From AGI to ROI: The 6 AI debates shaping enterprise strategy in 2024

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Patricia Martinez

Jul 02

How are enterprises adjusting to the GPU bottleneck in AI scaling?

Socrates

The GPU bottleneck: infrastructure realities

The surge in AI development has led to an unprecedented demand for specialized hardware, particularly GPUs.
Nvidia, the leading GPU manufacturer, has seen its market value skyrocket above $3 trillion.
This demand has created a supply crunch, driving up costs and extending wait times for this critical AI infrastructure.
However, the bottleneck isn't uniform across all AI applications, and many enterprise use cases focus on inference, which can run efficiently on non-GPU hardware.
Despite these developments, the overall trend points towards increasing computational demands, with AI labs and hyperscale cloud companies building massive data centers.
Most enterprise companies don't find themselves as constrained by GPU availability, as they use Azure, AWS, and Google's GCP clouds.

Content rights and LLM training: legal landmines ahead

The data used to train LLMs has become a contentious issue, with major implications for AI developers and enterprise users.
The New York Times and the Center for Investigative Reporting have filed suits against OpenAI, alleging unauthorized use of its content for training.
This legal battle highlights a crucial question: Do AI companies have the right to scrape and use online content for training without explicit permission or compensation?
Legal experts suggest it could take up to a decade for this issue to be fully resolved in the courts.
Businesses must understand the provenance of the data used to train the AI models they deploy, especially for finance and banking companies, which face big regulations around privacy and the usage of personal information.

Gen AI applications: transforming edges, not cores

While AI is transformative, its impact is currently more pronounced in enhancing existing processes rather than revolutionizing core business models.
Common applications include customer support chatbots, knowledge base assistants for employees, generative marketing materials, and code generation and debugging tools.
These applications are driving significant productivity gains and operational efficiencies, but not yet leading to massive revenue gains or business model shifts.
Executives at retail companies and the pharmaceutical industry are experimenting with AI to impact their core, though progress has been slower than anticipated.

Sandra Harris

Jul 02

What is the focus of VB Transform 2024?

Socrates

VB Transform 2024 focuses on enterprise generative AI, with the theme "Putting AI to work at scale". The event will explore critical debates shaping the AI landscape, featuring speakers from industry giants like OpenAI, Anthropic, Nvidia, Microsoft, Google, and Amazon, as well as AI leaders from Fortune 500 companies such as Kaiser Permanente, Walmart, and Bank of America.

Lisa416🔥

Jul 02

Has the LLM development race reached a plateau?

Socrates

The LLM race: a plateau in sight?

The race to develop the most advanced LLM has been a defining feature of the AI landscape since OpenAI's GPT-3.
As we enter the second half of 2024, a question looms large: Is the LLM race over? The answer appears to be yes, at least for now.
This matters because the differences between leading LLMs have become increasingly imperceptible, meaning enterprise companies can now select based on price, efficiency, and specific use-case fit rather than chasing the "best" model.
For enterprise leaders, this plateauing has significant implications. It means they should be leveraging the best individual LLMs for their specific purposes — and there are now hundreds of these LLMs available.

The AGI hype cycle: peak or trough?

As the pace of LLM breakthroughs slows, a larger question emerges: Have we reached the peak of inflated expectations in the AGI hype cycle? Our answer: Yes.
This matters because companies should focus on leveraging existing AI capabilities for real-world applications, rather than chasing the promise of AGI.
ChatGPT's release unleashed a wave of excitement about the possibilities of AI, but the steady improvements many had predicted failed to materialize.
The cost of feeding more data to these models has increased, while their frequent logical errors and hallucinations persist.

The GPU bottleneck: infrastructure realities

Is there a GPU bottleneck hurting the scaling of GenAI? Our answer: Yes, but it's more nuanced than headlines suggest.
The surge in AI development has led to an unprecedented demand for specialized hardware, particularly GPUs (Graphics Processing Units), that help run AI applications.
However, the bottleneck isn't uniform across all AI applications. While training large models requires immense computational power, many enterprise use cases focus on inference – running pre-trained models to generate outputs.
Despite these developments, the overall trend points towards increasing computational demands. AI labs and hyperscale cloud companies that are training advanced models and want to stay leaders are building massive data centers.