I'm unimpressed with this analysis. It seems pretty shallow and based on not very useful bad data. First, 2035 is very far away. The first modern smartphone ca

I'm unimpressed with this analysis. It seems pretty shallow and based on not very useful bad data. First, 2035 is very far away. The first modern smartphone came out 12 years ago. I bet in 2007 you could have analyzed the mobile startup space and come to the same conclusion. Projecting what benefits a revolutionary technology will bring us in the next 16 years by analyzing what startups exist right now does not seem like an effective way to make predictions. Uber (arguably the largest mobile-first company), came into existence only 10 years ago. The AI startups that will shape the industry of 2035 probably don't even exist yet. Then there is the lack of deep analysis of the three divisions in the current wave of AI, each of which have very different trajectories. There is: 1. Computer-world interaction. The work that focuses on giving computers new ways to interact with the world - speech recognition/synthesis, natural language understanding, computer vision, interpreting more specialized sensor data such as lidar or 3d imaging. 2. Data as an economy of scale. The work that focuses on using data to make better spending and planning decisions by leveraging vast amounts of historical data. 3. Exploratory research. Speculative research towards autonomous agents and AGI. Stuff like most reinforcement learning and other pure research that is not ready for production. Computer-world interaction is where most of the current set of startups seem to live. You can build on top of publicly available data and then finetune it for your needs with a relatively low amount of data, which means the barrier to using AI is not insurmountable. Example successful products using this type of AI - smart speakers, L2 autonomous driving (e.g. Autopilot), Pixel night camera, medical diagnosis. This is still in the early stages of commercialization, but the technology is improving at a ridiculous rate. This is mostly still in the late R&D/early commercialization phase so I would not expect many of the players in this space to be focused on profitability. What matters for the long-term projections here is whether the technology is advancing towards being viable for many real-world usecases within the next 5 years (IMO it is), whether the technology will be cost effective (less obvious but it looks promising with advancements in hardware and more efficient network designs) and whether it will be accessible to many companies (lots of good work happening here between AutoML research and MLaaS companies). Data as an economy of scale is where most of the near-term financial benefits of AI are being demonstrated. The successes I've seen are primarily confined to big companies since they have the data and resources needed to train and benefit from these models. There are some startups in the space (maybe the security startups mentioned in the article?), but I'm not sure exactly how they would build useful AI models without the large amounts of historical data that established companies have. It's possible startups in this space are sometimes guilty of overselling their 'AI'. However, big companies are finding a lot of success here - better logistics decisions, better fraud detection, better content/ad recommendations. Some of the techniques have existed for quite a while and we are only seeing benefits now because it has taken a long time to build up the data and ML infrastructure to apply those techniques. Other techniques such as graph neural nets are still in their early phases but are showing great promise. This is completely missed in the analysis done by the article because it only looked at startups. Finally there is exploratory research. Some of this is quite promising - RL as a technology is incredible, although RL as a useful tool for business still appears to be a ways away. While any advancements here would be great for the impact of AI, it doesn't really matter if none of them pan out - the current set of proven techniques will still have a huge impact. 'Companies' in this area are in many ways just research labs. I would not include them in my analysis - DeepMind and OpenAI.