments and iterative feedback processes.

How do domain knowledge and tacit knowledge differ?
Domain knowledge is information obtainable from literature, while tacit knowledge is experience gained on the ground and knowledge acquired from real-world environments like regulations.

📘 Essential Additional Information for the AI Era!

🌱 Success stories in the bio industry using AI

Case	Company	Achievement
Accelerated New Drug Development	Insilico Medicine	Shortening Drug Development Time
Climate Change Solutions	Ginkgo Bioworks	Successful Development of Carbon-Capturing Microorganisms

• Companies actively leveraging AI are rapidly growing in the market and demonstrating tangible results.
• By referencing these cases and establishing your own AI utilization strategy within your field, you can expect even greater results.

In conclusion

The era where AI handles coding has arrived. However, true competitiveness still lies in domain knowledge. The more complex and high-risk a field is, like the bio industry, the more essential it is to possess deep domain expertise. While AI boosts efficiency through automation, the power to define problems and understand the context of solutions remains a human responsibility. Cultivate your domain depth through literature reviews, field interviews, and AI-in-the-Loop experiments. This sustained effort will ultimately become your unique competitive edge, guiding you through the AI era.