Artificial intelligence investments reduce risks to critical mineral supply

Domain: earth_science
Published: 2024-08-24
Editorial outcome: minor_revision

Abstract
This paper employs insights from earth science on the financial risk of project development to present an economic theory of critical minerals. Our theory posits that back-ended critical mineral projects that have unaddressed technical and non-technical barriers, such as those involving lithium and cobalt, exhibit an additional risk for investors which we term the “back-ended risk premium”. We show that the back-ended risk premium increases the cost of capital and, therefore, has the potential to reduce investment in the sector. We posit that the back-ended risk premium may also reduce the gains in productivity expected from artificial intelligence (AI) technologies in the mining sector. Progress in AI may, however, lessen the back-ended risk premium itself by shortening the duration of mining projects and the required rate of investment by reducing the associated risk. We conclude that the best way to reduce the costs associated with energy transition is for governments to invest heavily in AI mining technologies and research.

Introduction
Unlike traditional energy sources, such as oil, coal, and gas, clean energy production and storage requires an unprecedented amount of critical minerals, such as copper, lithium, nickel, zinc, cobalt, and rare earth minerals1. Consequently, the shortage in the supply of critical minerals that are needed to achieve global net zero targets by 2050 has gained significant attention1. Recently, the International Energy Agency (IEA) estimated that the investment that will be required in critical minerals between 2022 and 2030 to realize global net zero by 2050 is ~360–450 USD billion, while the anticipated supply is between 180 and 220 USD billion, implying a shortfall of 180 to 230 USD billion2.

A key issue in meeting this shortfall is that there is a considerable time lag between investment and production. According to the IEA, exploring and producing some critical minerals can take 12.5 years2. One potential way to increase critical mineral production is via technological progress through artificial intelligence (AI), which can be used at all stages of the mining process. It has been argued that the mining industry is poised to reap the rewards of AI and data-driven approaches as it deals with a complex integrated value chain of exploration, extraction, and refining that has a history of incorporating high-technology systems in order to increase productivity3. Applying AI to mining databases may be useful in developing operations for decision-making support4. Automatization using AI could provide significant economic benefits for the mining industry through cost reduction, efficiency, and improving productivity5.

This paper uses insights from earth science about the financial risk of project developments for some key critical minerals to develop a theory that provides a less sanguine view of the potential for AI to address the shortfall in investment in critical minerals, at least in the short-to-medium term. Some key critical minerals have lower value to investors during the initial stages of project development due to both technical and non-technical risks6–8. This type of risk is called back-ended risk and contrasts with front-ended risk, which is present in more traditional mining projects in which investors place a high value on the project in the very early stages, reducing the cost of capital. Taking this observation as a starting point, in this work we develop an economic theory of what we call the back-ended risk premium. This theory states that for minerals that exhibit back-ended risk, the required rate of return by investors is higher than for front-ended risk mineral projects, leading to a back-ended risk premium. The required rate of return is defined as the minimum amount that an investor seeks when they embark on an investment or project9.

We show that the increase in the cost of capital due to this risk premium leads to a theoretical slowdown in technological progress. In the infancy of the AI revolution in the mining sector, the back-ended risk premium has the potential to reduce some of the expected gains from AI-inspired technologies. The extent to which this occurs will depend on whether, and to what extent, progress in AI can reduce the back-ended risk premium by shortening the duration of mining projects and lowering the risk faced by investors.

Results
Conceptualization of the back-ended risk premium
Many of the minerals considered critical for energy transition exhibit back-ended risk in project development caused by technical and non-technical risks occurring in the later stages of the project (six to eight years after the exploration starts)6–8. Examples of critical minerals that exhibit back-ended risk are bauxite, cobalt, lithium, graphite, niobium, nickel laterite, tungsten, scandium, rare earth, vanadium, and zinc. The existence of such back-ended risk means that investment is insufficient for these minerals to increase production in response to an increase in demand. This contrasts with the risk faced by other minerals—so-called front-ended risk minerals—such as gold, copper, and iron ore, for which the value of the project increases rapidly during the exploration stage. For these minerals, most of the investment needed for production occurs in the early stages of the project in response to an increase in demand.

In Fig. 1, we illustrate back-ended and front-ended risk project development and show how this relates to our proposed concept of the back-ended risk premium. The vertical axis represents the valuation (or share price) that the market places on the mineral project. The horizontal axis displays the different stages of development of the mining project (exploration, scoping, feasibility, development, and mining). At time T1 the valuation (or share price) of front-ended risk minerals is V2, which is much higher than the valuation of back-ended risk minerals (V1). Figure 2 shows that the valuations of back-ended and front-ended risk projects eventually converge after the development stage. The back-ended risk premium can be seen as the relative additional rate of return required by investors for back-ended minerals projects, relative to front-ended minerals projects, and in Fig. 1 is denoted by the gray-shaded area.

Figure 1 suggests that for back-ended critical minerals, the value to shareholders only increases in the latter stages of the project development, meaning that investment (and production) will be lower than the market equilibrium requires. Figure 2 illustrates the difference in risk premium between front-ended and back-ended risk projects or the additional risk that investors would incur investing in back-ended minerals compared to front-ended minerals. The vertical axis represents the required rate of return by investors, and the horizontal axis denotes the time between T1 and T2 from Fig. 1. From this figure, we can infer the following:
• Investors require a relatively larger return to be compensated by the back-ended risk premium.
• The back-ended risk premium decreases as a function of time.
• The back-ended risk premium is equal to the risk of front-ended minerals after T2.

Risk premiums for critical minerals
Figure 3 presents the non-technical risk premium for each of the critical minerals for 2022. The measurement of the non-technical risk premium is described in the methods, Supplementary Information Section 2, and in the Supplementary Data. Each of the critical minerals exhibits a non-technical risk premium, relative to the front-ended non-critical mineral benchmark, consisting of coal, gold, and iron ore. The non-technical risk premium varies from rare earth elements (30.6%) to nickel (2.9%), with the average being 15.7%. The heterogeneity in non-technical risk between critical minerals largely reflects the geographical distribution of specific critical minerals, with those exhibiting higher non-technical risk concentrated in countries having less attractive investment environments. In Supplementary Fig. 3, we show these results are robust to including the only major front-ended critical mineral (copper) in addition to coal, gold and iron ore in the benchmark of front-ended minerals.

Figure 4 presents the technical risk premium for 14 critical minerals for which we have data. The measurement of the technical risk premium is described in the methods and Supplementary Information Section 3. Tin has the highest technical risk premium, while copper and antimony have a technical risk discount, relative to the non-critical mineral benchmark. The average technical risk premium across the 14 critical minerals is 18.4%.

In Fig. 5, we present estimates of the total risk premium for the 13 critical minerals for which we have data on technical and non-technical risk. Rare earth elements, tin, manganese, tungsten, and zinc have the highest total risk premium. The only mineral showing a total risk premium discount is copper, reflecting its lower technical score relative to the benchmark.

Estimates of the back-ended risk premium
We provide conservative estimates of the cumulative back-ended risk premium depicted in Fig. 2 until 2035 under the IEA’s Sustainable Development Scenario (SDS) and Stated Policy Scenario (STEPS). STEPS is based on current policy settings, while SDS presents the best-case scenario for the clean energy transition. For each scenario, we calculate the total risk premium based on the weighted average of the market value of each critical mineral. Using the weighted average of the market value has the advantage that it considers differences in the relative importance of critical minerals to the clean energy transition. For the SDS, we calculate the total risk premium to be 32.5%, while for the STEPS the total risk premium is 31.6%.

Our estimates of the cumulative back-ended risk premium until 2035 are given in Fig. 6. The cumulative back-ended risk premium by 2035 ranges between USD 660 billion and USD 678 billion in the STEPS and SDS scenarios, respectively. Based on estimates described in the methods section, in our main analysis, we use 13.49% as the Weighted Average Cost of Capital (WACC) for the benchmark front-ended minerals. In Supplementary Figs. 4 and 5, as a sensitivity check on our main analysis, we alternatively assume that the WACC to calculate the back-ended risk premium is 12% or 16%. When the back-ended risk premium benchmark for front-ended minerals is estimated using a WACC of 12%, the cumulative back-ended risk premium decreases from USD 660-678 to USD 587-604, which can be considered a lower-bound estimate on the cumulative back-ended risk premium. In the upper bound case, when the back-ended risk premium benchmark for front-ended minerals is estimated based on 16%, the cumulative back-ended risk premium increases to the USD 775-804 billion range. This figure represents the additional cost of capital for back-ended projects and helps explain why investment in critical minerals is low even when long-term demand is high.

Using AI to address the shortfall in critical minerals
A key feature of the AI revolution is that many roles will be automated, and machine learning procedures that heavily depend on capital will replace mundane labor tasks10. In a large survey of machine learning researchers, the consensus among respondents was that AI will outperform humans in many activities over the next decade11. The expected gains in productivity in critical minerals production that are hoped will meet the shortfall in supply will be in automation of the labor force and high-level machine intelligence, in which capital is the key component at all stages of the mining industry production process from exploration to extraction5.

The back-ended risk premium theory, presented above, implies that the cost of capital for back-ended minerals is higher than that for front-ended minerals. Our findings suggest that, on average, the WACC for front-ended minerals is 13.49%, whereas, for back-ended minerals, it is 4.26% and 4.44% higher, depending on the scenario (17.75% and 17.93%, respectively). Thus, in a capital-intensive industry in which productivity gains are expected to be made in AI developments, the increase in capital cost because of the back-ended risk premium is expected to be economically large, lowering productivity growth in the critical minerals sector.

In Fig. 7, we employ a production possibility frontier (PPF) to show how the back-ended risk premium affects the potential to increase productivity in back-ended critical minerals via AI. The initial curve shows the combination between capital (K) and labor (L) that is required to produce the maximum amount of minerals for a firm or economy, in which the factors of production are any combination along the PPF of L1 and K1. The green line represents a potential increase in productivity due to AI-technologies which requires more investment in capital (technology). Here capital increases from K1 to K2 and the initial PPF rotates outwards and to the right to L1 and K2. The broken blue line represents the new PPF when the back-ended risk premium is considered. The blue line shows that some of the gains in production made by development in AI-technologies is lost due to the higher cost of capital, reflecting the back-ended risk premium. The loss in production is represented by the move from K2 to K3.

The analysis in Fig. 7 may be overly pessimistic. It overlooks the potential for AI to potentially reduce the back-ended risk premium by reducing the cost of capital for critical minerals mining projects. AI could reduce the back-ended risk premium by reducing the duration of mining projects and reducing the required rate of return on investment. We next consider the different ways in which AI could reduce the duration of mining projects and the required rate of return on investment, although we caution the reader that AI also has limitations, which we outline below. The potential benefits of AI for reducing the back-ended risk premium should be seen in light of those limitations.

There are multiple ways through which AI could potentially reduce the time from exploration to extraction in mining projects. One way is through improved mineral mapping. AI techniques, such as drone-based photogrammetry and remote sensing, can be used to automate the process of mineral mapping, making it possible to predict with greater accuracy regions with higher potential for new deposits12–15. Deep learning algorithms have been shown to provide a very high-level of accuracy in image recognition, which can be used for mineral resource mapping with surface and sub-surface image data12.

Supply-side risk stemming from the geographical concentration of critical minerals in a few countries has led to increased focus on the role of AI in detecting unconventional deposits of critical minerals in situ geological deposits of oil, gas, or coal mineral deposits from secondary by-products of anthropogenic processes16–19. An advantage AI has in such contexts is that a large amount of data has been collected through fossil fuel exploration on such geological deposits, which is well suited to machine learning20.

