5/5 (1)

I have explored generative AI tools like most students to make my life easier by summarizing articles and seeking simple explanations for complicated concepts and frameworks discussed in class. It is almost as if we have a digital tutor who has all the knowledge of the internet to answer all your questions. Just when I thought that GenAI has peaked, my friends who work as software developers shared how it has has a transformative impact on their day to day activities as they leverage it to generate code. There are many founders and enthusiasts with no formal training in computer science who now “vibe code” to build apps and tools for their companies as well. Tools like GitHub Copilot, CodeMakerAI, and Cursor have democratized software development and reshaped what it means to “code” in the post GenAI era.

Recent research suggests that developers using GitHub Copilot code 55% faster than those not using AI, raising the completion rate from 70% to 78%. I found these speed gains to be most pronounced in routine tasks such as documentation writing, boilerplate code generation, and refactoring existing functions. [1][2]

Quality Gains: The quality improvements are similarly impressive. While concerns are often raised about AI-enhanced code being “bad”, studies show that AI-enhanced code is more maintainable, more readable, and has fewer bugs than equivalent, human-written, code.

Better Contextual Awareness: Current AI tools struggle significantly with structures that require context to function properly across a lengthy codebase or long-lived project. In the future, systems should, at a minimum, build in persistent memory that learns from each interaction, remembers patterns unique to the project or task, and can adjust to the developer’s style over time. [3]

Natural Language Programming: Ultimately, it should be a goal to support conversational programming interfaces where users can “talk” to the computer in order to describe complex software development requirements and then the computer will produce a functional application. Recent improvements with multimodal AI suggest that goal is now, somewhat, in reach.

Collaborative AI Development: Future systems should allow for more team-centric AI assistance tools that again will potentially understand conventions across the project, team writing standards, and team workflow. This would allow for AI to function as an intelligent connector in group software development projects.

With the increasing involvement of AI in software development [4], the field that enabled it to scale the way it did in the first place, human-AI collaboration is more critical than ever. These tools can only be effective with active human guidance, innovative problem-solving, and high-quality oversight. Professionals must seek to learn how to effectively incorporate AI instead of seeing it as a threat to their livelihoods.

[1] https://www.harness.io/blog/the-impact-of-github-copilot-on-developer-productivity-a-case-study
[2] https://github.blog/news-insights/research/research-quantifying-github-copilots-impact-on-developer-productivity-and-happiness/
[3] https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/
[4] https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/unleashing-developer-productivity-with-generative-ai

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CRISPR gene editing has blurred the lines between the fields of biotechnology and digital transformation. The revolutionary molecular scissors is a perfect example of to understand disruptive innovation in the domain of life sciences through an information strategy lens. It has challenged the traditional biotechnology business models and created entirely new digital ecosystems.

The Technology and Market Disruption
CRISPR stared out as a basic defence mechanism for bacteria and it has rapidly evolved over the last decade to replace older gene editing technologies such as ZFNs and TALEN as it is simpler and more cost effective than them. The technology has been widely adopted and it has captured 80% of all gene editing clinical trials, thus exhibiting an exponential technology adoption as expected for a disruptive innovation. The global CRISPR market is set to grow from $2.87Bn to $12.22Bn in the next 10 years. This is largely driven by network effects and platform dynamics as often observed in digital business models.

Digital Business Model Innovation
CRISPR is fascinating from an Information Strategy perspective as it has led to the creation of new digital business models. CRISPR Therapeutics, a leading player in this space, has developed multi-side platforms that connect researchers, pharmaceutical companies, and healthcare providers through collaborative research partnerships, licensing agreements, and milestone payments.
Key features of a successful digital transformation demonstrated by this technology include:
Platform Ecosystems: connected networks that enable collaboration let biotechs, pharma giants, and academic institutes share their data and research with each other
Asset-Light Scaling: CRISPR tools are digitally distributed, thus they can be rapidly scaled across applications and research networks.
Data-Driven Value Creation: This technology generates vast genomic datasets that help create new revenue streams via analytics and drug discovery (now empowered and sped up by AI)
Moreover digital technologies such as Cloud Computing, AI and machine learning, and IoT sensors have been leveraged to process the genomic datasets and other monitoring and predictive applications.

Strategic Implications
CRISPR represents a coalition between traditional biotech models and more novel digital technology paradigms. This technology also showcases the convergence of physical and digital technologies. Although CRISPR is a biological tool, its effectiveness relies on the quality of the software and digital technology employed in the process.
In conclusion, CRISPR seems to be mirroring other digital transformations and it is likely that there will be an increasing shift from product to platform centric business models. Improvements in the data, algorithms, and ecosystems will be majorly responsible for further value creation than the core gene editing tools themselves

References:
1. How CRISPR is really changing lives: https://www.technologyreview.com/2023/03/07/1069475/forget-designer-babies-heres-how-crispr-is-really-changing-lives/
2. CRISPR Retools Life Itself – Trends eMagazine
3. Genetic Engineering Will Change Everything Forever: https://www.youtube.com/watch?v=jAhjPd4uNFY