However, when probing further into each letter, things can get somewhat obscure. Sure, it’s easy to name a few trending topics within each category (and almost always Carbon footprint and Diversity will be mentioned), but there’s much more depth behind each letter. There is however a balance to be struck, because it’s easy to get too deep into the weeds within a category, making it difficult to focus on what really matters.

As VC investors in software startups that build unique data solutions, we’ve tried to make sense of ESG by focusing on a few key topics within each ESG category as we work with entrepreneurs. These are topics we’ve determined to be impactful within our field and we hope to understand them already before making an investment. They are also important and tracked once we’ve made an investment and start working closely with the company towards its success.

We ensure that ESG is discussed in all boards where we have a board seat, and each year we survey our portfolio companies in order to measure what development has taken place. Ultimately our purpose is to ensure that ESG is part of the management agendas in all of the companies we work with.

Environmental

Given the fact that we work solely with software companies, one might hastily think that Environmental is the category we focus the least on, but that’s not the case. It’s often thought that since software companies don’t have any physical manufacturing or supply chains, there’s not that much to be done in order to reduce the carbon footprint. That’s however not the entire truth.

An often forgotten topic is the ever increasing energy consumption of complex task executions. The amount of power consumed by machine learning tasks is staggering and the exponential development could cause serious sustainability challenges in the future. Fortunately, smart software solutions can also save energy, e.g. by cutting infrastructure costs, which is the case in Sunlight.io, a Cambridge-based startup we recently invested in.

That being said, we do ask if our companies have an Environmental Policy in place and how they see their resource requirements and consumption going forward. We also want to understand how they travel and utilize videoconferencing and other modern digital collaboration tools.

Social

The Social category is very important for us and subsequently there are several questions in our annual survey addressing it. For example, we ask the company if they have the following policies in place:

• Health and Safety Policy
• Policy supporting anti-discrimination and potential migrant workers
• Policy supporting diversity

We also want to know if there are systems in place for identifying and addressing Health and Safety issues the company might face, or that the personnel might come across in the work or outside the office.

Furthermore, we ask what the percentage of women is in the company’s management team and in the board, how many different nationalities are represented in the company, and what the percentage of different age groups is.

Naturally we’re interested in learning about any interesting or tricky situations the company might have faced, and how those issues were resolved. Anonymized case studies can also be used for sharing knowledge and lessons learned across our portfolio.

Pay equality is an important aspect and this is also something we look into. We also want to understand what kind of insurances the company has in place and for whom.

Governance

The younger a startup is, the less they’ve typically paid attention to corporate governance. That’s understandable, but unacceptable as the company grows and the number of stakeholders increases.

As A-round VC investors, we are often the first institutional VC investors joining the company, which usually means that there are several basic things we need to put in place together with the company, starting from regular board meetings and systematic reporting.

In general, within the Governance category, we’re focused on what kind of policies the company has implemented and how they are enforced. Thus, we ask if the company has e.g. the following policies in place:

• Good Corporate Governance Policy
• Code of Conduct
• Policy / Guidelines for Information Security
• Policy on Corruption or Illicit financial actions

We are also interested in knowing if the company has formal employment contracts for everybody, if they’ve taken steps to ensure GDPR compliance, and whether or not there’s a whistleblowing system in place for the personnel to report e.g. bribery, corruption, money laundering, discrimination, or harassment.

Conclusion

It’s easy to see that each category of ESG is a world of its own and as such, each category can be expanded into a vast and complex realm. While a certain level of detail is needed to understand the facts, too much detail and complexity can make it hard to see the forest for the trees.

Once we’ve identified the topics that really matter in a particular case, it’s important to measure the development at regular intervals. We do it annually with an ESG questionnaire across our portfolio.

While we don’t pretend to be the world’s foremost experts on ESG, we have tried to take a systematic and serious approach to treating it when considering new investments and when working with our portfolio companies. The reason is obvious: startup teams that promote corporate responsibility, take diversity and equality seriously, and treat their employees well tend to deliver superior results.

This article was originally published on openocean.vc

Making sense of ESG was originally published in OpenOcean on Medium, where people are continuing the conversation by highlighting and responding to this story.

Let’s be clear, there are hundreds of early-stage tech investors out there, so it’s more than reasonable to ask what makes us different. Why should our new fund be your best bet for reaping the benefits of the ongoing data explosion and AI-driven business software boom in Europe?