A second way in which AI could reduce the risk associated with duration from exploration to extraction is by making it possible to more accurately calculate the duration of the extraction period of the mine14. One of the most important risks for all mining projects relates to the orebody itself - i.e. there is significant uncertainty about mineral resources21,22. One specific contributor to uncertainty being higher for critical minerals than conventional minerals in this early stage is that there are relatively few sources of data on critical mineral reserves. A second point of difference is that critical minerals are recovered as by-products from refining other metals. This mean that the metal supply responds not only to the price of the by-product metal but also to the price of the host metal, which affects reserve estimates23. Uncertainty about the potential to recover critical minerals from mine waste, such as tailings, is particularly acute24. A third important way in which critical minerals differ from fossil fuels is that the former can be recycled, although the implications of recycling on future reserves is difficult to quantify, adding to uncertainty about available reserves23. Mining and extraction methods are dictated by the geology of the orebody or orebodies. AI can be used to provide a more accurate depiction of the geology of the orebody and its associated uncertainties25. Several AI methods have been developed to predict the grade and recovery of mineral deposits, reducing the associated technical risk26.

A third avenue for reducing the time from exploration to extraction is through employing AI to improve mining productivity. Risks associated with drilling and blasting performance depend on proper rock fragmentation. AI can be used to predict rock fragmentation and provide real-time evaluation of drilling performance that improves efficiency12. Automated drilling can be fitted with sensors that target specific ores; hence, reducing exploration time27.

AI can also be used to reduce the required rate of return on investment in two main ways. One way is through reducing uncertainty with the risk of a blowout in the cost, which is particularly important for back-ended minerals given that the initial cost of capital is higher than for front-ended minerals. For example, AI can be used to forecast the capital cost of open-pit mining projects28. Equipment selection is the most important phase during mine planning. The capital investment in selecting the right equipment represents a major risk. AI algorithms can be used to reduce the risk with equipment selection12. Once the mine is in operation, AI can be used for predictive maintenance and management of equipment, minimizing repairs27.

AI could also reduce the required rate of return on back-ended projects through reducing the risk, particularly environmental risks, associated with such projects. There is evidence which shows that AI algorithms can be effective in reducing disasters and environmental hazards associated with energy mining29. This is particularly important in the case of some key back-ended critical minerals, such as cobalt and lithium. According to the U.S Geological Survey, more than 50% of the global proven lithium reserves are concentrated in the lithium triangle between Chile, Bolivia, and Argentina. Lithium in this region is mined from salt deserts or so-called salars30. Extracting lithium from salars has generated long-term environmental damage, with locals complaining that lithium mining has increased the prevalence of droughts, threatening livestock farming and drying out vegetation31. About half of the proven reserves of cobalt are located in Congo Kinshasa. Weak institutions, corruption, and conflict in the region exacerbate environmental risks associated with mining Cobalt. The technical risks associated with artisanal mining practices in Congo Kinshaha have caused environmental degradation, including deforestation, soil erosion, water pollution, and biodiversity loss. Unregulated mining activities, using mercury and other chemicals, and inadequate waste management practices can negatively affect ecosystems and local communities in the long term32.

The potential impact of AI on the rate of return and duration of the project are illustrated in Figs. 8 and 9, respectively. In Fig. 8, the potential impact of progress in AI on reducing the risk/rate on the back-ended risk premium is represented by the shift from RB to RAI while the volume of this reduction is represented by the area between the blue and green lines. Formally:

(Integral from T1 to T2 of f(RB) dt) - (Integral from T1 to T2 of f(RAI) dt) = BRP reduction by AI (risk=rate)

In Fig. 9, the impact of AI progress on reducing the mining project time is represented by the shift from TB to TAI (the area between the blue and the green lines represent the volume). Formally:

(Integral from T1 to T2 of f(TB) dt) - (Integral from T1 to T2 of f(TAI) dt) = BRP reduction by AI (time)

In Fig. 10, we demonstrate the effect of lowering the rate of the back-ended risk premium and a reduction in the duration of mining projects because of advancements in AI technology. For simplicity, we assume a 50% reduction in risk. Since our estimates are proportional, readers can infer proportional reductions at different percentages. A 50% reduction in risk resulting from AI improvements leads to a corresponding decrease in the back-ended risk premium, falling within the range USD 330 billion (STEPS) to USD 334 billion (SDS).

Investment in AI can eliminate the negative impact on the back-ended risk premium on investment in and production of critical minerals, which is key to achieving net zero. Various combinations of reductions in the risk/return associated with back-ended projects, as well as the duration of mining project time, may achieve a zero back-ended risk premium. Again, conclusions about the potential for AI to address the back-ended risk premium are subject to AI’s limitations outlined below.

Subject to these limitations, the implication of this result for the energy transition is that an immediate large-scale direct investment in critical minerals’ projects by governments between USD 660 and USD 678 billion is needed over the next decade. This amount is consistent with the shortage in investment estimated by the IEA2. The back-ended risk premium is an additional cost to achieve net zero 2050 that policymakers, thus far, have not considered when estimating the shortfall in investment.

What if meeting carbon net zero by 2050 is not possible?
Some studies have posited that, given existing mining constraints and known reserves of critical minerals needed for clean energy transition, replacing existing fossil fuel sources for energy requirements with renewable alternatives will not be possible by 2050. For example, one study finds that of 29 necessary metals in the lifecycle of renewable energy technologies, known reserves of eight metals might be depleted by 205033. Another study presents several scenarios for transition to carbon net zero, concluding that ultimately, global reserves of cobalt, nickel, and lithium may not be enough to resource the number of batteries needed to power the electric vehicles needed for clean energy transition34.

What does this mean for the back-ended risk premium and AI’s potential mitigation of this risk premium? Our interpretation is that the conclusion from such studies reinforces the importance of attracting investment in back-ended critical minerals to reduce the expected shortfall to realize the clean energy transition. It also highlights the importance of investment in AI applications in development and exploration to reduce the duration of mining projects and reduce the required rate of return on investment. Micheaux suggests that if existing critical mineral reserves are not sufficient to resource clean energy transition, a new social contract may be required that limits energy demand34. A pessimistic conclusion from this might be that if commitment to energy transition by 2050 is ignored this might erode the motivation for global support for net zero emissions from governments and lending agencies. In such circumstances, other solutions would need to be found, and financing investment in back-ended critical minerals would be less important, making the back-ended risk premium less important. However, this is unlikely. Given the global recognition of the threat posed by climate change, it is very unlikely that commitment to clean energy transition will be eroded even if meeting 2050 targets proves not to be feasible. If commitment to the clean energy transition did look like faltering, reducing expected demand for critical minerals, the added uncertainty would increase the back-ended risk premium by increasing the required rate of return on investment35. In this sense, the potential that political commitment to clean energy transition, broadly defined, could wane is a non-technical risk, which increases the uncertainty associated with investing in critical minerals, meaning investors require a higher rate of return. This applies a fortiori to critical minerals that have a high likelihood of running out before 2050. All things equal, the back-ended risk premium for these critical minerals will be higher because the uncertainty about their utility to the clean energy transition is particularly acute. However, it is important to note that the likelihood of a given mineral running out before 2050 is just one non-technical risk among many. Of the eight minerals that Moreau et al.33 conclude might be depleted by 2050, we consider four in this study (cobalt, nickel, tin, and zinc). It is noteworthy that each of these four critical minerals has a relatively low non-technical risk premium (see Fig. 3). More generally, most likely, even if critical minerals do not provide the full solution to resourcing the clean energy transition, they will at least still provide some of the solution, in conjunction with other initiatives, and further investment in critical minerals will be needed to facilitate this.

Limitations on AI as a solution to the back-ended risk premium
We show that improvements in AI have the potential to reduce the back-ended risk premium. However, AI is going to require further investment to ensure the required gains and there is uncertainty about whether and when the benefits of AI will be realized. While it is often touted that AI will lead to improvements in productivity in mining and a reduction in mining project risks, there is a distribution of AI applications in mining and mineral processing in terms of maturity on the Gartner hype cycle for developing new technologies36, with many technologies still early on the curve (especially at ‘peak of inflated expectations’). AI has experienced a number of ‘hype cycles’ in which unrealistic expectations have preceded periods of under-delivery, funding cuts, and slowdowns in investment in R&D37.

Recent meta-studies have also revealed a worrying tendency for overly optimistic AI performance reporting, due to data leakage and lack of reproducibility38,39. Messeri and Crockett argue that “AI solutions can exploit our cognitive limitations, making us vulnerable to illusions of understanding in which we believe we understand more about the world than we actually do”40. AI faces specific challenges in mining and mineral processing projects. One of the main challenges facing the mining sector in exploiting the potential of AI is addressing the skills gap, with many workers requiring reskilling or upskilling to take advantage of AI4,27,41.

A second set of challenges facing AI in mining studies is the lack of high-quality training data14,37,42. The application of AI has been limited by the use of small datasets26,42. Cost forecasting applications are typically based on relatively few data points4. Given that the exploration of critical minerals is relatively new and that there are relatively few critical minerals (and particularly back-ended critical minerals), the lack of databases and experimental data to train and test AI models is particularly acute.

A third related problem is that mining operations often take place in remote locations making access difficult. Abrasive materials, dust, and humidity that are commonplace in mines do not create a congenial environment for the deployment of digital technologies. In underground mining, installed sensors need to be resistant not only to dust and humidity but also to blasting that can damage the senses. Transmitting the data can be problematic due to the limited bandwidth of communication networks employed in underground and open-pit mining. Connectivity can be particularly weak and unstable in deep mine sites27. This makes the storage and transmission of useful data challenging43.

Methods
In this section, we describe how we constructed the non-technical and technical risk premiums and how we use this information to estimate the back-ended risk premium.

Non-technical risk premium
In Supplementary Section 2, we construct indexes for the non-technical risk of 16 major critical minerals and a benchmark index consisting of the average for coal, iron ore, and gold, which are three representative non-critical front-ended minerals6. These 16 critical minerals account for more than 90% of the market value of the 50 critical minerals, as defined by the U.S. Department of Energy. There is no geological data on proven reserves for the other 34 critical minerals, although it is expected that more knowledge on global proven reserves will become available as AI applications improve, making it possible to calculate non-technical risks for other critical minerals in the future. The non-technical risk indexes are the product of the investment attractiveness index from the Annual Survey of Mining Companies57, weighted by country-proven reserves of each individual mineral (see Supplementary Tables 1 and 2)30,58.

Formally, Non-technical risk = Sum (w c,m * S c)

Where w c,m is the proven reserves of critical mineral m in country c as a percentage of the world’s proven reserves of minerals and S is the investment attractiveness index score for country c from the Annual Survey of Mining Companies57.

Using these indexes, we construct a measure of the non-technical risk premium, which is the critical mineral non-technical risk expressed as a percentage of the non-technical risk of the non-critical front-ended mineral benchmark.

Formally, Non-technical risk premium = (IAI NC - IAI C) / IAI NC

Where IAI is the investment attractiveness index and NC and C denote non-critical minerals and critical minerals respectively.

Technical risk premium
Sykes et al.59 measured the technical risk of minerals for 49 minerals on the basis of six characteristics (crustal abundance, crustal concentration, ease of mining, ease of processing, criticality of use, and diversity of use). Details of these definitions are given in Supplementary Information Section 2. Each of the minerals was given a score of either zero, 0.5, or 1 for each characteristic. Thus, technical risk for each mineral takes a value between zero (lowest) and six (highest), with higher values indicating lower technical risk. In Supplementary Fig. 2, we show the scores of this measure of technical risk for 14 critical minerals and our benchmark of front-ended non-critical minerals, consisting of the average for coal, gold and iron ore. Using the technical risk index in Supplementary Information Section 3, we construct a measure of the technical risk premium, which is expressed as the percentage difference between the critical mineral technical risk and the technical risk of the non-critical mineral benchmark.

Formally, Technical Risk Premium = (TR NC - TR C) / TR NC

Where TR is the technical risk of the non-critical mineral benchmark (NC) and critical minerals (C), from Sykes et al.59.

