Those who choose to practice technology and innovation see it. Advancements in chipsets and compute power will jettison us to next-level processing. The shift from central, to graphical has moved to neural. It feels fast because it is fast. We’ve never had more ability to ‘do’ than now. This is when we, as humans, give that speed the direction it needs. This is where speed turns into velocity - and we influence the intended outcome. This is a phase change.

With most innovation, our new abilities bring subjectivity and opinion. We seek to find balance and reasoning - yet we have deep biases.

Negative Bias

• We tend to pay more attention to negative events than positive ones.

• We learn more from negative outcomes and experiences.

• We make decisions based on negative information more than positive data.

Negative bias impacts motivation, spreads bad news faster than good, and entangles itself within political ideology. Deep research has been done along party lines. Such differences in the negativity bias might explain why some people are more likely to value things such as tradition and security. In contrast, others are more open to embracing ambiguity and change. (Links below.)

To see the box, we have to stand outside it.

These are complex innovations in technology. There is no TL;DR summary for Physics Informed Neural Nets. 3D Gaussian Splatting for Real-Time Radiance Field Rendering doesn’t have Cliff’s Notes. Calculating the computational efficiency of deep learning models with FLOPs and MACs can’t be done on an iPhone app. Yet senior leadership continues to expect that we make it simple to understand. The requests for over-simplification are diminishing the organization’s ability to recognize change, thereby impacting its ability to react to it. These are complex processes, built to solve some of the world’s most WICKED problems. They deserve the respect of remaining complex. Learning tech is learning resistance. Knowing tech is knowing dependencies.

The good news.

The complexity of technology innovation is going to save the planet. We can now monitor global conditions in ways we never could before. Low Earth Orbit is giving us satellite connectivity across the world. MethaneSAT is circling the earth 15x daily monitoring and reporting changes in methane concentrations as small as three parts per billion. Earth-2 cloud platform is predicting climate change across the whole planet using simulation by AI supercomputers.

These platforms are, in part, available within the NVIDIA CUDA-X microservices software stack. They leverage advanced AI models and the CorrDiff generative AI model to produce high-resolution simulations 1,000 times faster and 3,000 times more energy efficient than current numerical models. Those are their words. Mine? Throw out that spreadsheet you’ve been so proud of for the last decade. It’s officially a bad habit.

ESG models that were written in 2019 now have a chance. By setting new nature-based targets alongside the often premature climate-based targets, large organizations can monitor, record, and validate targets in a very real and meaningful way. Outcome verification is crucial to the reporting process. The entire system has to work in sync with the natural rhythms of record. The earth is energy. The computers that record the earth are potential energy, their processors are kinetic. Innovation has made them more powerful and more efficient than ever before. We can now record it all. There’s no longer an excuse.

Time.

The funny thing about rhythms of record is time. The Clock of the Long Now, also called the 10,000-year Clock, is under construction at the direction of The Long Now Foundation. Built to promote a long-term cultural institution, the clock aims to provide a counterpoint to what is currently viewed as today’s “faster-cheaper” mindset and to promote “slower/better” thinking. The Foundation hopes to ‘creatively foster responsibility’ into the framework of the next 10,000 years. It will tick once every year. The century hand will advance every 100 years, and the cuckoo will come out every millennium for the next 10,000 years. The basic design principles are 1) Longevity 2) Maintainability 3) Transparency 4) Evolvability and 5) Scalability. These design principles should be at the forefront of all innovation engineering. The reality is, they aren’t. Orgs buy their solutions rather than taking the time to build them. ICP, TAM, and product-market-fit only apply to sales and marketing.

Back to Earth

If you could see into the future, would you? Every business is a climateTech business now. Weather is only a part of the equation. Globalism has expanded our network. We’re working in distributed teams with distributed tools. Our digital exhaust is everywhere. Data packets are colliding, systems are increasingly disparate, software dependencies are inherent, and IT is in full response mode. Fight or flight. Innovation needs to be at the center - not at the edge. This is the time to build. This is the time to create. This is the time to imagine. Jump in, both feet - mud, grit, warts and all. It’s complicated and hard, just like most things we look back on with appreciation.