Know your startup’s value so you can communicate it to investors

I’ve always told companies that investors have a much easier job than they do. To be good at their jobs, investors have to know how to do math and make decisions. As a business owner, you have to do both while also running your business.

The math piece can seem cumbersome, but it’s vital for understanding whether your company is creating or destroying value. A few simple metrics can demonstrate to investors the health and viability of your company, and they can show you which levers to pull that will best optimize your company for investor interest (and secure a higher price). But before you can ever hope to communicate your business’ value to an investor, you must understand it yourself.

The numbers are simple; it’s the calculations that are complex

Investment math itself is not complicated. In essence, it’s just about understanding whether your company is creating or destroying value by asking:

  • Where is your company investing its financial resourcesMost growing companies invest heavily in sales and marketing or research and development.
  • What is the return on this investment?  For example, how much gross profit (revenue x gross margin percentage) does a given sales and marketing investment produce?
  • How does that number compare to your cost of capital? If it’s higher, your company is creating value. If it’s lower, you’re destroying it.

Investors use this information to determine if their return would be higher than their expectation (e.g., 15% hurdle rate), should you continue down your current path of creating or destroying value. Then, they make their decision based on that calculation.

A caveat I’ll add here is that it’s not necessarily a deal-breaker if your company is declining in value. Oil rigs, after all, are considered investment assets, even though they are perpetually declining and will eventually run out (i.e., destroy all of their value). Although this article focuses on calculations that demonstrate value creation, all investment assets can be financed at the right price.

A deep dive into calculating value

One of the best metrics you can use to demonstrate value creation is your cohort-level return on investment. It’s a calculation most investors are familiar with, but it may not be as straightforward to companies who don’t see it as often. Again, while the metrics and concepts of investment math are simple, it’s the process of getting there that requires complex analysis.

Whether you are evaluating these metrics yourself or bringing in outside counsel to assist you, use the process below to show investors you are creating value.

Determine which information to analyze

The first step in calculating value is to understand which information from your income and cash flow statements to analyze as “investments.”

Start by dividing your capital allocation into three main buckets: short-term investments, long-term investments and expenses. In general, short-term investments will be the ones you want to focus on, but it’s helpful to walk through each.

  • Short-term investments (pay back within 24 months)

Decide which type of investor to target for raising capital

I recently wrote Should you raise venture capital from a traditional equity VC or a Revenue-Based Investing VC? Since then, I’ve talked with a number of other firms and greatly expanded my database: Who are the major Revenue-Based (RBI) Investing VCs?

That said, venture capital is just one of many options to finance your business, typically the most expensive. The broader question is, what type of capital should you raise, and from whom?  

I find many CEOs/CFOs default to approaching investors who have the most social media followers; who have spent the most money sponsoring events; or whom they met at an event. But, fame and the chance that you met someone at a conference do not logically predict that investor is the optimal investor for you. In addition, the best-known investors are also the ones who are most difficult to raise capital from, precisely because they get the most inbound.

The first step is to decide the right capital structure for your financing. Most CFOs build an Excel model and do a rough comparison of the different options. Some firms provide tools to do this online, e.g., Capital’s Cost of Equity estimator; Lighter Capital’s Cost of Capital Calculator; 645 Ventures’ cap table simulator. A similar, open-source, highly visual tool focused on VC is Venture Dealr.

For each of the major categories of investors, you can find online databases of the major providers. Major options include:

