Burst Capacity and The AI Arms Race
Three things are true of the AI revolution.
One - AI is ubiquitous. Generative AI-powered chatbots, prediction models, recommendation engines, self-driving taxis, AI-powered matchmaking(!), hyper-personalized content and more - there’s a new AI use-case every day!
Two - the quality of AI powering these use-cases hinges on the quality of the data used to train and fine-tune the underlying AI models. The world is therefore hungry for high-quality data - data that can only be generated and annotated by human beings.
Three - speed is key! Companies big and small are locked in an arms race - creating, improving, and releasing new LLMs and AI models every single day. Data scientists inside these companies are therefore devising and running new AI/ML experiments around the clock. But…requirements change rapidly, deadlines are subject to sudden shifts, and the size of the required datasets fluctuates unpredictably.
Innovators are in a bind because generating high-quality AI datasets rapidly is not a core competence. What can they do?
Enter burst capacity.
Burst Capacity - AI/LLM Developers’ New Best Friend
Burst capacity in the context of AI is an AI Data vendor’s ability to rapidly scale highly-qualified human resources and domain experts - up or down, and at a moment’s notice - to generate or annotate high-quality AI datasets.
Whether it's responding to a surge in demand for data annotation in preparation for a new product launch or quickly reallocating resources to fine-tune an existing dataset to meet custom quality metrics, burst capacity allows AI Data vendors to navigate the ebbs and flows of AI project requirements easily and reliably.
The Limitations of Traditional Staffing Models in Data Annotation
Historically, companies have relied on traditional staffing models characterized by long-term commitments and team sizes that are either fixed or scale slowly, over weeks. While this approach has historically worked in more predictable environments, it’s wholly inadequate for today's very-rapidly evolving AI landscape.
Maintaining a fixed workforce increases a vendor’s overhead and makes them uncompetitive because, during periods of low demand, fixed teams are woefully underutilized. On the other hand, the lead time required to onboard new team members from scratch can prevent a traditionally-staffed AI Data vendor from responding to clients’ urgent project needs.
In contrast, the burst capacity model offers a more flexible alternative, allowing AI data vendors to optimize resource allocation in real-time and adapt to changing client needs with agility.
Why The Burst Capacity Approach is Winning
The burst capacity model represents a paradigm shift in how companies approach staffing for AI data projects. Based on years of experience providing AI burst capacity services to some of the world’s largest AI builders, e2f has seen this approach deliver three key benefits:
Adaptability: Burst capacity enables AI data vendors to quickly adjust team sizes in response to fluctuating project requirements, ensuring optimal utilization.
Cost-effectiveness: By only staffing for active project phases, vendors can minimize overhead costs associated with maintaining a permanent workforce and remain competitive.
Speed: The ability to rapidly scale resources up or down allows vendors to meet tight deadlines and respond promptly to their clients’ dynamic needs.
Staffing Considerations to Support Burst Capacity
Given the unique and demanding nature of AI data projects, implementing an effective burst capacity staffing model requires careful planning and consideration. Once again, based on e2f’s deep expertise, here’s what we’ve learned:
Talent Acquisition: Identifying and building a pool of skilled and adaptable talent - aka various domain experts - that is ready to go, before clients have a need, is essential. This may involve leveraging freelance platforms, establishing partnerships with educational institutions, or investing in ongoing training programs.
Think Global: The world’s AI builders are globally distributed, their teams work around the clock, and turnaround times are often 24 - 48 hours. This means that AI vendors must also operate on a follow-the-sun model when it comes to project staffing and project management. AI Data vendors that are used to sourcing and managing global talent pools can successfully meet clients’ needs.
Project Management: Clear communication, well-defined objectives, and efficient workflows are essential for ensuring smooth collaboration across distributed teams. Project managers are key to any successful project - but in the context of AI burst capacity projects, project managers managing a large global talent pool of hundreds or thousands of domain experts that need to collaborate and turn around a project in just 24 - 48 can make or break the project!
Technology Infrastructure: Investing in scalable, cloud-based tools and platforms can streamline data annotation workflows and facilitate remote collaboration. From annotation tools to project management software, selecting the right technology stack is essential for maximizing efficiency and productivity.
Client Partnership: Building strong relationships with clients is paramount for understanding their evolving AI data needs and their custom quality metrics, and tailoring solutions for their project and company-specific needs.
Conclusion
The brave new world of AI requires AI data vendors to embrace the brave new world of the burst capacity staffing model. By embracing this model - and its agility, scalability, and efficiency - vendors can meet the dynamic demands of the AI industry and gain a competitive edge. AI builders may benefit from partnering with smaller, agile vendors that can more easily adopt the burst capacity model.
If you’re an AI/LLM builder that needs high-quality datasets turned around in 24-48 hours - or you’re a domain expert anywhere in the world that would like to serve the world’s AI builders as part of the e2f team, please contact us today.
