This is a thoughtful critique and I appreciate you raising it directly. You are right that fish sentience is getting more attention in the literature and it deserves more ethical scrutiny than it currently receives. We do not take the position that scaling aquaculture is straightforwardly good without tradeoffs. There are real welfare concerns and the industry has not always handled them well.

A few thoughts on where we stand:

On welfare specifically, our technology actually reduces stress on fish compared to the current manual process. Traditional phenotyping involves netting, anesthesia, and physical handling. Our system measures fish without any of that. Less handling means lower cortisol, lower mortality, and healthier animals. We are not neutral on welfare. We think better measurement tools should lead to better treatment, not just faster growth.

On plant based protein, you are right that our framing glosses over it. Plant based is more efficient on land use and we are not arguing against it. The reality is that billions of people rely on fish as their primary protein source today and that is not going to change overnight. We are trying to make the aquaculture that already exists more sustainable and humane, not argue that it is the only path forward.

On the scale of killing, I do not have a good answer for that. It is a massive number and I understand why that gives you pause. What I can say is that if aquaculture is going to exist at scale, we would rather it be done with better data, less stress on the animals, and more intentional breeding practices than the status quo.

We are doing exactly what you described with continuous calibration. We have essentially built our own in-house labeling, ingesting, and task assignment software for these tasks. Low confidence predictions get flagged for review, corrected labels feed back into training, and we retrain on a rolling basis. We also stratify our calibration datasets intentionally by time of day, tank conditions, and fish density rather than just grabbing random frames. Early on our datasets were too homogenous and the models would work great in testing then degrade in production. The architecture matters less than having a tight feedback loop between deployment and retraining.

On the welfare angle, yes we are thinking about this carefully. The data we collect includes body shape, fin integrity, spinal curvature, and other morphological traits that are signals of fish health and robustness, not just growth rate. Farms that care about sustainability can use this to select for fish that are healthy and resilient rather than just fast growing. The tool is neutral but the selection criteria are up to the breeder. We do not want to enable the same failure mode that happened with poultry.

The talent pipeline point is interesting too. You are right that most CV talent ends up in adtech or fintech. We have found that people get excited about working on something physical and tangible once they realize the problems are just as hard.

well these days inference can be done on CPUs depending on the size of a model. the OAK-D camera's especially are literally just computer. They are linux and you can SSH directly into them which is insane. The camera itself is shaped like a heat sink to dissipate heat. it's honestly phenomenal. I talked to the CEO of the company as well and I'm really impressed with their machine and supply chain. so all in all, high hopes for them.

This is a great comment. You are absolutely right about the data opportunity. The industry is so data sparse right now that even basic measurements at scale would be a step change. We are seeing that firsthand with our customer. They went from sampling a few dozen fish by hand to continuous measurement and the insights are already compounding.

Thank you for the fish pump link. We have looked at pump based systems as a way to create controlled measurement environments. You get consistent lighting, predictable fish orientation, and the fish are already moving through a constrained path. The challenge is you are still dealing with water turbidity, particulates, and bubbles in the flow which can mess with imaging. It is better than open water but not a free pass on the vision problems.

We have also been looking at pescalators which use an Archimedes screw design to lift fish out of the water. Some setups combine this with anesthetization for operations that require handling. The tradeoff is you are adding stress and complexity but you get a much cleaner imaging environment. There is no single right answer here and the best approach depends on the species, life stage, and what you are trying to measure. This is definitely technology that will develop over time as the industry matures.

What species are you working with in your aquaponics setup?

This is a really important point and something we think about a lot. You are absolutely right that chicken optimization has come with serious welfare tradeoffs. Breeding purely for growth rate without considering the animal's ability to actually live comfortably is how you end up with birds that cannot walk properly. We do not want to enable that trajectory for fish.

The good news is that the data we are collecting can be used to select for more than just growth. Body shape, fin integrity, spinal curvature, and other morphological traits are all signals of fish health and welfare. Farms that care about sustainability can use this data to breed fish that are robust and healthy, not just fast growing. The tool is neutral but the selection criteria are up to the breeder.

On the environmental side, our focus right now is on land based hatcheries and recirculating aquaculture systems rather than open net pens in the ocean. These closed systems avoid a lot of the dewilding and pollution concerns you mentioned. They are more expensive to operate but they keep farmed fish separate from wild populations and give you much more control over waste and water quality. And yes, reducing handling stress is a big part of what we are building. The manual process today involves netting, anesthesia, and physical manipulation. Our system can measure fish without any of that. Less stress on the animal and better data for the farmer.