Back-ended risk premium
The back-ended risk premium is estimated as follows:

Back-ended Risk Premium = Total Risk Premium x WACC

The total risk premium is the sum of the standardized technical and non-technical risk premium/discount with possible values in absolute values between zero and 1. WACC is the weighted average cost of capital for the average front-ended mining company. To estimate the WACC, we employ a random sample of 368 mining companies, spanning the period 2007 to 2023, giving a total of 2609 observations. The dataset consists of 368 mining companies engaged in mineral production worldwide, including but not limited to gold, copper, iron ore, and coal and is compiled from Bloomberg. In the Supplementary Data, we provide the dataset and all our calculations. For these firms, the WACC was 13.49%, which is consistent with the finding reported in Rasnosz60 that the WACC of mining companies typically ranges from 8% to 20%, with an average of approximately 14%. In our main analysis, we use 13.49% as the WACC. In sensitivity analysis, we use a lower bound value of the WACC of 12% and an upper bound value of 16%. Our benchmark estimation from Eq. 4 for the back-ended risk premium is 4.44% for the SDS and 4.26% for the STEPS.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This paper studied the financial risk associated with project developments for specific crucial minerals. It constructs a theory that presents a less optimistic perspective on the ability of AI to tackle the investment gap in critical minerals, especially in the short to medium run. An economic theory, called the \"back-ended risk premium\", was formulated to elucidate the phenomenon where investors place high value to mining projects during their very initial phases and to explain an increase in the cost of capital. The calculation indicates that the rise in capital costs resulting from this risk premium theoretically hampers technological advancement. Substantial advancements in AI that decrease the duration of mining projects could diminish the impact of this risk premium on the anticipated benefits derived from AI-driven technology.\n\nHowever, mining investment is influenced by factors such as minerals reserves, supply and demand, macroeconomic elements like Federal Reserve policy rates, geopolitical conditions, and numerous assumptions made in this paper that could be subject to scrutiny within this ever-evolving investment landscape. These simplified \"back-of-the-envelope\" calculations are insufficient for achieving a satisfactory estimation of the capital deficit and cannot offer guidance for government actions. The author is advised to conduct a more meticulous examination of at least 1 to 2 out of the 50 critical minerals, providing a comprehensive assessment of the risk premium and the deficit in capital.\n\nThe authors recommended substantial investment in AI-driven mining technologies to mitigate this capital shortfall, as well as reduce the back-ended risk premium. However, mining is a capital-intensive sector that demands significant equipment expenditure in addition to labor costs. Even for labor cost saving, it is still unclear how AI can automate some of the processes in critical mineral exploration and production, leading to uncertainties regarding the proposed viability. It is crucial for the authors to specify the areas in which the government (World Bank and the International Monetary Fund) should intervene to spearhead this AI mining investment.\n\nAs such, I would like to reject this paper for publication on Nature communications.", "reviewer_2": "Thank you for the opportunity to review your manuscript entitled “Artificial intelligence and the supply of critical minerals” to Nature Communications. The manuscript describes a novel economic theory that differentiates the types of risks associated with critical versus conventional raw materials. Overall, the manuscript is very well written and clearly describes the results and their implications. The topic of critical raw material supply constraints and artificial intelligence will likely be of broad interest. It was also great to see the analysis presented as an electronic supplementary table. My review is provided as an annotated PDF and a Word document. The annotated PDF highlights sentences that could be clarified and other minor comments. Below are more general comments for your consideration:\n(1) The technical and non-technical risks of critical raw materials projects and how they differ from conventional raw materials need to be discussed in detail in the introduction and Section 2. The assumption that there are disparate risk profiles for different critical raw materials goes to the core of the manuscript’s thesis, but is not described. Readers are referred to the literature for finding this rationale on their own, but the most important references mentioned in the main body of the manuscript do not appear in the bibliography. A quick google scholar search for the author profiles did not return results for “Trench 2013” and “Trench and Packey 2014”. In the absence of any further information, it is not obvious to me why these different risk profiles are commodity specific, since geography and political risks have the potential to impact all commodities. Please list and describe the technical and non-technical risk factors and how they vary systemically for each critical raw material. This kind of background information is needed to support the assumptions underpinning the analysis;\n(2) The papers also seems to assume that front- and back-end risks profiles are fixed for the conceptual section of the manuscript (Section 2). However, it is not obvious to me why these risks can’t be mitigated between discovery and development (or vice versa) to convert critical raw materials from back-ended to front-ended. Sections 3 and the text around equations 2 and 3 discuss the dynamic nature of these risk profiles within the context of AI, but I think some discussion on the other controlling factors for these risk profiles would help readers understand and interpret your results. Shortening the lead times of back-ended projects with AI seems to be distinct from the potential for AI, management, or policy interventions to be used, like a switch, to mitigate these risks all together. The probability that this could happen is higher for critical raw materials used in defence, where governments may directly intervene for national security considerations;\n(3) The paper assumes that 50% of critical raw materials exhibit a back-ended risk premium, however not all critical minerals are of equal importance to the renewable energy transition. Moreover, different critical minerals are required in different volumes and have different monetary values. My understanding is that an average proportion (i.e., 50%) was selected based on the number of minerals that are either front- or back-ended with no further information provided. If my interpretation is correct, the assumption made in this manuscript seems too simplistic. The range of proportions (60-40%) for front- versus back-end risks addresses this caveat to some extent, but I think readers would benefit from some discussion on the relative importance of each critical raw material to the energy transition and how this average proportion was selected;\n(4) I was also interested to learn that some critical minerals defined as front- or back-ended are in fact sourced from the same deposit type. For example, cobalt and nickel are both sourced, at least in part, from magmatic sulphide deposits. But cobalt is a back-end mineral; whereas nickel is a front-end mineral. The front- versus back-end risk paradigm and its fixture to commodities seems too simplistic for critical raw materials with multiple geological sources;\n(5) I got confused between the types of government invested being proposed as part of the current study. As you probably know better than me, Government investments in critical raw materials is pretty complex. Tax incentives, pools of patient capital for financing, and nationalization of the entire minerals sector could also be considered “government investment”. I think the policy section would benefit from some clarity over how investing in AI differs from the current government approach. At the same time, it may be worth highlighting that investments in AI specific to critical raw materials may be distinct from AI investment made by governments more generally. Your analysis also demonstrates the most important stages to apply this AI investment to reduce back-end premium (i.e., all AI applications between exploration and development, including data-driven land-use planning, prospectivity modelling, geophysical-geochemical-remote sensing surveys, drilling sensors, 3D resource modelling, pre-competitive ESG). Adding more specificity over the types of AI investments and the role of government could increase the impact of your work;\n(6) I am not an economist, but the idea that governments are the ones primarily responsible for reducing the back-ended risk premiums for the minerals sector seems controversial. Maybe this is a common assumption in this field of research. It is true that governments are already “investing” in different ways to de-risk mineral exploration. I work for a geological survey organization and so my job sort of depends on the idea that governments can support the discovery of critical raw materials. Investing in AI seems like a good idea to me because it is broad based and the rising tide of any technical breakthroughs could lift all boats. But surely the industry also has some role to invest in the type of AI that would shorten their own lead times and increase profits. Perhaps this is what you mean by “supranational organizations” and their role in reducing back-ended risk premiums. Please consider mentioning other industry-led initiatives that could reduce back-ended risk premiums in addition to government funding;\n(7) I think the title could be improved to describe what this study actually shows. For example, something like “Artificial intelligence investments reduce risks to critical mineral supply” or “Securing critical mineral supply chains through investments in artificial intelligence” would give readers a much better sense for your main conclusions and would likely increase your impact. In my view, your work is far more interesting than the current title suggests.\n\nOverall, this is a well written manuscript that will be of significant interest to a broad readership. The conceptual model and the analysis provides an economic perspective on the risks to the supply of critical raw materials. I am a geochemist by training and do not have enough expertise to place this study within the broader economics literature. The ideas and analysis seems novel to me, but other reviewers are needed to characterize the level of innovation within the context of economics. Nevertheless, I could follow the logic of the manuscript and can quite clearly see the significance of the work. I hope that my comments can help with your revision process. Thanks again for the opportunity to review your work. Congratulations and best wishes with your research.\nSincerely,\nChris", "reviewer_3": "The authors present an interesting discussion on risks and capital investment in mining projects, specifically in the context of a transition away from fossil fuels, and how artificial intelligence is predicted to affect the required capital investment.\n\nThe authors are commended for considering an interesting and important topic. However, some key considerations need to be addressed to ensure that the discussion is sufficiently critical and rigorous.\n\nThe first key consideration that requires more critical evaluation is the minerals supply outlook and associated impact on assumptions about investment strategies. Some publications suggest that replacing existing fossil fuel sources for energy requirements with renewable alternatives is not possible by 2050, given limited mining capacity (Micheaux, 2021; Moreau et al., 2019). If such a scenario is likely, then current assumptions for capital investment may not be valid. A new social contract may be required (limiting energy demand), commitment to energy transition by 2050 might be ignored (eroding the motivation for global support from the World Bank and International Monetary Fund, for example), or other departures from the assumed investment climate. The authors should mention the diverging scenarios reported in literature, and consider how the proposed concepts of back-ended risk premium and AI's potential mitigation of this risk premium would play out for such scenarios.\n\nThe second key consideration is the assumption that AI will necessarily lead to improvements in productivity or reduction in mining project risks. There is a distribution of AI and ML applications in mining and mineral processing in terms of maturity on the Gartner hype cycle, but many technologies are still early on the curve (especially at \"peak of inflated expectations\"). In general, recent meta-studies have also revealed a worrying tendency for overly optimistic AI and ML performance reporting in academic literature, due to data leakage and lack of reproducibility (Kapoor and Narayanan, 2022). AI and ML face specific challenges in mining and mineral processing projects (Estay et al., 2023), and may introduce more risks and delays. Although the authors do mention that AI would require further investment to ensure the required gains, the authors can share some relevant issues and citations with readers, as well as to introduce some of the associated uncertainty of AI benefit into their analysis.\n\nThe third key consideration relates to the front-end versus back-end mining project value-over-time curves. More detail and support need to be provided on why different minerals have these different profiles. What contributes to the uncertainty during scoping, feasibility, and development in these projects? Are these uncertainty contributions still valid, given the potential scenarios mentioned previously of not being able to attain an energy transition by 2050 or having a different global social contract in place (e.g., reduced energy demand)?