Decoding Nvidia CEO’s Views on “No Programming” And Future of AI Code

Updated on March 1 2024

When Nvidia CEO, Jensen Huang declares learning to program as no longer vital for kids given AI’s rapid evolution, his provocative message signifies both promise and fear when it comes to the state of computing technology and AI code generators.

On one hand, engineering barriers lowering through code generating algorithms show optimism on democratizing access to coding. Yet on the other, it also raises questions on the impact of these AI coding assistants on the future of coding and impact on jobs.

Lets examine what this statements means, the basis of it, the state of AI code today, how it has evolved over the last decade and what it entails in the times to comes including jobs.

What Exactly did the Nvidia CEO Say?

Speaking at the World Government Summit in Dubai this February, Nvidia CEO Jensen Huang verbalized AI’s rising capability to transform coding:

“You probably recall over the course of the last 10, 15 years almost everybody who sits on a stage like this would tell you it is vital that children learn computer science. Now, it’s almost the complete opposite.”

For Huang, advances like chatbots Claude and ChatGPT conclusively demonstrate that elaborate coding skills are no longer mandatory:

“It is our job to create computing technology such that nobody has to program and that the programming language is human. Everybody in the world is now a programmer. This is the miracle of AI.”

Also Read: Generative AI Code’s Trends in Software Development in 2024

Basis Behind What He Said

Huang’s eye-opening stance stems from the breakneck pace of progress in generative AI – systems able to generate original text, images or software with minimal human input by recognizing patterns in vast training datasets.

Whereas coding previously demanded rigorous study of languages like Python, C++, AI code assistants now translate natural language requests into executable code, lowering barriers considerably.

However, risks around biased, inaccurate output do persist in even the most advanced models today.

When we tried using code generated by ChatGPT for Appscribed, it needed considerable amount of time to edit code generated by GPT3.5 and make it usable. But with time, we got better at using ChatGPT and likewise GPT-4 was much more accurate.

Blindly accepting generated software as failproof risks severe consequences without reviews as these models are getting better as they get trained on more and more data.

Also Read: Analyzing Gemini 1.5: How Google’s Next-Gen AI Delivers 1 Million Tokens of Context

Key Players Driving AI Code Developments

Specific projects at the frontier of applying AI to ease programming include:

Name of the SoftwareWho Owns ItFeaturesActive Users/Subscribers
GitHub CopilotGitHub & OpenAIPredictive code generation, Multilingual capability, Continuous learningOver 100 million on GitHub; 1 Million plus use Copilot
Replit GhostWriterReplitReal-time code completion, Integrated coding environment, Syntax error prevention, Optimizations for speed and efficiencyOver 20 million developers on Replit
Amazon CodeWhispererAmazon Web ServicesContextual code suggestions, Support for multiple languages including Go, Rust, PHP, Ruby, Kotlin, C, C++, Shell scripting, SQL, and Scala, Security scanning, Free for individual usepart of AWS ecosystem
TabnineCodotaAI-powered code suggestions, supports multiple programming languages, test generation for automated software testingOver 1 million monthly users

Also Read: How to use Microsoft Copilot on Android and iOS?

Evolution of Coding

2000s – Teaching coding fundamentals and computer science theory is heavily prioritized globally.
2010s – Visual programming apps and games like Scratch gain widespread adoption making coding more intuitive to beginners.
2020s – Stunning leaps in language AI and release of models like GPT-3.5 lower access barriers considerably by generating software from descriptions in natural language alone.

The evolution clearly points to a future where most of the code will be written by AI coding assistants and the barrier to writing useful code will be significantly lower than what it is today.

Also Read: Analysing Magic’s “Coworker” Breakthrough: Active Reasoning and the Race to AGI

Programming In Near Future

Software Developer

Prominent AI thinker and investor Emad Mostaque predicts 90% of routine coding work itself could be fully automated by advanced algorithms over the next decade as capabilities improve from narrow AI to artificial general intelligence (AGI).

Already on crowdsourced code hub GitHub today, an estimated 10-15% of publicly shared projects employ some form of automation augmentation through tools like Copilot illustrating steady adoption by developers.

However, retaining enough human comprehension of code logic, architectural principles and software quality evaluation remains vital even when working in parallel with AI assistants on parts of builds.

The essence of programming – translating ideas and logic into efficient code – looks durable rather than replaceable outright any time soon.

Could Developer Jobs Face Extinction?

There are 2 ways to look at this scenario:


Most advanced algorithms today still lack creative contextual app knowledge and business logic – very human specialties needed to validate requirements and real world functioning through user empathy. So engineers focused on high value analysis, architecture and creative coding seem likely to retain relevance.

Additionally, entirely new specialties understanding AI itself like making models more understandable, accountable and ethically compliant present ripe opportunities. With retraining and upgrading to leverage AI as a collaborator rather than competitor, existing coding careers stand poised to transform rather than disappear outright.


Coding in general will be a lot more easier going forward and the developers at the bottom or middle of the skill pyramid may witness job losses and eventually no work. People with strong exposure to design, architecture and most importantly domain will find themselves uniquely placed to leverage AI and differentiate themselves and go on to form single person unicorns as famously quoted by OpenAI CEO, Sam Altman.

Also Read: How New Linkedin’s AI Chatbot Will Help Job Search

Why Retaining Coding Skills Still Matters

Even while applauding the accessibility improvements from AI coding tools, retaining foundational technical literacy stays vital rather than instantly discarding coding skills as irrelevant.

With auto-generated software still containing flaws at times, appreciating code quality markers allows properly vetting machine output instead of blind acceptance.

Deeper algorithmic awareness similarly nurtures quantitative aptitude to logically assess functionality, scale and performance – cornerstones of engineering. This will continue to be the most important use case for knowing how to code but with a caveat that not everyone will be required to do this.

So prudence favors retaining enough coding literacy alongside accelerators like Copilots rather than complete  replacement.


Rather than outright displacement, AI promises to complement coders once best practices evolve after an initial learning curve. Sharpening both human and machine capabilities in tandem points towards an emerging symbiosis – neither fully manual nor fully automatic extremes encapsulate the optimal balance. This is the most likely scenario for immediate to near future.

However, the pace of AI developments is becoming unpredictable. With tech giants like OpenAI, Google and Microsoft competing and collaborating with each other in AI code space, the odds of AI vs human coding seems to be tilting towards AI.

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