The New Frontiers of AI: Funding Challenges and Potential Solutions

Artificial intelligence (AI) continues to transform the way we live and work. From DeepMind's AlphaZero, which taught itself how to play chess through unconventional tactics, to MIT's halicin project that successfully killed resistant bacteria, the potential of AI is becoming increasingly apparent. However, with these advancements comes a surge in AI budgets, leading to an ongoing debate about where funding should come from. This article explores the challenges of AI funding and potential solutions that organizations can consider.

Increasing AI Budgets and Funding Dilemmas

As AI evolves and its applications multiply, the cost of implementing and maintaining these systems also increases. Enterprise-level Generative AI (GenAI) systems, for instance, can cost up to $30 per user per month. This is a significant rise compared to the costs of existing productivity applications. Consequently, organizations are grappling with the decision of whether to allocate these expenses to their IT budgets or seek unconventional sources of funding.

According to Forbes, organizations fall into two categories: early adopters who are willing to take risks and invest in AI, and more risk-averse CIOs who are hesitant about the financial implications. The challenge of funding AI initiatives is further heightened by the fact that companies, such as UPS, Google, and Spotify, have had to cut jobs to fund AI applications and talent, as reported by Fortune.

Funding AI from Lines of Business

One emerging trend is for organizations to draw funds from the lines of business (LOB) that will directly benefit from AI. This includes conceiving new products, enhancing existing ones, creating reports, improving sales productivity, and upskilling employees. By allocating costs to the LOBs, organizations can better justify the investment in AI as it directly contributes to enhancing the business and achieving competitive advantage.

Exploring Alternatives to Conventional AI Funding

Efforts are also underway to make AI more cost-effective. For example, OpenAI's CEO Sam Altman is seeking up to $7 trillion for a rival to AI chipmaker Nvidia, with the aim to create a more cost-effective and energy-efficient alternative to Nvidia's GPUs, as noted by Fortune. This initiative could potentially decrease the cost of AI, making it more accessible for organizations.

Moreover, AI companies are pouring money into new models and technologies to advance AI capabilities while reducing costs. This includes Lambda and Together, which provide access to data centers full of Nvidia's GPUs, as reported by SiliconANGLE.

Conclusion

As AI continues to evolve, organizations are faced with the challenge of funding these initiatives. While unconventional sources of funding are being explored, it is evident that CIOs need to investigate funding from the LOBs most likely to benefit from using GenAI. By aligning AI investments with business benefits, organizations can not only justify the cost but also gain early advantages in their respective markets.