Upgrade to Pro — share decks privately, control downloads, hide ads and more …

The State of AI 2019: Divergence

The State of AI 2019: Divergence

For entrepreneurs, executives, investors and policy-makers, 'The State of AI 2019: Divergence' is the accessible, comprehensive guide to the reality of AI today, what’s to come and how to take advantage. Download the full report at www.stateofai2019.com.

David Kelnar

June 27, 2019
Tweet

Other Decks in Technology

Transcript

  1. “The future is already here.
    It’s just unevenly distributed.”

    (William Gibson)
    The State of AI 2019: Divergence

    View full-size slide

  2. For entrepreneurs, executives, investors and policy-makers, The State of AI 2019 is an accessible, comprehensive guide to the
    reality of AI today, what’s to come and how to take advantage. Download at www.stateofai2019.com.
    Highlights:
    1. The race for adoption

    Increasing adoption of AI masks a growing divergence among nations, across industries and between companies.
    2. The war for talent

    While demand for AI professionals exceeds supply, winners and losers are emerging in the war for talent.
    3. The advance of technology

    Emerging techniques (transfer learning, reinforcement learning and generative AI) are enabling new possibilities.
    4. The Disruptors: Europe’s AI startups

    Our unique analysis of Europe’s 1,600 AI startups reveals a maturing ecosystem bringing creative destruction to new industries.
    5. The implications of AI
    AI will reshape sector value chains and enable new business models, while risking greater inequality and the erosion of trust.
    3
    The State of AI 2019: Divergence

    View full-size slide

  3. MMC Ventures is a research-led venture capital firm.
    We’ve backed over 60 early-stage, high growth
    technology companies since 2000.
    AI is a core area of research and investment.

    We’ve made 20 investments into many of the UK’s

    most promising AI companies.
    If you’re an early stage AI company, get in touch

    to see how we can accelerate your journey.
    www.stateofai2019.com
    We’re MMC Ventures
    4
    MMC Ventures Research
    David Kelnar: Partner & Head of Research

    Asen Kostadinov, CFA: Research Manager
    Explore MMC’s cutting-edge research at
    mmcventures.com/research.

    View full-size slide

  4. AI: Coined in 1956, a general term referring to hardware or
    software that exhibit behaviour which appears intelligent.
    Machine learning: Modern AI, which enables software to
    perform difficult tasks more effectively by learning through
    training instead of following sets of rules.
    Deep learning: A form of machine learning that approximates
    the function of neurons in a brain to solve problems.
    New to AI? Read our primer in www.stateofai2019.com
    5
    Before we begin…

    View full-size slide

  5. The State of AI 2019: Divergence
    1. The race for adoption

    View full-size slide

  6. AI may be the fastest paradigm shift in technology history.
    But increasing adoption masks a growing divergence
    between leaders and laggards – among nations, between
    industries and across companies.
    The race for adoption
    The State of AI 2019: Divergence 7

    View full-size slide

  7. The State of AI 2019: Divergence
    AI adoption is proliferating
    8
    One in seven large companies has deployed AI
    Source: Gartner, 2019 CIO Survey: CIOs Have Awoken to the Importance of AI, figure 1, 3 January 2019. Base: All answering, n = 2,882. What are your organisation’s plans in terms of artificial intelligence?
    One in seven large companies have deployed AI
    Enterprise plans to deploy AI
    No interest Plan to deploy in 2 to 3 years Will deploy in 12 to 24 months Will deploy in next 12 months Have already deployed
    9% 29% 25% 23% 14%
    • Today, one in seven large companies has adopted AI.
    • In two years, two thirds of large companies will have live AI initiatives.

    View full-size slide

  8. The State of AI 2019: Divergence
    AI is ‘crossing the chasm’
    9
    In 2019, AI ‘crosses the chasm’ to the early majority
    Source: Everett Rogers, Geoffrey Moore
    • By the end of 2019, over a third of enterprises will have deployed AI.
    • In 2019, AI ‘crosses the chasm’ to the early majority.
    • Adoption of AI has progressed rapidly from innovators and early adopters to the early majority.
    AI adoption is ‘crossing the chasm’ to the early majority
    Innovators Early adopters
    CHASM
    Early majority Late majority Laggards
    Population
    2.5% 34%
    34% 16%
    13.5%

    View full-size slide

  9. Adoption has been enabled by:
    • the prior paradigm shift to cloud computing
    • the availability of plug-and-play AI services from global technology vendors
    • a thriving ecosystem of AI-led software suppliers.
    10
    AI: the fastest paradigm shift in enterprise tech history?
    The State of AI 2019: Divergence
    Over three years, the proportion of companies with AI initiatives will
    have grown from one in 25 to one in three (Gartner).

    View full-size slide

  10. • Adoption is being catalysed by companies’ growing conviction in AI’s potential.
    • A greater proportion of executives believe AI will be a ‘game changer’ than any other
    emerging technology – including cloud, mobile, IoT, blockchain or APIs.
    11
    Great expectations are fuelling adoption
    The State of AI 2019: Divergence
    2019 CIO Agenda

    Which technology area do you expect will be a game-changer for your organisation.
    Top performers

    (n=230)
    Typical performers

    (n=2,329)
    Trailing performers

    (n=276)
    AI / Machine Learning 40% 25% 24%
    Cloud (including XaaS) 12% 10% 14%
    Mobile (incl. 5G) 7% 6% 5%
    Internet of Things 6% 10% 11%
    Blockchain 5% 4% 5%
    ERP 1% 3% 3%
    AI tops the list of technologies companies perceive as ‘game-changing’
    Source: Gartner, 2019 CIO Survey: CIOs Have Awoken to the Importance of AI, figure 1, 3 January 2019.

    View full-size slide

  11. 12
    Use of AI applications is advancing across a broad front
    The State of AI 2019: Divergence
    • Today’s enterprises are using multiple types of experiential and analytical AI applications.
    • One in ten enterprises now uses ten or more AI applications (Gartner).
    The State of AI 2019: Divergence
    Chatbots have displaced fraud detection as the top use of AI in 2019
    Does your organisation use any of these artificial intelligence (AI) based applications? 2019: n = 2,791; 2018: n = 2,672. Multiple responses allowed.
    Source: Gartner, 2019 CIO Survey: CIOs Have Awoken to the Importance of AI, figure 1, 3 January 2019.

    View full-size slide

  12. 13
    Popular use cases: asset management
    The State of AI 2019: Divergence
    • Investment strategy

    Improve a firm’s investment strategy by synthesising research and data, and incorporating broader
    data sets including unstructured information.
    • Portfolio construction

    Augment or automate an asset manager’s process of portfolio construction.
    • Risk management

    Improve risk management by incorporating broader data sets and enhanced analytical processing.
    • Client service

    Natural language AI enables client enrolment, support and self-service. Chatbots enable account
    managers to query client details and understand developments with client portfolios in seconds.

    View full-size slide

  13. 14
    Popular use cases: healthcare
    The State of AI 2019: Divergence
    • Diagnosis

    Replace complex, human-coded sets of probabilistic rules and identify subtle correlations between
    vast, multi-variate data sets to deliver scalable, automated diagnosis.
    • Drug discovery

    Synthesise information and offer hypotheses from the 10,000 research papers published daily;
    predict how compounds will behave from an earlier stage of the testing process; identify patients
    for clinical trials.
    • Monitoring

    Synthesise signals from inexpensive wearable devices worn by patients to deliver clinical-grade
    monitoring; enable large patient groups to be monitored in real-time by a single nurse.

    View full-size slide

  14. • Risk assessment

    Assess the risk of individual policies more accurately than rules-based systems by detecting

    non-linear patterns in multi-variate data sets and making more accurate projections.
    • Claims processing

    Automatically extract and classify structured and unstructured information from insurance policies
    and claim forms.
    • Fraud detection

    Identify fraudulent transactions, while reducing false positives, more effectively.
    • Customer service:

    Chatbots using natural language processing can offer 24/7 product information and answers to
    policyholders’ enquiries in a scalable, inexpensive and personalised channel.
    15
    Popular use cases: insurance
    The State of AI 2019: Divergence

    View full-size slide

  15. 16
    Popular use cases: law and compliance
    The State of AI 2019: Divergence
    • Case law, discovery and due diligence

    Natural language processing AI can identify, classify and utilise content from databases and
    unstructured documents at scale and speed, saving legal firms time and cost for document review.
    • Litigation strategy

    Analyse past judgements at greater speed, granularity and subtlety. By anticipating the probability
    of different outcomes, inform and enhance strategic decision-making.
    • Compliance

    Flag potential compliance breaches in real-time, before they occur, with sufficient accuracy to
    enable broad deployment.

    View full-size slide

  16. 17
    Popular use cases: manufacturing
    The State of AI 2019: Divergence
    • Predictive maintenance

    Identify subtle patterns in data from vibration, temperature, pressure and other sensors to identify
    leading indicators of equipment failure and save expensive, unplanned downtime.
    • Asset performance

    Improve the operation of high value assets, including gas and wind turbines, to optimise yield.
    •Utility optimisation

    Anticipate, and align, utility consumption with process requirements in real-time, lowering utility
    consumption by 5% or more.

    View full-size slide

  17. 18
    Popular use cases: retail
    The State of AI 2019: Divergence
    • Customer segmentation

    Access additional data sets (e.g. social media data) and undertake more granular analysis than
    rules-based systems allow, to optimise segmentation, channel selection and messaging.
    • Content personalisation

    Access additional unstructured data sets for analysis, and improved multivariate analysis to
    identify more subtle correlations than rules-based systems can detect.
    • Price optimisation

    Identify correlations within and between data sets, to better optimise for factors including price
    elasticity, revenue, profit, availability and phases in product’s lifecycle (introduction or end-of-life).

    View full-size slide

  18. 19
    Popular use cases: transport
    The State of AI 2019: Divergence
    • Autonomous vehicles

    Enable vehicles to sense and identify physical features and dynamics in their environment, from
    road lanes to pedestrians and traffic lights, with a high degree of accuracy.
    • Infrastructure and system optimisation

    Detect patterns and optimise complex data to address traffic, congestion and infrastructure
    challenges in transport systems.
    • Fleet management

    Optimise pick-ups, route planning and delivery scheduling to maximise asset utilisation, while
    considering economic, social and environmental impacts.
    •Control applications

    Address prediction and optimisation challenges presented by air traffic control, vehicle traffic
    signalling, and train control.

    View full-size slide

  19. 20
    Popular use cases: utilities
    The State of AI 2019: Divergence
    • Supply management

    Predict changes in supply, including those caused by the intermittency of renewable resources,
    enabling smaller reserves and cost savings.
    • Demand optimisation

    Identify detailed patterns in consumer behaviour to move consumption of energy from periods of
    peak use and high prices to times of lower demand and cost.
    • Security

    Identify abnormal patterns in network behaviour to detect breaches in network security that elude
    traditional programs.
    •Customer experience

    Chatbots, powered by deep learning algorithms, offer consumers self-service account
    administration, product information and customer service.

    View full-size slide

  20. ‘Deployed AI’ (% of companies)
    Source: Gartner
    ‘Deploying AI in the next 12 months’ (% of companies)
    Source: Gartner
    21
    Globally, China leads the race in AI adoption
    • Twice as many enterprises in Asia have adopted AI, compared with companies in North
    America, due to: government engagement; a data advantage; and fewer legacy assets.
    • Chinese companies have a dual advantage: more permissive policies than Europe
    regarding use of personal data; and less siloed data within companies.
    Deployed AI
    25%
    1%
    Deploying AI in the next 12 months
    30%
    1%
    Rapid adoption of AI overall masks a growing divergence – among countries, across industries and
    between companies.