\n\nThe fourth key consideration is the limited approach of the different scenarios considered. A top-down approach is used to assign low, medium, or high scenarios, and these three scenarios are evaluated. A more critical approach would be a bottom-up approach where the major variable factors are considered in a simple model of the back-ended risk premium through a Monte Carlo simulation (including ranges of AI benefits, uncertainty in back-ended risk premium profiles, different energy transition outlooks, etc.).\n\nThe manuscript needs a careful read-through to correct editorial issues, including typos. For example: note that in figure 7, the green arrow should point to the left, to indicate the potential reduction in project length that the authors suggest AI could lead to.\n\nReferences:\n- Michaux, S. P. (2021). Assessment of the extra capacity required of alternative energy electrical power systems to completely replace fossil fuels. Report, 42, 2021.\n- Moreau, V., Dos Reis, P. C., & Vuille, F. (2019). Enough metals? Resource constraints to supply a fully renewable energy system. Resources, 8(1), 29.\n- Kapoor, S., & Narayanan, A. (2022). Leakage and the reproducibility crisis in ML-based science. arXiv preprint arXiv:2207.07048.\n- Estay, H., Lois-Morales, P., Montes-Atenas, G., & Ruiz del Solar, J. (2023). On the Challenges of Applying Machine Learning in Mineral Processing and Extractive Metallurgy. Minerals, 13(6), 788."}, "author_response": "Response to Reviewers:\nReviewer #1\nThank you for your suggestions. Below we first restate your comment and then provide our response, including outlining how we have revised the paper.\n\nMining investment is influenced by factors such as minerals reserves, supply and demand, macroeconomic elements like Federal Reserve policy rates, geopolitical conditions, and numerous assumptions made in this paper that could be subject to scrutiny within this ever-evolving investment landscape. These simplified \"back-of-the-envelope\" calculations are insufficient for achieving a satisfactory estimation of the capital deficit and cannot offer guidance for government actions.\n\nHow might \"factors such as minerals reserves, supply and demand, macroeconomic elements like Federal Reserve policy rates, geopolitical conditions\" affect back-ended risk premium\nThanks for the comment. In the revised version, in a new Section 2.1 we calculate the total risk premium for 14 major critical minerals. We use these calculations to estimate the back ended risk premium. We have identified mineral reserves and geopolitical conditions in host countries and criticality and diversity of use - which affect demand relative to supply - as primary factors influencing the long-term back-ended risk premium.\n\nAdditionally, we've factored in supply and demand dynamics based on scenarios provided by the International Energy Agency (IEA), such as the Sustainable Scenario and Stated Energy Policy Scenario. These scenarios offer valuable insights into projected demand, which in turn informs our computation of the back-ended risk premium.\n\nHence, in the revised version, our estimates of the back-ended risk premium specifically account for mineral reserves, supply and demand dynamics and geopolitical conditions in host countries, which affect the investment attractiveness of each critical mineral.\n\nWhile macroeconomic factors like economic growth, inflation, and interest rates typically affect short-term mineral investment trends due to business cycles, we are primarily concerned with long-term horizons (spanning 5 to 20 years) in our study.\n\nHere, we assume that macroeconomic conditions have minimal impact on production over the long term. This perspective is supported by the research summarized in Chien (2015) and long-term growth theory elucidated in Romer (1990).\n\nThe author is advised to conduct a more meticulous examination of at least 1 to 2 out of the 50 critical minerals, providing a comprehensive assessment of the risk premium and the deficit in capital.\nThanks for the comment. In the revised version we calculate the technical risk premium and non-technical risk premium for 14 and 16 critical minerals respectively. Together these represent about 90% of the 50 critical minerals by proven reserves.\n\nWe then calculate the total risk premium, which is the sum of the technical and non-technical risk premium and the back-ended risk premium = total risk premium/cost of capital for non-critical minerals for the 14 critical minerals.\n\nThe new Section 2.1 reads as follows:\n\n2.1. Measuring Non-technical and Technical Risk Premiums\nIn this section we develop measures of the non-technical and technical risks premium and use these to compare the non-technical and technical risks of the main critical minerals.\n\n2.1.1 Non-technical risk premium\nWe construct indexes for the non-technical risk premium for 16 major critical minerals and a benchmark index, consisting of the average for coal, iron ore and gold, which are three representative non-critical front-ended minerals (Trench & Packey, 2012). These 16 critical minerals account for more than 90% of the market value of the 50 critical minerals, defined by the U.S. Department of Energy (2023).1 The indexes are the product of the investment attractiveness index from the Annual Survey of Mining Companies (Fraser Institute, 2022), weighted by country proven reserves of each individual mineral from the United States Geological Survey (USGS) (2022) and the BP Statistical Review of World Energy (2022).\n\nFormally,\n𝑁𝑜𝑛−𝑡𝑒𝑐ℎ𝑛𝑖𝑐𝑎𝑙 𝑟𝑖𝑠𝑘 =∑𝑛𝑤 ∗𝑠 (1)\n𝑖 𝑐,𝑚 𝑐\n𝑤 is the proven reserves of critical mineral m in country as a percentage of the world's proven reserves of\n𝑐,𝑐𝑚\nmineral m and 𝑆 is the investment attractiveness index score for country c from the Annual Survey of Mining\nCompanies conducted by the Fraser Institute (2022).2\n\nIn Figure 4, we show the non-technical risk for each of the 16 critical minerals and an equal-weighted average of three front-ended risk minerals (coal, iron ore and gold) for 2022. The index reflects the investment attractiveness for each mineral, in which a higher value is associated with less non-technical risk. The non-critical minerals benchmark has a median investment attractiveness index of 64.5, compared to the median of 54.5 for the 16 critical minerals. Thus, the non-technical risk of investing in the front-ended non-critical minerals benchmark is lower than each critical mineral. There is considerable heterogeneity in the non-technical risk of specific critical minerals. Rare earth elements has the highest non-technical risk/lowest attractiveness index of 44.7, followed by platinum (46.48), chromium (47.4), tungsten (47.7), graphite (47.8), barite (52.2), tin (54.3), manganese (53.9), antimony (55.0), cobalt (55.5), vanadium (55.0), lithium (58.2), bauxite (58.4), cooper (60.5), zinc (61.5) and nickel 62.7.3\n\nUsing these indexes, we construct a measure of the non-technical risk premium, which is expressed as the critical mineral non-technical risk expressed as a percentage of the non-technical risk of the non-critical front-ended mineral benchmark.\n\nFormally,\n𝑁𝑜𝑛−𝑡𝑒𝑐ℎ𝑛𝑖𝑐𝑎𝑙 𝑟𝑖𝑠𝑘 𝑝𝑟𝑒𝑚𝑖𝑢𝑛=\n(𝐼𝐴𝐼𝑁𝐶,−𝐼𝐴𝐼𝐶)\n(2)\n𝐼𝐴𝐼𝑁𝐶\nWhere 𝐼𝐴𝐼 is the investment attractiveness index and NC and C denote non-critical minerals and critical minerals respectively. Figure 5 presents the non-technical risk premium for each of the critical minerals for 2013-2022. Consistent with Figure 4, each of the critical minerals exhibits a non-technical risk premium, relative to the front-ended non-critical mineral benchmark. The non-technical risk premium varies from rare earth elements (28.7%) to zinc (4.8%), with the average being 15.7%. The heterogeneity in non-technical risk between critical minerals largely reflects the geographical distribution of specific critical minerals, with those exhibiting higher non-technical risk concentrated in countries having less attractive investment environments.4\n\n2.1.2 Technical risk premium\nSykes et al. (2016) measured the technical risk of minerals for 49 minerals on the basis of six characteristics (crustal abundance, crustal concentration, ease of mining, and ease of processing, criticality of use and diversity of use).5 Each of the minerals was given a score of either zero, 0.5 or 1 for each characteristic. Thus, technical risk for each mineral takes a value between zero (lowest) and six (highest), with higher values indicating lower technical risk.\n\nFigure 6 show the scores of this measure of technical risk for 14 critical minerals and our benchmark of front-ended non-critical minerals, consisting of the average for coal, gold and iron ore.6 Ten of the 14 critical minerals have higher technical risk than the non-critical mineral benchmark (3.5), with tin, manganese, rare earth elements and zinc exhibiting the highest technical risk (1.5-2). Copper and antimony have a score of 4 which reflect less technical risk, while bauxite and barite have the same technical risk as the non-critical benchmark.\n\nUsing the technical risk index in Figure 6, we construct a measure of the technical risk premium, which is expressed as the percentage difference between the critical mineral technical risk and the technical risk of the non-critical mineral benchmark.\n\nFormally,\n𝑇𝑒𝑐ℎ𝑛𝑖𝑐𝑎𝑙 𝑟𝑖𝑠𝑘 𝑝𝑟𝑒𝑚𝑖𝑢𝑛=\n(𝑇𝑅𝑁𝐶,−𝑅𝑅𝐶)\n(3)\n𝑇𝑅𝑁𝐶\nWhere TR is the technical risk of the non-critical mineral benchmark (NC) and critical minerals (C), from Sykes et al (2016). Figure 7 presents the technical risk premium for each of the 14 critical minerals. Consistent with Figure 6, tin has the highest technical risk premium, while copper and antimony have a technical risk discount, relative to the non-critical mineral benchmark. The average technical risk premium across the 14 critical minerals is 18.4%.\n\n2.1.3 Total risk premium for critical minerals\nIn Figure 8, we plot technical risk scores on the horizontal axis and non-technical risk scores on the vertical axis for 14 critical minerals (green dots) and the non-critical minerals benchmark (black dots). The values are normalised from 0 to 1 and inversed, such that values closer to the intercept of each axes denote lower technical and non-technical risks. Rare earth elements has the highest non-technical risk of 1 and also has a very high technical risk of 0.6. Tin exhibits the highest technical risk of 1 and the third largest non-technical risk of 0.25.\n\nIn Figure 9 we present estimates of the total risk premium for the 14 critical minerals for which we have data on technical and non-technical risk. The total risk premium is the sum of the standardized technical and non-technical risk premium/discount with possible values in absolute values between zero and 100. Rare earth elements, tin, manganese, tungsten and zinc have the highest total risk premium. The only mineral showing a total risk discount is copper, reflecting its lower technical score, relative to the benchmark.\n\nWe have also provided new estimates of the BRP under two scenarios proposed by the IEA (SDA and STEPS), where for each scenario we calculate the total risk premium based on the weighted average of the market value of each critical mineral. Using the weighted average of the market value has the advantage that it takes into account differences in the relative importance of critical minerals to the clean energy transition.\n\nThe new Section 2.2 reads as follows:\n\n2.2. Estimates of the back-ended risk premium\nNext, we provide conservative estimates of the cumulative back-ended risk premium depicted in Figure 3 until 2035 under the Sustainable Development Scenario (SDS) and Stated Policy Scenario (STEPS).7 For each scenario we calculate the total risk premium based on the weighted average of the market value of each critical mineral. Using the weighted average of the market value has the advantage that it takes into account differences in the relative importance of critical minerals to the clean energy transition. For the SDS, we calculate the total risk premium to be 32.5%, while for the STEPS the total risk premium is 31.6%.\n\nThe BRP is estimated as follows:\n𝐵𝑎𝑐𝑘−𝑒𝑛𝑑𝑒𝑑 𝑅𝑖𝑠𝑘 𝑝𝑟𝑒𝑚𝑖𝑢𝑛=𝑇𝑜𝑡𝑎𝑙 𝑅𝑖𝑠𝑘 𝑃𝑟𝑒𝑚𝑖𝑢𝑚∗𝑊𝐴𝐶𝐶 (4)\nWACC is the weighted average cost of capital for the average of front-ended mining company. To estimate the WACC, we employ a random sample of 368 mining companies, spanning the period 2007 to 2023, giving a total of 2609 observations. 8 For these firms, the WACC was 13.49%, which is consistent with the finding reported in Rasnosz (2017) that the WACC of mining companies typically ranges from 8% to 20%, with an average of approximately 14%. In our main analysis, we use 13.49% as the WACC. In sensitivity analysis, we use a lower bound value of the WACC of 12% and an upper bound value of 16%. Our benchmark estimation from Equation 4 for the BRP is 4.44% for the SDS and 4.26% for the STEPS.\n\nOur estimates of the cumulative BRP until 2035 are given in Figure 10. The cumulative BRP by 2035 ranges between USD 660 billion and USD 678 billion in the STEPS and SDS scenarios respectively. In Appendix A in Figure A2, we assume that the WACC to calculate the BRP is 12%, while in Figure A3 we assume the WACC is 16%. When the BRP benchmark for front-ended minerals is estimated using a WACC of 12%, the cumulative BRP decreases from USD 660-678 to USD 587-604, which can be considered a lower-bound estimate on the cumulative BRP. In the upper bound case, when the BRP benchmark for front-ended minerals is estimated based on 16%, the cumulative BRP increases to the USD 775-804 billion range. This figure represents the additional cost of capital for back-ended projects and helps explain why investment in critical minerals is low even when long-term demand (and prices) are increasing.\n\nThe authors recommended substantial investment in AI-driven mining technologies to mitigate this capital shortfall, as well as reduce the back-ended risk premium. However, mining is a capital-intensive sector that demands significant equipment expenditure in addition to labor costs. Even for labor cost saving, it is still unclear how AI can automate some of the processes in critical mineral exploration and production, leading to uncertainties regarding the proposed viability. It is crucial for the authors to specify the areas in which the government (World Bank and the International Monetary Fund) should intervene to spearhead this AI mining investment.\n\nThanks for your comment. We address this comment in the paper in two ways.\n\nFirst, we provide more detail on how AI could reduce the back-ended risk premium:\nAI could reduce the back-ended risk premium in two different ways; namely, via reducing the reducing the duration of mining projects and reducing required rate of return on investment.