  • Traditional equity venture capital and private equity. For early-stage startups in particular, I suggest Foundersuite*, Samir Kaji’s Master List of US Micro-VC’s and Shai Goldman’s database of VC funds at/below $200M in size. You can find other databases of investors at AngelList, CB Insights, Crunchbase, Dow Jones VentureSource, Pitchbook, Preqin, and Refinitiv Eikon
  • Revenue-based investing VC. See Who are the major Revenue-Based Investing VCs?
  • Venture debt. See FindVentureDebt and this comparison guide of debt options for SAAS companies. Watch out for double dipping, or interest on interest.
  • Merchant cash advances/factoring. See Debanked’s list.
  • Small Business Association Loans. Ravi Bhagavan, Managing Director, BRG Capital Advisors, said, “a low-cost and often convenient form of capital for small businesses is SBA loans, which are guaranteed by the Small Business Administration. SBA loans are $5k – $5M in size and are typically at a lower cost of capital compared to alternate forms of debt, since up to 85% of the loan is guaranteed by the SBA. Additionally, SBA loans have longer payment periods (5-25 years) than traditional forms of financing and come with less onerous ongoing disclosure requirements. However, SBA loans typically require a personal guarantee (PG) from the founder(s), who are scrutinized for income and credit history at the time of application. PGs can be quite daunting to founders because it puts their personal assets, including homes and investment accounts, on the line. SBA loans are available through SBA-approved banks and SBIC funds. SBICs make equity and debt investments of size $100k – $10M in qualifying small businesses. A good resource for looking up SBICs is here.” 
  • Crowdfunding, e.g., Republic*, Indiegogo*.  This option provides you capital and also market validation for desire for your product.  

Once you decide on the right category of investor, here are some tools I suggest using to find the optimal capital provider:

  • Most important, reference checking. I have a whitelist of investors I recommend to my portfolio — and a blacklist which I guide them to avoid.
  • Comparison websites: BitX, Fundera, GUD Capital, Lencred.com, Lendio, and NerdWallet Small Business Loans are all resources which can help you evaluate different options for small business financing, typically within a defined category of financing. Braavo specializes in financing app companies.
  • Financing supermarkets: Most investment firms start out with one asset class, and then over time they often add others. There are countless examples, e.g., most of the large B2B banks, Kapitus, Kalamata Capital, United Capital Source, etc. These firms can give you an apples-to-apples comparison of what different capital forms, albeit all from one provider, will cost you.

California’s new data privacy law brings U.S. closer to GDPR

Data privacy has become one of the defining business and cultural issues of our time.

Companies around the world are scrambling to properly protect their customers’ personal information (PI). However, new regulations have actually shifted the definition of the term, making everything more complicated. With the California Consumer Privacy Act (CCPA) taking effect in January 2020, companies have limited time to get a handle on the customer information they have and how they need to care for it. If they don’t, they not only risk being fined, but also loss of brand reputation and consumer trust — which are immeasurable.

California was one of the first states to provide an express right of privacy in its constitution and the first to pass a data breach notification law, so it was not surprising when state lawmakers in June 2018 passed the CCPA, the nation’s first statewide data privacy law. The CCPA isn’t just a state law — it will become the defacto national standard for the foreseeable future, because the sheer numbers of Californians means most businesses in the country will have to comply. The requirements aren’t insignificant. Companies will have to disclose to California customers what data of theirs has been collected, delete it and stop selling it if the customer requests. The fines could easily add up — $7,500 per violation if intentional, $2,500 for those lacking intent and $750 per affected user in civil damages.

Evolution of personal information

It used to be that the meaning of personally identifiable information (PII) from a legal standpoint was clear — data that can distinguish the identity of an individual. By contrast, the standard for mere PI was lower because there was so much more of it; if PI is a galaxy, PII was the solar system. However, CCPA, and the EU’s General Data Protection Regulation GDPR, which went into effect in 2018, have shifted the definition to include additional types of data that were once fairly benign. The CCPA enshrines personal data rights for consumers, a concept that GDPR first brought into play.

The GDPR states: “Personal data should be as broadly interpreted as possible,” which includes all data associated with an individual, which we call “contextual” information. This includes any information that can “directly or indirectly” identify a person, including real names and screen names, identification numbers, birth date, location data, network addresses, device IDs, and even characteristics that describe the “physical, physiological, genetic, mental, commercial, cultural, or social identity of a person.” This conceivably could include any piece of information about a person that isn’t anonymized.

With the CCPA, the United States is playing catch up to the GDPR and similarly expanding the scope of the definition of personal data. Under the CCPA, personal information is “information that identifies, relates to, describes, is capable of being associated with, or could reasonably be linked, directly or indirectly, with a particular consumer or household.” This includes a host of information that typically don’t raise red flags but which when combined with other data can triangulate to a specific individual like biometric data, browsing history, employment and education data, as well as inferences drawn from any of the relevant information to create a profile “reflecting the consumer’s preferences, characteristics, psychological trends, preferences, predispositions, behavior, attitudes, intelligence, abilities and aptitudes.”