As a matter of fact, there are only a handful of software expert VCs in Europe with hands-on experience in scaling software businesses to a billion-dollar valuation and who truly understand the technology and software stacks needed for building data-intensive software businesses.

Having always worked in-depth with data and analytics, OpenOcean is today focusing on areas such as advanced enterprise automation, artificial intelligence, new computational paradigms, and the next generation of application-driven infrastructure. We are working with a strong network of global thought-leaders in these areas and beyond.

To ensure continued thought-leadership, OpenOcean launched in 2018 the Data Series Network of data experts and leaders, which has rapidly grown to reach an audience of over 100k. In DataSeries, founders of our portfolio companies and other industry leaders meet and exchange experiences, get answers to difficult data and AI-related questions, and learn from the curated thought leadership content generated by the community. We benefit from DataSeries through world-class knowledge, hiring and advisory connections, deal-flow, and more. Already now we have 1000+ founders in the DataSeries Network, and the over 20 corporate partners include AWS, City.ai, IBM, MariaDB, Samsung Next, and many more.

Foreseeing major disruptions

The OpenOcean Team has repeatedly succeeded as visionary founders and business leaders in foreseeing major disruptions in the software and data markets and has capitalized on these unique insights and opportunities:

• Founding and investing in MySQL — the database for the Internet 2000–2008
• Building Nokia Interactive Advertising — innovating in mobile data 2006–2009
• Founding and investing in MariaDB — the data(base) foundation for the Cloud 2010 — current

In addition to driving these globally impactful data-centric innovations, the team has a strong track record of investing in category leaders creating the new layer of applications built on top of these underlying technologies reshaping the world, for example:

• Artificial Intelligence, much before it became hot (investments including Graphcore, Import.io, LoopMe, Nosto, RapidMiner)
• Mobile Identity and Payments, long before other investors saw the opportunity (Truecaller investment)
• Application-Driven Infrastructure and DevTools (investments such as Bitrise, MariaDB, and Platform.sh)
• Data disruption in enterprise verticals such as Marketing and Advertising (investments including Supermetrics, Unacast, and Verto Analytics)

Our DNA of having a deep understanding of the underlying technology and data stacks, which drive the growth of innovative new digital platforms and services, allows us to identify and invest in the next game-changing start-ups, whose unique solutions can rapidly be adopted globally.

Why Founders select OpenOcean

Founders and co-investors sense our integrity, respect for the entrepreneurial challenges ahead, and subject matter expertise, be it product or business related. The dialogue is usually intense, collaborative, and value-adding, already before OpenOcean has invested.

Upon investment, we help inspire the entrepreneurial teams to think long term and often expand their ambitions. We of course also assume regular board member duties, such as developing a strong and differentiated strategy; ensuring there is the right team to execute; the company is professionally governed; and we often conduct various workshops to help problem-solve or take areas to the next level. We help open doors globally to hire advisors or team members, bring top-notch follow-on investors, or introducing strategic partners and potential customers.

Benefits to LPs

For Limited Partner Investors (LPs), we can offer significant leverage through our deep presence in the European market:

1. Partnership beyond the capital, including industry thought leadership exchanges.
2. High-quality deal flow with the deep theme-driven approach. Potential participation in deal flow calls and/or quarterly tailored updates about the market.
3. Broad coverage of the European software opportunity (already invested in 9 European countries, actively covering the ground in all major European capitals and economies).
4. Case-specific opportunities to co-invest and or to compare analysis on investments.
5. Connection with OpenOcean’s broader ecosystem of founders, industry leaders, and investors, as well as via our data expert community DataSeries

OpenOcean Fund 2020 in a nutshell

Our next Fund 2020 will be a 10-year venture capital fund investing in European founders building unique B2B software with a focus on data-intensive businesses disrupting major industries.

The investment stage will be Series A and pre-emptive Series A. 65% of the Fund’s commitments will go to follow-on investments in the best performing companies. The Fund is seeking a minimum of 3.5x net return with a portfolio of 16–18 companies. The first closing of the fund is expected to be in Q2, 2020.

Contact us for more information

If you’d like to be part of our mission and want to join our Fund 2020, then please don’t hesitate to contact us. Tom Henriksson (tom@openocean.vc) and Niklas Rosenberg (niklas@openocean.vc) will gladly give you more information.

