The Impact of AI on Africa
A working thesis focusing on venture capital and the startup ecosystem in Africa.
Over the past 80 odd years we’ve gone through multiple major waves in technology innovation that have significantly improved our lives and the way we do things. I mentioned these in a previous note. I would characterise these as super cycle events that significantly improve productivity and have a transformative impact on various industries and society. We’re now experiencing the take-off of the next super cycle event, artificial intelligence (AI).
A while ago a good friend of mine asked my opinion on what impact the emergence of ChatGPT would have on Africa. At the time, I had no idea, but the open-ended activity we’re seeing in the AI space has been and continues to be exhilarating. Not implying anything, but this must be what it was like in the late ‘90s/early 2000s during the dot-com boom… Anyway, since moving into the venture capital (VC) space, I’ve been able to begin to form a working thesis that AI can be a force multiplier for the African VC and startup ecosystem.
The thesis is based on the fact that super cycle events such as the introduction of semi-conductors, personal computers, the internet, smartphones, and cloud computing (among other events) have improved productivity by orders of magnitude and reduced the costs of starting and running businesses.
For example, Amazon Web Services (AWS) has helped reduce startup costs in several ways. First, they introduced a pay-as-you-go pricing model, so startups only pay for the resources they use without any upfront costs. Second, AWS provides infrastructure services, such as virtual servers, storage, and databases, so startups don't have to invest in their own hardware. Third, AWS allows startups to easily scale their resources based on their needs, avoiding over-provisioning and unnecessary costs. Fourth, AWS offers server-less computing, which eliminates the need to manage servers and reduces idle time costs. Fifth, AWS provides managed services, reducing the need for specialised personnel and third-party service providers. Finally, startups can benefit from AWS's ecosystem, which includes pre-built solutions and support resources. Overall, AWS helps startups focus on their core business while minimising infrastructure expenses and operational overhead.
Similarly, the VC and startup ecosystem can benefit from AI due to the nature of VC in Africa.
In Africa, early-stage investing makes up most of the deal share*, more so compared to the larger, more developed regions.
The main pain points I’d like to think about for now are startup costs, product market fit, and due diligence.
Generally early stage investing (Angel - series A) is the riskiest class of venture capital investment - mainly because if the startup has a minimum viable product, the startup is yet to gain any insightful traction, is yet to generate revenues, and it is too early for anyone to know whether the startup is targeting the correct market, what adoption will look like, and ultimately whether the startup can weather various economic conditions, survive, and thrive. Uncertainties regarding survival will always be there, but AI can go a long way towards de-risking the early-stage asset class.
Reducing Startup Costs
Most startups are bootstrapped, where the founder uses either his own money, or borrows from friends and family to develop a minimum viable product, before they can get funding from an Angel investor or an early-stage VC firm. Using AI tools and agents, individuals can build a startup with very low costs. For example:
Putting together a team and spreading the work: AutoGPT can help you as a founder automate the coding process, create applications without hiring a developer, and help get you to market faster by automating the development process.
To eliminate the opportunity cost of not being able to write code and therefore execute on an idea: there are low-code and no-code platforms that can allow you to build websites, mobile apps, and business process automation.
To eliminate the opportunity cost of not being able to articulate and execute on an idea due to language barriers: Botlhale AI is laying the foundations for natural language models in different languages. What this will lead to is language translation to facilitate collaboration and participation between people speaking different languages, text-based communication, content generation in English regardless of whether the creator can speak English well, and language learning support allowing the founder to gradually improve fluency in a language.
Adding these capabilities to the existing suite of solutions, we’re encouraging more people to start businesses, and we’ve now de-risked startup costs.
Dealing with product market fit uncertainty
Have you heard of the “fake data” company? Tonic.ai uses machine learning to create synthetic data that is similar to real-world data, but that does not contain any personally identifiable information, which makes it safe to use for testing and development purposes, including testing software applications, developing machine learning models, creating training data for artificial intelligence models, and conducting data analysis.
Similar to how aeroplane pilots use a simulator when they train before they fly real planes, Tonic is laying the foundations for a “startup simulator” engine that incorporates market trends, user behaviour, customer demographics, and competitor analysis - generating realistic scenarios based on the integrated data and simulates the potential outcomes of different strategies. This engine would run different market simulations to help a startup develop and strengthen its minimum viable product (MVP) and enable the startup to:
Test different marketing strategies: such as different ad copy, landing pages, and offers. By testing different strategies, startups can learn what works best for their target market and gain a better understanding of what strategies are needed to gain product market fit.
Test different pricing models: by creating synthetic data that represents different customer segments, startups can learn what price point is most attractive to their target market. This information can then be used to set a price that is likely to be successful.