\n\nThere are multiple ways through which AI could reduce the time from exploration to extraction in mining projects. One way is through improved mineral mapping. AI techniques, such as drone-based photogrammetry and remote sensing, can be used to automate the process of mineral mapping, making it possible to predict with greater accuracy regions with higher potential for new deposits (Ali & Frimpong, 2020; Honarmand & Shahriari, 2021;\nJooshaki et al., 2021; Kohler et al., 2021). Deep learning algorithms have been shown to provide a very high level of accuracy in image recognition, which can be used for mineral resource mapping with surface and sub-surface image data (Ali & Frimpong, 2020).\n\nSupply-side risk stemming from geographical concentration of critical minerals in a few countries has led to increased focus on the the role of AI in detecting unconventional deposits of critical minerals in situ geological deposits of oil, gas or coal mineral deposits from secondary byproducts of anthropogenic processes (Chaterjee et al., 2022; Creason et al., 2023; Presley, 2024; Roth et al 2017). An advantage AI has in such contexts is that a large amount of data has been collected through fossil fuel exploration on such geological deposits, which is well suited to machine learning (Bishop & Robbins, 2024)\n\nA second way in which AI could reduce the risk associated with duration from exploration to extraction is through by making it possible to more accurately calculate the duration of the extraction period of the mine (Jooshaki et al., 2021). Mining and extraction methods are dictated by the geology of the orebody or oreobodies. AI can be used to provide a more accurate depiction of the geology of the oreobody and its associated uncertainties (Nwaila et al., 2022). Several AI methods have been developed to predict the grade and recovery of mineral deposits, reducing the associated technical risk (Gomez-Flores et al. 2022.\n\nA third avenue for reducing the time period from exploration to extraction is through employing AI to improve mining productivity. Risks associated with drilling and blasting performance depends on proper rock fragmentation. AI can be used to predict rock fragmentation and provide real time evaluation of drilling performance that improves efficiency (Ali & Frimpong, 2020). Automated drilling can be fitted with sensors that target specific ores; hence, reducing exploration time (Onifade et al 2023).\n\nAI can also be used to reduce the required rate of return on investment in two main ways. One way is through reducing uncertainty with the risk of a blow out in the cost, which is particularly important for back-ended minerals given that the initial cost of capital is higher than for front-ended minerals. For example, AI can be used to forecast the capital cost of open pit mining projects (Zhang et al., 2020). Equipment selection is the most important phase during mine planning. The capital investment in selecting the right equipment represents a major risk. AI algorithms can be used to reduce the risk with equipment selection (Ali & Frimpong, 2020). Once the mine is in operation, AI can be used for predictive maintenance and management of equipment, minimizing repairs (Onifade et al. 2023).\n\nAI could also reduce the required rate of return on back-ended projects through reducing the risk, particularly environmental risks, associated with such projects. You et al (2021), for example, review evidence which shows that AI algorithms can be effective in reducing disasters and environmental hazards associated with energy mining. This is particularly important in the case of some key back-ended critical minerals, such as Cobalt and Lithium.\n\nAccording to the US Geological Survey (2023), more than 50% of the global proven lithium reserves are concentrated in the lithium triangle between Chile, Bolivia, and Argentina. Lithium in this region is mined from salt deserts or so-called salars. Extracting lithium from salars has generated long-term environmental damage, with locals complaining that lithium mining has increased the prevalence of droughts, threatening livestock farming and drying out vegetation (Oritz, 2021). About half of the proven reserves of cobalt are located in Congo Kinshasa. Weak institutions, corruption, and conflict in the region exacerbate environmental risks associated with mining Cobalt. The technical risks associated with artisanal mining practices in Congo Kinshaha have caused environmental degradation, including deforestation, soil erosion, water pollution, and biodiversity loss. Unregulated mining activities, using mercury and other chemicals, and inadequate waste management practices can negatively affect ecosystems and local communities in the long term (Nkulu et al, 2018).\n\nSecond, we have rewritten Section 3.3 Policy recommendations to provide specific examples of \"the areas in which the government (World Bank and the International Monetary Fund) should intervene to spearhead this AI mining investment\":\n\nMost AI applications in mining have focused on the development and exploration phase, which is where AI is particularly useful in mitigating the technical risks (Kohler et al. 2021; Noreiga & Pourrahimian, 2022). One specific AI application in which governments and mining companies could invest is in data-driven prospectivity modelling where random forest (Flores et al 2020) and extreme learning machine models (Chen & Wu, 2017) have been used to more accurately predict recovery rates and reduce the time from exploration to extraction. A second related AI application is in mineral mapping in which drilling sensors, geophysical-geochemical-remote sensing surveys and 3D geological modelling can be used to more accurately predict locations with the most potential for mineral deposits (Acosta et al., 2019; Jindai et al., 2012; Tusa et al., 2019; Wang & Huang, 2012).\n\nA third area for potential investment would be in AI methods, such as artificial neural networks (Chatterjee et al, 2010) and extreme learning models (Jain et al, 2021), to predict the grade and recovery of mineral deposits. A fourth promising area for investment in AI is in the recovery of critical minerals from mining waste. AI has proved particularly useful in improving secondary recovery approaches, such as adsorption (Briao et al 2023; Sadi & Solemani, 2023), reducing time from exploration to extraction.\n\nLithium extraction in Chile, Bolivia, and Argentina is plagued by water scarcity. Scarcity of water and water management is one of the main challenges of mineral processing plants that lead to delays (Nwaila et al. 2022). Automated 'dry' or water-savvy sensing AI techniques can be employed to minimise these risks (Nwaila et al. 2022). The benefits of using critical minerals, such as lithium, to facilitate the clean energy transition will be muted if they are extracted in an environmentally unfriendly way. More generally, investment in AI powered environmental monitoring systems, such as smart earth technologies, could help mitigate the impact of minerals such as cobalt and lithium on the environment by detecting and addressing pollution in real time (Dhanwani et al., 2021). One of the biggest obstacles to the successful adoption of AI in addressing back-ended risk is the lack of data. Creating publicly available material datasets that can be used as a source of model training and testing is a common recommendation to further AI applications in mining (Maleki et al, 2022). Given the public goods nature of having open access material datasets, this is one area in which governments and international lending agencies in particular could invest.\n\nReferences cited in Response to Reviewer # 1\nChien, Y., 2015. What Drives Long-Run Economic Growth? Federal Reserve Bank of St. Louis, June, 1.\nRomer, P.M., 1990. Endogenous Technological Change. Journal of Political Economy, 98(5), pp. S71-S102.\n\nReviewer # 2\nThank you for reading our paper so closely and for your many very helpful suggestions. In addition to the suggested changes on the manuscript, which we have made, below we provide our responses to your comments. First, we restate your comment and then we provide our response, including how we have addressed it in the paper.\n\n(1) The technical and non-technical risks of critical raw materials projects and how they differ from conventional raw materials need to be discussed in detail in the introduction and Section 2. The assumption that there are disparate risk profiles for different critical raw materials goes to the core of the manuscript’s thesis, but is not described. Readers are referred to the literature for finding this rationale on their own, but the most important references mentioned in the main body of the manuscript do not appear in the bibliography. A quick google scholar search for the author profiles did not return results for “Trench 2013” and “Trench and Packey 2014”. In the absence of any further information, it is not obvious to me why these different risk profiles are commodity specific, since geography and political risks have the potential to impact all commodities. Please list and describe the technical and non-technical risk factors and how they vary systemically for each critical raw material. This kind of background information is needed to support the assumptions underpinning the analysis.\n\nThanks for the suggestion. In response, we incorporate a new Section 2.1 in which we calculate the technical risk scores and technical risk premium for 14 critical minerals, based on estimates in Sykes et al (2014). We also calculate the non-technical risk scores and non-technical risk premium for 16 critical minerals using the Fraser Institute Annual Survey of Mining Companies. These 14-16 critical minerals represent about 90% of the 50 critical minerals identified by the U.S. Dept of Energy (2023). We compare the risk profiles for these critical minerals with a benchmark average of gold, iron ore and coal, which we treat as representative front-ended non-critical minerals.\n\nThe new Section 2.1 reads as follows:\n\n2.1. Measuring Non-technical and Technical Risk Premiums\nIn this section we develop measures of the non-technical and technical risks premium and use these to compare the non-technical and technical risks of the main critical minerals.\n\n2.1.1 Non-technical risk premium\nWe construct indexes for the non-technical risk premium for 16 major critical minerals and a benchmark index, consisting of the average for coal, iron ore and gold, which are three representative non-critical front-ended minerals (Trench & Packey, 2012). These 16 critical minerals account for more than 90% of the market value of the 50 critical minerals, defined by the U.S. Department of Energy (2023).9 The indexes are the product of the investment attractiveness index from the Annual Survey of Mining Companies (Fraser Institute, 2022), weighted by country proven reserves of each individual mineral from the United States Geological Survey (USGS) (2022) and the BP Statistical Review of World Energy (2022).\n\nFormally,\n𝑁𝑜𝑛−𝑡𝑒𝑐ℎ𝑛𝑖𝑐𝑎𝑙 𝑟𝑖𝑠𝑘 =∑𝑛𝑤 ∗𝑠 (1)\n𝑖 𝑐,𝑚 𝑐\n𝑤 is the proven reserves of critical mineral m in country as a percentage of the world's proven reserves of\n𝑐,𝑐𝑚\nmineral m and 𝑆 is the investment attractiveness index score for country c from the Annual Survey of Mining\nCompanies conducted by the Fraser Institute (2022).10\n\nIn Figure 4, we show the non-technical risk for each of the 16 critical minerals and an equal-weighted average of three front-ended risk minerals (coal, iron ore and gold) for 2022. The index reflects the investment attractiveness for each mineral, in which a higher value is associated with less non-technical risk. The non-critical minerals benchmark has a median investment attractiveness index of 64.5, compared to the median of 54.5 for the 16 critical minerals. Thus, the non-technical risk of investing in the front-ended non-critical minerals benchmark is lower than each critical mineral. There is considerable heterogeneity in the non-technical risk of specific critical minerals. Rare earth elements has the highest non-technical risk/lowest attractiveness index of 44.7, followed by platinum (46.48), chromium (47.4), tungsten (47.7), graphite (47.8), barite (52.2), tin (54.3), manganese (53.9), antimony (55.0), cobalt (55.5), vanadium (55.0), lithium (58.2), bauxite (58.4), cooper (60.5), zinc (61.5) and nickel 62.7.11\n\nUsing these indexes, we construct a measure of the non-technical risk premium, which is expressed as the critical mineral non-technical risk expressed as a percentage of the non-technical risk of the non-critical front-ended mineral benchmark.\n\nFormally,\n𝑁𝑜𝑛−𝑡𝑒𝑐ℎ𝑛𝑖𝑐𝑎𝑙 𝑟𝑖𝑠𝑘 𝑝𝑟𝑒𝑚𝑖𝑢𝑛=\n(𝐼𝐴𝐼𝑁𝐶,−𝐼𝐴𝐼𝐶)\n(2)\n𝐼𝐴𝐼𝑁𝐶\nWhere 𝐼𝐴𝐼 is the investment attractiveness index and NC and C denote non-critical minerals and critical minerals respectively. Figure 5 presents the non-technical risk premium for each of the critical minerals for 2013-2022. Consistent with Figure 4, each of the critical minerals exhibits a non-technical risk premium, relative to the front-ended non-critical mineral benchmark. The non-technical risk premium varies from rare earth elements (28.7%) to zinc (4.8%), with the average being 15.7%. The heterogeneity in non-technical risk between critical minerals largely reflects the geographical distribution of specific critical minerals, with those exhibiting higher non-technical risk concentrated in countries having less attractive investment environments.12\n\n2.1.2 Technical risk premium\nSykes et al. (2016) measured the technical risk of minerals for 49 minerals on the basis of six characteristics (crustal abundance, crustal concentration, ease of mining, and ease of processing, criticality of use and diversity of use).13 Each of the minerals was given a score of either zero, 0.5 or 1 for each characteristic. Thus, technical risk for each mineral takes a value between zero (lowest) and six (highest), with higher values indicating lower technical risk.\n\nFigure 6 show the scores of this measure of technical risk for 14 critical minerals and our benchmark of front-ended non-critical minerals, consisting of the average for coal, gold and iron ore.14 Ten of the 14 critical minerals have higher technical risk than the non-critical mineral benchmark (3.5), with tin, manganese, rare earth elements and zinc exhibiting the highest technical risk (1.5-2). Copper and antimony have a score of 4 which reflect less technical risk, while bauxite and barite have the same technical risk as the non-critical benchmark.