Pioneering data-intensive software ecosystems from Europe was originally published in OpenOcean on Medium, where people are continuing the conversation by highlighting and responding to this story.

Why Berlin you might ask? Well, we already have offices in three important European tech hubs, London, Helsinki, and Amsterdam. Naturally we try to visit other major tech hubs as often as possible, but we haven’t spent as much time in Berlin as we would’ve liked. So far we’ve only invested in two Berlin based startups, EyeEm and HeavenHR.

Berlin is often ranked among the Top 3 tech hubs in Europe and there are several data points supporting this, such as these from State of European Tech 2018:

• Number of Developers (about 100K),
• Number of tech-related Meetups (since 2013 about 13 300),
• Number of funded companies (since 2013 almost 860), and
• Venture Capital invested (almost €2B in 2018).

Also, based on our own deal-flow, it seems like Berlin is today producing much more than just marketplace startups à la Rocket Internet. We’ve seen many interesting B2B2C startups, as well as delicious data-intensive enterprise software solutions.

In general, Germany is in second place in Europe (after the UK) in producing billion dollar companies. Since 2003, Germany has produced eleven $1B+ companies and three of them were added in 2018.

From the OpenOcean Team, I will be in Berlin for the entire four weeks (February 13th to March 15th, 2019), and during the SuperReturn week (SuperVenture, February 26–27th, 2019) several of us will be in town, as Katya Almasque and Tom Henriksson will also be participating in the event.

On a personal note, I really look forward to spending a full month in Berlin, meeting a lot of people and broadening my horizons. It’s experiences like these that energize me and keep me creative. It’ll also be a great reality-check on how far my School German takes me.

Please don’t hesitate to contact me (niklas@openocean.vc) if you’d like to meet in Berlin over a cup of coffee and have a chat about Life, the Universe, or anything Startup/VC related.

Then we take Berlin was originally published in OpenOcean on Medium, where people are continuing the conversation by highlighting and responding to this story.

The data explosion can be attributed to several different technological advances. Increasing computing power and the Internet of Things (IoT) are often mentioned as reasons, but there have also been many innovations and developments in software for unstructured data. It’s good to remember that a big chunk of all new data created is still unstructured, such as social media posts, video, audio, and pictures.

Data is important in many ways, and if used properly — meaning that privacy and regulations are being honored–it can make all the difference. For example, by driving better decision making on a number of different levels, ranging from individuals to businesses (even societies), data can help us in succeeding and thriving in an ever-changing competitive world. Data is also the fuel that makes machine learning tick.

OpenOcean’s mission is to invest in data-intensive and delicious software companies. In particular we look for relentless entrepreneurs who build revolutionary products that have a significant impact on how we conduct business or lead our lives in the future. If you’re curious about what we mean by delicious software, please have a look at our Investment Thesis in a Nutshell. The data-intensive part is a bit more self explanatory and in essence it means that data is being aggregated and used in a smart way when building the product and developing the business, or that the product itself is data-analytics software.

At OpenOcean we look and evaluate the data-intensivity of software companies already at early stages in our investment process. We think it’s unwise to leave the data evaluation to later stages of the process, e.g. to the confirmatory due diligence stage. Here are some of the elements we try to understand when looking at a company from a data perspective:

• Maturity of current data sets: This means evaluating the volume and structure of the data that is used. We also look at data clean-up processes. In essence we try to understand if the company is collecting and using the right data.
• Maturity of KPIs: We investigate what KPIs have been put in place to manage and improve the company’s operations.
• Maturity of the system: This means evaluating the data collection processes, the infrastructure, the flexibility of the data collection, and what tools are being used.
• Maturity of data strategy: We look at the company’s goals, process, and roadmap for gathering relevant future data.

Because we speak so much about data-intensivity, we often get the following (very reasonable) question: how data-intensive are you yourselves as venture capital investors?

At OpenOcean we’ve done a lot of thinking around data and how it can be used for better decision making and automation. One outcome of this is our proprietary Haystack Deal-Flow Software, which enables us to efficiently gather and look at a large amount of relevant venture deals with automated qualification (scoring) done by algorithms. We’ve been using Haystack in production use since September 2018 and the results have so far been very promising.

To summarize, there is no doubt in our minds that smart aggregation and deep usage of data are essential building blocks for fast-growing successful software companies. That’s why we pay special attention to the data perspective when evaluating deals. It’s also safe to assume that data will be increasingly important for any company that wants to succeed in the future and that’s why we’ve started to develop our own investment operations into a more data-driven direction.