    View full-size slide

  21. Adoption of AI is uneven across, and within, sectors
    Source: Gartner
    22
    Sector adoption is in a state of flux
    The State of AI 2019: Divergence
    Divergence is also evident across industries:
    • ‘Early adopters’ (financial service and high-tech companies)
    invested early in AI, sustained their investment, and today
    maintain a lead.
    • ‘Movers’ (retail, healthcare and media) have awoken to AI’s
    potential and are rapidly catching up.
    • Government agencies, education companies and charities are
    laggards in AI adoption. Utilities
    Wholesale trade
    Government
    Education
    Charity
    Natural Resources
    Financial Services
    Health (Providers)
    Transport
    Manufacturing
    Media
    Health (Payers)
    Retail
    Telecoms
    Software & IT Services
    Insurance
    Adoption of AI is uneven across, and within, sectors
    Percentage of respondents that have
    deployed AI or plan to within 12 months
    48%
    46%
    44%
    44%
    44%
    42%
    40%
    39%
    38%
    37%
    35%
    33%
    32%
    26%
    26%
    25%
    Vulnerable members of society may be among the last
    to benefit from AI.

    View full-size slide

  22. Leaders and laggards face different adoption challenges
    23
    Leaders and laggards face different challenges to adoption
    Source: S. Ransbotham, P. Gerbert, M. Reeves, D. Kiron, and M. Spira, “Artificial Intelligence in Business Gets Real,” MIT Sloan Management Review and The Boston Consulting Group, September 2018.
    • Among companies, laggards are struggling to gain leadership support for AI and to

    define use cases.
    • Leaders’ challenges have shifted from ‘if’ to ‘how’. Leaders are seeking to overcome the
    difficulty of hiring talent and addressing cultural resistance to AI.
    20%
    40%
    60%
    80%
    100%
    0%
    Attracting,
    acquiring and
    developing the
    right AI talent
    Competing
    investment
    priorities
    Security concerns
    resulting from
    AI adoption
    Cultural
    resistance to
    AI approaches
    Limited or
    no general
    technology
    capabilities
    (e.g. analytics,
    data, IT)
    Lack of
    leadership
    support for
    AI initiatives
    Unclear or no
    business case for
    AI applications
    Passives Experimenters Investigators 2017 data
    What gets in the way of AI adoption?
    Pioneers
    Leaders and laggards face different challenges to adoption

    View full-size slide

  23. The State of AI 2019: Divergence
    There is a growing gulf in corporate understanding of AI
    24
    • Among AI laggards, fewer than two in ten
    understand the implications of AI.
    • Among leaders the reverse is true; eight in
    ten understand its dynamics.
    There is a gulf between leaders’ and laggards’ understanding of the implications of AI
    Source: “Reshaping Business With Artificial Intelligence”, MITSloan Management Review in collaboration with The Boston Consulting Group
    Leaders are extending their advantage in AI
    through better understanding, faster learning
    and greater investment.

    View full-size slide

  24. The smart are getting smarter
    Source: “Reshaping Business With Artificial Intelligence”, MITSloan Management Review in collaboration with The BCG.
    AI leaders are extending their advantage through greater investment
    Source:“Artificial Intelligence in Business Gets Real,” MIT Sloan Management Review and The BCG, September 2018.
    25
    Leaders are learning faster and investing more
    The State of AI 2019: Divergence
    • The smart are getting smarter. In the last year, between half and two thirds of AI leaders
    improved their understanding of AI to a great extent. Just a tenth to a fifth of laggards did so.
    • Leaders are extending their advantage through greater investment. Nine in ten AI pioneers
    have increased their investment in AI in the past year compared with one in five passives.
    10%
    20%
    30%
    40%
    50%
    60%
    70%
    0%
    Passives Experimenters Investigators Pioneers
    Fig. 4b: How much are organisations learning about AI?
    11%
    20%
    50%
    69%
    Percentage of respondents whose understanding of AI
    has changed a lot or to a great extent in the past year
    10%
    20%
    30%
    40%
    50%
    60%
    70%
    80%
    90%
    0%
    Passives Experimenters Investigators Pioneers
    Percentage of respondents with an increased investment in AI in the past year
    19%
    62% 62%
    88%
    Percentage of respondents with an increased
    investment in AI in the past year

    View full-size slide

  25. The State of AI 2019: Divergence
    Laggards are falling further behind in AI strategy
    26
    While more companies have an AI strategy, the proportion of laggards with an AI strategy is unchanged
    Source: S. Ransbotham, P. Gerbert, M. Reeves, D. Kiron, and M. Spira, “Artificial Intelligence in Business Gets Real,” MIT Sloan Management Review and The Boston Consulting Group, September 2018.
    • Laggards’ sense of urgency regarding AI is weakening.
    • The proportion of companies that have implemented an AI strategy has increased – but the
    proportion of laggards that have done so is unchanged.
    How are organisations planning for AI?
    Developing a strategy for
    AI is urgent for our organisation
    Percentage of all respondents
    We have a strategy for what
    we are going to do with
    AI on our organisation
    Percentage of respondents
    who expressed urgency
    for an AI strategy
    Somewhat or strongly agree Do not somewhat or strongly agree 2017 data
    60% 39% 61% 67% 33% 69% 31% 85% 15%
    40%
    57% 14% 86% 50% 50% 63% 37% 90% 10%
    43%
    Investigators Pioneers
    Experimenters
    Passives
    OVERALL

    View full-size slide

  26. The State of AI 2019: Divergence
    AI initiation is shifting from the C–suite to IT department
    27
    Initiation of AI projects has shifted from the C-Suite to the IT department
    Source: Gartner (June 2018)
    • Two years ago CEOs, CIOs or CTOs initiated two thirds of AI projects.
    • Today, just one in eight survey respondents highlight corporate leadership as the primary initiator
    of AI projects. Interest in AI has shifted from the C-suite to the IT department.

    View full-size slide

  27. The State of AI 2019: Divergence
    Companies prefer to buy, not build, AI
    28
    Nearly half of companies favour buying AI solutions from third parties
    Source: Gartner (June 2018).
    • Nearly half of companies favour buying AI solutions from third parties.
    • A third of companies intend to build custom solutions.
    Buy an AI solution and then
    customise it to our industry
    Buy an AI solution already
    customised to our industry
    Build custom solution internally
    using open source where possible
    Outsource custom solution
    development to third party
    Wait for AI to be embedded in
    our favoured software products
    Build custom solution internally
    Other
    0 5 10 20
    15 25 30
    Sourcing strategies for AI initiaves
    Percentage of respondents
    18
    27
    BUY = 44%
    BUILD = 33%
    1
    10
    11
    18
    15
    Communications = 32%
    Education = 25%
    Government = 25%

    View full-size slide

  28. Workers’ views vary widely regarding the impact of AI on their activities
    Source: Survey Analysis: How AI Will Impact Industries From the Workers’ Perspective, Gartner 2018
    On balance, workers expect AI to decrease job security
    Source:Survey Analysis: How AI Will Impact Industries From the Workers’ Perspective, Gartner 2018
    29
    Workers are concerned about job security
    The State of AI 2019: Divergence
    • Workers expect AI to increase the safety, quality and speed of their work.
    • As companies’ AI agendas shift from revenue growth to cost reduction initiatives, workers are
    concerned about job security.
    0
    Fig. 19: Workers views regarding the impact of AI on their activities vary widely
    Percentage of respondents
    AI impact on jobs
    Variety
    % DECREASE % INCREASE
    Job security
    Customer contact
    Interesting
    Safety
    Co-worker collab
    Quality
    Pace of work
    21
    -20
    14
    -33
    16
    -22
    15
    -22
    23
    -18
    38
    -8
    34
    -10
    34
    -17
    0
    -20
    20
    40
    Safety
    AI impact on jobs
    (net % of respondents)
    Quality
    Pace
    Interest
    Co-worker
    collaboration
    Customer contact
    Variety
    Job security
    How AI will impact industries from the workers' perspective

    View full-size slide

  29. The State of AI 2019: Divergence
    2. The war for talent

    View full-size slide

  30. While supply of AI talent is increasing, demand for AI
    professionals continues to exceed supply.
    Winners and losers are emerging in the competition

    to capture talent.
    High job satisfaction is intensifying the war for talent.
    The war for talent
    The State of AI 2019: Divergence 31

    View full-size slide

  31. The State of AI 2019: Divergence
    Demand for AI talent has doubled in 24 months
    32
    • In the UK, job listings for AI roles have increased 485% since 2014 (Indeed).
    •Demand for AI talent has doubled in 24 months.
    •There is a gulf between demand and supply, with 2.3 roles available for every AI professional.
    AI-related job postings as a proportion of total job postings have doubled in 18 months
    Source: Indeed
    Fig. 1: AI-related job postings as a proportion of the total have doubled in 18 months
    (AI-related postings per million postings)
    0
    200
    400
    600
    800
    1000
    1200
    2015 2016 2017 2018
    AI-related postings per million postings

    View full-size slide

  32. 33
    • Machine learning has become the top emerging field of employment in the United States.
    • Greater supply is being driven by: high pay; the inclusion of AI modules in university computer
    science courses; companies’ investment in staff training; and AI technology companies ‘pump
    priming’ the market with free resources.
    • Over time, AI tools offering greater abstraction will make AI accessible to less specialised developers.
    In the US, machine learning is the top emerging job
    Source: LinkedIn
    Technology companies are offering free educational AI resources
    Source: Google, Udacity
    Brand Partner
    Full Stack Developer
    Director of Data Science
    Unity Developer
    Big Data Developer
    Full Stack Engineer
    Customer Success Manager
    Sales Development Representative
    Data Scientist
    Machine Learning Engineer
    0x 1x 2x 3x 4x 5x 6x 7x 8x 9x 10x
    Fig. 2: In the US, machine learning is the top emerging job
    Increase in individuals’ listed professions
    compared with five years ago
    Supply is increasing…

    View full-size slide

  33. • AI demands advanced competencies in mathematics, statistics and programming.
    • AI developers are seven times more likely to have a Doctoral degree than other developers.
    • Estimates vary; there may be as few as 22,000 highly-trained AI specialists (Element) and up to
    300,000 AI researchers and practitioners within broader technical teams (Tencent).
    60% of AI developers have a Master’s or Doctoral degree
    Source: Kaggle
    AI developers are seven times more likely to have a Doctoral degree than others
    Source: Kaggle, StackOverflow
    Fig. 5: 60% of AI developers have a Master’s or Doctoral degree
    (AI developers’ level of education)
    Master's degree
    Bachelor's degree
    Doctoral degree
    Other
    42%
    32%
    16%
    11%
    0%
    10%
    20%
    30%
    40%
    50%
    60%
    Secondary
    school
    Some
    university
    Bachelor’s
    degree
    Master’s
    degree
    Doctoral
    degree
    AI Developers
    General Developers
    Developers (%)
    Fig. 6: AI professionals have a greater proportion of advanced degrees
    Developers’ levels of education
    34
    …but the pool of AI talent remains small
    The State of AI 2019: Divergence

    View full-size slide

  34. 35
    Over time, AI democratisation will mitigate talent shortages
    The State of AI 2019: Divergence
    • Over time, a larger talent pool and more accessible AI tools will alleviate much of the shortfall
    in AI talent.
    • Governments’ investment in education – in science, technology, engineering and mathematics
    (STEM) subjects – will be vital for countries to broaden their pools of AI talent.
    • AI courses and resources from universities and technology companies, and market demand, will
    also boost supply.
    • Development environments offering higher levels of abstraction will also reduce the burden
    on developers’ knowledge. In 2005, NumPy abstracted portions of required mathematics. In
    2010 and 2015, Python libraries and TensorFlow progressively abstracted network development.