Know the rules, know the data

These regulations aren’t checklist rules; they require big changes to technology and processes, and a rethinking of what data is and how it should be treated. Businesses need to understand what rules apply to them and how to manage their data. Information management has become a business imperative, but most companies lack a clear road map to do it properly. Here are some tips companies can follow to ensure they are meeting the letter and the spirit of the new regulations.

  • Figure out which regulations apply to you

The regulatory landscape is constantly changing with new rules being adopted at a rapid rate.  Every organization needs to know which regulations they need to comply with and understand the distinctions between them. Some core aspects CCPA and GDPR share include data subject rights fulfillment and automated deletion. But there will be differences so having a platform that allows you to handle a heterogenous environment at scale is important.

  • Create a privacy compliance team that works well with others

What AI startups need to achieve before VCs will invest

Funding of artificial intelligence-focused companies reached approximately $9.3 billion in the U.S. in 2018, an amount that will continue to rise as the transformative impact of AI is realized. That said, not every AI startup has what it takes to secure an investment and scale to success.

So, what do venture capitalists look for when considering an investment in an AI company?

What we look for in all startups

Some fundamentals are important in any of our investments, AI or otherwise. First, entrepreneurs need to articulate that they are solving a large and important problem. It may sound strange, but finding the right problem can be more difficult than finding the right solution. Entrepreneurs need to demonstrate that customers will be willing to switch from what they’re currently using and pay for the new solution.

The team must demonstrate their competence in the domain, their functional skills and above all, their persistence and commitment. The best ideas likely won’t succeed if the team isn’t able to execute. Setting and achieving realistic milestones is a good way to keep operators and investors aligned. Successful entrepreneurs need to show why their solution offers superior value to competitors in the market — or, in the minority of cases where there is an unresolved need — why they’re in the best position to solve it.

In addition, the team must clearly explain how their technology works, how it differs and is advantageous relative to existing competitors and must explain to investors how that competitive advantage can be sustained.

For AI entrepreneurs, there are additional factors that must be addressed. Why? It is fairly clear that we’re in the early stages of this burgeoning industry which stands to revolutionize sectors from healthcare to fintech, logistics to transportation and beyond. Standards have not been settled, there is a shortage of personnel, large companies are still struggling with deployment, and much of the talent is concentrated in a few large companies and academic institutions. In addition, there are regulatory challenges that are complex and growing due to the nature of the technology’s evolutionary aspect.

Here are five things we like to see AI entrepreneurs demonstrate before making an investment:

Demonstrate mastery over their data and its value: AI needs big data to succeed. There are two models: companies can either help customers add value to their data or build a data business using AI. In either case, startups must demonstrate that the data is reliable, secure and compliant with all regulatory rules. They must also demonstrate that AI is adding value to their own data — it must explain something, derive an explanation, identify important trends, optimize or otherwise deliver value.

With the sheer abundance of data available for companies to collect today, it’s imperative that startups have an agile infrastructure in place that allows them to store, access and analyze this data efficiently. A data-driven startup must become ever more responsive, proactive and consistent over time.

AI entrepreneurs should know that while machine learning can be applied to many problems, it may not always yield accurate predictions in every situation. Models may fail for a variety of reasons, one of which is inadequate, inconsistent or variable data. Successful mastery of the data demonstrates to customers that the data stream is robust, consistent and that the model can adapt if the data sources change.

Entrepreneurs can better address their customer needs if they can demonstrate a fast, efficient way to normalize and label the data using meta tagging and other techniques.

Remember that transparency is a virtue: There is an increased need in certain industries — such as financial services — to explain to regulators how the sausage is  made, so to speak. As a result, entrepreneurs must be able to demonstrate explainability to show how the model arrived at the result (for example, a credit score). This brings us to an additional issue about accounting for bias in models and, here again, the entrepreneur must show the ability to detect and correct bias as soon as they are found.