They say data drives our world today. We say data will drive humanity tomorrow!

Looking for Data was originally published in OpenOcean on Medium, where people are continuing the conversation by highlighting and responding to this story.

I remember hearing the first time about ESG many years ago while working at a corporate bank. A person from the Markets Division tried to explain to the rest of us why yet another three-letter-acronym was going to be important. Most people in the audience were yawning and thinking “yeah, yeah, more regulation and rules to oppress us, blabla…”.

The thing is that this early ESG ambassador was absolutely right. ESG quickly became an important topic among institutional investors and eventually others in the food chain also started paying attention to it.

If you ask any sane person if he or she thinks that environmental or social factors are important, the answer is going to be yes. But, as Morpheus said, “there’s a difference between knowing the path and walking the path”. This is especially true in the investment world.

You might think that investors would change their behaviour just because “it’s the right thing to do”. Unfortunately that’s not always the case. Typically investors change their behaviour only when there are strong economical reasons for doing so. Fortunately, when it comes to ESG, investors have now realised that they can actually produce better long-term returns by investing in companies that not only are innovative and great but also have their ESG matters in order.

It’s becoming increasingly difficult, if not even impossible, to put together a VC fund without ESG being part of the strategy. The limited partner investors (LPs) investing in VC funds simply won’t invest if ESG is not your priority. The same applies to LPs when they’re fundraising. They won’t get money from larger institutional investors like pension funds or sovereign wealth funds unless they can be sure that ESG matters are in order.

Which brings us to the most important part of the ecosystem, i.e. startup founders who ultimately are the key drivers of value. If you as a founder want to raise VC funding for scaling your business, you have to be aware of ESG and understand what it requires from you.

Keeping our own house in order

If we as VC investors are setting demands for startups regarding ESG, then it’s fair to expect that our own house is in order as well. After all, a VC firm like OpenOcean is like any other company. We believe ESG is crucial for us to succeed in our mission, which is “to invest in and support the building of successful enduring companies, create long term value, and generate superior returns for investors”.

We’ve adopted the Ten Principles of the United Nations Global Compact and we support the Six Principles of United Nations Principles of Responsible Investment. We also adhere to the OECD Principles of Corporate Governance. Furthermore, we’re members of the European Venture Capital Association (“Invest-Europe”) and the Finnish Venture Capital Association (FVCA), which means that we support and act in accordance with their standards and guidelines.

All new OpenOcean employees are educated in ESG related matters and we talk about these important topics and principles regularly e.g. at our team offsite meetings.

Evaluating companies during the investment process

As a leading VC investor OpenOcean recognises the key role that improvement of ESG matters means for long-term value creation. Consequently our aim is to invest responsibly while following our investment strategy, which is to invest in data-intensive and delicious software companies in Europe.

We try to keep ESG matter in our mind during all stages of the investment process, but when a company enters the “Evaluation” stage there’s a formal Deal Evaluation Matrix that must be completed. The evaluation matrix contains various aspects that predict startup success, such as scalability, unit economics, market size, team composition — and these days also ESG.

ESG is also examined in the Due Diligence phase of the investment process (i.e. a term sheet has already been signed) and that’s when we really dig deep into the fundamentals of a company from a technical, financial, and legal viewpoint, often assisted by external experts and advisors.

Working together with our portfolio companies

In the early phases of a startup’s lifecycle there might not be too many ESG matters to worry about. Also, certain environmental aspects don’t apply to software products in the same way as to physical products. Nevertheless, the larger a company grows the more attention you need to put on aspects like human rights, labour rights, the environment, and anti-corruption.

We aim to set clear expectations on how to promote corporate responsibility within all our portfolio companies and to support them by providing guidance and tools. Most often we do this by being active members in the board of directors, which ultimately is responsible for delivering business objectives.

A recent development is that we have formulated an ESG questionnaire for our portfolio companies based on Invest-Europe’s ESG Due Diligence Questionnaire. Our ambition is to gather first responses during Q4 2018 and summarise the results in a new ESG section of our Fund Investor Report. Thereafter we will regularly request responses to the same questionnaire, which enables us to:

1. Ensure that ESG matters are high on the portfolio company’s management agenda,
2. Monitor the ESG status and progress across our portfolio, and
3. Report to our LPs regularly on the ESG situation.