    View full-size slide

  35. • AI professionals are among the best paid developers and their salaries continue to increase.
    • Half enjoyed salary growth of 20% or more in the last three years.
    AI professionals are among the highest paid developers
    Source: StackOverflow
    Most AI professionals’ salaries have increased in the last three years
    Source: Kaggle
    Mobile developer
    Game or graphics developer
    Educator or academic reseracher
    Designer
    Front-end developer
    Database administrator
    QA/test developer
    System administrator
    Back-end developer
    Desktop or enterprise applications developer
    Full stack developer
    Embedded applications/devices developer
    Data or business analyst
    Data scientist or machine learning specialist
    DevOps specialist
    Engineering manager
    $0 $10 $20 $30 $40
    Median annual salary ($000s)
    $50 $60 $70 $80 $90
    Fig. 8: AI professionals are among the highest paid developers
    (Global average developer salaries by category)
    Global average developer salaries by category
    5%
    10%
    15%
    20%
    25%
    30%
    35%
    40%
    45%
    50%
    0
    Declined Unchanged
    (+/- 5%)
    Higher
    (+6% to +19%)
    Higher
    (+20% or more)
    Fig. 11: Most AI professionals’ salaries have increased in the last three years
    3%
    18%
    19%
    45%
    Compensation changes in the past three years
    for data scientists/machine learning engineers
    36
    Today, talent shortages are sustaining high salaries
    The State of AI 2019: Divergence

    View full-size slide

  36. • Data scientists enjoy among the greatest salary premium relative to their level of experience.
    AI professionals are paid highly relative to their level of experience
    Source: StackOverflow
    $40,000
    $50,000
    $60,000
    $70,000
    $80,000
    $90,000
    6 7 8 9 10 11
    Average years of professional programming experience
    Median salary ($USD)
    Fig. 10: AI professionals are paid highly relative to their level of experience
    (Developer salary vs. years of professional programming experience)
    Developer salary vs. years of professional programming experience
    Number of respondents
    10,000 20,000
    DevOps specialist
    Engineering manager
    CTO/CEO/etc
    Product manager
    Data scientist
    Full-stack developer
    Embedded/devices developer
    Back-end developer
    QA or test developer
    Data or business analyst
    Mobile developer
    Game or graphics developer
    Educator or academic researcher
    Desktop of enterprise application developer
    System administrator
    Database administrator
    Designer
    Front-end developer
    Today, talent shortages are sustaining high salaries
    37
    The State of AI 2019: Divergence

    View full-size slide

  37. Technology and financial services firms are absorbing nearly 60% of AI talent
    Source: The Burtch Works Study – May 2018. N=2,212 . Shows distribution of data scientists by sector.
    44%
    Technology
    10%
    Other
    14%
    Financial Services
    6%
    Healthcare/
    Pharma
    2%
    Academia
    1%
    Gaming
    1%
    Government
    5%
    Retail & CPG
    9%
    Advertising/Marketing
    8%
    Consulting
    Fig. 13: Technology and Financial Services absorb nearly 60% of AI talent
    (distribution of data scientists by industry)
    • The technology and financial services sectors are absorbing 60% of AI talent.
    • The ‘brain drain’ from academia to industry is real and will have mixed implications, catalysing
    AI’s impact while moving value from the public domain to select, private companies.
    Winners and losers are emerging in the war for talent
    38

    View full-size slide

  38. • Three quarters of AI professionals are satisfied in their current role.
    • To optimise hiring and retention, companies should align roles to AI professionals’ primary
    motivators – learning opportunities, environment, access to preferred technologies and impact.
    Three quarters of AI developers are satisfied (6/10 or better) with their current role
    Source: Kaggle
    Learning, office environment and technologies are AI developers’ primary motivators
    Source: Kaggle
    Fig. 14: Three quarters of AI developers are satisfied (>6/10) with their current roles
    (AI developers’ job satisfaction)
    Respondents (%)
    AI developers’ job satisfaction (scale of 1 to 10)
    0%
    5%
    10%
    15%
    20%
    25%
    1 2 3 4 5 6 7 8 9 10
    0%
    10%
    20%
    30%
    40%
    50%
    60%
    70%
    80%
    Diversity
    Work remotely
    Leader reputation
    Company funding/financials
    Industry
    Publishing opportunties
    Commute
    Department
    Impact
    Job title
    Management
    Experience level
    Compensation
    Technologies
    Office environment
    Learning
    Fig. 15: Learning, office environment and the technologies they will use are
    AI developers’ primary motivators
    % respondents rating factor “very important”
    High job satisfaction is intensifying the war for talent
    39
    The State of AI 2019: Divergence

    View full-size slide

  39. • Company websites and technology job boards are less effective than engaging with
    recruiters, friends, family and colleagues, according to those already employed in the field.
    The most effective route into AI is engagement with recruiters
    Source: Kaggle
    1. Recruiter
    2. Friend, family, colleague
    3. General job board
    4. Other
    5. Company website
    6. Career fair or recruiting event
    7. Tech job board
    EMPLOYED IN FIELD
    1. Company website
    2. Tech job board
    3. General job board
    4. Friend, family, colleague
    5. Recruiter
    6. Career fair or recruiting event
    7. Other
    ENTERING FIELD
    Fig. 16: The most effective route into AI work is engagement with recruiters
    How do you look for, or find, work?
    40
    The State of AI 2019: Divergence
    New talent follows sub-optimal paths to employment

    View full-size slide

  40. The State of AI 2019: Divergence
    3. The advance of technology

    View full-size slide

  41. The advance of technology
    The State of AI 2019: Divergence 42
    Advances in AI technology are creating new possibilities.
    Custom silicon is enabling a new generation of AI hardware.
    Emerging software techniques – reinforcement learning,
    transfer learning and generative AI – are delivering
    breakthroughs in multiple domains and decoupling progress
    from the constraints of human experience.

    View full-size slide

  42. • Vendors are optimising or customising hardware to support the use of popular deep learning
    frameworks, enabling faster system training and performance with common AI frameworks.
    • NVIDIA’ Tesla GPUs accelerate matrix computations to optimise deep learning on a range of frameworks.
    Certain neural networks can be trained in one third of the previous time and operate 4x faster.
    Tesla GPUs enable suitable neural networks to be trained in a third of the previous time
    Source: NVIDIA
    Tesla GPUs allow suitable neural networks to operate four times faster than previously
    Source: NVIDIA
    43
    Tensor architectures are accelerating deep learning
    The State of AI 2019: Divergence
    8X Tesla P100
    8X Tesla V100
    0 4 8 12 16
    Time to solution in hours
    (Lower is better)
    Server config: Dual Xeon E5-2699 v4 GHz | 8X NVIDIA®
    Tesla® P100 or V100 | ResNet-50 Training on MXNet for
    90 Epochs with 1.28M ImageNet Dataset
    Title TBC
    15.5 hours
    5.1 hours
    Tesla P4
    1X CPU
    Tesla V100
    0 10X 20X
    27X
    7X
    30X 40X 50X
    Performance normalised to CPU
    Workload: ResNet-50 | CPU: 1X Xeon
    E5-2690v4 @ 2.6 GHz | GPU: Add 1X Tesla P4 or V100
    Title TBC

    View full-size slide

  43. Tensor architectures are accelerating deep learning
    44
    • Google’s Tensor Processing Unit (TPU) is an application- specific integrated circuit (ASIC)
    designed specifically to accelerate AI workloads on the popular TensorFlow framework.
    • Pods of next-generation TPUs can reduce training time for select algorithms from

    five hours to seven minutes.
    Google’s next-generation TPUs reduce further the time required to train an algorithm
    Source: Google
    Title TBC
    ResNet-50 302.0
    minutes
    183.0
    minutes
    60.0
    minutes
    7.1
    minutes
    TPU v2 TPU v3 TPU v3 Pod TPU v3 Pod
    NMT
    SSD
    40.4
    minutes
    26.3
    minutes
    9.7
    minutes
    96.9
    minutes
    74.9
    minutes
    17.8
    minutes
    Number of chips
    4 4 16
    4
    256
    4

    View full-size slide

  44. • Custom silicon, designed from inception for AI, offers transformational performance,
    capability similar to existing systems for a fraction of the power or space, and greater versatility.
    • Graphcore’s intelligence processing unit (IPU) combines a bespoke, parallel architecture with
    custom software to offer greater performance than existing systems.
    Graphcore’s IPU is designed, from inception, for AI
    Source: Graphcore
    Graphcore’s IPU could deliver 200-fold performance improvements in selected tasks
    Source: Graphcore
    45
    The post-GPU era: custom silicon offers new possibilities
    The State of AI 2019: Divergence
    GPU (<2ms latency)
    GPU (<7ms latency)
    IPU (<7ms latency)
    GPU (<5ms latency)
    IPU (<5ms latency)
    IPU (<2ms latency)
    0 10000 20000
    242X
    182X
    30000 40000 50000 60000 70000
    Inferences per second
    Single layer inference

    View full-size slide

  45. • A ‘barbell’ effect is emerging as a new class of AI hardware is optimised for edge
    computing instead of the data centre.
    • Edge computing moves the processing of data from the cloud to the ‘edge’ of the internet –
    to devices where data is created: autonomous vehicles; drones; sensors and IoT devices.
    • Increasingly, edge computing is required as devices proliferate, and issues of connectivity
    and latency demand on-device processing in multiple contexts.
    46
    Custom silicon is taking AI to the edge
    The State of AI 2019: Divergence
    In 2019, as well as enabling next generation AI in the cloud, custom
    silicon will transform AI at the edge by coupling high performance
    with low power consumption and small size.

    View full-size slide

  46. • As quantum computing matures it will create profound opportunities for progress in AI.
    • Quantum-powered AI will enable humanity to address previously intractable problems, from
    personalised medicine to climate change.
    • While nascent, quantum computing is advancing rapidly. Google is developing quantum
    neural networks. In 2018, an Italian team of researchers developed a functioning quantum
    neural network on an IBM quantum computer.
    47
    Quantum computing is unlocking profound opportunities
    The State of AI 2019: Divergence
    Interested in learning more about quantum computing?
    Listen to ‘Quantum Leap’ – Episode 6 – of our ‘Beyond the Hype: AI’ podcast:

    => bit.ly/2LrNhn1 (Web)
    => apple.co/2GwMXEU (iTunes)

    View full-size slide

  47. Reinforcement learning is enabling powerful AI agents
    48
    Reinforcement learning enabled AlphaGo Zero, a system developed by DeepMind to play Go, to achieve unrivalled
    capability after 40 days of play
    Source: Google DeepMind
    • Instead of learning from training data, reinforcement learning (RL) systems learn through
    exploration. Progress towards a specified goal is rewarded and reinforced.
    • Developments in RL are enabling the creation of powerful AI agents.
    • AlphaGo Zero (Oct 2017), an RL system developed to play Go, achieved unparalleled capability within
    40 days by playing against itself. AlphaZero (Dec 2017) can play a range of games at even higher levels.
    Elo Rating
    Days
    -2000
    -1000
    0
    1000
    2000
    3000
    4000
    5000
    Title TBC
    0 5 10 15 20 25 30 35 40
    40 days
    AlphaGo Zero supasses all other versions of AlphaGo and,
    arguably, becomes the best Go player in the world. It does
    this entirely from self-play, with no human intervention and
    using no historical data.
    AlphaGo Zero 40 blocks AlphaGo Lee AlphaGo Master

    View full-size slide

  48. Reinforcement learning is enabling effective multi-agent collaboration
    Source: OpenAI. AI agents playing Defence of the Ancients 2 (Dota2)
    Reinforcement learning enabled the OpenAI 5 team to surpass rapidly most human teams
    Source: OpemAI
    49
    Reinforcement learning is enabling multi-agent collaboration
    The State of AI 2019: Divergence
    Drafting
    Picking
    Composition
    Mirror Death Prophet,
    Witch Doctor, Gyrocopter, Earthshaker, Fidehunter
    Mirror Necrophos, Lich,
    Crystal Maiden, Viper, Sniper
    2,000
    3,000
    4,000
    5,000
    6,000
    22 April 6 May
    OpenAI Five
    OpenAI Dev Team
    Blitz + Audience
    Amateur Team
    Semi-Pro Team
    Test Team A
    Test Team B
    Caster Team
    20 May 3 June 17 June 1 July 15 July 29 July 12 August
    Reinforcement learning enabled the OpenAI 5 team to rapidly surpass the performance of most human teams
    Approximate Team MMR
    Date (2018)
    • Developments in RL are enabling groups of agents to interact and collaborate with each other.
    • OpenAI 5, an RL-powered team of agents developed to play the game Defence of the Ancients 2
    (DOTA2), improved rapidly and can defeat all but the top professional human players.