Great question. We are specifically targeting vertically integrated farms which are usually massive scale operations with hundreds of thousands to millions of fish. These facilities already have significant labor costs for trained technicians and geneticists doing manual phenotyping and sorting. We are not going after small scale manual farmers or trying to be an eFishery type business that serves smallholders.

The labor dynamics are also different at these large farms. The work we are automating is repetitive, physically demanding, and hard to staff consistently. Most of the farm managers we talk to are not trying to replace people, they are struggling to find enough workers willing to do this work in the first place. Automation at this scale tends to shift jobs rather than eliminate them entirely. And thank you for the kind words on the website! We have gotten mixed reviews on the cursor so enjoy it while it lasts lol

Thanks! Yes turbidity has been one of our bigger challenges. Water clarity can shift dramatically throughout the day depending on feeding, fish activity, and weather. We have had to build our calibration datasets to capture that variance otherwise the quantized models degrade fast in production. We are also experimenting with different lighting setups to cut through particulate but it is still a work in progress :)

We are hiring, would love to chat if you have some time.

Great question. We are building our entire labeling and data management system in house. Early on we tried existing platforms but they did not fit our workflow. We have a lot of video data and need custom labeling for things like keypoints, body outlines, and deformity classification that off the shelf tools do not handle well. Building it ourselves is cheaper at our scale, gives us tighter integration between labeling, training pipelines, and deployment, and lets us iterate faster. We can assign tasks to annotators, version datasets, and push models to edge devices from one system. When you are trying to close the loop between data collection on farm and deployment you cannot afford fragmented tooling.

Thank you! Goa has a way of making the ocean feel like part of everyday life. It is hard to grow up there and not think about fishing and seafood at some point.

Would love to connect with your cohort. North East India has a ton of freshwater aquaculture potential and we are always looking to learn from founders working in different geographies and species.

You are right that aqua startups need each other. The industry is so fragmented and underserved that collaboration makes more sense than competition at this stage.

yes it's definitely capital intensive for sure. feedback on the website is noted, will make the changes on the next release.

Thank you!

Whoosh has really interesting tech more focused on the fish transport side with products that move fish from tank to tank while performing some operations.

Our initial focus with inspection is taking high quality images of fish to pull insights needed for maximizing efficiency and improving breeding programs. We have designed our system to easily drop-in to the current operations so it is seamless.

We are usually interacting with anesthetized fish and farms are already doing manual tasks with this. We are also integrating camera's into existing workflow that do not handle the fish to improve a farms insight into their own farms.

There are many tasks within farms that are extremely manual that are a bottleneck, this is especially prevalent in offshore farms and marine species.

thank you, appreciate it

Shinkei definitely has cool tech! Aquaculture has already surpassed commercial fishing in terms of production and has become the cheapest source of protein in many countries. We are excited to help the industry grow even further.

The broader point we were trying to make is that as population grows and arable land decreases, the protein mix will need to shift. Fish have a feed conversion ratio of around 1.0 to 1.5 compared to 6 to 10 for beef. They do not require land for grazing and aquaculture has significantly lower greenhouse gas emissions than most terrestrial livestock. That does not mean aquaculture is the only path forward but it is likely a bigger part of the equation than it is today, especially in regions where seafood is already a primary protein source. We are not arguing against plant based diets or other solutions. Just that aquaculture is underinvested relative to its potential and the infrastructure to scale it efficiently does not exist yet. That is the gap we are trying to fill.

I like it, but it should just be visual. It jacks my default scrollpad/mouse behavior, which is the annoying part.

Agreed. Feels icky. Made me want to leave the page as quickly as possible again.

^ no way this was tested by anyone with eyes before it was deployed

I am a fan of the fish cursor. We should make the internet quirky again. This is like a modern take on Geocities websites, and they should do things like fish cursors now while they still can before a board of VCs comes in and makes them remove the fish cursor.

Thanks! The quantization tradeoffs have been a grind. We do not have an exact number but we found that a few thousand images was not enough once you account for the variance on farm. Lighting changes throughout the day, water clarity shifts between feedings, fish density varies by tank. Early on our calibration sets were too homogenous and the INT8 models would work great in testing and then fall apart when conditions shifted.

We also found that segmentation required significantly fewer images compared to keypoint pose detection models. Segmentation generalizes faster since you are just finding body boundaries. Keypoints are more finicky because anatomical landmarks vary a lot more across species, life stages, and body deformation while swimming. We had to be much more intentional about diversity in the keypoint training data. What made the difference overall was building calibration sets that intentionally captured edge cases. Low light, high turbidity, dense occlusion, different life stages. We also started stratifying by time of day and tank conditions rather than just grabbing random frames. It is still not perfect but the models are much more stable now.