\n\nUsing the technical risk index in Figure 6, we construct a measure of the technical risk premium, which is expressed as the percentage difference between the critical mineral technical risk and the technical risk of the non-critical mineral benchmark.\n\nFormally,\n𝑇𝑒𝑐ℎ𝑛𝑖𝑐𝑎𝑙 𝑟𝑖𝑠𝑘 𝑝𝑟𝑒𝑚𝑖𝑢𝑛=\n(𝑇𝑅𝑁𝐶,−𝑅𝑅𝐶)\n(3)\n𝑇𝑅𝑁𝐶\nWhere TR is the technical risk of the non-critical mineral benchmark (NC) and critical minerals (C), from Sykes et al (2016). Figure 7 presents the technical risk premium for each of the 14 critical minerals. Consistent with Figure 6, tin has the highest technical risk premium, while copper and antimony have a technical risk discount, relative to the non-critical mineral benchmark. The average technical risk premium across the 14 critical minerals is 18.4%.\n\n2.1.3 Total risk premium for critical minerals\nIn Figure 8, we plot technical risk scores on the horizontal axis and non-technical risk scores on the vertical axis for 14 critical minerals (green dots) and the non-critical minerals benchmark (black dots). The values are normalised from 0 to 1 and inversed, such that values closer to the intercept of each axes denote lower technical and non-technical risks. Rare earth elements has the highest non-technical risk of 1 and also has a very high technical risk of 0.6. Tin exhibits the highest technical risk of 1 and the third largest non-technical risk of 0.25.\n\nIn Figure 9 we present estimates of the total risk premium for the 14 critical minerals for which we have data on technical and non-technical risk. The total risk premium is the sum of the standardized technical and non-technical risk premium/discount with possible values in absolute values between zero and 100. Rare earth elements, tin, manganese, tungsten and zinc have the highest total risk premium. The only mineral showing a total risk discount is copper, reflecting its lower technical score, relative to the benchmark.\n\n(2) The papers also seems to assume that front- and back-end risks profiles are fixed for the conceptual section of the manuscript (Section 2). However, it is not obvious to me why these risks can’t be mitigated between discovery and development (or vice versa) to convert critical raw materials from back-ended to front-ended. Sections 3 and the text around equations 2 and 3 discuss the dynamic nature of these risk profiles within the context of AI, but I think some discussion on the other controlling factors for these risk profiles would help readers understand and interpret your results. Shortening the lead times of back-ended projects with AI seems to be distinct from the potential for AI, management, or policy interventions to be used, like a switch, to mitigate these risks all together. The probability that this could happen is higher for critical raw materials used in defence, where governments may directly intervene for national security considerations;\n\nIt's clear from our analysis that governments prioritize supporting raw materials for national defence over those for energy transition. This underscores our policy recommendation advocating for supranational intervention through organizations like the International Monetary Fund and The World Bank. Individual country policies are unlikely to sufficiently support critical minerals investment, especially considering the reluctance of nations with significant proven reserves, such as Australia, Argentina, Brazil, Congo, Chile, and Indonesia. While some domestic policies, like the Inflation Reduction Act in the U.S. and the EU Fit for 55 initiatives, exist to support critical minerals investment, they primarily focus on promoting clean energy production. However, these policies fall short in areas where proven reserves are scarce, namely the U.S. and Europe. This discrepancy is concerning, especially given projections from the IEA indicating a significant shortage in investment in the critical minerals sector by 2050. Therefore, greater international cooperation and policy intervention are essential to address this gap and ensure adequate investment in critical minerals production globally.\n\n(3) The paper assumes that 50% of critical raw materials exhibit a back-ended risk premium, however not all critical minerals are of equal importance to the renewable energy transition. Moreover, different critical minerals are required in different volumes and have different monetary values. My understanding is that an average proportion (i.e., 50%) was selected based on the number of minerals that are either front- or back-ended with no further information provided. If my interpretation is correct, the assumption made in this manuscript seems too simplistic. The range of proportions (60-40%) for front- versus back-end risks addresses this caveat to some extent, but I think readers would benefit from some discussion on the relative importance of each critical raw material to the energy transition and how this average proportion was selected.\n\nIn the revised version we calculate the risk premium for the 14 main critical minerals, which together represent about 90% of critical minerals using the U.S. Department of Energy's classification of critical minerals. For each scenario (SDS and STEPS) we calculate the total risk premium based on the weighted average of the market value of each critical mineral. Using the weighted average of the market value has the advantage that it takes into account differences in the relative importance of critical minerals to the clean energy transition.\n\nOf the 14 critical minerals, we find that they are all back ended except copper (see Figure 9), which is widely recognised in the existing work of Sykes and Trench (in their various papers and presentations) as a major front-ended critical mineral.\n\nIn short, in the revised version, we no longer assume that 50% of critical minerals are back-ended, but through calculating the total risk premium for the 14 critical minerals for which data are available, relative to a non-critical mineral benchmark, we empirically examine which critical minerals are back-ended. We also weight their importance to the clean energy transition, using their market value, when calculating the back-ended risk premium.\n\nWe now empirically show that about 80% of critical minerals by market value are back-ended, given that (a) we cover about 90% of all critical minerals by market value and find that copper is the only major front-ended critical mineral.\n\n(4) I was also interested to learn that some critical minerals defined as front- or back-ended are in fact sourced from the same deposit type. For example, cobalt and nickel are both sourced, at least in part, from magmatic sulphide deposits. But cobalt is a back-end mineral; whereas nickel is a front-end mineral. The front- versus back-end risk paradigm and its fixture to commodities seems too simplistic for critical raw materials with multiple geological sources;\n\nThe lack of understanding regarding the investment risk profile in critical minerals, particularly in terms of front-ended and back-ended risk, has been a key motivation for our research. The literature is in its very infancy in this topic. In section 2.2, we present the first actual estimations, derived directly from data, shedding light on the risk profiles of individual critical minerals. Our findings suggest that copper is the only major critical mineral, by market value, exhibiting front-ended risk characteristics. In particular, our findings suggest that both cobalt and nickel are back-ended critical minerals. In aggregate is also clear that critical minerals exhibit more technical and non-technical risk compared to heavily used minerals such as coal, iron ore and gold. These insights align with observations made by Sykes et al. (2014) and Trench and Packey (2014), who were among the first earth scientists to endeavour to untangle the non-technical and technical risks associated with critical minerals. By building upon their work and utilizing empirical data, our research adds depth to the understanding of the risk landscape surrounding critical minerals investments.\n\n(5) I got confused between the types of government invested being proposed as part of the current study. As you probably know better than me, Government investments in critical raw materials is pretty complex. Tax incentives, pools of patient capital for financing, and nationalization of the entire minerals sector could also be considered “government investment”. I think the policy section would benefit from some clarity over how investing in AI differs from the current government approach. At the same time, it may be worth highlighting that investments in AI specific to critical raw materials may be distinct from AI investment made by governments more generally. Your analysis also demonstrates the most important stages to apply this AI investment to reduce back-end premium (i.e., all AI applications between exploration and development, including data-driven land-use planning, prospectivity modelling, geophysical-geochemical-remote sensing surveys, drilling sensors, 3D resource modelling, pre-competitive ESG). Adding more specificity over the types of AI investments and the role of government could increase the impact of your work;\n\nThanks for this comment. In the revised version, we have completely rewritten the policy suggestions stemming from our analysis as follows:\n\nThe main problem that back-ended critical minerals has is that they provide lower value to investors during the initial stages of project development and exploration, due to technical and non-technical risks. Our analysis suggests that the most important stages to invest in AI to reduce the back-end premium is in applications between exploration and development.\n\nIt is important to highlight that investing in mining in these stages is distinct from investment in AI made by governments, the private sector and global funding agencies more generally. In 2021-2022, the United States Federal Government, as the global leader in AI investment, spent US$ 3.3 billion on AI, while worldwide private sector investment in AI was US$ 91.9 billion (Stanford, 2023). In comparison, mining companies were expected to spend just US$ 218 million on AI globally in 2024 (Mining Technology, 2024) with only a fraction of this invested in AI in supporting development and exploration in back-ended critical minerals.\n\nOur suggestions below for areas of investment in AI apply to governments, the private sector and lending agencies such as the World Bank and IMF. The argument for government investment in AI applications is that it is broad based and the benefits of technical breakthroughs can extend beyond a single firm or sector. However, there is also a strong argument for the critical minerals sector investing in AI to reduce the back-ended risk premium, given it is in their interests to attract investment. Improvements in AI in the development and exploration phase can reduce the back-ended risk premium through both reducing the duration of the project and reducing the required rate of return on investment\n\nMost AI applications in mining have focused on the development and exploration phase, which is where AI is particularly useful in mitigating the technical risks (Kohler et al. 2021; Noreiga & Pourrahimian, 2022). One specific AI application in which governments and mining companies could invest is in data-driven prospectivity modelling where random forest (Flores et al 2020) and extreme learning machine models (Chen & Wu, 2017) have been used to more accurately predict recovery rates and reduce the time from exploration to extraction. A second related AI application is in mineral mapping in which drilling sensors, geophysical-geochemical-remote sensing surveys and 3D geological modelling can be used to more accurately predict locations with the most potential for mineral deposits (Acosta et al., 2019; Jindai et al., 2012; Tusa et al., 2019; Wang & Huang, 2012).\n\nA third area for potential investment would be in AI methods, such as artificial neural networks (Chatterjee et al, 2010) and extreme learning models (Jain et al, 2021), to predict the grade and recovery of mineral deposits. A fourth promising area for investment in AI is in the recovery of critical minerals from mining waste. AI has proved particularly useful in improving secondary recovery approaches, such as adsorption (Briao et al 2023; Sadi & Solemani, 2023), reducing time from exploration to extraction.\n\nLithium extraction in Chile, Bolivia, and Argentina is plagued by water scarcity. Scarcity of water and water management is one of the main challenges of mineral processing plants that lead to delays (Nwaila et al. 2022). Automated 'dry' or water-savvy sensing AI techniques can be employed to minimise these risks (Nwaila et al. 2022). The benefits of using critical minerals, such as lithium, to facilitate the clean energy transition will be muted if they are extracted in an environmentally unfriendly way. More generally, investment in AI powered environmental monitoring systems, such as smart earth technologies, could help mitigate the impact of minerals such as cobalt and lithium on the environment by detecting and addressing pollution in real time (Dhanwani et al., 2021). One of the biggest obstacles to the successful adoption of AI in addressing back-ended risk is the lack of data. Creating publicly available material datasets that can be used as a source of model training and testing is a common recommendation to further AI applications in mining (Maleki et al, 2022). Given the public goods nature of having open access material datasets, this is one area in which governments and international lending agencies in particular could invest.