Conclusion

We’ve come a long way since the early days of ESG and that’s a very good thing for everybody in the startup ecosystem and for future generations on the planet. Today you can’t start a VC fund without having ESG as a natural part of your investment strategy. And as a startup founder you need to have your answers ready when VCs start to probe around ESG matters.

Who cares about ESG? was originally published in OpenOcean on Medium, where people are continuing the conversation by highlighting and responding to this story.

Our observation is based on our own deal-flow (OpenOcean logs about 2000 European deals annually) and on VC deal statistics (e.g. obtainable from Pitchbook). The low volume of InsurTech startups is also something FinTech oriented VCs quite often talk about.

Now, before you flood us with “Wait, haven’t you noticed this and that startup?” I will just say yes, there are a number of good and successful InsurTech startups out there. The point here is that the number is much lower than you’d expect, given the magnitude of the opportunity at hand.

What could be the reason for this scarcity?

I can’t give you an exact scientific answer, but having worked both with insurances and banking I’ve got a pretty good sense how consumers and businesses think about these services.

Many people feel that insurances are boring and unpleasant, because insuring is associated with unpleasant events (e.g. having a serious accident) and risk mitigation, which is always less sexy than risk taking. Banking on the other hand is associated with opportunities and positive risk taking and is thus much more attractive.

I’m by no means claiming that insurances would be less important than banking. I’m just pointing out how the majority of people feel about it. Who do you think has the better chance of getting an audience with a CFO? The Account Executive who wants to present a new insurance solution? Or the Relationship Manager who wants to show new financing solutions?

Another contributing factor might be that business schools teach much less about insurances than about finance. Look at the curriculum of any business school and you’ll find it’s filled with courses related to asset pricing and portfolio management, but you’re lucky if you can find a few insurance related courses. Most people who graduate from business schools don’t have the faintest idea how an insurance product is priced.

All of the above mentioned issues probably contribute to the fact that only a few startup founders feel that the insurance sector is where they want to make a difference.

A sector full of opportunities

You can slice and dice the insurance industry in many different ways, e.g. Property & Casualty (P&C) vs. Life insurances and Business vs. Private insurances, and in each segment you’ll find opportunities. Why?

First and foremost the insurance industry is a highly conservative industry with a lot of big old incumbents who have mastered the skill of protecting their existing business. The barriers built have come at the cost of innovation. Yes, you can buy insurances and report claims on the web, but in terms of product innovation only a few things have occurred during the past 30 years, with perhaps Health insurances being the exception.

Second, most consumers and businesses pay too much for their insurances. The reason for this is that while insurance companies are masters in indexing their premiums, most customers are lazy at comparing and changing their insurance providers. You’ll have no trouble remembering the last time an insurance company raised your premiums but you’ll be hard pressed in remembering the last time your terms and conditions (i.e. your cover and services) were automatically improved. It’s no wonder that many large European insurers are highly profitable with combined ratios well below 85% (which means that over 15% of premiums collected goes to profit).

Third, oftentimes consumers and businesses don’t have the optimal insurance solution or cover. The reason for this is that while your life circumstances (or business stage) changes and evolves, insurances are slow or inflexible to reflect these changes. This easily results in you (or your family, or your business) either being over or under covered, of which the latter is of course worse.

Finally, the insurance industry is a huge market and people might not even realize exactly how big it is. In Europe alone P&C and Life insurance premiums were €1030B in 2017. Globally the same number was €3660B.

Innovation is possible within most lifecycle stages

If you look at the lifecycle of an insurance solution from a customer’s perspective you can roughly divide it into the following steps:

1. Recognising your risks and deciding how to manage those risk (not all risks are worth insuring).
2. Discovering what insurance alternatives you have for the risks you want (or have to) to insure.
3. Buying your insurance policy, i.e. obtaining the optimal solution in terms of coverage, duration, terms and conditions, and premium.
4. Getting assistance from the insurance solution provider when you have questions or issues regarding your risk or cover.
5. Filing a claim and having a smooth claims handling and indemnification process in case you experience an accident/incident.
6. Calibrating and changing your solution as your circumstances change.
7. Renewing or terminating your insurance solution.

I firmly believe that within almost every stage there’s room for innovation and disruption. It’s also possible that the innovation could be brought to the market by other parties than insurance carriers. Not all participants in the ecosystem have to be licensed insurance carriers, nor do they have to be licensed brokers. However, it’s prudent to admit that the insurance business is a heavily regulated business and capital costs for insurance carriers in particular have gone up significantly due to Solvency II.