The AI stack that’s changing retail personalization

Consumer expectations are higher than ever as a new generation of shoppers look to shop for experiences rather than commodities. They expect instant and highly-tailored (pun intended?) customer service and recommendations across any retail channel.

To be forward-looking, brands and retailers are turning to startups in image recognition and machine learning to know, at a very deep level, what each consumer’s current context and personal preferences are and how they evolve. But while brands and retailers are sitting on enormous amounts of data, only a handful are actually leveraging it to its full potential.

To provide hyper-personalization in real time, a brand needs a deep understanding of its products and customer data. Imagine a case where a shopper is browsing the website for an edgy dress and the brand can recognize the shopper’s context and preference in other features like style, fit, occasion, color etc., then use this information implicitly while fetching similar dresses for the user.

Another situation is where the shopper searches for clothes inspired by their favorite fashion bloggers or Instagram influencers using images in place of text search. This would shorten product discovery time and help the brand build a hyper-personalized experience which the customer then rewards with loyalty.

With the sheer amount of products being sold online, shoppers primarily discover products through category or search-based navigation. However, inconsistencies in product metadata created by vendors or merchandisers lead to poor recall of products and broken search experiences. This is where image recognition and machine learning can deeply analyze enormous data sets and a vast assortment of visual features that exist in a product to automatically extract labels from the product images and improve the accuracy of search results. 

Why is image recognition better than ever before?

retail and artificial intelligence

 

While computer vision has been around for decades, it has recently become more powerful, thanks to the rise of deep neural networks. Traditional vision techniques laid the foundation for learning edges, corners, colors and objects from input images but it required human engineering of the features to be looked at in the images. Also, the traditional algorithms found it difficult to cope up with the changes in illumination, viewpoint, scale, image quality, etc.

Deep learning, on the other hand, takes in massive training data and more computation power and delivers the horsepower to extract features from unstructured data sets and learn without human intervention. Inspired by the biological structure of the human brain, deep learning uses neural networks to analyze patterns and find correlations in unstructured data such as images, audio, video and text. DNNs are at the heart of today’s AI resurgence as they allow more complex problems to be tackled and solved with higher accuracy and less cumbersome fine-tuning.

How much training data do you need?

The AI stack that’s changing retail personalization

Consumer expectations are higher than ever as a new generation of shoppers look to shop for experiences rather than commodities. They expect instant and highly-tailored (pun intended?) customer service and recommendations across any retail channel.

To be forward-looking, brands and retailers are turning to startups in image recognition and machine learning to know, at a very deep level, what each consumer’s current context and personal preferences are and how they evolve. But while brands and retailers are sitting on enormous amounts of data, only a handful are actually leveraging it to its full potential.

To provide hyper-personalization in real time, a brand needs a deep understanding of its products and customer data. Imagine a case where a shopper is browsing the website for an edgy dress and the brand can recognize the shopper’s context and preference in other features like style, fit, occasion, color etc., then use this information implicitly while fetching similar dresses for the user.

Another situation is where the shopper searches for clothes inspired by their favorite fashion bloggers or Instagram influencers using images in place of text search. This would shorten product discovery time and help the brand build a hyper-personalized experience which the customer then rewards with loyalty.

With the sheer amount of products being sold online, shoppers primarily discover products through category or search-based navigation. However, inconsistencies in product metadata created by vendors or merchandisers lead to poor recall of products and broken search experiences. This is where image recognition and machine learning can deeply analyze enormous data sets and a vast assortment of visual features that exist in a product to automatically extract labels from the product images and improve the accuracy of search results. 

Why is image recognition better than ever before?

retail and artificial intelligence

 

While computer vision has been around for decades, it has recently become more powerful, thanks to the rise of deep neural networks. Traditional vision techniques laid the foundation for learning edges, corners, colors and objects from input images but it required human engineering of the features to be looked at in the images. Also, the traditional algorithms found it difficult to cope up with the changes in illumination, viewpoint, scale, image quality, etc.