\n\n(6) I am not an economist, but the idea that governments are the ones primarily responsible for reducing the back-ended risk premiums for the minerals sector seems controversial. Maybe this is a common assumption in this field of research. It is true that governments are already “investing” in different ways to de-risk mineral exploration. I work for a geological survey organization and so my job sort of depends on the idea that governments can support the discovery of critical raw materials. Investing in AI seems like a good idea to me because it is broad based and the rising tide of any technical breakthroughs could lift all boats. But surely the industry also has some role to invest in the type of AI that would shorten their own lead times and increase profits. Perhaps this is what you mean by “supranational organizations” and their role in reducing back-ended risk premiums. Please consider mentioning other industry-led initiatives that could reduce back-ended risk premiums in addition to government funding;\n\nPlease see response to your previous point. In the revised version, we make it clear that investing in mining between development and exploration is distinct from investment in AI made by governments, the private sector and global funding agencies more generally. We also make it clear that we are talking about investment in AI made by governments, the private sector and global funding agencies (not just government).\n\n(7) I think the title could be improved to describe what this study actually shows. For example, something like “Artificial intelligence investments reduce risks to critical mineral supply” or “Securing critical mineral supply chains through investments in artificial intelligence” would give readers a much better sense for your main conclusions and would likely increase your impact. In my view, your work is far more interesting than the current title suggests.\n\nThanks for suggesting this. We have changed the title accordingly.\n\nReviewer #3\nThank you for your comments. Below, we first restate your comment and then provide our response, including outlining how we have revised the paper.\n\nThe first key consideration that requires more critical evaluation is the minerals supply outlook and associated impact on assumptions about investment strategies. Some publications suggest that replacing existing fossil fuel sources for energy requirements with renewable alternatives is not possible by 2050, given limited mining capacity (Micheaux, 2021; Moreau et al., 2019). If such a scenario is likely, then current assumptions for capital investment may not be valid. A new social contract may be required (limiting energy demand), commitment to energy transition by 2050 might be ignored (eroding the motivation for global support from the World Bank and International Monetary Fund, for example), or other departures from the assumed investment climate. The authors should mention the diverging scenarios reported in literature, and consider how the proposed concepts of back-ended risk premium and AI's potential mitigation of this risk premium would play out for such scenarios.\n\nThanks for raising this point. In response, we add a new Section 3.1:\n\n3.1. What if meeting carbon net zero by 2050 is not possible?\nSome studies have posited that given existing mining constraints and known reserves of critical minerals needed for clean energy transition that replacing existing fossil fuel sources for energy requirements with renewable alternatives will not be possible by 2050. For example, Moreau et al (2019) find that of 29 necessary metals in the lifecycle of renewable energy technologies, known reserves of eight metals might be depleted by 2050. Micheaux (2021) presents several scenarios for transition to carbon net zero concluding that ultimately global reserves of cobalt, nickel and lithium may not be enough to resource the quantity of batteries needed to power the electric vehicles needed for clean energy transition.\n\nWhat does this mean for the back-ended risk premium and AI's potential mitigation of this risk premium? Our interpretation is that the conclusion from such studies reinforces the importance of attracting investment in back-ended critical minerals to reduce the expected shortfall to realize the clean energy transition. It also highlights the importance of investment in AI applications in development and exploration to reduce the duration of mining projects and reduce required rate of return on investment. Micheaux (2021) suggests that if existing critical minerals reserves are not sufficient to resource clean energy transition that a new social contract may be required that limits energy demand. A pessimistic conclusion from this might be that if commitment to energy transition by 2050 is ignored this might erode the motivation for global support for net zero emissions from governments and lending agencies. In such circumstances, other solutions would need to be found and financing investment in back-ended critical minerals would be less important, making the back-ended risk premium less important. However, this is unlikely. Given the global recognition of the threat posed by climate change, it is very unlikely that commitment to clean energy transition will be eroded even if meeting 2050 targets prove not to be feasible. If commitment to clean energy transition did look like faltering, reducing expected demand for critical minerals, the added uncertainty would increase the back-ended risk premium by increasing the required rate of return on investment (Carruth et al., 2000). Most likely, as Micheaux (2021) acknowledges, even if critical minerals do not provide the full solution to resourcing the clean energy transition, they will at least still provide some of the solution, in conjunction with other initiatives, and further investment in critical minerals will be needed to facilitate this.\n\nThe second key consideration is the assumption that AI will necessarily lead to improvements in productivity or reduction in mining project risks. There is a distribution of AI and ML applications in mining and mineral processing in terms of maturity on the Gartner hype cycle, but many technologies are still early on the curve (especially at \"peak of inflated expectations\"). In general, recent meta-studies have also revealed a worrying tendency for overly optimistic AI and ML performance reporting in academic literature, due to data leakage and lack of reproducibility (Kapoor and Narayanan, 2022). AI and ML face specific challenges in mining and mineral processing projects (Estay et al., 2023), and may introduce more risks and delays. Although the authors do mention that AI would require further investment to ensure the required gains, the authors can share some relevant issues and citations with readers, as well as to introduce some of the associated uncertainty of AI benefit into their analysis.\n\nThanks for pointing this out. In response, we add a new section 3.2:\n\n3.2. Limitations on AI as a solution to the back-ended risk premium\nWe show that improvements in AI have the potential to reduce the back-ended risk premium. However, AI is going to require further investment to ensure the required gains and there is uncertainty about whether and when the benefits of AI will be realized. While it is often touted that AI will lead to improvements in productivity in mining and a reduction in mining project risks, there is a distribution of AI applications in mining and mineral processing in terms of maturity on the Gartner hype cycle for developing new technologies (Linden & Fen, 2003), with many technologies still early on the curve (especially at 'peak of inflated expectations'). AI has experienced a number of 'hype cycles' in which unrealistic expectations have preceded periods of under-delivery, funding cuts and slowdowns in investment in R&D (McCoy & Auret, 2019).\n\nRecent meta-studies have also revealed a worrying tendency for overly optimistic AI performance reporting, due to data leakage and lack of reproducibility (Kapoor & Narayanan, 2023; Rosenblatt et al, 2024). Messeri and Crokett (2024, p. 49) argue that \"AI solutions can exploit our cognitive limitations, making us vulnerable to illusions of understanding in which we believe we understand more about the world than we actually do\".\n\nAI faces specific challenges in mining and mineral processing projects. One of the main challenges facing the mining sector in exploiting the potential of AI is addressing the skills gap with many workers requiring reskilling or upskilling to take advantage of AI (Noriega & Porrahimian, 2002; Onifade et al 2023; Young & Rogers, 2022).\n\nA second set of challenges facing AI in mining studies is the lack of high-quality training data (Jooshaki et al, 2021; Maleki et al., 2022; McCoy & Auret 2019). The application of AI has been limited by the use of small datasets (Gomez-Flores et al. (2022; Maleki et al., 2022). Cost forecasting applications are typically based on relatively few data points (Noreiga & Pourrahimian, 2022). Given that exploration of critical minerals is relatively new and that there are relatively few critical minerals (and particularly back ended critical minerals) the lack of databases and experimental data to train and test AI models is particularly acute.\n\nA third, related problem, is that mining operations often take place in remote locations making access difficult. Abrasive materials, dust and humidity that are commonplace in mines do not create a congenial environment for the deployment of digital technologies. In underground mining, installed sensors need to be resistant not only to dust and humidity, but also blasting that can damage the senses. Transmitting the data can be problematic due to the limited bandwidth of communication networks employed in underground and open pit mining. Connectivity can be particularly weak and unstable in deep mine sites (Onifade et al 2023). This makes the storage and transmission of useful data challenging (Etsay et al. 2023).\n\nThe third key consideration relates to the front-end versus back-end mining project value-over-time curves. More detail and support need to be provided on why different minerals have these different profiles.\n\nThanks for the comment. In the revised version we calculate the technical risk premium and non-technical risk premium for 14 and 16 critical minerals respectively. Together these represent about 90% of the 50 critical minerals by proven reserves.\n\nWe then calculate the total risk premium, which is the sum of the technical and non-technical risk premium and the back-ended risk premium = total risk premium/cost of capital for non-critical minerals for the 14 critical minerals.\n\nWe add a new Section 2.1 on the risk profiles as follows:\n\n2.1. Measuring Non-technical and Technical Risk Premiums\nIn this section we develop measures of the non-technical and technical risks premium and use these to compare the non-technical and technical risks of the main critical minerals.\n\n2.1.1 Non-technical risk premium\nWe construct indexes for the non-technical risk premium for 16 major critical minerals and a benchmark index, consisting of the average for coal, iron ore and gold, which are three representative non-critical front-ended minerals (Trench & Packey, 2012). These 16 critical minerals account for more than 90% of the market value of the 50 critical minerals, defined by the U.S. Department of Energy (2023).15 The indexes are the product of the investment attractiveness index from the Annual Survey of Mining Companies (Fraser Institute, 2022), weighted by country proven reserves of each individual mineral from the United States Geological Survey (USGS) (2022) and the BP Statistical Review of World Energy (2022).\n\nFormally,\n𝑁𝑜𝑛−𝑡𝑒𝑐ℎ𝑛𝑖𝑐𝑎𝑙 𝑟𝑖𝑠𝑘 =∑𝑛𝑤 ∗𝑠 (1)\n𝑖 𝑐,𝑚 𝑐\n𝑤 is the proven reserves of critical mineral m in country as a percentage of the world's proven reserves of\n𝑐,𝑐𝑚\nmineral m and 𝑆 is the investment attractiveness index score for country c from the Annual Survey of Mining\nCompanies conducted by the Fraser Institute (2022).16\n\nIn Figure 4, we show the non-technical risk for each of the 16 critical minerals and an equal-weighted average of three front-ended risk minerals (coal, iron ore and gold) for 2022. The index reflects the investment attractiveness for each mineral, in which a higher value is associated with less non-technical risk. The non-critical minerals benchmark has a median investment attractiveness index of 64.5, compared to the median of 54.5 for the 16 critical minerals. Thus, the non-technical risk of investing in the front-ended non-critical minerals benchmark is lower than each critical mineral. There is considerable heterogeneity in the non-technical risk of specific critical minerals. Rare earth elements has the highest non-technical risk/lowest attractiveness index of 44.7, followed by platinum (46.48), chromium (47.4), tungsten (47.7), graphite (47.8), barite (52.2), tin (54.3), manganese (53.9), antimony (55.0), cobalt (55.5), vanadium (55.0), lithium (58.2), bauxite (58.4), cooper (60.5), zinc (61.5) and nickel 62.7.17\n\nUsing these indexes, we construct a measure of the non-technical risk premium, which is expressed as the critical mineral non-technical risk expressed as a percentage of the non-technical risk of the non-critical front-ended mineral benchmark.\n\nFormally,\n𝑁𝑜𝑛−𝑡𝑒𝑐ℎ𝑛𝑖𝑐𝑎𝑙 𝑟𝑖𝑠𝑘 𝑝𝑟𝑒𝑚𝑖𝑢𝑛=\n(𝐼𝐴𝐼𝑁𝐶,−𝐼𝐴𝐼𝐶)\n(2)\n𝐼𝐴𝐼𝑁𝐶\nWhere 𝐼𝐴𝐼 is the investment attractiveness index and NC and C denote non-critical minerals and critical minerals respectively. Figure 5 presents the non-technical risk premium for each of the critical minerals for 2013-2022. Consistent with Figure 4, each of the critical minerals exhibits a non-technical risk premium, relative to the front-ended non-critical mineral benchmark. The non-technical risk premium varies from rare earth elements (28.7%) to zinc (4.8%), with the average being 15.7%. The heterogeneity in non-technical risk between critical minerals largely reflects the geographical distribution of specific critical minerals, with those exhibiting higher non-technical risk concentrated in countries having less attractive investment environments.18\n\n2.1.2 Technical risk premium\nSykes et al. (2016) measured the technical risk of minerals for 49 minerals on the basis of six characteristics (crustal abundance, crustal concentration, ease of mining, and ease of processing, criticality of use and diversity of use).19 Each of the minerals was given a score of either zero, 0.5 or 1 for each characteristic. Thus, technical risk for each mineral takes a value between zero (lowest) and six (highest), with higher values indicating lower technical risk.\n\nFigure 6 show the scores of this measure of technical risk for 14 critical minerals and our benchmark of front-ended non-critical minerals, consisting of the average for coal, gold and iron ore.20 Ten of the 14 critical minerals have higher technical risk than the non-critical mineral benchmark (3.5), with tin, manganese, rare earth elements and zinc exhibiting the highest technical risk (1.5-2). Copper and antimony have a score of 4 which reflect less technical risk, while bauxite and barite have the same technical risk as the non-critical benchmark.