“Thousands of analyst- and associate-hours to accomplish”… that one definitely struck home. You’ve done a fantastic job drilling down to the roots of what financial transactions entail and how machine learning can disintermediate many of the roles within the traditional chain-of-command in banking. However, I have to disagree on the intrinsic value of expert intuition. From my experience in the banking, tech and entertainment worlds, ‘intuition’ is frequently just subconscious pattern recognition, which is notoriously unreliable, especially as future outcomes diverge further and further away from past trends. Ultimately, most of the higher-level banking positions (i.e. managing directors) essentially spend their days relationship-building (i.e. wining and dining clients) to win business. In other words, not adding value but ingratiating themselves with corporate decision makers. Expanding on your thesis, I do believe a two-person shop (one with finance theory expertise, one with data science expertise) combined with powerful ML could prove to be the next, and superior, iteration of what financial advisory looks like tomorrow.
Fantastic synthesis of the technical and philosophical components of genomics research. I believe it’s entirely possible our (great-)grandchildren look at sequencing as we do indoor plumbing: fundamental to basic health yet unbeknownst to our less civilized ancestors. While very optimistic for its application, I am curious about the distribution of this newfound power. Will genomic sequencing be like the internet, a democratizing force that lifts all (if not most) boats? Or does the potential to optimize for certain traits and eliminate diseases drive a greater wedge between those with money to burn vs. everyone else? And in the case of the latter, what role should government play (if any), when there’s too much power concentrated in the hands of a few companies?
Very cool topic, pretty unique blend of old-school and new-tech. But with just one harvest per year, farmers can be risk-averse and thus reluctant to try new technology, especially when they have generations of experience & proven growing techniques… should Deere even try to convince the small family farms (90% of US farms are < $350k annual net income) to buy in or is the ROI for this tech infeasible for anyone but the largest players? Longer-term, will high-density, controlled-environment vertical greenhouses replace traditional farms?
Appreciate the perspective, and completely agree that given the upside-downside asymmetry, malicious agents have an inherent advantage in the battle for voting security. While new tech brings its own vulnerabilities, perhaps machine learning can better combat the threat than open innovation… to your point, Voting Village is an open-book that exposes ES&S to more sophisticated attacks and breaches, while ML has the potential to anticipate and preemptively neutralize maneuvers that humans haven’t even yet thought of (see: AlphaGo). Is this instead a problem that should be outsourced to cyber-attack specialists and artificial intelligence experts as opposed to a hardware manufacturer?
I’m a big fan as well of Steam’s leveraging of human capital without the commensurate overhead costs, but if I were to channel my inner Steve Jobs (or Henry Ford), one could argue that the fans rarely know what they want until we create it from them. In that sense how would you as management balance the incremental nature of crowd-sourced innovation vs. step function improvements driven by the company, but at higher cost? Moreover, besides the risks associated with inappropriate content, how can the company maintain a consistent brand / value proposition when they hand the narrative over to the public?
Well-written and very insightful, thanks for sharing. No doubt, innovative tech has the greatest cost-saving potential in massive & highly-complex organizations, provided you get buy-in from the higher-ups / existing contractors. However, given the double-edged nature of technology, do you foresee any new threats arising from this technology… you mentioned cyber-attacks, but perhaps more directly, manufacturing defects or 3D-printing being used by enemy combatants?
First of all, fantastic job distilling and synthesizing a number of complex concepts into a cohesive narrative. A near-term, practical question: what’s the estimated cost for a 3D-printed organ transplant and how does that compare to the alternative? While prices would likely decrease along the adoption curve, the initial cost could determine how rapidly this technology can meaningfully penetrate the market. Longer-term, and more philosophical: in the case of success, what impact do you predict beyond the medical aspect? A decrease in human trafficking and longer lifespans? Or increased disparity between the rich vs. poor and black market for cheap transplants?