    View full-size slide

  49. Reinforcement learning decouples progress from human knowledge
    50
    • RL is well suited to creating agents that can perform autonomously in environments for which we
    lack training data.
    • Progress in RL is significant because it decouples system improvement from the constraints of
    human knowledge.
    • RL enables researchers to “achieve superhuman performance in the most challenging domains
    with no human input” (DeepMind).
    The State of AI 2019: Divergence

    View full-size slide

  50. Transfer learning can offer strong initial performance, faster improvement and better 

    long-term results
    Source: Torry, Shalvik
    Interest in transfer learning has grown 7-fold in 24 months
    Source: Google trends
    51
    Transfer learning enables more adaptable systems
    • Transfer learning (TL) enables programmers to apply elements learned from previous challenges
    to related problems.
    • Interest in TL has grown seven-fold in 24 months and is enabling a new generation of systems with
    greater adaptability.
    • TL- powered models are improving the state of the art in language processing in areas of universal
    utility – text classification, summation, text generation, question answering and sentiment analysis.
    Interest in transfer learning has grown 7-fold in 24 months
    1.0
    2.0
    3.0
    4.0
    5.0
    6.0
    7.0
    8.0
    APR-13
    JUN-13
    AUG-13
    OCT-13
    DEC-13
    FEB-14
    APR-17
    JUN-17
    AUG-17
    OCT-17
    DEC-17
    FEB-18
    APR-18
    JUN-18
    AUG-18
    OCT-18
    DEC-18
    FEB-19
    APR-14
    JUN-14
    AUG-14
    OCT-14
    DEC-14
    FEB-15
    APR-15
    JUN-15
    AUG-15
    OCT-15
    DEC-15
    FEB-16
    APR-16
    JUN-16
    AUG-16
    OCT-16
    DEC-16
    FEB-17
    Relative interest in transfer
    learning (April 2013 = 1.0)
    Performance
    Transfer learning can offer strongerinitial performance,
    faster improvement and better long-term results
    Training
    with transfer
    without transfer
    higher start
    higher slope
    higher asymptote

    View full-size slide

  51. 52
    Transfer learning is delivering leaps in language AI
    The State of AI 2019: Divergence
    • ELMo (‘Embeddings from Language Models’, March 2018): improved the state of the art for a broad
    range of natural language tasks including Q&A and sentiment analysis.
    • ULMFiT (‘Universal Language Model Fine-tuning for Text classification’, May 2018): underscored that TL can
    be applied to language processing tasks and introduced techniques for fine-tuning language models. With
    100 labelled examples, ULMFiT matched the performance of systems trained with 100-fold more data.
    • OpenAI (mid-2018): Impressive results on a diverse range of language tasks from a single starting point;
    task-agnostic model outperformed models with architectures crafted for specific tasks including
    question answering.
    • BERT (Bidirectional Encoder Representations from Transformers, Oct 2018): Open-sourced by Google, an
    RL-based language processor achieved state-of-the-art results on 11 natural language benchmarks.

    View full-size slide

  52. 53
    Transfer learning will unlock further advances in AI
    The State of AI 2019: Divergence
    • Transfer learning is enabling complex systems to interact with the real world.

    Training AI systems, such as autonomous vehicles, can be laborious, expensive or dangerous.
    Simulation enables emulation of environments instead of capturing real-life data. Applying transfer
    learning enables learnings from a simulation to be applied to real-world assets.
    • Transfer learning offers adaptability and progress towards AGI.

    By offering greater adaptability, TL supports progress towards artificial general intelligence (AGI)

    i.e. systems that can undertake any intellectual task a human can perform.

    While AGI is far from possible with current AI technology, developments in TL are enabling progress.

    View full-size slide

  53. “I think transfer learning is the key to general intelligence.
    And I think the key to doing transfer learning will be the
    acquisition of conceptual knowledge – knowledge that is
    abstracted away from perceptual details of where you
    learned it, so you can apply it to a new domain.”
    Demis Hassabis, DeepMind
    The State of AI 2019: Divergence 54
    Transfer learning will unlock further advances in AI

    View full-size slide

  54. GANs’ ability to create lifelike media has rapidly improved
    Source: Goodfellow et al, Radford et al, Liu and Tuzel, Karras et al, https://bit.ly/2GxTRot
    GANs can generate artificial images that appear real (none of these individuals exist)
    Source: NVIDIA
    55
    Generative AI will transform media and society
    The State of AI 2019: Divergence
    • Generative Adversarial Networks (GANs) enable the creation of artificial media, including
    pictures and video, with exceptional fidelity.
    • GANs will deliver transformational benefits in sectors including media and entertainment,
    democratising the creation of high quality content at scale and low cost. GANs will also present
    profound challenges to societies – beware ‘fake news 2.0’.

    View full-size slide

  55. Generative AI: two networks working in opposition
    56
    GANs operate with two networks working in opposition
    Source: Naoki Shibuya
    • GANs operate by two networks – a ‘generator’ and ‘discriminator’ – working in opposition to
    create increasingly lifelike media.
    • The generator receives a random input, such as a matrix of numbers, and follows a series of
    mathematical transformations to convert the input into a picture.
    • The discriminator assesses whether the image received from the generator is authentic or has
    been artificially generated, with a feedback cycle between the networks promoting progress.
    DISCRIMINATOR
    GENERATOR
    Training data
    Latent sample
    -0.19972104,
    -0.27617624,
    -0.7449326,
    -0.02522890,
    -0.86428853,
    -0.84710504,
    -0.41719925,
    -0.88549566,
    -0.42638235,
    -0.0455994,
    -0.305852,
    -0.60752668,
    -0.14700781,
    -0.26471074,
    -0.71651508,
    -0.65559716,
    -0.71335986,
    -0.82961057,
    -0.81311934,
    -0.42092858,
    -0.42457545,
    -0.39863341,
    -0.26192929,
    -0.18518651,
    Generated
    image
    1 (Real)
    0 (Fake)
    Title TBC
    The State of AI 2019: Divergence

    View full-size slide

  56. 57
    Generative AI has use beyond pictures and video
    The State of AI 2019: Divergence
    • While GANs are frequently used to create images, their utility is broader. GANs enable:
    - Alternative media: create music or text in the style of particular individuals.
    - System Training: improve the training of AI classification systems.
    - Data manipulation: Add/remove features in data by combining GANs with autoencoding techniques.
    - Data normalisation: Normalise data from different sources.
    - Network security: Detect anomalies in network access or activity.
    - Data creation: Produce additional training data to improve the accuracy of AI classifiers.

    View full-size slide

  57. The State of AI 2019: Divergence
    4. The Disruptors:

    Europe’s AI startups

    View full-size slide

  58. 1 in 12 European startups is now an ‘AI startup’

    putting AI at the heart of its value proposition.
    Europe’s 1,600 AI startups are maturing, bringing creative
    destruction to new industries and navigating unique
    capital dynamics. While the UK is the powerhouse of
    European AI, Germany and France may extend their influence.
    Entrepreneurs’ greatest challenges are access to data,
    recruiting talent and moving AI from ‘lab to live’.
    The Disruptors: Europe’s AI startups
    The State of AI 2019: Divergence 59

    View full-size slide

  59. Europe is home to 1,600 early stage AI software companies
    60
    • We individually reviewed the activities, focus and funding of 2,830 companies classified 

    by popular tools as AI startups, in the 13 EU countries most active in AI.
    • Together, these countries – Austria, Denmark, Finland, France, Germany, Ireland, Italy, the 

    Netherlands, Norway, Portugal, Spain, Sweden and the UK – also comprise 90% of EU GDP.
    • In approximately 60% of cases – 1,580 companies – there was evidence of AI material to

    a company’s value proposition.
    The State of AI 2019: Divergence

    View full-size slide

  60. One in twelve new European startups is an AI company
    Source: MMC Ventures (2018 data to October)
    • In 2013, one in fifty new startups embraced AI.
    • Today, one in twelve put AI at the heart of their value proposition.
    • We have entered the era of AI entrepreneurship.
    AI entrepreneurship is becoming mainstream
    61
    The State of AI 2019: Divergence
    0
    1%
    2%
    3%
    4%
    5%
    6%
    7%
    8%
    9%
    0.0pp
    0.2pp
    0.4pp
    0.6pp
    0.8pp
    1.0pp
    1.2pp
    1.4pp
    1.6pp
    1.8pp
    2011
    2010 2012 2013 2014 2015 2016 2017 2018
    AI startups as % of all startups founded each year
    AI startups as % of all startups founded each year
    AI startups as % of all startups founded each year (left axis)
    YoY change in percentage points (right axis)
    YoY change in percentage points

    View full-size slide

  61. • Two years ago, just one in 20 AI startups were ‘growth’-stage companies that had passed beyond
    Angel and Seed funding rounds.
    • Today, one in six European AI companies is a ‘growth’-stage company with over $8m of funding.
    • Expect: acquisitions to recycle capital and talent; startups competing with ‘scale-ups’ as well as
    incumbents; and increasing competition for talent.
    Six in ten startups are at the Angel or Seed stages Larger ecosystems are typically more mature; Spain is an exception
    Source: MMC Ventures, Beauhurst, Crunchbase, Tracxn
    The European AI ecosystem is maturing
    62
    Six in ten AI startups
    are at the Angel or Seed stages
    Angel
    27%
    Early
    Stage
    28%
    Seed
    29%
    Growth
    16%
    0% 20% 40% 60% 80% 100%
    Fig. X: Larger ecosystems are typically more mature; Spain is an exception
    31%
    26%
    28%
    29%
    28% 28%
    34%
    25%
    19%
    19%
    27%
    27%
    27%
    16%
    16%
    39%
    21%
    35%
    32%
    32%
    11%
    38%
    24%
    9%
    6%
    18%
    26%
    All
    Seed
    Early Stage
    Growth
    Angel
    UK
    France
    Germany
    Spain
    Sweden
    Finland
    25%
    Source: MMC Ventures, Beauhurst, Crunchbase, Tracxn.