It’s also worth pointing out that the above mentioned list only deals with the core business of insuring risks. The cake becomes much more attractive when you add all sorts of interesting ancillary products and services on top of it, for example fast and easy access to high quality healthcare providers in case of illness or injury.

OpenOcean is actively looking for InsurTech opportunities

OpenOcean is an early-stage (Series A) venture capital firm investing across Europe with offices in London, Helsinki, and Amsterdam. With an entrepreneurial background from building several category-defining software businesses, the firm engages with founders to build and scale global companies delivering data-intensive and delicious software solutions.

Among our portfolio companies you’ll find Verifly, the leading digital provider of episodic Drone Liability and Commercial General Liability (CGL) insurance solutions. With an elegantly designed app and a totally new customer experience they tap perfectly into the new opportunities and demands of the gig economy. We are continuously looking for new innovative InsurTech startups, so please contact us if you’re building something interesting in this space and want to discuss how the industry should be shaken up.

Where are all the InsurTech startups? was originally published in OpenOcean on Medium, where people are continuing the conversation by highlighting and responding to this story.

At OpenOcean we have a very pragmatic view on AI and we like to keep our feet firmly on the ground. We don’t spend much time worrying about The Singularity or Paperclip Maximizers. We are not too worried about potential social unrest due to displacement of jobs. In fact, we don’t think anything super radical has emerged within AI research since the 1980’s.

What has changed is the infrastructure speed, scale, and availability, which has enabled us to use bolder algorithms to solve more ambitious problems. The recent developments in hardware and Graphics Processing Units (GPUs) combined with cloud services have democratized AI. What used to be run in specialized computing labs can now be deployed to the cloud quickly and at a low cost. Today anyone can build AI enabled software as long as you have access to relevant data. And data, as it turns out, is the thing that really makes AI tick.

The ups and downs of AI fashionability

It’s worthwhile to remember that way back in the 1980’s there was a similar AI boom as the one we’re now witnessing. Everybody was writing about it and many businesses jumped on the bandwagon. Early AI pioneers were featured on magazine covers and valuations of “AI enabled” businesses skyrocketed.

But as the 1990’s came along AI was already forgotten and what used to be a “hot technology” never caught on as a mainstream thing. Interest in pursuing academic careers within AI research plummeted. There were simply too many obstacles for absorbing AI into the mainstream. The hardware wasn’t good enough and it was difficult to integrate it into existing systems. There were no established design methodologies and development was difficult. After a while AI got a reputation as a failed attempt and for a long time the term itself became taboo.

All that has of course changed now. Today everybody talks about AI, but few have a clear picture of what AI can do today and in the near future. What follows is a summary of what we think is important to keep in mind when evaluating AI.

AI is a big field

Not only is it a big field but it’s also diverse in terms of topics and methods used. The main AI conferences, such as IJCAI, ICML, AAAI, and NIPS, attract many attendees but most of them only understand a small part of what goes on in the conference. It’s rare to find a person who can keep up to date with all the subfields like constraint propagation, deep learning and stochastic search. If someone says they know what’s going on right now in AI, it’s safe to assume that they only know a small part of the big picture.

No such thing as “an artificial intelligence”

AI is a collection of methods and ideas for building software that can emulate a small portion of the things we humans do with our brains. It’s possible to develop new AI algorithms and use existing algorithms to build software that can do something neat, such as play Chess or identify faces. But almost always it’s the case that the same software can’t both play Chess and identify faces. For different tasks you need different systems and it’s important to understand the limitations of a system.

AI is an old field

The recent advances in deep learning have their roots in neural network research from the 1980s and 1990s. That research in turn was based on ideas and experiments from the 1940s. Often development within AI consists of minor variations and improvements on previous research. When IBM’s Deep Blue beat Garry Kasparov in Chess the core of the program was the Minimax algorithm, first implemented by Alan Turing in the 1940s.

Data is key but you have to know how to use it

The word learning in machine learning means that the algorithms depend on data used as a training set. One of the reasons AI/ML adoption has exploded during the past five years is that more and more data is constantly being generated. But even if data is the key ingredient in ML you have to know how to use it.

