“Over the last couple of years, a billion new people have joined the super-connected world. Billions more around the developing world, now, walk with a high-speed computer in their pockets. And yet, they don’t have a bank account, a formal education or access to most of the services we take for granted in the U.S. Imagine the possibilities… imagine how you can change the lives of billions of people.”
This is how I closed the Stanford class about venture opportunities in emerging ecosystems three years ago. Looking back, when I first began teaching the course, I could only count on the brilliant and spontaneous minds seated in front of me to help me foresee the possibilities.
I recognized that it was hard to imagine them from the trenches. So, I mostly stuck to describing the macro opportunities and the barriers that had prevented local entrepreneurs from making it big (leaving the majority of the world unable to unlock the benefits of their ideas): material, cultural and adoption walls.
Indeed, starting a tech company in emerging economies is an enormous feat that faces innumerable roadblocks due to poor access to capital, lack of support networks and an inadequate talent pool. Even if a founder is able to gain traction against these odds, scaling is hard because of poor infrastructure, an ill-suited financial sector and uncertainty in the legal and political contexts.
Perhaps the biggest challenge of all is accessing local markets. Potential client bases lack purchasing power, a reliable internet connection and sufficient education levels to operate in the digital world; some lack the motivation to climb out of poverty. Consequently, smartphone penetration alone did not really prepare developing economies for the new Uber of X or the Airbnb for X. However, it did create the most propitious environment to build thousands of X + AI solutions, setting the stage for the upcoming revolutionaries: homegrown AI-first innovators.
The best indicator of why machine learning technologies will shape the world more deeply than anybody predicted is how fast the open source movement in the field is moving. Companies such as IBM, Microsoft and Google are opening parts of their most advanced algorithms. Elon Musk, Reid Hoffman, Jessica Livingstone and other visionaries launched the OpenAI initiative to foster collaboration and democratize access for founders: “Deep learning is an empirical science, and the quality of a group’s infrastructure is a multiplier on progress. Fortunately, today’s open-source ecosystem makes it possible for anyone to build great deep learning infrastructure.”
Anyone, anywhere, any time! Indeed, over the last couple of years, AI research reached a tipping point precipitated by a combination of low-cost ultra powerful computing, progress in algorithm design and access to large sources of data. OpenAI believes accessing AI capabilities should be as easy as launching a website.
Creativity is a better predictor of lifetime accomplishments than IQ or school performance.
By now, you must be convinced that the world will be eaten by intelligent software — literally in the scariest scenarios. If you are a technologist, you can almost touch the future. You can feel a car stop automatically as it arrives at your destination. You can hear the door open automatically. Without looking, you see yourself jumping off and heading directly to a highlighted table. A 165-degree personalized latte, perfectly flavored to your morning palette, is already waiting for you. You virtually wave a quick “see ya” to your gaming pals before you drop your Oculus Rift 6 and start your real-life day. You know the future will be awesome in the Valley. Facebook and Tesla are poised to own what’s next…
Meanwhile, in the rest of the world, automation will transform billions of lives in simpler but more profound ways: from getting a decent primary education to providing spot prices for crops, as well as access to fair credit and personally matched job opportunities. Billions of smartphones, the best sensors on earth, are already widely deployed. I believe local entrepreneurs will own that part of the future. They will lead this revolution because local problems will be finally solved at a cost that the majority will be able to afford.
Most of the traditional barriers founders face will be eradicated as most tech solutions will be detached of local infrastructure and local non-market environments — think of local currencies, for example. This, in turn, will attract part of the whopping $10 billion in financing already backing some 1,500 AI startups from 70 countries (Venture Scanner). And this is projected to rise more than fourfold in 2017 (Forrester Research). Technical teams around the world will be connected to the global AI community for collaboration and support. These local idealists will be empowered to lead a new wave of innovation by leveraging their proximity to local problems, by accessing unique local data and by better understanding the humans they want to serve.
