[cross-posted on Substack under a different title]

Claude ranked top startups assuming AI goes well. These could be good ESG-esque investments for businesses as of August 18, 2026. Some of these are applicable to most/all businesses, and all of these are relevant for VC firms or businesses with VC arms. It will also be good to see how this plays out in the future. 

"Rank them via moral weights — how much they can provide to people in the economy (economic value) for people, sustainability (their environmental footprint, water usage, etc.), ability for misuse (e.g., surveillance technology), and ability for benefit (e.g., how well it delivers on important, tractable, and neglected social problems)." 

How do we give an objective metric? We have a metric for the economy but that does not tell us how good the company is or will be. Startups in the AI business right now do not generate returns and many are heavily in debt. So we must pivot to funding stages in which anything below series-C makes sense (let's say under $50M, although the ones I have included here are well below this) because that is where these companies are most likely to (1) have a following/reporting with references that are citable and (2) compose of diverse stakeholders in which many business investors (and perhaps individuals) can join.

We should go off of companies that are currently integrating with AI. So startups in the tech industry. Why? AI is probably the future. And theoretical applications can be tied more closely to yes/no metrics: "Does the customer base include defense/intelligence contracts, is facial recognition or biometric tracking a core product line, is the company on the Commerce Department Entity List, does it have a published Responsible Scaling Policy with any teeth?"

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What does assuming AI goes well mean? Assuming AI is not misaligned and kills all of humanity? Sure. But more importantly, we should generally orient this ranking toward a world where AI starts generating sufficient returns by 2030. Where are we now and is this feasible? Let's see:

"As of mid-2026: worldwide AI spending is projected to reach $2.59 trillion in 2026, yet only 6% of organizations qualify as AI 'high performers' with measurable bottom-line impact, per McKinsey's survey of nearly 2,000 companies."

Gwkinvest frames 2026–2030 as "the crucial test for AI commercialization," where at that time investments must start generating returns or "face potential write-downs."

It seems AI companies themselves have no plan on how they manage to accomplish this, unfortunately. Let's put 2030 down as an optimistic threshold, but one that AI companies must hit to save the industry[?] and beat these estimates. 

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I am going to rank 8 of the best funding-stage startups based on (1) how much they can provide to people in the economy (economic value) for people, (2) sustainability (their environmental footprint, water usage, etc.), (3) ability for misuse (e.g., surveillance technology), and (4) ability for benefit (e.g., how well it delivers on important, tractable, and neglected social problems).

How do we narrow down the field so we do not get AI slop on our search results for general startups that would take years to individually evaluate? For that I should make assumptions that you should largely agree with to narrow down the field, and for some contestable ones I have clarified in parenthesis. I should also note that some metrics may have repeating justifications and/or a "low" ranking in cases in which there is no connection to the field. For example, "low" economic value for biosecurity (the very first ranking) does not just mean economic value will be indeterminate, but that there is no obvious connection between improving biosecurity and simultaneously generating economic value over and above potentially saving humanity. This would also be double counting the "human benefit" metric. Finally, in this context is important to note we are talking about small companies in an early funding stage and (likely) small ESG business investments. 

• Biosecurity / pandemic defense [low econ, low sustainability, high misuse (since safety research could improve potential attack capabilities, too), high benefit]

• AI Governance / Interpretability [mid econ, mid sustainability, low misuse, high benefit (I would like to note here that, of course, sustainability and economic returns depend on how AI is regulated in general. However, the upsides and goals in this field may be too broad for calculated investments, for example, given that the "AI governance" field cares for things ranging from consumer protection to global power conflicts. So the link here between investment and our metric impact is not direct.)]

• Agricultural AI for low-resource regions [high econ, high sustainability, low misuse, high benefit]

• Low power chips [mid econ, mid sustainability (see water usage app above), low misuse, mid benefit (isolated communities may benefit)]

• Drug discovery / biotech [high econ, low sustainability, high misuse (bioweapons), high benefit]

• Climate disaster modeling [mid econ (helping stop climate change could contribute trillions to the economy, help affected communities thrive economically, but single investments won't really move the needle), high sustain, low misuse, high benefit (climate-destabilized communities will benefit)]

• Algorithmic credit-scoring [high econ, low sustain, high misuse, mid benefit (I can see this being much more negative for the average consumer than positive, credit scores are limiting)]

What is the safest field here based on these assumptions? It seems, surprisingly, agricultural AI and climate disaster modeling investments would be, based on these weights, the best fields for companies to make ESG investments. 

So from there we can compile our list (given by Claude and edited/fact-checked by me): 

Agricultural AI 

• Aydi (MENA region) — raised $7.5 million in seed funding in September 2025, led by COTU Ventures, Daltex, and Nuwa Capital. Its ORTH platform combines satellite data, weather information, and AI models to give agronomist-level advice automatically for every farm plot, reporting over 20% early yield/efficiency improvements. Free tier exists for farmers — good economic-access signal.

• AgroScout — ~$10.8M raised; uses drones, satellite, and weather data with deep learning to detect and monitor crop disease in the field.

• Avalo (USA) — ~$15M raised; plant biotech company using AI and evolution to build a more sustainable/resilient food system. Leans more biotech than pure software — worth noting for sustainability.

• Instacrops (Latin America, YC-backed) — gives farmers IoT sensors combined with satellite and drone data, reporting a 12% average yield increase, live on 300+ farms across Latin America with $200K in monthly revenue. Funding sits at ~$6M. New data suggests 260 farms have seen 20% yield gains / 30% water savings after their respective AI pivots. 

• Heritable Agriculture (Google X spinout) — received a $5 million grant funding from the Bill and Melinda Gates Foundation, which combines AI with genomics to develop climate-resilient crops for low-income countries. Note: this is grant money, not equity funding. Still could be considered on the list for business investments/partnerships.

Climate/disaster modeling AI 

• FloodMapp (Australia) — has raised a high of $10 million, fusing hydrological models with machine learning for real-time street-level flood forecasts, and cut flood-related disruptions by up to 40% in trials (but this is unconfirmed) with Australian emergency services. I've included this here because they have lots of institutional credibility / it may be worth for businesses to do more research and come to their conclusions here. Very practical positives in investing if data is clarified.  

• Previsico (UK) — raised seed funding in March 2025 from Burnt Island Ventures and Foresight Capital Ventures to expand into the US market, delivers location-specific flood predictions from real-time weather and sensor data.

• Reask — climate risk intelligence firm; uses machine learning and climate physics to model tropical cyclones, droughts, and wildfires, with a database of 200+ high-resolution tropical cyclone simulations. 

https://score-impact-ai.base44.app [note that the app includes the justifications explained above, and also included numerical rankings for the companies on top of the work that I did —  I think that all 8 recommended startups deliver smart ESG investments for businesses, so take these rankings as unofficial.]

This is a heuristic assuming businesses will check relevant details before making decisions. All sources are public.