Deep learning, on the other hand, takes in massive training data and more computation power and delivers the horsepower to extract features from unstructured data sets and learn without human intervention. Inspired by the biological structure of the human brain, deep learning uses neural networks to analyze patterns and find correlations in unstructured data such as images, audio, video and text. DNNs are at the heart of today’s AI resurgence as they allow more complex problems to be tackled and solved with higher accuracy and less cumbersome fine-tuning.

How much training data do you need?

The case against Grace Hopper Celebration

We’ve heard the criticisms that there were fewer black women speakers than white men at Grace Hopper Celebration in the past, but event organizers heard our complaints and created an entire conference pathway and new grants for “women of color from underrepresented groups and women from untapped pathways.”

We feel better now that our panels include hijabi and transgender women. The work done by women of color and others to broaden our understanding of diversity and inclusion in these spaces cannot go without recognition.

But at the end of it all, my question after a long day of panels and handshakes is, why? What are we really doing here? What ideas are we planting and fostering behind our massive paywall? Are we breaking down barriers for future generations, or simply congratulating ourselves for reaching the upper echelons of women who have vaulted them? Are we pushing to change toxic systems, or asking women to change themselves to navigate them?

Who are we benefiting and elevating with our efforts?

What we can say about the majority of corporate women is that we are currently wealthy and educated. What we can say about many corporate women in the American tech sector is that we are white or Asian-American, heterosexual, abled and a plethora of other dimensions of privileged. Through most of our women in tech events, we self-select into a space where others are educated like us, or aspire to be educated like us, and erect barriers to the tune of thousands of dollars and up to a week off from work/school. Conferences tout scholarships to offset the cost of attendance for the up and coming generation of tech women, but often times those students are required to show existing proclivities to STEM.

Extending resources to students who already have exposure to STEM biases our outreach to those with privilege already; low-income schools in California are four times less likely to offer AP computer science A courses than high-income schools, according to an independent study done by the Kapor Center. Unfortunately, it’s hard to make a case to allocate resources any other way when these events rely on corporate sponsorship and attendance and a business case must be made for return on investment (re: tech talent pipeline).

The following is a (non-comprehensive) list of recommendations for improving the way we build power as women in tech:

1. Increase economic accessibility by supporting smaller conferences

Attending a conference costs more than its ticket price, so increasing accessibility must be more comprehensive than offering scholarships. Some examples of questions to ask ourselves as organizers: will attendees with mobility needs spend more than others for their travel and lodging? Are students who receive financial aid more fearful about taking days off?

At first glance, these questions seem like they can be addressed by throwing money at the problem — more scholarships for disabled and lower-income attendees, easy! But trying to level the playing field in this manner is an exercise in futility; bringing a few lucky underprivileged people into our space does little to address the underlying hierarchy. A better way to look at it is to ask how we can make the benefits available to those of us with privilege equally accessible to those with less.

Smaller, regional events usually cost less to host and attend and spread value more widely. New speakers can practice leadership, attendees can network with professionals in their local area, and students can receive more attention and mentorship. Resources move into local communities and nonprofits instead of into recruiting pipelines for tech giants. Some examples of regional conferences targeting minorities but with more granular goals are CodeNewbies, AfroTech and Take Back Tech. These are the efforts we need to support if we want to effectively grow power in our communities that don’t already have it.

2. Focus on systemic change

If every takeaway from your event is how women can change their actions, then it might be a shallow event. Women and others are not held down because we cry at work, or because we take maternity leave, but because of how those around us perceive those things. Challenging ourselves to change our perceptions is more difficult but ultimately more valuable than stifling our authentic choices and personality to be more convenient.

It’s important to ask ourselves why we, a group of traditionally mistreated professionals, are gathering. Why are we sharing our stories of vulnerability and to what end are we building our collective strength? Marginalized people coming together helps consolidate our power so that we can change the system we’re in. It’s a form of collective action — when dozens of women want maternity leave, their employer is more inclined to provide it than when one woman asks alone. When multiple women talk to each other and realize they’ve been harassed by the same co-worker, they feel empowered to do something about it. We organize and gather so we can change injustices.