One spring morning in 2017, a 40-year-old mother of three living on the outskirts of Bangalore feels a small lump in her right breast. She immediately called her mother, who urges her to visit the local clinic that recently acquired a state of the art mammography scanner. When she got there, as she stood in line, she could see the white artifact, the size of a vending machine, in an empty room. The lights were off.
“Is that the machine?,” she asked. “Why do I need to wait three months for my consultation? No one is using it!” The man behind the desk responded, “Well, we have the scanner, but our only radiologist moved to another city and we haven’t been able to find a replacement.”
Medical equipment is often useless without the manpower — i.e. experience and intelligence — of a specialist, and three months is overly sufficient breeding time for cancer. Waiting three months could be the difference between life and death. India, like most developing economies, faces a chronic shortage of medical doctors. India has 0.7 doctors per 1,000 people — lower than China’s 1.5 or the United States’ more than 2 and France’s 3.5, according to WHO. Thankfully, in India and other countries with similar challenges, nurses and paramedics have become the cornerstone of their healthcare systems. Unfortunately, even if they could be taught to operate a mammography scanner, they can seldom detect masses or micro calcifications.
Rohit Kumar Pandey, Tathagato Rai Dastidar and Apurv Anand want to solve the problem caused by the chronic shortage of trained medical practitioners. They are part of the team that founded SigTuple, an Indian startup that is building a platform to provide healthcare solutions by detecting different diseases using machine learning software. It promises to automatically analyze medical images and data to aid diagnosis.
AI can empower entrepreneurs to create, imagine and innovate at entirely new levels.
A Computer Science PhD, former director of Amex’s Big Data labs and now SigTuple’s Chief Scientist Officer, Tathagato believes the only way healthcare services can reach more people and take advantage of infrastructure is to make doctors more efficient. In the future, lack of specialists or lack of local infrastructure should not be a barrier for better women’s health. Long distances and translation issues in a country with more than 100 different spoken languages will no longer prevent the unprivileged from gaining access to basic services wherever they live.
Nurses will be enhanced by AI to heal anyone, teachers will be empowered to teach at a personalized pace and local journalists will be liberated of language constraints to give citizens more sources of information. It has long been established that solving local problems, as opposed to importing global solutions from rich countries, should be the calling of native entrepreneurs.
Still, today, many founders choose to launch and scale copycats that can only cater to the upper classes in emerging markets. They are going after technology early adopters who have decent purchasing power. Automation will soon make services in poor countries cheaper than they have ever been. Solving local problems at scale will now become economically feasible. So these founders have the advantage of being on the ground and living first-hand the problems they will solve.
Even the best Stanford storytelling techniques will never be as powerful as living the real and deep frustration caused by a problem hurting your “own” on a daily basis.
I have an investor friend who loves drones. He often flies his latest addition in front of his office, where he questionably experiments attaching objects on top of the lenient quadrupeds. The difference between this investor and any other gadget-obsessed VC is that Mbwana’s office is not on Sand Hill Road or SOMA, but in front of the African Savannah.
Until now, I had never understood his fascination for overpriced flying “toys.” Today, computer vision and image processing will be able to monitor land use or deforestation programs, drastically improve efficiency for farming and even check for flood risk. He bets governments and development agencies will start using them more and more. Mbwana knows better than any other VC, because he knows the local terrain. And local terrain is data.
What I do know is that these transforming applications of deep learning will come from developing economies.
“Admit it: Do you still have that idealized view of a Masai holding a feature phone checking market prices, popularized by the media?,” writes Mbwana on his Savannah Fund’s blog.
Knowing the land and the local organization to get data may very well make the Masai farmer fantasy become a reality. And Mbwana will be there to help founders do exactly that. He knows that successfully integrating the power of drones and computer vision technologies to solve problems in Africa is only half the challenge. Partnering with governments and corporates will be a necessity not only to reach the consumer but to get access to data.