\n\nUsing the technical risk index in Figure 6, we construct a measure of the technical risk premium, which is expressed as the percentage difference between the critical mineral technical risk and the technical risk of the non-critical mineral benchmark.\n\nFormally,\n𝑇𝑒𝑐ℎ𝑛𝑖𝑐𝑎𝑙 𝑟𝑖𝑠𝑘 𝑝𝑟𝑒𝑚𝑖𝑢𝑛=\n(𝑇𝑅𝑁𝐶,−𝑅𝑅𝐶)\n(3)\n𝑇𝑅𝑁𝐶\nWhere TR is the technical risk of the non-critical mineral benchmark (NC) and critical minerals (C), from Sykes et al (2016). Figure 7 presents the technical risk premium for each of the 14 critical minerals. Consistent with Figure 6, tin has the highest technical risk premium, while copper and antimony have a technical risk discount, relative to the non-critical mineral benchmark. The average technical risk premium across the 14 critical minerals is 18.4%.\n\n2.1.3 Total risk premium for critical minerals\nIn Figure 8, we plot technical risk scores on the horizontal axis and non-technical risk scores on the vertical axis for 14 critical minerals (green dots) and the non-critical minerals benchmark (black dots). The values are normalised from 0 to 1 and inversed, such that values closer to the intercept of each axes denote lower technical and non-technical risks. Rare earth elements has the highest non-technical risk of 1 and also has a very high technical risk of 0.6. Tin exhibits the highest technical risk of 1 and the third largest non-technical risk of 0.25.\n\nIn Figure 9 we present estimates of the total risk premium for the 14 critical minerals for which we have data on technical and non-technical risk. The total risk premium is the sum of the standardized technical and non-technical risk premium/discount with possible values in absolute values between zero and 100. Rare earth elements, tin, manganese, tungsten and zinc have the highest total risk premium. The only mineral showing a total risk discount is copper, reflecting its lower technical score, relative to the benchmark.\n\nWhat contributes to the uncertainty during scoping, feasibility, and development in these projects? Are these uncertainty contributions still valid, given the potential scenarios mentioned previously of not being able to attain an energy transition by 2050 or having a different global social contract in place (e.g., reduced energy demand)?\nWhat contributes to the uncertainty during scoping, feasibility, and development in these projects?\nUncertainty during scoping, feasibility and development in these projects stem from technical and nontechnical risks. While the risk, and associated uncertainty, varies across critical minerals, they are higher, on average, for critical minerals than front-ended conventional minerals. We add the following additional material in the paper on uncertainty:\n\nOne of the most important risks for all mining projects relates to the orebody itself - ie. there is significant uncertainty about mineral resources (Al-Bakri et al., 2023; Dominy et al., 2002). One specific contributor to uncertainty being higher for critical minerals than conventional minerals in this early stage is that there are relatively few sources of data on critical mineral reserves. A second point of difference is that critical minerals are recovered as by-products from refining other metals. This means that the metal supply responds not only to the price of the by-product metal, but also to the price of the host metal, which affects reserve estimates (Spiers et al., 2015). Uncertainty about the potential to recover critical minerals from mine waste such as tailings is particularly acute (Suppes & Heuss-Aßbichler, 2021). A third important way in which critical minerals differ from fossil fuels is that the former can be recycled, although the implications of recycling on future reserves is difficult to quantify, adding to uncertainty about available reserves (Spiers et al., 2015).\n\nAre these uncertainty contributions still valid, given the potential scenarios mentioned previously of not being able to attain an energy transition by 2050 or having a different global social contract in place (e.g., reduced energy demand)?\nIf commitment to energy transition by 2050 looks like faltering or other solutions to addressing climate change that do not rely on critical minerals gain favour, this adds to the uncertainty associated with investing in critical minerals, reinforcing the back-ended risk premium. In this sense, the potential that political commitment to clean energy transition, broadly defined, could wane is a nontechnical risk, which increases the uncertainty associated with investing in critical minerals, meaning investors require a higher rate of return.\n\nWe address this in the new Section 3.1 as described in response to your first point.\n\nThe fourth key consideration is the limited approach of the different scenarios considered. A top-down approach is used to assign low, medium, or high scenarios, and these three scenarios are evaluated. A more critical approach would be a bottom-up approach where the major variable factors are considered in a simple model of the back-ended risk premium through a Monte Carlo simulation (including ranges of AI benefits, uncertainty in back-ended risk premium profiles, different energy transition outlooks, etc.)\n\nIn the revised version of the paper, we've made significant strides in quantifying non-technical and technical risks associated with critical minerals by utilizing precise calculations based on actual data. These calculations are detailed in Supplementary Material 1, allowing for transparency and reproducibility of our methodology. By employing a weighted average, factored by market value, we've approximated the true back-ended risk premium (BRP) more accurately. Our analysis indicates that the BRP stands at around 4-4.5% over non-critical minerals, depending on the selected scenario.\n\nWe've made significant enhancements to our methodology, particularly in refining our main assumption regarding the proportion of total critical mineral value exposed to back-ended risk premium (BRP). Here's how we've improved our approach:\n\nMarket Value Estimation: We have estimated the market value of the top 14 critical minerals, which collectively represent roughly 90% of the total value.\n\nBRP Estimation for Each Mineral: We then calculated the BRP for each critical mineral. For instance, our analysis showed that copper constitutes about 12% of the expected market value of critical minerals by 2040, based on scenarios provided by the International Energy Agency (IEA).\n\nDetermining Exposed Value: By multiplying the estimated market value by the proportion of each mineral's back-ended risk premium contribution, we determined the portion of total critical mineral value exposed to the back-ended risk premium. In the case of copper, the calculation would be 90 x 0.12 = 10.8. Thus, subtracting this value from 90 yields 79.2%. This indicates that approximately 80% of the total value of critical minerals are subject to the back-ended risk premium.\n\nFurthermore, we've provided estimations for both non-technical and technical risk premiums. Our approach relies on reputable sources such as the U.S. Geological Survey and the Fraser Institute, ensuring the reliability of our findings. While our paper focuses on these initial approximations, we believe our methodology can be expanded upon in the future using more detailed and readily available data. With advancements in big data and AI technologies, it will be possible to gather more accurate information on proven reserves and country mining risk profiles. This, in turn, will enhance our understanding of the BRP for each mineral, informing critical energy transition policies more effectively.\n\nBy continuing to refine our methodologies and leveraging emerging technologies, we can contribute to a more nuanced understanding of the risks associated with critical minerals, ultimately guiding informed decision-making in the transition towards sustainable energy.\n\nThe manuscript needs a careful read-through to correct editorial issues, including typos. For example: note that in figure 7, the green arrow should point to the left, to indicate the potential reduction in project length that the authors suggest AI could lead to.\n\nThank you for notice that we corrected old figure 7 (new figure 13). The manuscript has been corrected including editorial issues and typos."}, {"round": 2, "reviewer_comments": {"reviewer_2": "Thank you for your detailed responses to the review.", "reviewer_3": "The authors are commended for the work put in to further refine their manuscript based on the feedback of the reviewers.\n\nFeedback on rebuttals on original considerations:\n1. Minerals supply outlook\nThe added section 3.1 in the revised manuscript is appreciated. However, there is still ambiguity around minerals that have a high likelihood of running out before 2050, or where the production requirement (no matter the reserve) is simply impossible to achieve. The inserted section 3.1 does not quite adequately address these issues.\n\n2. Limitations of AI\nThe added section 3.2 in the revised manuscript is appreciated. However, it feels siloed against other sections which hype up AI again, without mentioning the limitations. Be sure to remind the reader of the AI limitations in the context of listing the potential AI applications in other sections as well. It is now also remiss to not mention the role of generative AI and large language models in the article.\n\n3. Different project value-over-time curves for different minerals\nThe added work in the manuscript is appreciated.\n\n4. Sources of uncertainty\nThe added work in the manuscript is appreciated. Note that the concern around the mineral supply outlook and its impact on the uncertainty landscape is not adequately addressed (see point 1 above).\n\nSome further comments:\n- The new article title may be too optimistic. I suggest \"The potential of artificial intelligence to reduce risks to critical mineral supply\", which should imply that it is not clear cut whether AI will be able to live up to the task.\n- The editorial quality of equations and graphs is low. Consider professional services to improve data visualization, and make use of more appropriate mathematical syntax (e.g., not using an asterisk as a multiplication sign).\n- Proofreading is required. It seems that more typos have appeared in the additional sections."}, "author_response": "Reviewer #3\n\n1. Minerals supply outlook\nThe added section 3.1 in the revised manuscript is appreciated. However, there is still ambiguity around minerals that have a high likelihood of running out before 2050, or where the production requirement (no matter the reserve) is simply impossible to achieve. The inserted section 3.1 does not quite adequately address these issues.\n\nAnswer:\nOur position is that: \"If commitment to clean energy transition did look like faltering, reducing expected demand for critical minerals, the added uncertainty would increase the back-ended risk premium by increasing the required rate of return on investment (Carruth et al., 2000). In this sense, the potential that political commitment to clean energy transition, broadly defined, could wane is a nontechnical risk, which increases the uncertainty associated with investing in critical minerals, meaning investors require a higher rate of return\". With respect to the comment that \"there is still ambiguity around minerals that have a high likelihood of running out before 2050\", our argument is that if there is a high likelihood that the e critical mineral would run out before 2050 this would increase the associated nontechnical risk and exacerbate the back-ended risk premium for these critical minerals. However, the risk of running out is just one nontechnical risk among many. In response, we add the following in Section 3.1 to clarify our argument:\n\n\"This applies a fortiori to critical minerals that have a high likelihood of running out before 2050. All things equal, the back-ended risk premium for these critical minerals will be higher because the uncertainty about their utility to the clean energy transition is particularly acute. However, it is important to note that the likelihood of a given mineral running out before 2050 is just one nontechnical risk among many. Of the eight minerals that Moreau et al (2019) conclude might be depleted by 2050, we consider four in this study (cobalt, nickel, tin and zinc). It is noteworthy that each of these four critical minerals have a relatively low nontechnical risk premium (see Figure 5)\".\n\n2. Limitations of AI\nThe added section 3.2 in the revised manuscript is appreciated. However, it feels siloed against other sections which hype up AI again, without mentioning the limitations. Be sure to remind the reader of the AI limitations in the context of listing the potential AI applications in other sections as well. It is now also remiss to not mention the role of generative AI and large language models in the article.\n\nAnswer:\nIn response, where we discuss the advantages of AI, in two places we caution the reader.\n\"We next consider the different ways in which AI could reduce the duration of mining projects and the required rate of return on investment, although we caution the reader that AI also has limitations, which we outline below. The potential benefits of AI for reducing the back-ended risk premium should be seen considering those limitations\"\n\n\"Again, conclusions about the potential for AI to address the back-ended risk premium is subject to AI's limitations outlined below\"\n\nSome further comments:\n- The new article title may be too optimistic. I suggest \"The potential of artificial intelligence to reduce risks to critical mineral supply\", which should imply that it is not clear cut whether AI will be able to live up to the task.\n\nAnswer:\nThe current title was suggested by the second reviewer, which we gratefully adopted. We prefer the current title.\n\n- The editorial quality of equations and graphs is low. Consider professional services to improve data visualization, and make use of more appropriate mathematical syntax (e.g., not using an asterisk as a multiplication sign).\n\n- Proofreading is required. It seems that more typos have appeared in the additional sections.\n\nAnswer:\nWe carefully proofread the manuscript and use the appropriate mathematical syntax as requested."}]

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