Biggest surprise of 2018
The biggest surprise for 2018 has been the quality of start-ups I’ve seen in Central and Eastern Europe (CEE). After Brexit I wanted to find a new angle for my EIS (Enterprise Investment Scheme) fund so started looking outside of the UK. Maybe not such a surprise for people in CEE but for a UK centric VC it was a very pleasant one!
Right across CEE I have seen high quality deal flow. Yes, it always helps to have partners/ecosystem to provide a filter but I’m still very captivated.
The three things that stand out are:
Quality and drive of founders
I find a consistency of drive, ambition and passion – maybe in the UK I’ve become used to a more conservative approach to business. Most of all I find the execution often further ahead than in the UK. This may be down to quicker B2B sales cycles in CEE and maybe it’s easier to get to the right decision maker. In the UK the average deal sizes are larger however outside the UK I’m seen higher volumes which as a VC gives you the confidence the company can scale.
In the UK salary costs for developers are extremely high. I see some very good ideas who just manage to get to the MVP stage. However, in CEE a similar company with the same budget will have made significantly more progress on the software development side due to the more affordable resources.
Willingness to enter new markets
In the UK I believe some of our start-ups find it hard to move out of their comfort zone. In CEE I see that going into new markets with different languages and cultures is less of a concern and embraced with enthusiasm.
I believe the future is bright for CEE start-ups and many of my colleague are waking up to this and I predict that in 2018 there will be more investment flowing from the UK to CEE.
My top 5 technology trends in CEE for 2018
These are the areas that I see being hot in 2018. Venezia Capital take a thematic approach to investment and believe these areas will see significant growth and investment in 2018.
The ability to use AI to enhance decision making, reinvent business models and ecosystems, and remake the customer experience will drive the payoff for digital initiatives.
Given the volume of AI start-ups, it’s clear that interest is growing. A recent Gartner survey showed that 59% of organisations are still gathering information to build their AI strategies, while the remainder have already made progress in piloting or adopting AI solutions.
Intelligent things use AI and machine learning to interact in a more intelligent way with people and surroundings. Some intelligent things wouldn’t exist without AI, but others are existing things (i.e., a camera) that AI makes intelligent (i.e., a smart camera.) These things operate semi-autonomously or autonomously in an unsupervised environment for a set amount of time to complete a particular task. Examples include a self-directing vacuum or autonomous farming vehicle. As the technology develops, AI and machine learning will increasingly appear in a variety of objects ranging from smart healthcare equipment to autonomous harvesting robots for farms.
As intelligent things proliferate, expect a shift from stand-alone intelligent things to a swarm of collaborative intelligent things. In this model, multiple devices will work together, either independently or with human input. The leading edge of this area is being used by the military, which is studying the use of drone swarms to attack or defend military targets.
Cloud to the Edge
Edge computing describes a computing topology in which information processing and content collection and delivery are placed closer to the sources of this information. Connectivity and latency challenges, bandwidth constraints and greater functionality embedded at the edge favors distributed models. Enterprises should begin using edge design patterns in their infrastructure architectures — particularly for those with significant IoT elements. A good starting point could be using colocation and edge-specific networking capabilities.
While it’s common to assume that cloud and edge computing are competing approaches, it’s a fundamental misunderstanding of the concepts. Edge computing speaks to a computing topology that places content, computing and processing closer to the user/things or “edge” of the networking. Cloud is a system where technology services are delivered using internet technologies, but it does not dictate centralized or decentralised service delivering services. When implemented together, cloud is used to create the service-oriented model and edge computing offers a delivery style that allows for executions of disconnected aspects of cloud service.
Conversational platforms will drive a paradigm shift in which the burden of translating intent shifts from user to computer. These systems are capable of simple answers (How’s the weather?) or more complicated interactions (book a reservation at the Italian restaurant on Parker Ave.) These platforms will continue to evolve to even more complex actions, such as collecting oral testimony from crime witnesses and acting on that information by creating a sketch of the suspect’s face based on the testimony. The challenge that conversational platforms face is that users must communicate in a very structured way, and this is often a frustrating experience. A primary differentiator among conversational platforms will be the robustness of their conversational models and the API and event models used to access, invoke and orchestrate third-party services to deliver complex outcomes.
Continuous Adaptive Risk and Trust
Digital business creates a complex, evolving security environment. The use of increasingly sophisticated tools increases the threat potential. Continuous adaptive risk and trust assessment (CARTA) allows for real-time, risk and trust-based decision making with adaptive responses to security-enable digital business. Traditional security techniques using ownership and control rather than trust will not work in the digital world. Infrastructure and perimeter protection won’t ensure accurate detection and can’t protect against behind-the-perimeter insider attacks. This requires embracing people-centric security and empowering developers to take responsibility for security measures. Integrating security into your DevOps efforts to deliver a continuous “DevSecOps” process and exploring deception technologies (e.g., adaptive honeypots) to catch bad guys that have penetrated your network are two of the new techniques that should be explored to make CARTA a reality.
Credits: Kasey Panetta - Gartner