Negotiating with multiple entities across sub-Saharan Africa is not easy, and local entrepreneurs and hands-on investors have a clear advantage. Moreover, as innovation in business models and tech accelerates, the outdated or sometimes total lack of regulation in developing economies can play in one’s favor, albeit riskily. While the FAA has already regulated drone flying, curtailing innovation in a nascent industry in the U.S., most emerging markets have yet to address it. So Mbwana will have the chance to support founders pushing the envelope in unregulated countries — and maybe bring solutions to the U.S. once local regulations approve.
In the early hours of a cold night in 2012, a young Mexican artist, Pia Camil, and architect, Mateo Riestra, welcomed their first son. They gave him what must be the most Mexican name of all: Guadalupe.
Having his first baby touched Mateo profoundly. That year, the young father launched a Kickstarter campaign for a project that had become urgent. He knew Disney and Mattel would entertain and distract Lupe, but he felt his son needed a different type of toy that would better equip him with more important skills to get a head start in the world.
After a successful campaign, Mateo decided to drop his design studio and start a toy company called Lupe Toys — with the mission of leveraging nature’s intelligence to develop gamesome educational experiences. Wanting to have more impact, he joined NUMA Mexico, Mexico’s affiliate of a French global accelerator, to transform his indie toy company into an edtech startup. After months of exploration, the focus turned on the development of an IoT-based gaming system running on a machine learning platform that could measure and increase children’s creativity.
Creativity is a better predictor of lifetime accomplishments than IQ or school performance. Imagine a generation of kids around the world benefiting from a personalized learning experience powered by machine learning to become more creative and, in turn, more successful.
Starting a tech company in emerging economies is an enormous feat that faces innumerable roadblocks.
Mateo’s ambitious journey to transform education did not come from a stay at Singularity or from a lab in Israel. Love sparked it. Explain to a social media wizard with no kids how it feels to see your baby marvel when her creativity is empowered. It’s impossible to understand that feeling — even if you provide the best analytical tool to analyze millions of Facebook timelines. Try to explain a Mexican “Albur,” a vulgar ironic Mexican joke, to the wittiest British data scientist. To borrow from Shakespeare, “There are more things in heaven and earth, Horatio, than are dreamt of in your philosophy.”
In short, you need to understand words beyond Google’s search gold mine — you need cultural context and the experience of hearing the tone that often precedes the joke. Teaching to understand deep feelings or cultural references will require entrepreneurs who understand local humans. Life can only teach life, and not a successive jumbo round of financing. Beyond simply eliminating repetitive tasks and outsourcing entire professions to software, AI will put people at the center of software development. AI can empower entrepreneurs to create, imagine and innovate at entirely new levels to drive not only growth, but happiness.
The fourth industrial revolution is here. While large tech companies will focus on cutting-edge solutions, and corporates in developing economies will miss yet another wave of innovation, AI-first entrepreneurs in emerging markets will bring a revolution to address the problems brought by a “hot, flat and crowded world.”
I believe the only true barrier for these entrepreneurs is doubting that only they can make these things happen. Will Tathagato’s software save lives in India? Will Mbwana back the next drone unicorn? Will Mateo educate new, more-creative minds? I don’t know. What I do know is that these transforming applications of deep learning will come from developing economies.
Now that you’ve reached the end of your quick diagonal read, this may feel just like any other post about AI paraphrasing The Economist or a16z. But, it’s not about artificial neural networks or about training machines to think. It’s about human will. It’s an outcry for battle written for every founder working hard from emerging ecosystems around our planet. Even if they still feel the odds are against them and see walls being built, AI may very well be the tool they needed to truly make it big. Maybe now they can start a company built to solve a local problem and scale to change the world for the better.
This post is about a better world brought by human ingenuity. It’s about a human opportunity, an invitation to founders and investors in advanced economies to come and help us change the lives of billions of humans. Come join the movement to help mankind move forward for a better, fairer future. It’s time!