    Funding categories: Angel (<$0.5m), Seed ($0.5-2.0m), Early Stage ($2.0-8.0m), Growth (>$8.0m)

    View full-size slide

  62. With twice as many AI startups as any other country, the UK is the powerhouse of European AI entrepreneurship
    Source: MMC Ventures,
    Beauhurst, Crunchbase, Tracxn
    • The UK is the heartland of European AI with a third of the continent’s AI startups.
    • UK strengths include: talent from a quarter of the world’s top 25 universities; extensive venture capital;
    many successful AI scale-ups and exits; and a global financial services hub.
    • Germany and France are thriving hubs where high quality talent, increasing volumes of capital and an
    expanding roster of successful AI companies are creating feedback loops of growth and investment.
    The UK is the powerhouse of European AI
    63
    479
    UK
    196
    Germany
    217
    France
    166
    Spain
    75
    Ireland
    66
    Italy
    45
    Portugal
    43
    Austria
    32
    Norway
    36
    Denmark
    49
    Finland
    73
    Sweden
    103
    Netherlands
    Fig. X: The UK is the heart of European AI entrepreneurship

    View full-size slide

  63. The ‘UK 500’: A map of early stage UK AI companies
    64
    The State of AI 2019: Divergence
    Cambridge Quantum Computing
    UK AI Landscape (Early stage companies)
    ANGEL / SEED
    EARLY STAGE / GROWTH
    Skim Technologies
    Instadeep Memgraph
    Seldon
    Invacio
    Grakn Labs Hadean
    Graphcore
    Fetch.AI
    Academy of Robotics Evolve Dynamics
    ANGEL / SEED
    FiveAI
    EARLY STAGE / GROWTH
    Oxbotica
    Latent Logic
    Focal Point Positioning
    Sky-Futures
    Accelerated Dynamics Baro Vehicles
    Headlight AI Humanising Autonomy Intelligent Robots
    Machines With Vision Predina Wayve
    React AI Third Space Auto
    ANGEL / SEED
    EARLY STAGE / GROWTH
    SLAMcore
    MicroBlink
    Spectral Edge WeSee
    Blue Vision Labs
    Emteq
    Alchera Technologies FaceSoft
    TouchByte
    Vize.ai
    Visio Ingenii
    Machine Medicine Synthesia
    EARLY STAGE / GROWTH
    Telectic
    EARLY STAGE / GROWTH
    Audio analytic
    Ditto AI
    Mind Foundry
    Prodo.ai
    ANGEL / SEED
    Bottr.co Chatbot Agency
    Prowler.io
    Neuron soundware
    CloudNC
    Senseye
    QiO Technologies
    EARLY STAGE / GROWTH
    ANGEL / SEED
    EARLY STAGE / GROWTH
    Ai Build Flexciton
    Materialize.X
    Thingtrax
    ANGEL / SEED
    Mouldbox Houseprice.AI PerchPeek
    Pupil
    ANGEL / SEED
    EARLY STAGE / GROWTH
    AskPorter
    Skyscape
    Ecosync
    Brytlyt Hivemind
    Instrumental Jukedeck Myrtle Software Mogees Popsa Spirit AI
    ANGEL / SEED
    Capito Systems Kami.ai
    Poly AI
    CrowdEmotion DAM Good Media
    AI Music Antix
    Let's Enhance
    Lobster Logically
    infloAi Kompas
    DeepAR
    Deep Innovations
    Gameway
    Factmata
    Pimloc Thingthing
    Snaptivity
    B2B
    Angel/Seed: <$2m
    Early stage/Growth: >$2m
    Funding category
    New company (since 2017)
    B2C
    KEY
    iProov
    Speechmatics
    RETAIL
    OTHER
    LANGUAGE & SOUND
    COMPUTER VISION
    AI INFRASTRUCTURE
    Core Technologies
    AUTONOMOUS SYSTEMS
    Sector
    Cortexica Dressipi
    Artlimes
    Cocoon Emotech Mallzee Metail
    Snap Tech Spoon Guru Thread
    Aura Vision Boldmind
    ANGEL / SEED
    EARLY STAGE / GROWTH
    Hoxton Analytics JCC Bowers Measmerize
    Cadouu CartMe
    Orpiva
    Pasabi Proximus See Fashion ThirdEye
    Save your wardrobe
    Olvin
    EDITED
    PROPERTY & REAL ESTATE
    Gousto
    Winnow
    Nuritas
    Henchman
    ANGEL / SEED
    EARLY STAGE / GROWTH
    Dinabite Limited
    IntelligentX VideraBio
    FOOD & BEVERAGE
    MANUFACTURING MEDIA & ENTERTAINMENT
    Function
    Energi Mine
    SenSat
    Limejump nPlan
    EARLY STAGE / GROWTH
    ClauseMatch
    Eigen Technologies
    Luminance
    Cognitiv+
    ThoughtRiver
    DoNotPay
    ANGEL / SEED
    EARLY STAGE / GROWTH
    Crowd Connected
    Verv by Green Running
    Calipsa Disperse.io
    ANGEL / SEED
    Green Running Grid Edge NumberEight
    TravelAI
    OpenCapacity
    Wluper
    Rovco Ginie.ai
    INFRASTRUCTURE & TRANSPORT
    Cambridge Medical Robotics
    Exscientia GTN
    Drayson Technologies
    BrainWaveBank
    BenevolentAI
    Babylon Health
    Amiko BioBeats Cydar Medical Deontics
    eCUORE
    Antiverse Biorelate
    Auro
    ANGEL / SEED
    EARLY STAGE / GROWTH
    LabGenius Lifebit
    Cambridge Humanae
    Cambridge Bio-Augmentation Systems
    Aigenpulse Causaly Chronomics
    HealthUnlocked Healx Kheiron Medopad Snap40 Synthace Thriva
    Your.MD
    Visulytix Viz
    Sentireal
    ThinkSono
    Sime Diagnostics Transformative
    HEALTH & WELLBEING LAW
    FitWell InnersightLabs InsideDNA
    Kraydel
    Kaido Kiroku OME Health Resurgo Genetics
    PetaGene
    Optellum Peptone
    ANGEL / SEED
    EARLY STAGE / GROWTH
    Quantemplate Quantexa Vortexa
    ArrayStream
    AdviceGames Artificial Labs Banked Blue Lion Research Bud
    Abaka
    FINANCIAL SERVICES
    Oxademy Synap
    BridgeU
    Gamar Kwiziq
    ANGEL / SEED
    EARLY STAGE / GROWTH
    Century Lingumi
    Lingvist
    EARLY STAGE / GROWTH
    Global Surface Intelligence
    Hummingbird Technologies
    Dogtooth Technologies
    HeraSpace Observe
    ANGEL / SEED
    Cervest
    KisanHub
    Xihelm
    Optimal Labs
    AGRICULTURE EDUCATION
    Tractable
    Procensus
    PriceHubble
    Plum
    Kirontech
    Essentia Analytics
    Arkera Floodlash
    BMLL Technologies Digital Contact FriendlyScore
    ForwardLane
    Cleo AI Cytora
    Mosaic Smart Data Multiply TradeTeq
    Acuity Trading AlgoDynamix Brolly
    Alpha-i
    CUBE DealX Ducit.ai Knox EA
    G-Research Metafused
    Digital Clipboard Financial Network Analytics
    Chip EnAlgo
    Oseven Telematics
    Proportunity
    Yedup Zoral
    Sns Analytics
    ProvidensAI Torafugu Tech Win FX Financial Innovation
    TradeRiser
    Spixii
    RightIndem
    UK AI Landscape (Early stage companies)
    ANGEL / SEED
    EARLY STAGE / GROWTH
    Skim Technologies
    Instadeep Memgraph
    Seldon
    Invacio
    Grakn Labs Hadean
    Graphcore
    Fetch.AI
    Academy of Robotics Evolve Dynamics
    ANGEL / SEED
    FiveAI
    EARLY STAGE / GROWTH
    Oxbotica
    Latent Logic
    Focal Point Positioning
    Sky-Futures
    Accelerated Dynamics Baro Vehicles
    Headlight AI Humanising Autonomy Intelligent Robots
    Machines With Vision Predina Wayve
    React AI Third Space Auto
    ANGEL / SEED
    EARLY STAGE / GROWTH
    SLAMcore
    MicroBlink
    Spectral Edge WeSee
    Blue Vision Labs
    Emteq
    Alchera Technologies FaceSoft
    TouchByte
    Vize.ai
    Visio Ingenii
    Machine Medicine Synthesia
    EARLY STAGE / GROWTH
    Telectic
    Audio analytic
    Prodo.ai
    Prowler.io
    Brytlyt Hivemind
    ANGEL / SEED
    Capito Systems
    Poly AI
    B2B
    Angel/Seed: <$2m
    Early stage/Growth: >$2m
    Funding category
    New company (since 2017)
    B2C
    KEY
    Spe
    LANGUAGE
    COMPUTER VISION
    AI INFRASTRUCTURE
    Core Technologies
    AUTONOMOUS SYSTEMS
    Sources: MMC Ventures, Beauhurst, Crunchbase, Tracxn
    Additions or corrections? Email us at [email protected]

