Erica Andersen

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smartR AI

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08 Jul 2024

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Edinburgh

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smartR AI

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Is the $500 Billion Investment Boom Building for the Future or a Cobweb Cycle?

The Race for AI Infrastructure Recently, President Trump announced the Stargate Initiative, a staggering $500 billion investment aimed at accelerating the creation of AI datacenters. With major players like Microsoft already committing $80 billion in capital spending this year, Meta investing over $20 billion for AI infrastructure alone, and simi

27 January 2025 Artificial Intelligence and Financial Services

Are SLMs the End of Traditional ERP and CRM?

With all the hype over agentic AI and the cartel's bigger is better for business thinking, the disruptive power of Small Language models continues to go under the radar. Small Language Models (SLMs) are poised to revolutionize the way businesses interact with Enterprise Resource Planning (ERP) and Customer Relationship Management (CRM) systems. By

26 January 2025 Artificial Intelligence and Financial Services

The Evolution of AI Training Efficiency: Emerging Trends and Market Implications Recent developments

Recent developments in artificial intelligence training methodologies are challenging our assumptions about computational requirements and efficiency. These developments could herald a significant shift in how we approach AI model development and deployment, with far-reaching implications for both technology and markets. The Emergence of Efficien

20 January 2025 Artificial Intelligence and Financial Services

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Latest comments

ESG vs Generative AI: A Growing Dilemma in Corporate Ecosystems

When you read the latest on AI's hunger for power, then you know you need to choose your AI solution wisely if you want to maintain your ESG and Green profile.

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SCOTi's small footprint means less energy consumption and a smaller carbon footprint. Small but perfectly formed there is rarely a need for server upgrades, which makes SCOTi® AI the greenest GPT around.
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Try SCOTi AI today and discover the power of private, secure, and compliant data management! Happy to do a show and tell if you have a spare 30 mins neil.gh@smartr.ai

25 Sep 2024 09:06 Read comment

The software ownership dilemma

Thank you for your informed opinon. It's always good to hear back from others in the community with AI insights.

22 Aug 2024 10:26 Read comment

The software ownership dilemma

Thank you, Ketharaman, for your insightful comment. Your perspective, rooted in decades of enterprise software sales experience, is invaluable.

I agree with your assessment of how COTS and SaaS vendors have historically maintained ownership of their software, including customer-specific data and customizations. Your point about vendors merging these customizations into future versions is astute and often overlooked.

Regarding GenAI, I concur that the initial models contain vast amounts of vendor-supplied training data. However, I believe the situation becomes more complex when we consider model specialization using proprietary company data.

Unlike traditional SaaS products, fine-tuning an AI model with company-specific data fundamentally alters the model itself. The weights of the neural network are adjusted, effectively embedding aspects of the proprietary data into the model's structure. This creates a unique challenge: the data becomes inseparable from the "software" (i.e., the model).

This inseparability is where I foresee potential issues. If a vendor were to use this specialized model for another client, they would inadvertently be transferring knowledge derived from one customer's proprietary data to another. This scenario is fundamentally different from traditional software customizations, where features can be more easily isolated and repurposed.

That said, I appreciate your prediction that GenAI vendors will find it easier to maintain total ownership. It's certainly possible that they will develop novel strategies to navigate these complexities.

Perhaps the solution lies in developing more sophisticated methods of model specialization that can clearly delineate between base knowledge and customer-specific insights. This could allow vendors to maintain control while ensuring customer data privacy.

I'm curious about your thoughts on potential solutions to this dilemma. How do you envision GenAI vendors balancing ownership rights with the need to protect customer-specific knowledge?

15 Aug 2024 08:51 Read comment