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As I reflect on the current trend of investors pushing for automation and replacement of human skilled developers through AI, I'm reminded of . Are we risking the long-term benefits of software development by prioritizing short-term gains?
As a seasoned software and cloud architect, I have analyzed some facts that warrant consideration:
Meanwhile, the current layoffs and difficulty in finding new skilled developers
It's essential to step back and assess the situation.
The idea that we can create a Large Language Model (LLM) that can generate software as good as the best developer in the world might seem appealing, but it's a short-sighted approach. Even if LLMs could produce high-quality code, they would still be constrained by their training data and unable to surpass the capabilities of human developers.
To understand why LLMs are constrained by the data used to train them, let's dive into their basics. At their core, LLMs are statistical machines that produce coherent text by leveraging patterns and randomness. However, this approach comes with inherent constraints: they lack the nuance of human intuition, the capacity for diverse coding approaches, and the ability to seamlessly integrate new domain knowledge. The primary reason for these limitations is that LLMs can only generate responses based on the data they were trained on. As a result, their growth and evolution are directly tied to the quality and novelty of the data used to train them. If the new data doesn't introduce fresh perspectives, creativity, diversity, or domain expertise, the models will not be able to advance beyond their current capabilities, effectively capping their potential for improvement.
Furthermore, if we rely solely on LLMs, assuming LLM can generate accurate software source code, companies will struggle to differentiate themselves.
Instead, I propose we envision current LLM capabilities as a foundational seed for future technological advancements. By collaborating with humans, LLMs can generate new data under the human creativity guidance, leading to unprecedented code diversity. This closed-loop system will create a snowball effect, where new data will be generated, curated, and used to improve future models.
LLMs have the potential to unlock numerous opportunities, certainly exceeding the number of roles that may be automated or replaced in the short term. In my company, we're embracing this collaborative approach, recognizing that human developers and LLMs can work together to achieve unprecedented long-term quality and efficiency growth.
This reality is not yet achieved, however, looking in depth, performing various analysis I could realize that LLMs excel at generating human-consumable assets but require additional support or complex RAGs when creating machine-consumable assets which are very often not reliable without human verification.
Challenges and Questions are welcome
As a seasoned software and cloud architect, I have analyzed some facts that warrant consideration:
Meanwhile, the current layoffs and difficulty in finding new skilled developers
It's essential to step back and assess the situation.
The idea that we can create a Large Language Model (LLM) that can generate software as good as the best developer in the world might seem appealing, but it's a short-sighted approach. Even if LLMs could produce high-quality code, they would still be constrained by their training data and unable to surpass the capabilities of human developers.
To understand why LLMs are constrained by the data used to train them, let's dive into their basics. At their core, LLMs are statistical machines that produce coherent text by leveraging patterns and randomness. However, this approach comes with inherent constraints: they lack the nuance of human intuition, the capacity for diverse coding approaches, and the ability to seamlessly integrate new domain knowledge. The primary reason for these limitations is that LLMs can only generate responses based on the data they were trained on. As a result, their growth and evolution are directly tied to the quality and novelty of the data used to train them. If the new data doesn't introduce fresh perspectives, creativity, diversity, or domain expertise, the models will not be able to advance beyond their current capabilities, effectively capping their potential for improvement.
Furthermore, if we rely solely on LLMs, assuming LLM can generate accurate software source code, companies will struggle to differentiate themselves.
Instead, I propose we envision current LLM capabilities as a foundational seed for future technological advancements. By collaborating with humans, LLMs can generate new data under the human creativity guidance, leading to unprecedented code diversity. This closed-loop system will create a snowball effect, where new data will be generated, curated, and used to improve future models.
LLMs have the potential to unlock numerous opportunities, certainly exceeding the number of roles that may be automated or replaced in the short term. In my company, we're embracing this collaborative approach, recognizing that human developers and LLMs can work together to achieve unprecedented long-term quality and efficiency growth.
This reality is not yet achieved, however, looking in depth, performing various analysis I could realize that LLMs excel at generating human-consumable assets but require additional support or complex RAGs when creating machine-consumable assets which are very often not reliable without human verification.
Challenges and Questions are welcome