Moore's Law is Slowing Down. What is it and How Does This Impact EdTech?

student using VR headset

For years, Moore's Law—predicting that the number of transistors on a chip would double roughly every two years—has been the driving force behind the exponential growth of computing power. But as we have come close to reaching the edges of what is physically possible on an atomic scale, this doubling rate has slowed.

We can see the impacts of this when looking at how new cell phone upgrades now focus on small changes like camera pixels and battery life, instead of dramatic changes with each new model. 

Interestingly, though, from what I’ve seen, while the era of traditional CPU scaling might be drawing to a close, GPUs (Graphics Processing Units) are stepping up to continue the legacy of innovation that Moore's Law began. 

Originally designed to handle the heavy lifting in rendering graphics, GPUs are now at the forefront of processing complex algorithms, particularly in areas like artificial intelligence (AI) and big data. 

From my perspective, this is where the real opportunity lies for education. As we look to the future of AI in Education as well as Extended Reality (XR), this shift is likely to have some meaningful implications for the EdTech sector. 

And it’s worth exploring how this transition impacts schools, educators, and our students.

What This Means for EdTech

When we look at the potential impacts of this shift to GPUs, we can expect to see some significant impacts in the following areas:

Creating Richer Learning Environments: The parallel processing power of GPUs is enabling the creation of more sophisticated, interactive learning tools. Whether we’re talking about AI-driven personalized learning platforms or extended reality (XR) experiences that immerse students in new interactive and deep learning virtual worlds, GPUs are making these advanced technologies more accessible and effective.

Enhancing Data-Driven Decision Making: Schools and districts have more data for their students than the NFL has for players. And they are already increasingly relying on data to inform instructional strategies and student interventions. With the enhanced capabilities of GPUs, we can process vast amounts of data more efficiently, allowing for real-time analytics that help educators make informed decisions on the fly. This kind of responsiveness is crucial for addressing the diverse needs of today’s students. This new data work can lead us to predictive scheduling for students and assisting with helping them choose their own educational adventure through our schools and districts.

Maximizing Cost Efficiency: While GPUs might come with a higher upfront cost, their ability to handle complex computational tasks more efficiently can actually lower costs in the long run. While many schools and districts may not set up GPU farms, they are likely to utilize solutions that allow them to achieve more with less, reducing the need for large-scale hardware investments and improving energy efficiency. This is particularly important as schools face tighter budgets and growing demands for tech-enhanced learning.

Implications for Schools and Educators

So, what does this really mean for those of us in education? Here are a few key points to consider:

• Curriculum Updates: As we integrate more GPU-driven technologies into the classroom, it’s essential that our curricula evolve to include training in these areas. Students will need to be prepared for a world where AI, data science, and advanced computing are the norms, not the exceptions.

• Infrastructure Investment: Schools will need to rethink their IT infrastructure to fully leverage the power of GPUs. This isn’t just about buying new hardware; it’s about strategic investment in a technology ecosystem that will support innovative teaching and learning practices, as well as enhanced operations, for years to come.

• Professional Development for Educators: Our teachers are at the heart of education, and they need to be equipped to use these new tools effectively. Investing in professional development that focuses on integrating GPU-powered technology into instruction is crucial for maximizing its impact on student learning.

• The Haves and Have Nots: As always, we must consider this issue. Advanced technologies like GPUs should not widen the gap between well-resourced and under-resourced schools. We need to advocate for policies and funding that ensure all students have access to the benefits these technologies can bring.

Let's Wrap IT Up

While the slowing of Moore’s Law might seem like a challenge, it’s also an opportunity—an opportunity to rethink how we approach technology in education. GPUs offer a way to extend the legacy of innovation that Moore’s Law represents, but it’s up to us to harness this potential in ways that truly benefit our students.