The point that “using Epic is easier than trying to piece together better options from various software vendors” is really key. I have looked at investing in many healthcare IT businesses, and the most prevalent theme in the EHR sector is consolidation to a single vendor across all modules. Hospitals don’t want and can’t afford to piece together best of breed solutions across practices–they all prefer to deal with a single provider out of simplicity, and Epic and Cerner have been the two biggest beneficiaries of this trend. The key difference between these two leaders is in their approach–Epic tends to build their modules in-house and is aggressively non-interoperative, consistent with Judy Faulkner’s style. Cerner on the other hand has been much more acquisitive and tends to be more open. At the same time, third party interoperability vendors are becoming more prevalent, and may ultimately be able to circumvent Epic’s fairly closed architecture. In the foreseeable future, though, I don’t see Epic losing any market share–if anything, they are continuing to gain.
Interesting and powerful post – I am curious what other forms of data could be brought to bear in conjunction with the national hotline data that might make the big data analysis even more powerful. For example, I would guess there are marketplaces or message boards on the dark web that can be scraped, and, using natural language processing, provide additional data that can be helpful for law enforcement. As another example, I read about a service recently where hotel visitors can upload pictures of their hotel rooms to a database–traffickers often upload pictures of their victims as a form of advertisement, and this service could help locate and rescue victims by matching the advertisements to known hotels in the database in order to determine where the victims are located. Innovative digital solutions like these should increasingly be able to solve the trafficking problem in the future.
The application of virtual and augmented reality is really interesting here. I know several other home brands are pursuing similar strategies, but in my opinion it’s too early to tell if the VR/AR applications here are truly value-add or more simply gimmicks. Also, I wonder if companies like Wayfair that don’t design their own products may have difficulty procuring the CAD files necessary to produce the digital 3D images. Suppliers may not want to provide these files for security / privacy reasons, and it is likely unrealistically costly for Wayfair to recreate the files themselves.
Interesting article–really makes you think about how dynamic the early stages of a new industry can be. Several companies right now are developing robotic pharmacy dispensing systems that essentially serve as robotic vending machines for pharmacies. I had thought this was a really innovative concept and would likely take over the pharmacy industry until reading about PillPack–the delivery component is so much more convenient that it will probably win out as a model over robotic dispensers, which I imagine most of those companies haven’t even considered.
Rudi Gassner brings up an interesting point about the target market, though. The users that will benefit most from this service are the elderly, but this is also the segment least likely to use technology solutions like Pillpack. I wonder how they will be able to overcome this target market discrepancy via marketing.
Very interesting point that Uber’s current competitive advantage in self-driving cars lies in the size of their existing network, whereby they should be able to train algorithms using their current drivers. I have been thinking a lot about which company is most likely to emerge as the early leader in SDCs–Uber’s strength is not only the point that you bring up regarding their network, but also that they have a proven track record of taking on regulators and winning. Google, however, will likely be able to offer the lowest cost service over the long-term, since they should be able to subsidize fares by displaying ads inside the vehicles.
I’m not as concerned about the ethical issue of prioritizing patient vs. pedestrian safety. I believe the issue is likely to be a corner-case where there isn’t a very clearly identifiable “safest move”. However, I believe to the extent that this logic does need to be built into the algorithms, the algorithm will have to essentially always prioritize the safety of the passenger. Otherwise, lack of passenger trust in the system could erode the viability of SDCs as a service.
I am not as concerned about Solar City’s profitability or business model as others may be. Over the long-term, solar energy is the only energy source that makes sense for the planet, since it is the only energy source external to the planet. All other sources are either fundamentally limited in amount (i.e. coal, gas) or are incapable of the scale necessary to power society (e.g. wind, geothermal). I believe Solar City may simply be riding out the trend lines until 1) the cost-per-watt of solar energy becomes materially less than coal and 2) battery technology advances to the point where weather patterns no longer impact the viability of solar. At that point, the vast majority of electricity production, not just for homes but for the entire grid, will begin to shift to solar, and Solar City will have the infrastructure, sales force, and expertise necessary to lead the drive. Short term profitability is not meaningful compared to that larger goal.
Excellent post! The statistic that fast fashion clothing is worn on average less than five times is staggering — it really makes you re-evaluate whether or not fast fashion actually has the price/value proposition that it is perceived to have. It’s also very interesting that this is an environmental implication that most consumers aren’t aware of. How can consumers and regulators become so outraged by single-use paper and plastic bags when clothing has become almost single use?
I also wonder about Inditex’s motives. Are they facing regulatory pressure to become more environmentally friendly? Are they marketing their eco-friendliness in an attempt to gain consumer trust and improve their brand image? I personally haven’t seen any environmentally-oriented marketing from Inditex, so it seems plausible that it is actually a “save the world” motivation rather than profit-driven. As others have commented, though, there is certainly more that they could be doing, but it is a good start.
Interesting post. I am curious as to the mix of participants in carbon trading markets, and what role they all play. Do large corporations rely on firms like Mercurial to provide liquidity for these types of transactions? Or are carbon trading firms more of an obstruction, extracting value from a system that is intended to facilitate business-to-business transactions? My guess would be the former–the presence of market makers like Mercurial serves to incentivize firms to reduce their emissions by signaling to the market that there are buyers willing to consume their credits, which is a good thing for the world. However, the thought of high-frequency traders profiting from international carbon emissions arbitrage admittedly has a slightly predatory feel. Would be interested to read more about the role and effects of these market participants.
I am surprised to see so many comments with the position that UberPOOL is likely more harmful than good to the environment. Several of the comments seem to focus on how a non-zero percentage of Uber rides are incremental (i.e. if the option was not available, I would have chosen to walk or bike). The blog post author cites 8% as the incrementality estimate — even if this number is slightly low, I believe this effect is more than made up for by the newness of Uber’s fleet, not just today but especially in the coming years. Once Uber’s fleet is mostly autonomous, I expect it will simultaneously become almost entirely electric. Once we are in that world, ~8% incremental rides or more will cause negligible environmental harm compared to the environmental benefits derived from electric vs. gas.
Very interesting post and creative topic. I am curious if the energy efficiency AI algorithm comes with a corresponding performance cost. Without knowing the details, it seems like there could be an unfortunate consequence where optimizing data centers for energy efficiency may not lead to the same resource allocations as optimization for speed, redundancy, or other key parameters. It would be interesting to hear how Google thinks about this trade-off, and how they choose to internally prioritize energy efficiency vs. raw performance.
Another question that comes to mind for me is how global warming impacts the performance of existing data centers. I know that many data center operators choose to build their facilities in colder locations in order to take advantage of the climate’s natural cooling effects. However, with global warming, data center efficiency may be hurt as a result, and centers may have to move to even colder locations.