The paper discusses a setting in which an existing dataset/trained model is augmented/refined by adding additional datapoints. Issues of how to price the new data are discussed in a high level, abstract way, and arguments against retrieving the new data for free or encrypting it are presented. Overall, the paper is of an expository nature, discussing high-level ideas rather than actually implementing them, and does not  experimentally or theoretically substantiate any of its claims. This makes the technical contribution rather shallow. Interesting questions do arise, such as how to assess the value of new data and how to price datapoints, but these questions are never addressed (neither theoretically nor empirically). Though main points are valid, the paper is also rife with informal statements  and logical jumps, perhaps due to the expository/high-level approach taken in discussing these issues. Detailed comments:The (informal) information theoretic argument has a few holes. The claim is roughly that every datapoint (~1Mbyte image) contributes ~1M bits of changes in a model, which can be quite revealing. As a result, there is no benefit from encrypting the datapoint, as the mapping from inputs to changes is insecure (in an information-theoretic sense) in itself. This assumes that every step of stochastic gradient descent (one step per image) is done in the clear; this is not what one would consider secure in cryptography literature. A secure function evaluation (SFE) would encrypt the data and the computation in an end-to-end fashion; in particular, it would only reveal the final outcome of SGD over all images in the dataset without revealing any intermediate steps. Presuming that the new dataset is large (i.e., having N images), the "information theoretic" limit becomes ~N x 1Mbyte inputs for ~1M function outputs (the finally-trained model). In this sense, this argument that "encryption is hopeless" is somewhat brittle. Encryption-issues aside, the paper would have been much stronger if it spent more effort in formalizing or evaluating different methods for assessing the value of data. The authors approach this by treating the ML algorithm as a blackbox, and using influence functions (a la Bastani 2017) to assess the impact of different inputs on the finally trained model (again, this is proposed but not implemented/explored/evaluated in any way). This is a design choice, but it is not obvious. There is extensive literature in statistics and machine learning on the areas of experimental design and active learning. Both are active, successful research areas, and both can be provide tools to formally reason about the value of data/labels not yet seen; the paper summarily ignores this literature. Examples of imprecise/informal statements: "The fairness in the pricing is highly questionable" "implicit contracts get difficult to verify" "The fairness in the pricing is dubious" "As machine learning models become more and more complicated, its (sic) capability can outweigh the privacy guarantees encryption gives us" "as an image classifier's model architecture changes, all the data would need to be collected and purchased again" "Interpretability solutions aim to alleviate the notoriety of reasonability of neural networks"