    View full-size slide

  64. The ‘UK 500’: A map of early stage UK AI companies
    65
    The State of AI 2019: Divergence
    ANGEL / SEED
    Ai Build Flexciton
    Materialize.X
    Thingtrax
    ANGEL / SEED
    Mouldbox Houseprice.AI PerchPeek
    ANGEL / SEED
    AskPorter
    Skyscape
    Ecosync
    CrowdEmotion DAM Good Media
    AI Music Antix
    Let's Enhance
    Lobster Logically
    infloAi Kompas
    DeepAR
    Deep Innovations
    Gameway
    Factmata
    Pimloc Thingthing
    Snaptivity
    Cybertonica
    Fraugster
    Ravelin
    Featurespace
    ANGEL / SEED
    EARLY STAGE / GROWTH
    Fractal Labs
    Aire
    Rimilia
    Pace
    Fluidly
    ANGEL / SEED
    EARLY STAGE / GROWTH
    FRAUD DETECTION
    FINANCE HUMAN RESOURCES
    PredictiveHire
    hackajob
    GoSay Grad DNA
    Filtered Headstart App
    Beamery Qlearsite Rotageek
    Saberr StatusToday
    ANGEL / SEED
    EARLY STAGE / GROWTH
    Potentially
    Metaview MeVitae
    Stitched TalentHunter.AI Wevolve
    JamieAi
    InteriMarket
    Human
    Darktrace RepKnight
    Senseon
    Alchemy Data Cyberlytic
    Barac
    Encode
    Corax
    CyberSparta Elemendar
    ANGEL / SEED
    EARLY STAGE / GROWTH
    Honeycomb Technologies
    VChain Technology
    Cybershield
    CYBERSECURITY
    Aistemos Amplyfi
    EARLY STAGE / GROWTH
    Black Swan Data Cazana
    Bird.i Gyana Logical Glue Callsign
    Behavox
    Audit XPRT Berry Technologies CoVi Analytics
    Exonar Eyn
    AimBrain Hazy Onfido
    Tessian
    Traydstream Limited
    Sum&Substance
    ANGEL / SEED
    EARLY STAGE / GROWTH
    WaymarkTech
    BotsAndUs
    DigitalGenius Gluru
    Hutoma
    ANGEL / SEED
    EARLY STAGE / GROWTH
    Action.AI
    Enterprise Bot
    True AI
    Synthetix
    Constellation AI
    Humley
    Sentient Machines
    Samim.ai
    Massive Analytic Peak Quorso Rezatec Ripjar Semantic Evolution Signal Media Simudyne
    ANGEL / SEED
    10x Airfinity causaLens
    Analytics Intelligence Chorus Intelligence Data quarks Flumes Hertzian
    Policy Radar
    Reportbrain SeeQuestor Sensing Feeling
    Satalia Singular Intelligence Terrabotics
    illumr Krzana
    Kite Edge
    illumr Ohalo
    Migacore Technologies Oxford Semantic Technologies
    Hello Soda
    CUSTOMER SERVICE
    BI & ANALYTICS COMPLIANCE
    Artlimes Aura Vision Boldmind
    ANGEL / SEED
    Hoxton Analytics JCC Bowers Measmerize
    Cadouu CartMe
    Orpiva
    Pasabi Proximus See Fashion ThirdEye
    Save your wardrobe
    Olvin
    Decibel Insight Fresh Relevance
    Brandwatch
    LoopMe MiQ
    Buzz Radar
    Admedo Fospha
    ADTYPE adverttu
    Artios
    ANGEL / SEED
    EARLY STAGE / GROWTH
    Carsift Chattermill
    Concured Creative AI Crystal Apps CustomSell Digital MR
    DaVinci11
    MediaGamma Perfect Channel
    Storystream
    Realeyes
    Qubit
    Jampp
    FindTheRipple Firedrop Mercanto Metageni Mobile Acuity
    Nudgr
    Phrasee
    Ignition Ai Maybe*
    Advizzo ArtuData
    Aiden Bibblio
    rais
    Platform360 Vaix
    Swogo
    MARKETING
    &
    ADVERTISING
    ANALYTICS/OPTIMIZATION TARGETING
    BoomApp
    ANGEL / SEED
    EARLY STAGE / GROWTH
    Selerio
    Echobox
    Adoreboard Colourtext
    ANGEL / SEED
    EARLY STAGE / GROWTH
    Genus SentiSum
    Big Data for Humans Codec
    ANGEL / SEED
    EARLY STAGE / GROWTH
    AshTV
    Popcorn Metrics
    Otus Labs Personalyze
    Idio
    iotec Pixoneye
    Permutive
    DataSine
    Visii
    ANGEL / SEED
    EARLY STAGE / GROWTH
    Donaco
    SENTIMENT ANALYSIS
    AUGMENTED CONTENT PURCHASE DISCOVERY/
    RECOMMENDATION
    Function
    IT
    Cardinality
    4th Office Mettrr Rainbird Technologies
    Global App Testing re:infer Recordsure
    Redsift
    Automorph
    Ampliphae
    ANGEL / SEED
    EARLY STAGE / GROWTH
    Aria Networks
    BigHand
    Autto Beneficiary.io CompareSoft
    Automated Intelligence
    Diffblue
    Trint Vivid-q
    Context Scout
    Retechnica
    Nexus Frontier Tech
    Cyanapse Digital Taxonomy jClarity
    Fantoo
    Fedr8 Fraim Linguamatics Mudano Rossum
    Skipjaq Thingful Unity {Cloud}Ware
    TextRazor
    Synthesized
    Spot Intelligence Spotlight Data
    PROCUREMENT
    Previse
    keelvar
    Matchdeck
    ANGEL / SEED
    EARLY STAGE / GROWTH
    SALES
    R&D
    FeedStock
    Klydo
    Sparrho
    Patsnap
    GlassAI
    ANGEL / SEED
    EARLY STAGE / GROWTH
    DueDil
    Cognism
    GrowthIntel
    Artesian Solutions
    Conversity Kluster Intelligence
    SalesSift
    Synoptic Technologies
    ANGEL / SEED
    EARLY STAGE / GROWTH
    Netz
    EnigmaMS
    UK AI Landscape (Early stage companies)
    ANGEL / SEED
    EARLY STAGE / GROWTH
    Skim Technologies
    Instadeep Memgraph
    Seldon
    Invacio
    Grakn Labs Hadean
    Graphcore
    Fetch.AI
    Academy of Robotics Evolve Dynamics
    ANGEL / SEED
    FiveAI
    EARLY STAGE / GROWTH
    Oxbotica
    Latent Logic
    Focal Point Positioning
    Sky-Futures
    Accelerated Dynamics Baro Vehicles
    Headlight AI Humanising Autonomy Intelligent Robots
    Machines With Vision Predina Wayve
    React AI Third Space Auto
    ANGEL / SEED
    EARLY STAGE / GROWTH
    SLAMcore
    MicroBlink
    Spectral Edge WeSee
    Blue Vision Labs
    Emteq
    Alchera Technologies FaceSoft
    TouchByte
    Vize.ai
    Visio Ingenii
    Machine Medicine Synthesia
    EA
    A
    P
    Prowler.io
    Brytlyt Hivemind
    A
    C
    P
    B2B
    Angel/Seed: <$2m
    Early stage/Growth: >$2m
    Funding category
    New company (since 2017)
    B2C
    KEY
    L
    COMPUTER VISION
    AI INFRASTRUCTURE
    Core Technologies
    AUTONOMOUS SYSTEMS
    Sector
    EARLY STAGE / GROWTH
    FINANCIAL SERVICES
    EARLY STAGE / GROWTH
    EARLY STAGE / GROWTH
    AGRICULTURE EDUCATION
    Sources: MMC Ventures, Beauhurst, Crunchbase, Tracxn
    Additions or corrections? Email us at [email protected]

    View full-size slide

  65. France and Germany are increasing their share
    of European AI entrepreneurship
    Source: MMC Ventures, Beauhurst, Crunchbase, Tracxn
    The dynamics European AI entrepreneurship are in flux
    66
    The State of AI 2019: Divergence
    UK
    France
    Germany
    Spain
    Ireland
    Netherlands
    Sweden
    Italy
    Finland
    Portugal
    Austria
    Denmark
    Norway
    0%
    5%
    -5%
    -10%
    10%
    15%
    20%
    25%
    30%
    35%
    40%
    Fig. X: France and Germany are increasing their share of European AI entrepreneurship
    Share of European AI startups
    Change in share over the last six years (% points)
    • While the UK remains the powerhouse of European AI,
    its share of Europe’s AI startups (by volume) has
    slightly decreased in recent years.
    • Brexit could accelerate this dynamic. If free
    movement of workers between the EU and UK ends,
    visas are unforthcoming or rhetoric is unwelcoming,
    the UK’s access to talent could be inhibited.
    • France, Germany and other hubs may extend their
    influence in the decade ahead, spreading the
    benefits of entrepreneurship more evenly

    across Europe.

    View full-size slide

  66. Italy, Sweden and Germany ‘punch above their weight’

    in core technology
    • Two thirds of of Europe’s ‘core technology’

    (sector-agnostic) AI startups are located in the

    UK, Germany and France.
    • However, adjusting for countries’ ‘size’ – their
    number of AI startups – Italy, Sweden and
    Germany ‘punch above their weight’ and

    are core technology hubs.
    The European core AI technology hubs
    67
    The State of AI 2019: Divergence
    Italy
    Sweden
    Germany
    Finland
    Austria
    Denmark
    Norway
    Spain
    UK
    France
    Netherlands
    Ireland
    Portugal
    0
    5%
    10%
    15%
    20%
    25%
    30%
    Fig. X: Core technology startups as % of AI startups
    Core technology startups as % of AI startups
    Average across all countries
    % of Europe’s Core Tech
    25%
    30%
    Fig. X: Core technology startups as % of AI startups
    Core technology startups as % of AI startups
    Average across all countries
    % of Europe’s Core Tech
    Source: MMC Ventures, Beauhurst, Crunchbase, Tracxn

    View full-size slide

  67. • Nine in ten of Europe’s 1,600 AI startups sell to other businesses (B2B).
    • Just one in ten AI startups sells directly to consumers (B2C).
    • But B2C AI is on the rise. In 2018, a quarter of new AI startups were B2C.
    • B2C entrepreneurs are mitigating or circumventing the ‘cold start’ data challenge.
    Nine in ten AI startups are B2B
    Source: MMC Ventures, Beauhurst, Crunchbase, Tracxn
    B2C AI is on the rise – a quarter of new AI companies are B2C
    Source: MMC Ventures, Beauhurst, Crunchbase, Tracxn
    Nine in ten AI startups are B2B, but B2C AI is on the rise
    68
    The State of AI 2019: Divergence
    Fig. X: Nine in ten of the AI startups in our universe are B2B
    B2B
    B2C
    87%
    13%
    0%
    10%
    20%
    30%
    40%
    50%
    60%
    70%
    80%
    90%
    100%
    2011
    2010
    pre
    2010
    2012 2013 2014 2015 2016 2017 2018*
    Fig. X: The share of new AI startups focusing on B2C is increasing
    B2C B2B
    90%
    90%
    94% 90% 87% 87% 90% 87% 83% 75%
    10%
    10%
    6% 10% 13% 13% 10% 13% 17% 25%
    0%
    10%
    20%
    30%
    40%
    50%
    60%
    70%
    80%
    90%
    100%
    2011
    2010
    pre
    2010
    2012 2013 2014 2015 2016
    Fig. X: The share of new AI startups focusing on B2C is increasing
    B2C B2B
    90%
    90%
    94% 90% 87% 87% 90% 87%
    10%
    10%
    6% 10% 13% 13% 10% 13%

    View full-size slide

  68. • There is a higher proportion of B2C AI companies in sectors where data is readily available.
    • The sectors attracting the highest proportion of new AI startups have been: finance;

    health & wellbeing; and media & entertainment.
    Media, finance, and health attract a higher percentage of B2C companies
    Source: MMC Ventures, Beauhurst, Crunchbase, Tracxn
    Half of new AI startups target the finance, health or media sectors
    Source: MMC Ventures, Beauhurst, Crunchbase, Tracxn , Startups founded in the last 24 months.
    B2C AI is growing in sectors with available data
    69
    The State of AI 2019: Divergence
    Finance
    Infrastructure & Utilities
    Health & Wellbeing
    Media & Entertainment
    Retail
    Transport & Travel
    0% 20% 40% 60% 80% 100%
    Fig. X: B2B/C dynamics by sector
    79%
    69%
    53%
    86%
    73%
    74%
    21%
    31%
    47%
    14%
    27%
    26%
    B2C
    B2B
    5%
    10%
    15%
    20%
    25%
    0%
    Finance Health &
    Wellbeing
    Media &
    Entertainment
    Retail Transport
    & Travel
    Fig. 5: Almost one in four of the sector AI startups founded
    in 2017/8 targets the financial services space
    23%
    17%
    10%
    10% 9%

    View full-size slide

  69. • Nine in ten AI startups address a need in a specific ‘vertical’ (business function or sector).
    • Just one in ten is developing a core, sector-agnostic ‘horizontal’ AI technology.
    • The proportion of core technology providers will remain modest as global technology
    companies offer an extensive and expanding suite of core AI technologies.
    One in ten AI startups focuses on core tech
    Source: MMC Ventures, Beauhurst, Crunchbase, Tracxn
    The proportion of ‘horizontal’ core technology providers has remained consistent over time
    Source: MMC Ventures, Beauhurst, Crunchbase, Tracxn , Startups founded in the last 24 months.
    AI entrepreneurship remains vertically-focused
    70
    Fig. X: One in ten AI startups focuses on core tech
    Core technologies
    Vertical (Sector/Function)
    88%
    12%
    0%
    10%
    20%
    30%
    40%
    50%
    60%
    70%
    80%
    90%
    100%
    2013 2014 2015 2016 2017
    Fig. X: Vertical/Horizontal dynamics of new AI startups
    43% 41% 44% 44% 37%
    11% 15% 11% 10% 11%
    45% 44% 45% 46% 52%
    Sectors Functions Core Technologies
    0%
    10%
    20%
    30%
    40%
    50%
    60%
    70%
    80%
    90%
    100%
    2013 2014 2015 2016 2017
    43% 41% 44% 44% 37%
    11% 15% 11% 10% 11%
    45% 44% 45% 46% 52%
    Sectors Functions Core Technologies
    0%
    10%
    20%
    30%
    40%
    50%
    60%
    70%
    80%
    90%
    100%
    2013 2014 2015 2016 2017
    Fig. X: Vertical/Horizontal dynamics of new AI startups
    43% 41% 44% 44% 37%
    11% 15% 11% 10% 11%
    45% 44% 45% 46% 52%
    Sectors Functions Core Technologies
    0%
    10%
    20%
    30%
    40%
    50%
    60%
    70%
    80%
    90%
    100%
    2013 2014 2015 2016 2017
    Fig. X: Vertical/Horizontal dynamics of new AI startups
    43% 41% 44% 44% 37%
    11% 15% 11% 10% 11%
    45% 44% 45% 46% 52%
    Sectors Functions Core Technologies