Conversations where the whole room may not agree with you can be more impactful than the ones that earn you the most laughs and nods. Challenge your audience; discomfort is where we grow. If you’re holding an event for allies, make them earn the title of ally. Catch yourself when you fall to the instinct of making everyone feel good when your goal is to make a difference.

3. Support grassroots-led change instead of corporate-lead change

Let’s not forget who the greatest winners are after a Women @ Qualcomm weekend, a Microsoft Women in Technology Event or Grace Hopper Celebration — the event organizer.

They recruit from the highly qualified pool of attendees while cultivating positive PR for valuing diversity, gaining much more overall than any one individual, though a single person may stand to gain from the opportunity. Companies have made a major push for students and employees from underrepresented groups to stay in the “tech talent pipeline.” As from any affirmative action, there are positive outcomes from that, but there are also studies that find that the pipeline has not addressed deeper issues with workplace cultures, power asymmetries, and harassment.

Put another way, companies often recruit diversity in ways that bring value to themselves without taking responsibility for the quality of life of those within the pipeline. It’s important to remind ourselves that these are not purely philanthropic goals for corporations and that recruitment and retention are to their benefit. At the very least, we’re entitled to substantive policy change in exchange for our labor.

Grassroots and community-led change is better than corporate-led change if our goal is to empower and further the opportunities for women. We must create opportunities for leadership and support efforts that truly build our strength. We should be fearless in asking for real change. By all means, do the work within the companies and within the mainstream conferences if that empowers you, but be wary of the ways that you might be keeping power in already powerful communities and keep your goals in sight. Don’t be afraid to ask why, even for things that seem to have the best of intentions. Even well-meaning systems can perpetuate harmful power dynamics if those of us within them aren’t constantly questioning and pushing back.

D2C companies deliver customer delight and simplicity

As the holiday season approaches, I can feel the tension in the air: how do I make my gifts stand out?

Thankfully, there are so many fun direct to consumer (D2C) categories — from bath salts to plants, to even organic fertilizer.

A New York City-based VC firm once asked us, “there are so many products that are getting launched in the direct to consumer route. It’s good that you track them. But can you tell us which segment is likely to go direct to consumer?” In other words, they were asking us to be psychics.

We aren’t, but I never let that question go.

There are many reasons why a brand can go D2C. You could unbundle every category on Amazon and there could be a case made for going direct to consumer. Several brands that do just that, but Amazon is not the obvious place to look for all answers.

Let’s take the example of plants and fertilizer. I want to gift a plant this holiday season, but I have two problems: I don’t know which plant to pick for my friend because I don’t know his preferences, and even if I find the right plant, I don’t know whether he’ll be able to keep it alive.

Generally, when people consider purchasing a plant, it’s not because they woke up after having a startling dream about a fern or a ficus that won its heart — it’s more likely that they looked at an empty balcony while sipping their morning coffee and thought it needed a touch of green. People aren’t buying plants; they’re buying better visuals, and a potted palm tree is a vehicle to their preferred emotional state.

But what if he’s unable to take care of the plants? Should I just buy some really good candles instead? Rooted, an online plant store, sorts its offerings using criteria like the amount of light required and how frequently a plant needs to be watered. As a result, I found Tim, a snake plant that’s “virtually indestructible and adaptable to almost any conditions.”

Some products are complex. No two plants are the same, and no two plant buyers are identical, either. It’s complicated. You can walk into a nursery and get the plant you are drawn towards and read the instructions wrapped inside, but the onus is still on you to help it thrive.

Companies like Rooted and Bloomscape know that you are buying an emotional state, so they help you avoid post-purchase dissonance. Instead, they offer a customer-focused product experience that starts with choosing the right plant and includes an onboarding kit that educates users, all contained within a continuous positive feedback loop delivered through carefully designed, friendly, educational content.

By going direct to consumer, brands can personalize the buying experience, optimize customer enjoyment and use, educate them at the right cadence, and ultimately, help them successfully harvest the emotions they were seeking.

