An inclusive business model is delivering value right now
The furious momentum of the GenAI hype cycle has lost a bit of steam with the recent tech stock market wobble for industry giants and press coverage of businesses questioning the value of GenAI. Nobody is more dialed into this reality check than the CIO who must balance the high expectations of the technology from the company’s leadership team with the realities of the costs, the complexity of implementation, and the return on investment. At this year’s Flash Memory Summit, those conversations about GenAI were front and center for the entire event.
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The excitement phase is understandable with predictions from organizations like McKinsey suggesting it could inject trillions into the global economy and usher in the next Industrial Revolution. Every size business is interested in improving the customer experience, lowering costs, improving efficiencies, and working smarter with insights from their data to stay competitive and relevant.
Access
Until recently, the barriers to entry were steep for GenAI due to its restricted access for small and medium size businesses. Only a select few could handle the $1M investment for equipment and the ongoing expenses of training and managing data on LLMs. While ChatGPT is fine for consumer use, it currently has little business value because it is trained on data that isn’t tailored to individual enterprises. Adding another layer of complication is the need to keep sensitive data on premises rather than store it in the cloud to avoid becoming locked into very expensive and variable cloud services and expose it to security risks.
GenAI is following the natural evolution of new technology and widespread adoption. In the 2000s Internet boom, investments were made quickly in the early days until many key companies lacked profitable models, leading to the Dot.com bubble burst. It did not take off until 2004, when the rise of smartphones shifted the Internet business model and numerous companies leveraging the technology are still profitable, global leaders today. The smartphone essentially enabled Internet and cloud companies to gain direct access to global users, fostering a multitude of innovations from the application industry.
For GenAI to truly reach profitable status, businesses of all sizes must have access to the technology at an affordable rate and have confidence that valuable data stays secure. That means that AI must directly engage with businesses right at the edge of where business happens– workstations, servers, etc. instead of the clouds and walled gardens of the hyperscalers.
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Affordability
This scenario is exactly what I faced as the General Manager and President of Phison North America. I have to understand customer needs, make mission-critical decisions, ensure timely product launches, and foster an innovative culture with data-driven decisions powered by our global team of 75% engineers. We recognized the call to unlock the power of GenAI for our internal use to stay competitive whilst also building a more accessible solution for a variety of SMB use cases.
We faced the classic ‘make or buy’ decision. Finding the existing options to be cost-prohibitive, we chose to invest in building our own solution that will democratize AI: aiDAPTIV+. With aiDAPTIV+ ,companies can scale boundaries without busting the budget. Now organizations and companies of all sizes can unlock the power of GenAI while owning and training their data, keeping it on premises, and building their own ecosystem of partners for their unique needs at a price point of $40K for equipment with additional fees for electricity and power.
This is a game changer because most enterprises can afford it, rely upon it, and operate it without requiring additional engineers and IT staff to run it. In less than a year, we have found 12 distinct use cases and over 100 enterprises are using it. At FMS 2024, our solution won Best of Show for the most innovative AI application.
Now even users with limited resources are empowered to train large language models. This end-to-end AI appliance solution allows system integrators to build turn-key large language model training systems from start to successful finish. With a simplistic user experience that turns raw data into tokenized data, it runs the fine-tuning process with Llama-3 70B precision and offers the ability to ask questions about the data, all from a local domain on premises. Data can be fine tuned data in about 4 hours which is sufficient for most business applications. Sure, the multi GPU big systems (the Ferraris of AI) are needed for applications like keeping Teslas on the road but most businesses would opt for a Toyota version of a GenAI solution to take the journey, arrive on time, and not break the budget. All without the need to hire a professional driver and road crew to make it work!
My message to CIOs everywhere is that it is not time to panic and walk away from GenAI solutions. Innovation will continue the democratization of GenAI, paving the way for an even playing field for enterprises of all sizes.
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