    View full-size slide

  70. • More AI startups – one in five – serve the health & wellbeing sector than any other. Healthcare is a focal
    point for AI entrepreneurship.
    • In the coming decade, developers will have a greater impact on the future of healthcare than doctors.
    • Activity is thriving given profound new opportunities for process automation and cost reduction and
    a tipping point in stakeholders’ openness to innovation.
    More AI startups – one in
    five – serve the health &
    wellbeing sector than any
    other
    Source: MMC
    Ventures, Beauhurst,
    Crunchbase, Tracxn
    Health & wellbeing is a focal point for AI entrepreneurship
    71
    21%
    Health & Wellbeing
    18%
    Finance
    12%
    Media & Entertainment
    6%
    Education
    5%
    Agriculture
    11%
    Retail
    8%
    Transport & Travel
    8%
    Infrastructure
    & Utilities
    Fig. X: One in five AI sector startups in our universe focuses on Health & Wellbeing

    View full-size slide

  71. The UK is the heartland of European healthcare AI
    Source: MMC Ventures, Beauhurst, Crunchbase, Tracxn
    • The UK is home to a third of Europe’s health & wellbeing AI startups.
    • UK entrepreneurs benefit from: leading universities for medicine and high quality teaching hospitals;
    successful healthcare scale-ups stimulating talent; and increasing expenditure and openness to
    innovation within the National Health Service (NHS).
    The UK is the heartland of European healthcare AI
    72
    Fig. X: Share of European health & well being startups
    35%
    1%

    View full-size slide

  72. More startups – one in four – serve the marketing function than any other
    Source: MMC Ventures, Beauhurst, Crunchbase, Tracxn
    • Among AI startups serving a business function, more – a quarter – serve the marketing function

    than any other.
    • Customer service and IT departments also enjoy a rich ecosystem of suppliers.
    Marketing and customer service teams are well served
    73
    The State of AI 2019: Divergence
    23%
    Marketing
    16%
    IT
    16%
    Customer Service
    9%
    Operations
    7%
    Sales
    4%
    Compliance
    8%
    BI & Analytics
    8%
    HR
    Fig. X: Marketing accounts for one in four AI function startups

    View full-size slide

  73. The UK has four in ten of Europe’s AI marketing startups (% of European AI marketing startups)
    Source: MMC Ventures, Beauhurst, Crunchbase, Tracxn
    France is Europe’s hub for AI Customer Service (% of European AI Customer service startups)
    Source: MMC Ventures, Beauhurst, Crunchbase, Tracxn
    The UK leads in marketing AI, France for customer service
    74
    The State of AI 2019: Divergence
    Fig. X: Share of European customer service startups
    22%
    1%
    Fig. X: Share of European marketing startups
    39%
    1%
    • The UK contributes half of Europe’s AI marketing startups.
    • France is Europe’s hub for customer service AI with a fifth of Europe’s customer service AI startups.

    View full-size slide

  74. • While currently underserved, the operations 

    function is benefitting from an influx of new,

    AI-led startups in the last 24 months.
    • AI is expanding the ‘envelope’ of automation – 

    the breadth and value of processes susceptible

    to digital mechanisation and robotic process 

    automation (RPA).
    • Over time, domain-focused vendors may build 

    defensibility through domain expertise, workflow 

    integrations, data network effects and referenceability.
    An influx of AI startups is driving process automation
    75
    The State of AI 2019: Divergence
    Among new startups addressing a business function, one in seven
    serve operations teams
    Source: MMC Ventures, Beauhurst, Crunchbase, Tracxn
    5%
    10%
    15%
    20%
    25%
    0%
    Customer service Marketing Operations IT HR
    Fig. X: Almost one in five of the function AI startups
    founded in 2017/8 focuses on customer service
    % AI start-ups founded since 2017 serving a business function
    20%
    15% 15%
    13% 13%

    View full-size slide

  75. Since 2015, AI companies have raised larger investment rounds
    Source: Crunchbase, MMC Ventures
    AI companies are raising larger rounds at all stages of maturity
    Source: Crunchbase, MMC Ventures
    AI companies raise larger amounts of capital
    76
    • AI companies raise larger volumes of capital than traditional software companies across

    all stages of maturity.
    • Additional capital may be required for the longer journey to ‘minimum viable product’ and

    the high cost of AI talent.
    • An imbalance between supply and demand for capital (many venture capital firms chasing 

    limited high quality opportunities) is also inflating round sizes.
    0
    0.2
    0.4
    0.6
    0.8
    1.0
    1.2
    1.4
    1.6
    -20%
    -10%
    0%
    10%
    20%
    30%
    40%
    50%
    60%
    2012 2013 2014 2015 2016 2017 2018
    Fig. X: Since 2015, AI companies have raised larger investment rounds
    Median funding (US$m)
    AI premium over software
    Software ex AI AI AI premium
    0
    5
    10
    15
    20
    25
    0%
    10%
    20%
    30%
    40%
    50%
    60%
    70%
    80%
    Seed Series A Series B Series C
    Fig. X: AI companies are raising larger rounds at all stages of maturity
    Median funding (US$m)
    AI premium over software
    Software ex AI AI AI premium

    View full-size slide

  76. Core technology companies attract a disproportionate share of funding
    Source: MMC Ventures, Beauhurst, Crunchbase, Tracxn
    • While core (‘deep tech’, sector-agnostic AI) technology companies comprise a tenth of AI startups, 

    they attract a fifth of venture capital investment.
    • The cost of world-class talent, extended time to achieve a minimum viable product, and alternative 

    revenue models (e.g. licensing agreements) can increase cash requirements.
    Core tech attracts a disproportionate share of funding
    77
    5%
    10%
    15%
    20%
    25%
    30%
    35%
    40%
    45%
    50%
    0%
    Core Technology Function Sector
    Fig. X: Covers companies founded since 2014
    Share of funding
    19%
    12%
    34%
    41%
    47% 47%
    Share of total (number)

    View full-size slide

  77. A greater proportion of AI startups are highly valued
    Source: Dealroom.co, MMC Ventures
    A greater proportion of AI startups are highly valued (detail – lower values)
    Source: Dealroom.co, MMC Ventures
    AI companies attract premium valuations
    78
    The State of AI 2019: Divergence
    • AI companies are securing higher valuations as well as more capital.
    • Beyond investment mechanics (larger rounds require higher valuations to avoid excessive dilution), and
    industry fundamentals, an imbalance in demand for capital and its supply is increasing valuations.
    • With AI entrepreneurship becoming mainstream, this valuation tailwind is likely to reduce.
    AI startups Software (ex AI) startups
    Proportion of companies
    Valuation bracket (€m)
    0%
    10%
    20%
    30%
    40%
    50%
    60%
    70%
    AI startups valuation data 1
    0–5m 5m–10m 10m–15m 15m–20m 20m–25mm 25m–50m 50m–100m
    AI startups Software (ex AI) startups
    Proportion of companies
    AI startups valuation data 2
    0–1.0m 1.1m–2.0m 2.1m–3.0m 3.1m–4.0m 4.1m–5.0m
    0%
    10%
    20%
    30%
    40%
    50%
    60%
    Valuation bracket (€m)

    View full-size slide

  78. 79
    • Recruiting AI talent is challenging. Startups compete with multiple categories of competitors (large
    technology companies, banks, professional service firms and other early stage companies) for a limited
    pool of AI practitioners.
    Data, talent and productising AI are key challenges
    Competition for AI talent, the limited availability of training data, and the difficulty
    of moving AI from ‘lab to live’ are entrepreneurs’ key challenges.
    “Access to talent, and its competitiveness is the

    biggest challenge.”
    David Benigson, Signal Media
    “London has one of the best pools of talent

    in the world – and is the main reason we’re here.”
    Fabio Kuhn, Vortexa

    View full-size slide

  79. 80
    • Access to initial training data sets poses a challenge. Startups are mitigating the difficulty by drawing
    on a diverse range of data sources, developing powerful use cases for access to client data, and by
    implementing a data acquisition strategy from early in their lives.
    Data, talent and productising AI are key challenges
    “We started collecting data very early in our journey.”
    Timo Boldt, Gousto
    “It’s a classic chicken and egg problem. Early customers, and thus data,
    are hard to get when you don’t have any existing reference clients.”
    Tim Sadler, Tessian
    The State of AI 2019: Divergence

    View full-size slide

  80. 81
    • Developing production-ready AI difficult. Entrepreneurs recommend moving ‘from lab to live’ as soon as
    possible, testing development systems on low-risk real-world data. Cross-functional collaboration is key.
    Data, talent and productising AI are key challenges
    “Taking what works well in a lab and getting it to work in a diverse
    and sick population is a big challenge.”
    Chris McCann, Current Health
    “The real world is full of black swans and exceptions. We’ve learned
    to overcome them by getting great at cross-functional collaboration
    and constant monitoring of risk.”
    Timo Boldt, Gousto
    The State of AI 2019: Divergence

    View full-size slide

  81. 82
    • Our AI Playbook is an accessible, step-by-step guide to taking advantage of AI in your company.
    • A blueprint for success with best practices in AI: strategy; data; talent; development; production;

    and regulation & ethics.
    Our AI Playbook: your guide to taking advantage of AI
    The State of AI 2019: Divergence
    Download the Playbook free at

    mmcventures.com/research

    View full-size slide

  82. The State of AI 2019: Divergence
    5. The implications of AI

    View full-size slide

  83. The implications of AI
    The State of AI 2019: Divergence 84
    AI will have profound implications for companies and societies.
    AI will reshape sector value chains, enable new business models
    and accelerate cycles of creative destruction.
    While offering societies numerous benefits, AI poses risks of

    job displacement, increased inequality and the erosion of trust.

    View full-size slide

  84. AI offers innovation, efficacy, velocity and scalability
    Source: MMC Ventures
    AI offers innovation, efficacy, velocity and scalability…
    85

    View full-size slide

  85. AI will disrupt companies and markets by enabling:
    • New market participants
    • Shifts in sector value chains
    • New business models
    • New commercial success factors
    • Changes in companies’ competitive positioning
    • Shifts in skills and organisational design
    • Accelerated cycles of innovation
    86
    …with significant implications for markets
    The State of AI 2019: Divergence

    View full-size slide

  86. • By automating capabilities previously delivered by human professionals, AI will

    reduce the cost and increase the scalability of services.
    • Markets including healthcare and financial services will see broader global participation.
    87
    AI will enable new market participants…
    The State of AI 2019: Divergence
    • Healthcare example: medical diagnosis is expensive for people in
    developed nations and inaccessible to many in developing economies.
    • AI-powered automated diagnosis available at negligible marginal cost,
    coupled with extensive smartphone penetration, will reduce the cost and
    increase access to primary care – enabling an influx of new

    market participants.

    View full-size slide

  87. • AI will change where, and the extent to which, profits are available
    within a value chain.
    • Value chains in sectors including insurance, legal services and transport
    will be reshaped.
    88
    …and drive shifts in sector value chains
    The State of AI 2019: Divergence
    • Transport example: 42% of revenue from global insurance premiums come
    from car insurance. As AI-powered autonomous vehicles gain adoption,

    the frequency of car accidents will reduce.
    • The profitability of car insurance policies will fall – by as much as 81%

    in the UK (Autonomous Research).
    • How will insurers adjust to a collapse in profitability of much of their revenue?
    • Multiple sector participants must plan for a profound shift in their sector’s
    value chain.

