Resource Tech Innovation: Beyond the Surface

How Resource Innovation and Finance Fuels Tomorrow's Breakthroughs

David here. In the next 7 min:

💡What’s New - topics and trends shaping our electric future.

🤔 Opinion - The -isms of energy transformation.

🛠️ Tools and Data - opensource tools to speed your work

💡What’s New: Trends and Topics in Perspective

Geopolitics - Here’s recent expert testimony before the House on critical mineral supply. Battery metals have been pumping with legislation for mineral-based domestic energy. Even the DOD is paying for critical mineral assessments. Wondering how a geologic census is done? Here you go. You can access all that data in the USGS Mineral Deposit Database. Resource supply is rising to meet demand. Even the Taliban is mining its $T mountain of minerals. Canada and the US have stepped up their Lithium game. And the Swedish government is bankrolling the Epiroc robotics platform.

Deep Sea Mining - The fate of The Metals Company deep-sea mining project is uncertain as financial and social pressures mount. From a macro perspective: “When the prices of onshore minerals reach a certain level, countries will engage in large-scale development, and relevant environmental protection measures will gradually become clearer”.

Tech (Space and AI) - The US National Geospatial Intelligence Agency is touting the impact of AI. Automated geo analytics platforms are slowly emerging from companies like Archetype and Danti. And the usefulness of AI in finding economic deposits is on display for the public. Global analytics in the browser via WASM sounds interesting (see an early example). Check out this use of Deep Learning to predict spatial distribution of lead based on multispectral satellite images. If you know what embeddings are, you understand their use in similarity search; so imagine that for the entire planet’s surface. A report recently claimed a $70B market for satellite services by 2030, and (I believe) most companies will soon realize their every move can be tracked on a geologic scale–ESG is a risk to manage. UP42 has an provenance and assessment recommendation worth considering. Meanwhile, the optimism for space resources is palpable. I think we’ll get there, as several new mining equipment platforms are demonstrating. There’ve been >48k patents filed and granted in mining in the past three years, and there’s a review of the top IP trends. Nevertheless, a recent article by a LinkedIn user is spot on about challenges to adoption (namely retraining and high initial investment). Finally, do abandoned mines present an opportunity for tech companies?

Jobs, Education, and Prof. Growth - Here is a site to track jobs, even university professorships, in resource markets. Bear in mind that AI competency carries a premium. You can browse GitHub for coursework and code notebooks like this one from PennState. Spain recently posted its Spatial Data Bootcamp, and Poland its 2023 geospatial summer camp. Check out this handbook on using QGIS for mineral exploration, or this primer on how metals are found in groups and why. You can also find out how multispectral satellite data is useful in mineral mapping. The Colorado School of Mines (Ivy League in this industry) has a great data repo too. Don’t neglect the importance of mining claims on US federal lands. There’s also an exploration focused website that collects entertaining and helpful videos on resource topics. If you’re a mining professional, please consider starting an EdX (or Udemy) course like this one as a side gig.

Finance - When you want to implement large mining projects, you will inevitably hear about project finance. Here’s a free intro course and a comprehensive commercial series on the topic. Udemy has one too. YouTube also has some (pt 1 and pt 2). LinkedIn has advice on funding projects if you want to mine yourself. Or check out this in-depth look. Within resource financing, there are various niches. Each mineral is different because there are different mining and processing steps unique to the ore it’s found in. You also have to finance the processing facility. Anyone interested in becoming an expert should find some chunky books (this textbook or this one, but also debt and discounted cash flow analysis). There are case studies with details on how deals get done in steel, or at salt projects in Australia. For a look at the size and cost of typical funding, check out a sample size and timeline. The business of mining is fraught with uncertainty, which is why there’s a lot of opportunity!

🤔 Opinion

Just as there are 4 points to move on a compass, I find there are 4 ways to view any economic news. Pessimism, Optimism, Rationalism, Realism.

While there is tremendous expectation in technology markets today…

Pessimism (bad news first)

  • Mining indices and large companies are showing signs of decline. This is normal since commodities correlate strongly with the overall economy. Rising interest rates push down consumer spending. Orders decline.

    FRED steel product manufacturing is flatlining

    Same for primary metals orders

    Cobalt futures are down.

    Lithium futures are in rapid decline as well.

  • Despite obvious media interest in AI, I have not seen many tech jobs advertised within mining–and I’ve been watching! It could be early.

  • Even if the US ramps up domestic mineral production, can it expect to manufacture EV cars at a rate that’s competitive to China?

  • Pertaining to space mining, how do we expect to get mining equipment automated on the moon if we cannot even get to level 4 autonomous driving here on earth?

  • And the biggest question overall in the energy transformation: Who’s going to fund it? Consumer bank accounts are shrinking, and credit debt is at an all time high. The average cost of an EV car is +$10k an ICE. Manufacturing costs have to come down for the cost of the cars to fall. But those costs only seem to be rising in today’s high-inflation environment.

Optimism

  • At the same time markets are declining, more tech startups within resource markets are being funded by venture capital and government.

  • When pessimism is strong in any market, I am attentive. Oil and Gas, just like coal, are not dead…not by any measure that counts.

  • $70B for satellite services which can tie into AI may not be completely accurate, but anywhere in that ballpark is interesting.

  • High investment cost and retraining are the same costs experienced in automotive when I was at BMW a year ago. But these can be overcome. If you’re in resources, it’s a safe-ish bet your boss will be coming to you soon with new technology. You’d be a shoe in for a promotion if you already know how to use it.

  • Overall, it’s a great time to be an accredited investor in resource tech. Venture capital can fill in gaps that equity markets and banks leave as economic tides roll out.

Rationalism

  • Increased data resolution and real-time mapping startups make many applications possible. But to make them economic, you need AI, IoT, and cloud compute. Those are the safer bets, IMO.

  • Bigger companies that resist technology and change may have cash flow today, but tomorrow’s prospects are brighter for smaller firms (junior miners, etc) who are fast to adopt. They can easily be purchased to add value to larger firms.

Realism

  • Time expectations differ wildly between software tech entrepreneurs and mining engineers that are better acquainted with physical constraints. The average time to start a mine producing is 3 years, in addition to substantial regulatory hurdles. That doesn’t include the time and capital to get processing facilities online!

  • I suspect few people in technology truly understand finance or its outsized impact on tech industrialization. Successful miners and startup firms need to do what they do well, but also manage the enormous financial risks of resource projects so capital can flow in.

🛠️ Tools and Data

Opensource drill hole visualization in QGIS

Satellite internet coverage assessment tool.

Streamline the analysis of geospatial and remote sensing data - geohashes, pre-processing of Synthetic Aperture Radar (SAR) datasets from Sentinel-1, ALOSPALSAR, and TerraSAR-X.

A github topic list for image segmentation worth browsing.

A github collection of highly ranked Jupyter extensions worth checking out. Also check out the parent repo, “best of

Helps language models improve their ability to follow natural language instructions by using the model's own generations to create a large collection of instructional data.

I was awe struck when I saw my first solar farm not long ago.

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