The continuous growth and advancements in Generative AI models have now become foundational for a new wave of startups, leading us to question: What’s next in the future of Generative AI technology?
The Future of Generative AI: Standardization
Broadly, we believe Generative AI model abstractions will converge to create synergies between disparate elements, including prompts, APIs, and query parameters. This is a necessary step to streamline backend integration for the ecosystem of applications built on top of these models and to, in turn, drive model usage.
A core aspect of the coming Generative AI journey will involve standardization, simplifying processes, and easing decision-making for third-party application developers. We are developing a coordination compass to assist Omega portfolio companies, ensuring they are ready to align with these future capabilities.
Organizations like OpenAI, Google, and Meta are at the forefront, creating model recipes tailored to various sectors. Such recipes promote technology openness in regulated industries and enable developers to build around them, thereby defining collective ecosystem guidelines.
Generative AI and the Evolution of Computational Needs
On the computation side, with enterprises valuing stability and predictability, computational power is becoming a pressing issue, especially during this time of peak technological innovation.
The architecture of Generative AI models is similar to transformer-based models, which inherently require big data. The advent of standardized models will raise the question of whether more specialized computation architectures are better, faster, and cheaper than standard GPUs. We predict increasing compute infrastructure specialization over the next few years, much like the transition from GPUs to ASICs in Bitcoin mining.
As models become more standardized, we foresee a surge in optimized computing, provoking us to contemplate cost development and deployment efficiency over the next 12 to 36 months. While current Generative AI product shipping costs may be high, we believe that the efficiency curve could experience accelerated improvement.
Generative AI: Advent of Specialized Models and Distillation
With the paradigm of research labs shifting away from general-purpose models towards collective intelligence, we envision a future where models become smaller, more efficient, and specialized. As models become smarter, they can teach through a process called distillation, where the model only needs to understand the essentials to perform its task optimally.
How Generative AI is Disrupting Traditional Industries
On the application side, we’re observing AI disrupt traditional industries such as the Media and Entertainment industry. Across media, Generative AI opens up possibilities for large-scale personalization, with AI reviewing millions of content pieces worldwide and focusing on popular stories and themes. With the advent of video models, the cost of generating content is set to drop drastically.
With AI, the Education and Healthcare sectors are poised to experience significant disruption. Models can provide adaptive learning, taking into account individual learning styles, and provide expert medical advice. Coding is another area where language models can have a profound impact. As applications leverage different underlying models to work together, they can optimize the code further, reducing the need for entry-level coders.
As to the impact of AI on enterprise software, our view is that AI can indeed enhance process workflow automation, particularly when multiple AI models work together.
Regarding safety concerns, we highlighted the need for industry regulation, owing to the potential threat posed by the rapid emergence of AI applications. AI models have the potential to transition from being a technological novelty to a potential threat swiftly. It’s crucial we’re prepared to manage these risks.
The Role of Partnerships in Generative AI Development
As new Generative AI foundation models and Large Language Models are released, the complexity of these models means that there are a limited number of entities that possess the sophistication to build meaningful applications around them.
Therefore, partnerships with larger entities become appealing for standalone startups developing Generative AI foundation models. More specifically, the role of companies such as Stable Diffusion in the ecosystem will be to extract the best from AI research, develop benchmark models, standardize them, and support their ecosystems.
Looking Ahead: The Future of Generative AI and Its Impact
As Omega Venture Partners, we are excited about the transformative journey Generative AI offers, with a focus on standardization, specialized models, and the potential disruption of traditional sectors. The road ahead may be filled with challenges, but the prospect of building a new world with AI holds immense potential and opportunities.