This approach works for any category that is perceived to be complex. Whether it’s coffee, wine, food supplements or plants, these products are complex experiences that need to be tailored to customers, and the education process could be overwhelming. Brands that get it right can achieve the right experience by going direct to consumer.

People are generally resistant to change, but they love brands that can help them find a better version of themselves. Fear of the unknown and making the wrong decision ends in post-purchase dissonance; bad brands introduces dissonance, while a good brand attenuates this fear. The good or the bad is determined by the onboarding experience, intuitive design, content, online support, customer reviews and after-sales experience.

Like batteries that store power, brands store emotional states, positive and negative; a consumer’s interaction with Comcast taps into a different range of emotions than a visit to an Apple Store.

Creating comfortable footwear, for example, requires complex engineering; with unique types for walking, cycling and running, how do you figure which one is right for you? Nike Fit, an app released this year, uses AI to help customers find the optimal fit for their foot.

“Three out of every five people are likely to wear the wrong size shoe,” the company said in a statement. “Length and width don’t provide nearly enough data to get a shoe to fit comfortably. Sizing as we know it is a gross simplification of a complex problem.” The AI even tells you if your right foot is larger than your left and recommends the best sneaker; emotions unlocked! It’s no wonder Nike’s doubling down on its D2C channels.

Ultimately, a brand that performs well is a brand that has recognized and solved a customer’s problem; ecommerce and D2C are mediums that to do precisely that. A good brand offers good experience design that brings simplicity to a complex product, magically making it seem familiar.

Relocating Indonesian capital will impact nation’s startup ecosystem

Recently reelected, Indonesian President Joko Widodo announced a desire to move the nation’s capital from Jakarta to the East Kalimantan region, citing environmental concerns, the most exigent of these being the fact that Jakarta is literally sinking due to the uncontrolled extraction of groundwater. Widodo said he wished to separate Indonesia’s government from its business and economic hub in Jakarta.

However, what would a move from Jakarta do to Indonesia’s burgeoning startup economy?

Shifting administrative governmental hubs

According to Widodo, studies have determined that the best site for the proposed new capital is between North Penajam Paser and Kutai Kertanegara, both located in East Kalimantan. The basis of this selection is due to studies highlighting the region’s relative protection from natural disasters, especially when compared to other regions. This would definitely be a benefit for the governmental heart of Indonesia, ensuring continuous administrative functions in a disaster-prone region. Other governments have separated administrative centers from their economic hubs with varying degrees of success, with some examples being Brazil’s creation of Brasília, as well as Korea’s projected move from Seoul to Sejong.

What is most interesting to note from prior examples is that these newer branched-out cities are non-surprisingly, heavily government-centric. In Brasília, roles tied to the government make up nearly 40% of all jobs, while in Sejong, a lack of facilities like public transit and commercial mall space cause many to commute into Sejong for government work, instead of permanently settling in the area. Given the semi-undeveloped nature of East Kalimantan, these anecdotes are quite troubling if the government is actually moving to North Penajam Paser or Kutai Kertanegara.

These facts raise the question of economic impacts of such governmental moves. In fact, one may even opine that while these moves do allow for governmental growth, ultimately, they may hurt the country economically due to a divestment between both government and economic hubs. In this specific instance, it is most important to analyze the impact of such a move on Indonesia’s startup economy, as the nation is one the world’s leaders in startup growth.

Indonesia’s startup economy

Indonesia has emerged as a startup hub within Southeast Asia in recent years, with its population of over 260 million marking it as the world’s fourth-most populous country. Additionally, Indonesia’s mobile-first population has enabled the full embrace of the internet era, with 95% of all internet users in Indonesia connected to the web via a mobile device.

Similarly, startup growth has boomed in the island archipelago, with several Indonesian-based unicorns disrupting local, regional, and global economies. Softbank-backed ecommerce giant Tokopedia is currently in talks for a pre-IPO funding round, while emerging super-app Gojek controls significant portions of the ride-sharing industry in Asia, simultaneously expanding into separate industries to include digital payments, food delivery, and even video-streaming. Additionally, online travel portal Traveloka (in which Expedia has a minority stake) has recently entered the financial services space, furthering its impact within Asia. These specific examples of high-growth startups demonstrate a population hungry for innovation, further driving the developing startup economy.

