Welcome to the first edition of the CxO Corner, where each month we'll sit down with an enterprise technology leader whom we respect and admire to highlight their role, work, and perspectives on the past, present, and future of the technology landscape. New York is well known as the financial capital world, but is also home to many of the early buyers of emerging tech across its diverse industries, from financial services and media to healthcare, real estate, and transportation.
We're kicking off our inaugural post of the CxO Corner with Bill Murphy, Senior Managing Director and Chief Technology Officer at Blackstone, on his journey in technology and how entrepreneurs can be successful with tech executives like himself. Read below on Bill’s best practices and lessons learned; you’ll find insights for fellow technology executives and entrepreneurs alike.
Note: this article was originally published on TechCrunch on June 28, 2017.
Cloud computing is driving growth at 3 of the 5 most valuable companies in the world. AI will impact jobs only as quickly as AI-powered business software evolves. These are just two of the ramifications of disruptions in enterprise technology permeating mainstream media.
Yet the inner workings of the tightly knit enterprise software industry are rarely publicized. Most talented engineers flock to Instagram and Snapchat where they help The Kardashians hyper-optimize selfies. Taking part in the B2B subculture of Silicon Valley feels like a second-rate option.
Enabling Companies of All Sizes to Centralize, Reuse, and Productionize Data Science
We’re excited to announce that Work-Bench is joining Google’s new fund focused on AI and machine learning investments, Madrona, Rakuten Ventures & Osage University Partners to invest in Algorithmia’s $10.5M Series A.
To the outside world, data science can look like a lot of razzle and dazzle. But at an enterprise grade and scale, it requires an inordinate amount of work to truly get a model into production. From siloed data sets and strict data governance policies, to a lack of central repositories for machine learning models in an organization, there’s no way to manage the full lifecycle of algorithm development - let alone the infrastructure to quickly and efficiently deploy and host compute intensive AI models. What’s needed is a seamless platform to build and deploy machine learning models - a Github and Heroku for AI development at the Fortune 1000.
Enter Algorithmia. The company’s mission is to make state of the art algorithms accessible and discoverable. They’ve built a public marketplace and common API for algorithms, functions, and models that run as scalable microservices, allowing anyone to leverage the latest in AI research from top universities and add a layer of intelligence to existing applications.
Clayton Christensen recently tweeted that “any strategy is (at best) only temporarily correct.” The paradox behind this statement is that great managers hyper-optimize their business lines for profit only to see new entrants come in to take all the money off the table. This type of perfect competition can shoot a company off the edge of a cliff.
Creative disruption a la Apple and Amazon is the answer, but little is discussed of startups catapulting their own successes into new markets. We usually only hear of the overnight success stories like Facebook. But with technology change reaching tornado speed and the pathway from David to Goliath longer than ever, change is important for startups to embrace early and often. For entrepreneurs eager to endure in the competitive and complicated markets of enterprise software, I’d like to offer up an anecdote of our portfolio company vArmour evolving its platform strategy for a new sprint at competitive advantage in the security software industry.
Combining Word-Class Machine Learning With Financial Services Expertise
Our mantra at Work-Bench, which you can see printed in vinyl right when you enter our workspace, is that great things happen at the intersection of suits and hoodies.
I don’t think that a company of ours has ever better exemplified this more than Merlon Intelligence, which is why we’re thrilled to announce that Work-Bench is joining in their $7.65M raise led by Data Collective with participation from Fenway Summer and Nyca Partners.
Too often companies from the Valley and beyond approach Wall Street thinking that they can solve their problems better, faster, and cheaper, however they usually lack the necessary context and domain expertise to fully appreciate the problem at hand (including existing tooling and workflows).
When we come across those rare founders who have an extraordinary vision for how the world should be and who pair it with deep customer empathy and an ability to execute, it gets us excited.
Such is the case with Merlon Intelligence, where one day last year we got a call from someone at a bank we work closely with, who said that we had to check out an incredible new startup solving Anti-Money Laundering (AML) and Know Your Customer (KYC) compliance unlike anything they’d seen before in the market.