Part 1 of 3 — this blog post was authored by three Insight Venture Partners leaders: Hilary Gosher (Managing Director), Lonne Jaffe (Managing Director) and Gary Survis (Venture Partner). To review “ABC’s of Tech in 2018 – Part 2″ click here

Artificial Intelligence (AI)

The what?  AI and machine learning has emerged as one of the most important phenomenon in technology today. In particular, neural networks and deep learning have shown sudden improvements in performance that have surprised even the most experienced machine learning practitioners. The landscape has started to shift away from consulting companies doing custom, bespoke projects. In 2017, we saw the emergence of real software companies selling turnkey cross-industry applications with machine learning at their core, in domains such as cybersecurity, fraud detection, and churn analytics. Many of these new applications being fueled by machine learning and AI are built on open source infrastructure being made available by vendors such as Google and Amazon Web Services, who developed the capabilities for internal use and are now making them available through their public cloud infrastructure platforms.

2018 and beyond. In 2018, machine learning and AI will continue to move up the stack, as vendors who have created use case-specific applications across industries as varied as healthcare, education, retail, financial services, and government start to achieve scale. Machine learning will be a key ingredient in the next generation of software applications. With AI-powered, industry-specific applications, scale can provide the largest and fastest growing vendor in each segment with an insurmountable data advantage—on top of all of the usual brand and cost advantages enjoyed by the scale leader in software. More customers will result in a better product, with value accruing to all of a vendor’s customers as well as to the vendor iteself and its investors. There will also be an increase in “machine learning theatrics” as companies pretend to use machine learning to get improved valuations. 


The what? Blockchain technology, underpinning Bitcoin and hundreds of other cryptocurrencies and distributed cryptographic systems, represents the integration of three key innovations: proof of work, public key cryptography, and distributed ledger technology. Blockchains and their associated cryptocurrencies allow for the creation of a tamper-proof public ledger for recording information or running programs—a kind of distributed, immutable database that doesn’t rely on management by a central entity like a large corporation. A well-designed blockchain can provide both an incentive for participants to run the distributed ledger and computation system, as well as a cryptographic distributed consensus mechanism that maintains the system’s integrity even if all of the participants don’t trust each other. 

2018 and beyond. As the market capitalization of cryptocurrencies exceeded $600 billion, with Bitcoin alone trading at a market cap of more than three times that of Goldman Sachs, 2017 also saw the emergence of the “Initial Coin Offering.” High profile, early-stage projects such as Filecoin raised hundreds of millions of dollars in capital in mere hours. Some big-name VCs have amended their charters to allow them to acquire and serve as custodians for cryptocurrency tokens. The Long Island Ice Tea Co. changed its name to Long Blockchain and saw its market cap increase by more than 300%, as did multiple other publically traded companies, raising the specter of the .com excesses of the late 1990s. Legacy companies have started to market distributed database technologies as “private blockchains” to ride the wave. 

The global computer network running the Bitcoin blockchain alone is consuming more energy than of 150 of the world’s countries, causing concern over the sustainability of more energy-intensive consensus mechanisms such as proof-of-work. While few blockchains have seen real traction beyond the speculation use case, the large influx of capital is driving real innovation in technology and protocols that may power a new generation of companies. In particular, blockchains and cryptocurrencies are showing promise as a way to incentivize early participants in a network to participate a system before scale network effects kick in. This could help marketplace-style businesses and lower-level computing protocols and platforms achieve “escape velocity” and enjoy network effects more quickly.  


The what? The CRISPR gene editing technique, adapted from the immune system of bacteria, allows researchers to edit DNA at precise locations, turning off or on specific gene expressions. Over the last few years, CRISPR has been used for a wide variety of accomplishments, including removing Huntington’s disease from the genes of mice, improving the biofuel production capacity of algae, slowing the growth of cancer cells, creating synthetic bacteria-like organisms, and removing HIV from cells. 

2018 and beyond. Progress applying CRISPR technology to new use cases is proceeding at a rapid pace, and there is a corresponding large body of activity—in many ways still in its infancy—around applying advanced computer modeling and machine learning techniques to genomic analysis. Advanced computer software and enormous amounts of data are needed to allow scientists to determine which genes to edit and how to edit them to get desired results. As with machine learning, increasing success with CRISPR technology will provoke an important, global conversation to tackle questions of ethics and safety.