I ran digital ads for a presidential campaign, and Twitter is right to ban them

As the digital director for Congressman Seth Moulton’s 2020 presidential campaign, I was responsible for everything the campaign did on the internet: the emails you claim to hate, the videos we hoped would go viral, the online infrastructure that supported organizers in the field, and more. But our biggest investment of both time and money, by far, was in digital advertising.

For our campaign and many others, digital ads were the single biggest expense outside of payroll. Yet these ads are terrible for campaigns, toxic for democracy and are even bad for the companies who profit off them. Last week, Twitter CEO Jack Dorsey took a bold first step in banning political ads — Facebook CEO Mark Zuckerberg and Google CEO Sundar Pichai should follow suit.

Digital ads are one of the most important channels for acquiring new supporters and serving them that all-important question: “Will you chip in $10, $5, or whatever you can to support our campaign? Even $1 helps!” When the Democratic National Committee announced in February that presidential candidates would need a minimum of 65,000 individual donors to qualify for the first two debates, acquiring these small dollar donors became a do-or-die priority for campaigns.

The trouble is, when 25 campaigns are competing in a Democratic donor market that had just five competitors in 2016, and when each campaign is desperate to acquire new donors, prices go up. Way up.

We — and I suspect many others — routinely ran what were supposed to be revenue-generating ads at a loss, spending $10, $20, or even $30 in order to acquire one new donor and their contribution of as little as $1. This is a terrible deal for campaigns: they hemorrhage cash in order to lose money acquiring more, costing weeks or months of valuable runway, all while Facebook pockets the difference. At scale, the consequence is massive: the remaining 18 Democratic candidates have already spent over $53 million on Facebook and Google this cycle, most of it these kinds of ads.

This is $53 million — plus millions more from prolific former candidates like Sen. Kirsten Gillibrand and Gov. Jay Inslee — which would have otherwise been invested in infrastructure to turn out voters and help Democrats in November no matter who is the nominee. Instead, it went straight into Facebook and Google’s coffers.

These ads are toxic to our democracy.

Due to short online attention spans, the character limits that enforce them and the engagement algorithms that act as gatekeepers to the digital world, campaigns must distill complex issues down to a two sentence pseudo-essence that would leave even debate moderators unsatisfied. And if you want to have a prayer of anyone clicking on your ad, it had better be as inflammatory as possible — people click when they’re angry.

The easiest way to do this is to simply make things up, something most campaigns would never consider, but which Zuckerberg made clear in congressional testimony this week his platform would happily enable. Companies like Facebook and Google force us to present voters with a world that is black and white, in which all nuance is distraction, and in which civic engagement is something that can be done from your phone for just $1 (Unless you’d like to make this a monthly recurring donation? Your support has never been more crucial!). This does not an informed, healthy democracy make.

Political ads are not even good for the companies that serve them. On a quarterly earnings call the same day as Dorsey’s announcement, Zuckerberg estimated that political ads run by candidates would make up just 0.5% of Facebook’s 2020 revenue. Assuming similar performance to the previous 12 months, in which Facebook earned $66 billion, this would be about $330 million in political ad revenue.

In exchange, Facebook has earned itself years of bad PR, increased regulatory risk as congressional leaders are beginning to see it as a national security problem, and even existential risk as leading presidential candidate Sen. Elizabeth Warren has vowed to break up the company if elected. All over revenues that hardly even justify the opportunity cost of Zuckerberg’s hours of preparation for congressional hearings.

So who benefits from these kinds of ads? Those who want to create a chaotic information environment in the United States in which facts are subjective, reality is ephemeral and the only information you can trust comes from the people manipulating social media to feed it to you. It is therefore no surprise that one of the first organizations to condemn Dorsey’s decision was the Russian state-sponsored media outlet Russia Today.

Presented with a choice between minuscule revenues and existential risk, between patching a bug in American democracy and abetting Russian propaganda, Dorsey made a wise choice for both his bottom line and his country. Zuckerberg and Pichai would do well to follow his lead.