 you have cost across the organization, across the value chain, if you think about it. And what I have mentioned here, a bunch of items is like what we talked about, the data processing cost, additional pipeline creation cost, additional aggregation, which we needed to create cost, additional aggregation for inferences or derivations. Then you have the transfer cost. Okay, that is something, right? So if you are doing transfer of data from your system, there is always a transfer cost, data transfer cost associated if you are sending a data set. And if you have to, if you're bound by SLA to send a daily data set, you have a value cost as well in terms of the data transfers. And additional algorithm run has a cost as well, right? Any algorithm on a large data set beyond a particular capability is going to incur lots of cloud computing cost or any on-prem computing cost or however you are operating. So if I have to spend all these additional costs, what are my incentives, right? So I have to give this data and to give this data, I am incurring additional costs. So what are my incentives to give this additional data? Am I going to get a large incentive? So that is partly, should be addressed by the pricing problem as well because the value can increase down the line. Say it one month, the data value is so much and three months down the line, it is so much. So how is it going to be the incentivization going to happen? You as a company who's providing is going to be participant of that incentivization, right? So this is something to be thought about as a broad ecosystem as a whole. And the other thing is, if you think about it, startups are already running into multiple challenges. One of the, we talked about it in the intro slide, one of the foundational principles of the NPD tractors. They want to create a level playing field where everybody is able to take data and achieve scale as soon as possible. In taking data, we figured there are a bunch of challenges in terms of creating new pipelines, in terms of verifying the quality, putting the gates and all these and having the contractual and all these, as well as for giving the data out, we saw a bunch of processing costs, new aggregations, security considerations and all these things. If you think about it, a startup might be in a completely different business, what they're trying to do. And within three months, they achieved a scale of data where they are forced by this draft to register themselves as a data business. Now, they have to spend money and resource, time, people as well as actual rupees to get this, to get this done. Do they have the resources or Big Tech has the resources? In my opinion, probably the bigger companies or better place to either put in these new pipelines or procure new data and process them faster. So if you think about it, the equation is bit swayed towards a person who has a deeper pockets rather than a startup in terms of how it is. Probably in terms of ideation startup has, I would say, advantage there, but in terms of actual execution, definitely they don't stand to gain from this whole thing.