The dawn of the digital age brought forth a range of technological advancements, reshaping industries and redefining norms. In the realm of software engineering, generative AI coding assistants, including tools like GitHub Copilot and Tabnine, epitomise this wave. Drawing from the impact of foundational models like OpenAI’s GPT and Anthopic’s Claude, these tools interpret natural language inputs to suggest and generate code snippets, amplifying developer productivity. Notably, GitHub Copilot now underpins a staggering 46% of coding tasks, enhancing coding speed by an impressive 55%.
Energy efficiency and ESG: Amidst the rapid expansion of generative AI, the emphasis on Environmental, Social, and Governance factors is intensifying, making energy-efficient code an urgent priority. To put this into perspective, the training of GPT-3 is estimated to have consumed 1,287 MWh of energy, resulting in emissions of over 550 tons of carbon dioxide equivalent. This is comparable to one person making 550 round trips between New York and San Francisco – and that’s before the model is even launched to consumers.
The environmental impact doesn’t stop at the training phase. For instance, integrating LLMs into search engines could potentially lead to a fivefold increase in computing power, resulting in substantial carbon emissions. Efficient code is important in curbing emissions while still enabling businesses to get the most out of AI.
The challenges of code optimisation
Navigating the complexities of code optimisation is far from straightforward, and it is often littered with challenges. One of the major challenges among these is the scarcity of accomplished performance engineers, a niche segment of professionals that require salaries upwards of £500k in London, which may be a significant hurdle for many organisations.
Compounding this is the time-intensive and iterative nature of optimisation, necessitating a cyclical process of code refinement, testing, and analysis that even the best of engineers find daunting – especially within expansive codebases that make a comprehensive view difficult to attain.
Further to this, there are resource limitations that exist in the process. Large codebases require significant human resources for improvement; a codebase with a million lines of code could require up to 70 top developers, across several stages from testing to backend orchestration, further extending the optimisation timeline.
These challenges also come with a cost. In 2020, the Cost of Poor Software Quality in the US alone exceeded the $2.08 trillion mark. This underscores the urgent need for innovative strategies in code optimisation.
This staggering figure includes expenditures on rework, lost productivity, and customer dissatisfaction resulting from subpar code. Addressing this trillion-dollar problem demands a new approach to code optimisation.
Already we are seeing instances of AI being used to supercharge code optimisation to overcome these challenges. AI can automatically identify inefficiencies, generate enhanced code versions, recommend optimal code changes and more. This could be one way that businesses can turn this potential performance headache into a genuine competitive advantage.
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