Peter Lee, the CEO of Rapidminer, explained during the 2018 OpenOcean Annual Summit that “the number one challenge we see across the board is that there’s this false expectation that ML, just because you have a ton of data, is actually going to find a meaningful signal”. He continued to drive home his point by explaining how the best way to predict flight delays might not be to only analyze data generated by a myriad of sensors, but to also look at mundane data like why airplane toilets or coffee pots are malfunctioning or why the heaters are off.

Much of AI is human ingenuity

There’s a reason why researchers and developers specialize in specific AI applications, such as robotics or translation. When building a system for solving a certain problem, a lot of domain knowledge is typically required and included in the system. For example, you might provide special inputs to the system or use prepared training data or even reformulate the problem in order to make it easier to solve. To understand the extent to which an “AI solution” is automatic and what is due to human domain knowledge, you might want to test how the system works on a slightly different problem.

Don’t pretend to be AI enabled

Just because there’s a lot of hype around AI doesn’t mean you have to pretend to be AI enabled. It’s still quite possible to build a tremendously successful software business without any machine learning. Algorithms you will need of course, but they don’t necessarily have to be machine learning algorithms. It’s all about the problem you’re trying to solve and AI/ML is suitable for certain types of problems. Too often the term AI is used to describe techniques that have never been thought of as AI, just in order to ride-out the buzz-wave.

Conclusion

Our pragmatic view on AI doesn’t mean we are pessimistic about the technology or the opportunities involved. On the contrary, we firmly believe that AI will enable startups to tackle even more complex and relevant problems in the future. The challenge is to be able to cut through the hype and understand what AI can do and what it can’t do.

Often when we talk about AI we should in fact be talking about ML, which can be viewed as a subset of AI. It’s ML that will deliver most of the “AI disruptions” of the near future and the tricky part is to identify the application areas besides the obvious ones, like fraud detection or e-commerce recommendations.

Finally, for ML to really be able to deliver its magic you need lots of data and clever ways of using and aggregating it. That’s why we here at OpenOcean like software that not only is delicious but also data intensive.

How to cut through the AI hype was originally published in OpenOcean on Medium, where people are continuing the conversation by highlighting and responding to this story.

It was during an OpenOcean hosted software event last fall that Nirav Tolia, co-founder and CEO of Nextdoor, reminded the audience that you should always ask yourself if your product is a vitamin or a painkiller.

This question is popular among venture capitalists and Kevin Fong, a venture capitalist in the Bay Area, is known for saying:

“We divide business plans into three categories: candy, vitamins, and painkillers. We throw away the candy. We look at vitamins. We really like painkillers. We especially like addictive painkillers!”

As in our everyday lives, vitamins are nice to have and there are undoubtedly benefits in getting your vitamins, but you can easily survive without taking them.

Pain on the other hand is much more concrete and acute. When in pain, you tend to seek something that alleviates it — and the sooner you get it the better. If your pain is really excruciating, you’re probably not going to spend much time price shopping or bargaining.

Clearly, if you’ve managed to develop a painkiller and your target customers realize that they are in pain, you’re in a good spot. However, achieving this dual position can be quite difficult.

The much more common path is to start out as a vitamin and work your way towards a painkiller. Just think about some of the unicorns of our time (e.g. LinkedIn, Slack, Twitter, and Zendesk). Few of them started out as painkillers and interestingly enough, you could argue that some of them started out as candy, i.e. not as vitamins.

Whether or not your product is seen as a vitamin or a painkiller depends on your audience’s situation and context. The fact that you see your product as a clear-cut painkiller doesn’t necessarily mean that your potential customers see it that way. If you’re convinced that you are right, and you’ve got evidence to support it, then you need to educate and convince your audience that they are in fact experiencing pain. This is a challenging communication and marketing task.

Remember that there might also be differences across markets. For example, Truecaller (an OpenOcean portfolio company) is definitely a painkiller in India, where spam calls and switching of pre-paid phone numbers are prevalent, but more of a vitamin in Western Europe.

The fact that most venture capitalists tend to favour painkillers over vitamins and candy doesn’t mean you can’t be successful by developing vitamins or candy. It’s probably just a bit harder and you might first have to make your product free in order to get people hooked. But there are many examples from the gaming industry proving that candy — and addictive candy in particular — can be massively successful too!

From Vitamins To Painkillers was originally published in OpenOcean on Medium, where people are continuing the conversation by highlighting and responding to this story.