    42%
    of the revenue from
    global insurance
    premiums comes 

    from car insurance.
    Source:

    Autonomous Research

    View full-size slide

  88. • AI, growth of ‘x-as-a-service’ consumption, and subscription payment
    models will obviate select business models and enable new ones.
    • Expect new business models in sectors ranging from healthcare to
    transport and insurance.
    89
    AI will enable new business models…
    Transport example: AI will transform the economic fabric of ownership

    and insurance.
    • Historically, wasteful private vehicle ownership has been required to provide
    spontaneity, convenience, security and privacy.
    • On-demand access to a fleet of autonomous vehicles will provide the same

    at a disruptive low cost.
    • Expect ‘transport-as-a-service’: pay a fixed, low monthly fee for all-you-can-eat
    access to an autonomous fleet - and reduced private vehicle ownership.
    • ‘Downstream’ models (of repair centres, charging centres) will also be transformed.
    Cars are parked for an
    average of
    96%
    of their lives.
    Source: UITP Millennium

    Cities Database

    View full-size slide

  89. New technology paradigms offer significant benefits but demand new competencies.
    Winners in the age of AI will develop the new competitive success factors required for AI.
    Success factors for the age of AI include the ability to:
    • possess the vision to embrace AI and the organisational changes it requires
    • leverage large, non-public data sets to train market-leading algorithms
    • evaluate the opportunities and risks of sharing training data with partners and competitors
    • attract, develop, retain and integrate data scientists
    • form effective partnerships with ‘best-of-breed’ third-party AI software providers
    • diligence AI partners effectively
    • understand and respond to regulatory challenges posed by AI
    • effect a mindset shift to software that provides probabilistic instead of binary recommendations
    • manage workflow changes resulting from AI implementations
    • manage challenges of organisational design and culture as AI augments and replaces personnel
    90
    …and demand new commercial success factors

    View full-size slide

  90. New platforms, disruptors, leaders and laggards will emerge as AI disrupts companies’ competitive positioning:
    Providers of AI:
    • Platforms: With vast data sets, world-class AI teams and extensive resources, select GAIM vendors (Google, Amazon,
    IBM and Microsoft) will accrue value as platforms that support the provision of AI. GAIM lack the strategic desire or
    data advantage to address myriad domain-specific use cases, presenting opportunities for Disruptors.
    • Disruptors (early stage, AI-first software companies tackling business problems in a novel way using AI): enable SMBs
    and Enterprises who embrace them while eroding the value of those that do not.

    Buyers of AI (SMBs and Enterprises):
    • Leaders will emerge in key industries by: anticipating the shifts in value chains and business models caused by AI;
    taking advantage of large, proprietary data sets to train AI algorithms; having the organisational ability to deploy AI
    effectively; and by having sufficient resources and reputation to attract high quality AI talent.
    • Laggards lack the will or organisational ability to embrace AI. While some lack the foresight to adapt, more will falter
    due to limited organisational capability.
    91
    AI will cause shifts in companies’ competitive positioning…
    The State of AI 2019: Divergence

    View full-size slide

  91. • As AI gains adoption the skills that companies seek and companies’ organisational
    structure will change.
    • Currently, companies’ engagement with data scientists is limited; a mix shift to
    employing data scientists is likely.
    • Hiring for adaptability will be increasingly important, as the range of tasks supported
    or undertaken by AI systems increases.
    92
    …and drive changes in skills and organisational design
    The State of AI 2019: Divergence
    • Professional services example: global professional services and consulting firms
    average 5,000 to 15,000 in-house analytics professional; fewer than 8% are data
    scientists (MMC Ventures) .
    • Tomorrow’s leaders are aggressively expanding their data science teams; time to
    market is key given competitive advantage through data network effects.
    41%
    of companies are
    considering the impact

    of AI on future skill
    requirements.
    Source: PWC

    View full-size slide

  92. Cycles of innovation, adoption and consumption are compressing
    Source: European Environment Agency, based on Kurzweil
    AI will accelerate cycles of innovation
    93
    The State of AI 2019: Divergence
    • By reducing the time required for process-driven work, AI will accelerate the pace of innovation.
    • This may compress cycles of creative destruction, reducing further the period of time for which all but a
    select number of super-competitors maintain value.
    Cycles of innovation, adoption and consumption are compressing
    Time before mass use
    Long Short
    Black and white television 26
    Invention available to the general public
    1926
    1873
    1897
    1876
    1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010
    1983
    1991
    1991
    1979
    1975
    1951
    Computer 16
    Mobile phone 13
    Compact disc 12
    7
    10
    World Wide Web
    Smartphones
    31
    Radio
    Telephone 35
    Electricity 46
    Colour television 18
    Years neccessary for an invention
    to be used by a quarter of the US population
    33 years
    In 1965, average company

    duration in S&P500.
    14 years
    By 2026, est. average company

    duration in S&P500.
    Source: Insight

    View full-size slide

  93. In addition to numerous benefits, AI presents challenges and risks for societies:
    • AI-powered automation may displace jobs
    • Biased systems could increase inequality
    • Artificial media will undermine trust
    • AI offers states greater control and poses trade-offs between privacy and security
    • Autonomous weapons may increase conflict between nations
    94
    To societies, AI presents risks as well as benefits
    The State of AI 2019: Divergence

    View full-size slide

  94. • AI will directly enable the automation of select occupations that involve routine and repetition – from truck-driving
    (3.6 million US jobs) to telemarketing.
    • AI will augment and then displace workers in some more complex roles, while reducing the need for additional
    workers to be hired as companies expand.
    • However: analysis of UK census data since 1871 shows: historically, contracting employment in agriculture and
    manufacturing (due, in part, to automation) have been more than offset by rapid growth in alternative (caring,
    creative, technology and business service) sectors (Deloitte).
    • Greater automation of manual and business service roles may concentrate employment further in occupations
    resistant to automation – including care work and teaching.
    95
    AI-powered automation may displace jobs
    The State of AI 2019: Divergence
    • Whether or not, over time, AI creates more jobs than it destroys, the short period in which select
    workers could be displaced, with a reduction in the availability of similar roles, could prevent
    those who lose their jobs being rapidly re-absorbed into the workforce. Social dislocation,
    with political consequences, may result.

    View full-size slide

  95. AI-powered facial recognition systems misgender 1% of lighter-
    skinned males but 35% of darker- skinned females
    Source: J Buolamwini, M.I.T. Media Lab, via The New York Times
    96
    Biased AI systems could increase inequality
    The State of AI 2019: Divergence
    • Theoretically, AI can free decision-making from human bias

    by finding objective patterns in large data sets.
    • In practice, AI systems learn by processing training data.

    Available training data reflects systemic historic biases,
    particularly regarding gender and race.
    • Incomplete or inadequate training data are causing AI systems to
    behave problematically, particularly to minorities.
    • Example – in a popular data set used to train facial recognition systems,

    75% of faces are male and 80% are white.
    • As a result of poor training data, facial recognition systems that offer gender 

    classification misgender 1% of lighter-skinned males but up to 7% of

    lighter-skinned females (Buolamwini, Gebru).

    View full-size slide

  96. • AI systems are being used to make a growing range of decisions that have a significant impact

    on individuals’ lives (credit, recruitment).
    •“If we fail to make ethical and inclusive AI, we risk losing gains made in civil rights and

    gender equity under the guise of machine neutrality.” Joy Buolamwini
    There are
    potential harms
    from algorithmic
    decision-making
    Source: Megan Smith via gendershades.org
    97
    Biased AI systems could increase inequality
    INDIVIDUAL HARMS
    HIRING
    EMPLOYMENT
    INSURANCE & SOCIAL BENEFITS
    HOUSING
    EDUCATION
    ILLEGAL DISCRIMINATION UNFAIR PRACTICES
    COLLECTIVE
    SOCIAL HARMS
    CREDIT
    DIFFERENTIAL PRICES OF GOODS
    ECONOMIC LOSS
    LOSS OF LIBERTY
    INCREASED SURVEILLANCE
    SOCIAL
    STIGMATISATION
    STEREOTYPE REINFORCEMENT
    DIGNITARY HARMS
    LOSS OF
    OPPORTUNITY
    Potential harms from algorithmic decision-making

    View full-size slide

  97. 98
    Biased AI systems could increase inequality
    The State of AI 2019: Divergence
    To avoid ‘automating inequality’ developers can:
    • recognise the challenge, as a starting point for action;
    • develop diverse teams that reflect the communities they serve;
    • create balanced, representative data sets;
    • deploy rigorous ethics and testing frameworks for system validation.
    There is a battle going on for fairness, inclusion and justice in the digital world.”

    (Darren Walker, via The New York Times).

    View full-size slide

  98. Given video of former President Obama, researchers
    synthesised photorealistic , new lip-synched video
    Source: Suwajanakorn, Seitz and Kemelmacher-Shlizerman
    • Generative Adversarial Networks (‘GANs’) are an emerging AI technique that enable the creation of

    artificial media, including pictures and video, impossible to differentiate from real content.
    • GANs will democratise the creation of content, enabling the production of media at scale and low cost.
    • GANs also enable the creation of damaging counterfeit media in which individuals appear to perform
    actions they have not undertake or speak alternative dialogue.
    99
    Artificial media may undermine trust – ‘fake news 2.0’
    • In addition to damaging individuals, artificial media
    will undermine trust. If any media can be
    counterfeit, all media is open to challenge.
    • In the age of artificial media, society

    will grapple with challenges of truth and trust.
    • Adversaries recognise that sowing doubt and
    confusion to divide populations can be more powerful
    than direct action.

    View full-size slide

  99. • AI-powered facial recognition has unprecedented capability. To what extent
    will citizens and governments sacrifice anonymity to prevent crime?
    • AI with real-time analytics are enabling the high-tech surveillance state
    with greater capacity for social control. China intends to combine real-
    time recognition with social scoring to inhibit ‘undesirable' behaviour.
    100
    AI presents new trade-offs and offers greater state control
    The State of AI 2019: Divergence
    “You don’t need people’s cooperation for us to be able to recognise

    their identity” (Huang Yongzhen, Watrix, via the Associated Press).

    A citizen is
    captured on

    CCTV an average of
    68 times
    per day.
    Source: Cheshire
    Constabulary Camera
    Survey

    View full-size slide

  100. • AI-powered computer vision systems, AI-based decision-making algorithms and improved
    robotics are enabling humanoid and aerial drones with greater capability and autonomy.
    • While the risk of ‘killer robots’ turning against their masters is overstated, less considered is the
    possibility that conflict between nations may increase if the human costs of war are lower.
    101
    Autonomous weapons may increase conflict
    The State of AI 2019: Divergence
    A country that thinks twice about sending young people into conflict may be
    more adventurous if the only assets in harm’s way are equipment.

    View full-size slide

  101. MMC Ventures is a research-led venture capital firm.
    We’ve backed over 60 early-stage, high growth
    technology companies since 2000.
    AI is a core area of research and investment.

    We’ve made 20 investments into many of the UK’s

    most promising AI companies.
    If you’re an early stage AI company, get in touch

    to see how we can accelerate your journey.
    www.stateofai2019.com
    Get in touch
    102
    MMC Ventures Research
    David Kelnar: Partner & Head of Research

    Asen Kostadinov, CFA: Research Manager
    Explore MMC’s cutting-edge research at
    mmcventures.com/research.

    View full-size slide