The Future of Work After COVID-19

Three things I’ve learned about career planning from the 2008 financial crisis that can help us navigate the current recession

In 2020, a black swan crashed the global economy — Coronavirus. Just as the outbreak in China appeared to be easing, the number of infections has surged in Europe and the United States. Millions of Americans and Europeans have been forced to work from home.

Government officials and academics have revised expectations and warned that unemployment would rise to 7.4 percent by the end of 2020. The U.S. Secretary of the Treasury committed to doing everything in his power to prevent the unemployment rate from rising to 20 percent.

All this is reminiscent of the global financial crisis that happened a decade ago. The crisis started in 2007 as the subprime mortgage market in the United States depreciated. Hundreds of thousands of Americans lost their jobs every month, with the unemployment rate eventually climbing to 10.2%, the highest point in 26 years.

Unemployment has also risen in Spain, the U.K. and many more countries in Asia. Against the backdrop, some of the new graduates of the time chose to continue their studies, to pursue an advanced program, to study abroad or exchange students, while some struggled to find jobs.

What can we learn from past experiences to help us navigate the recession around the corner? And what’s different this time?

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4 Reasons why AI/ML is not the new SaaS?

AI is predicted to be one of the top segments in cloud computing. However, AIaaS and SaaS could not have been more different.

* Hey all, I will be running a couple of “remote AI/ML Masterminds” for people in AI/ML to exchange thoughts and stay connected. I will share more information in the newsletter. Sign up here if you haven’t.

Over 40% of companies plan to deploy AI solutions by the end of this year, according to the recent Gartner Survey. However, the high infrastructure costs and needs for AI/ML expertise are daunting for most organizations. That’s why AIaaS (AI-as-a-service) or MLaaS began to emerge.

Over 40% of companies plan to deploy AI solutions by the end of this year — Gartner.

Popularized by Salesforce, SaaS refers to a licensing model in which software is centrally hosted for customers to access via a browser. SaaS companies also sometimes offer free trials (freemium) to encourage adoption.

Recurring revenue and decreasing costs make the SaaS business model more predictable, profitable and scalable. Therefore, it has become an extremely attractive model for both entrepreneurs and investors.

Since Salesforce went public in 2004, there have been over 70 SaaS IPOs. On average, SaaS companies have significantly outperformed the market. SaaS companies tend to have higher gross margins and lower R&D expenses because they don’t need to support multiple versions or technology stacks.

Similarly, AIaaS allows companies to utilize off-the-shelf AI solutions instead of building their own teams and infrastructures from scratch. AIaaS provides better scalability and flexibility to users by minimizing the upfront investment.

AI will increase 5X from 2019 by the year 2023.

AI enthusiasts also expect that AIaaS business model can help drive adoption and profitability. Gartner’s prediction that cloud-based AI will increase 5X from 2019 by the year 2023 seems to confirm the trend.

Companies from tech giants like Amazon and Microsoft to startups started to offer AIaaS including chatbots, digital assistants, cognitive APIs, and machine learning frameworks. Leveraging these services can simplify the complicated process and lessen the computation burden of deploying AI.

AIaaS seems to be a magic pill to solve all your problems. It enables a rapid and cost-saving AI deployment so companies don’t need to depend on in-house AI experts inhouse who are currently lacking everywhere. Hosting models in the cloud so they can continuously improve with more data also brings out the biggest benefits of ML.

However, the reality is, deploying AIaaS poses many more challenges than SaaS. And here’s why.


Will This Crisis Help set Autonomous AI on the Right Course?

The COVID-19 pandemic serves as a wake-up call to all AI, robotics, and driverless car startups: stop building eye-dazzling demos and talking about the future possibility of general-use AI. Instead...

Millions of Americans have started to work from home amidst the current pandemic. Retailers have struggled with supply while nervous consumers are hoarding everything from toilet paper to hand soap.

Across the globe, Chinese e-commerce giant JD began testing a level-4 autonomous delivery robot in Wuhan and running its automated warehouses 24 hours a day to cope with a surge in demand.

Suddenly, autonomous machines need to be better than just proof of concept. They can no longer depend on onsite engineering support for edge cases. They must be robust enough to work independently across various real-life situations.

In some ways, the epidemic accelerates an automated future that’s already on its way. It has exposed problems that have long existed in the AI venture scene: buzzwords and hype cloud people’s judgment, making it difficult to see real progress.

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The Single Biggest Reason Why AI/ML Companies Fail to Scale?

And Three Things You Can do to Avoid it Happening to you.

“What’s the accuracy of this machine learning (ML) model?”

“How long is the training time?”

“How much training data do you need?”

Working for a company that builds machine learning software for robotics, I hear these questions every day. Machine learning has become a shiny object that everyone wants to pursue. Over 80% of the companies are looking into at least one AI project.

Users generally want to know how long it would take to onboard a new item and how well the models perform or generalize. They want a way to measure the overall cost against performance. However, answers to the above questions don’t give you a full picture. Even worse, they are misleading.

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Three Questions Every ML Product Manager Must Answer

Last week we hosted our second monthly AI & ML PM event in San Francisco. Five product managers who work daily with consumer or enterprise machine learning (ML) products from smaller startups to...

Last week we hosted our second monthly AI & ML PM event in San Francisco. Five product managers who work daily with consumer or enterprise machine learning (ML) products from smaller startups to larger tech companies joined us for a vibrant and diverse discussion.

We covered a wide range of different topics from the types of problems that are best suited for ML, unique challenges in managing ML products, use-case selection as well as prioritization, common mistakes in building ML products, best practices working with ML researchers or engineers, and the essential skills required to succeed as an ML PM.

And here are some of my favorite conversations.

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