Test different product features: by creating synthetic data that represents different customer segments, startups can learn what features are most important to their target market. This information can then be used to prioritise the development of new features and ensure that the product is meeting the needs of the target market.
With the existence of such a simulator, we’ve now de-risked product market fit.
Strengthening Due Diligence
Credit risk engines - tools used by financial institutions to assess the credit worthiness and potential risks of lending money - laid the foundation for an AI engine that can further de-risk early stage investing for VCs by improving (read: fool-proofing) the due diligence stage in the following ways:
Automated Data Analysis: AI-powered algorithms can analyse vast amounts of data, including financial statements, market research, customer reviews, and industry trends. By automating the data analysis process, AI can quickly identify patterns, anomalies, and insights that may be crucial for evaluating a startup's potential. This saves time and allows investors to focus on interpreting the results rather than manually sifting through data.
Market Research and Competitive Analysis: AI can assist in conducting comprehensive market research and competitive analysis. By utilising natural language processing and machine learning, AI algorithms can analyse news articles, industry reports, social media data, and other sources to gather insights on market dynamics, competitive landscape, and emerging trends. This information helps investors understand the market opportunity and assess a startup's competitive positioning.
Risk Assessment: AI can help identify and assess risks associated with potential investments. By analysing historical data and market indicators, AI algorithms can identify red flags, such as financial instability, legal issues, or regulatory compliance concerns. AI can also evaluate a startup's intellectual property portfolio to assess its uniqueness and potential for future growth.
Predictive Analytics: AI algorithms can leverage historical data to generate predictive analytics, offering insights into a startup's future performance. By analysing factors such as revenue growth, customer acquisition, churn rates, and market trends, AI can provide forecasts and projections that assist investors in evaluating the startup's growth potential and expected returns on investment.
Natural Language Processing for Document Analysis: AI-powered natural language processing techniques can analyse startup-related documents, such as business plans, pitch decks, legal agreements, and contracts. This enables investors to extract key information, identify risks, and compare startups more efficiently. AI can also detect inconsistencies, omissions, or discrepancies within documents, alerting investors to potential issues.
Sentiment Analysis: AI can analyse public sentiment, customer reviews, and social media discussions about a startup to gauge its brand perception and customer satisfaction levels. Sentiment analysis can provide investors with insights into a startup's reputation, product-market fit, and overall customer sentiment, helping them assess its market potential and competitive advantage.
Pattern Recognition: AI algorithms can identify patterns and correlations within datasets, highlighting meaningful insights. For example, AI can identify patterns in customer behaviour, revenue generation, or operational efficiency. These insights enable investors to assess the scalability, sustainability, and potential risks associated with a startup's business model.
The existence of such a tool would mean we’ve de-risked due diligence inaccuracies.
In conclusion, the emergence of artificial intelligence (AI) as the next super cycle event holds immense potential for the venture capital (VC) and startup ecosystem in Africa. By leveraging AI technologies, such as AutoGPT, low-code/no-code platforms, and a startup simulator, we can address key pain points in early-stage investing, including high startup costs, product-market fit uncertainties, and due diligence challenges.
Reducing startup costs through AI-powered tools will enable more individuals to start businesses, fostering a more active entrepreneurship ecosystem throughout Africa. Additionally, de-risking product-market fit with market simulators and utilising synthetic data for testing and development allows startups to refine their minimum viable products and make informed decisions about marketing strategies, pricing models, and product features.
Moreover, AI-driven due diligence tools can facilitate comprehensive data analysis, market research, risk assessment, predictive analytics, document analysis, sentiment analysis, and pattern recognition. This streamlines the investment evaluation process, saving time and enabling VC firms to make more informed decisions about early-stage investments.
The knock-on effects of these advancements mean that VC investors can allocate lower ticket sizes to early-stage startups, given reduced capital requirements in the early stages. Enhanced due diligence and de-risked investing attract more capital, leading to a surge in funding opportunities for innovative solutions across Africa. As more individuals are encouraged to solve problems, the economy benefits from increased productivity, job creation, and income growth. Moreover, the injection of capital into the startup ecosystem allows for the tackling of larger-scale challenges, such as energy, health, food security, and general inefficiencies in emerging economies, at a lower cost.
While this working thesis explores the potential impact of AI in Africa, the ongoing super cycle event of AI signifies an exciting period of growth and transformation. By leveraging AI technologies effectively, Africa's venture capital and startup ecosystems can experience a positive shift, driving innovation, economic development, and improved quality of life.
*Data compiled using CB Insights State of Venture Report 2022