A Deep Dive into Elon Musk’s Investments: The Makings of a Billionaire

Key Highlights

Career History
Goes All-In with His Businesses
Creates Ecosystems around Himself
Creative Financing Methods
Polarizing Opinions

Since founding Zip2 over 22 years ago, Elon Musk has gone on to start another two businesses and invest in countless others. He is one of the most admired leaders in the world, gaining respect for the ambitious nature of his activities and sheer dedication towards ensuring that they succeed.

His endeavors naturally receive a lot of press, and countless armchair analysts pore over the viability self-driving cars or if mankind can colonize Mars. In this article, I am going to leave those pontifications aside and purely focus on Musk the investor. What are the tactics and game plans that he follows when funding his companies—or, indeed, other people’s companies? What are the lessons we can learn from his investment track record and success? If you are interested in a deeper look into his background and the companies that he is involved in, I highly recommend Tim Urban’s writings on Musk and his businesses.

To do this, I will first lay out the career of Elon Musk’s investments and entrepreneurial activities from a high-level perspective, and then review the trends and tactics that he has followed. In researching this, I found that Musk clearly learns from his mistakes and experiences, adapting his future actions thereafter.

A Concise Timeline of Musk’s Investing Career to Date

Phase 1. Musk the Early-stage Entrepreneur

After dropping out of Stanford in 1995, Musk founded Zip2 with his brother Kimbal using $28,000 borrowed from their father. It was intended to be an online consumer version of the Yellow Pages linked to mapping visuals. After VC Mohr Davidow later invested $3 million, Musk was relegated to the role of CTO and the business pivoted towards a B2B offering aimed at newspapers. With his ownership diluted to 7%, his influence waned over the direction of the business and eventually in 1999 it sold to Compaq for $307 million; Musk earned $22 million from the deal.

In November of the same year, Musk invested $10 million of his Zip2 proceeds into founding X.com, one of the first attempts at online banking. In March of 2000, it merged with Peter Thiel’s Confinity platform, which was its major competitor at the time. Later that year, Musk was again ousted as CEO, this time by Thiel (but he remained on the board) and the business rebranded to the now ubiquitous PayPal in 2001. By February of 2002, the business had IPOed, but then eight months later it was bought by eBay for $1.5 billion and Musk pocketed $180 million from the sale.

Phase 2. Musk the Multi-tasker

With a batting average of 1.000 from successful startups, you would have expected Musk to perhaps take a step back after the PayPal sale. Instead he entered into a grueling phase of his career which can be characterized by the running of a string of concurrent entrepreneurial ventures.

As with X.com, he didn’t waste any time after the PayPal sale and he established SpaceX in the same year. April 2004 was when he first dipped his toes into the world of Tesla through a $6.35 million investment in its Series A round. At this point in time, he was also continually investing into the formation of SpaceX and he was following-on in future funding rounds of Tesla with rising largesse and frequency. Despite not “founding” Tesla, his influence rose continuously, stemming from his invaluable operational and financial input.

Moving on to 2006, following Musk’s suggestion and encouragement, his cousins Peter and Lyndon Rive established SolarCity. Musk invested seed capital into the business and assumed the role of chairman. He followed on in three more subsequent rounds between 2007 and 2012.

2008 was a nadir for Musk and potentially the most pivotal point of his career. He was fighting on three fronts to build companies with significant scope and ambition. From a liquidity perspective, his situation reached a flashpoint, with the seemingly infinite PayPal money now reaching its end. A divorce from his first wife also added a $20 million bill that year. Through loans from friends, Musk managed to continue on his course and some unexpected gifts arrived via the first proceeds from his angel investing portfolio (Everdream and Game Trust).

Tesla’s IPO in 2010 marked the end of this era and allowed Musk to consolidate financially and move onwards with his evolving strategy.

Phase 3. Musk the Conglomerator

With Tesla starting to turn the corner, now was the turn of SpaceX, which began to get positive traction through successful trials and contracts. The realms of his three companies began to slowly intertwine, in terms of deals between them and their common narrative of forging a sustainable future for mankind. Indeed, despite IPOing in 2012, four years later SolarCity merged with Tesla, with the apparent synergies and vertical integration opportunities receiving blessing from 85% of shareholders.

In this period, Musk’s angel investing takes a turn towards AI and biotech focused startups. He parlayed a significant return of $92 million from an investment in DeepMind (bought by Google) and made other investments into the sectors via Halcyon Molecular, Vicarious, and NeuroVigil between 2010 and the present day.

His net worth during this period grew exponentially, from $2 billion in 2012 to $16 billion in 2017 (Forbes). Tesla’s share price also skyrocketed after the introduction of the Model 3 paved a way for the beginning of mass-market consumer electric vehicles. SpaceX, while still private, raised funding in July 2017 at a $21.2 billion valuation. The rapid growth of his net worth over the last six years is shown below:

Chart 1: Elon Musk's Net Worth over Time

Musk’s Investments by the Numbers

Below is a chronological timeline of Musk’s investing activities, showing inflows and outflows. The data was sourced via a triangulation of CrunchbasePitchbook, and news article sources:

Chart 2: Timeline of Elon Musk's Investing Activities

A summary of each company that he has invested in or founded is shown with more detail in the table below. The only evidence of a Musk investment being a complete flameout is that of Halcyon Molecular, which burned through investors’ cash and shuttered within 2 years. Of his exits, two were strong successes (Everdream and DeepMind) and two returned their capital (Game Trust and OneRiot). Of his current “live” portfolio, he will be sitting on some significant paper gains from his investment in Stripe. At the other end of the spectrum, Mahalo which now exists as Inside.com has been an investment of over 10 years for Musk and, despite various pivots, is unlikely to provide a successful outcome anytime soon.

Table 1: Summary of Elon Musk’s Investments

Outcome color coding: green = successful, yellow = average/flat, red = bad

Date of First Investment Age Company Total Invested (a) Final Outcome Musk’s Proceeds Sources
Summer 1995 24 zip2 $28k M&A – Compaq 1999 $22mn 12
March 1999 27 X.com/PayPal $15mn IPO, then M&A – eBay 2002 $180mn 123
December 2002 31 SpaceX $100mn (b)
January 2003 31 Everdream $1m M&A – Dell 2007 $15mn 12
April 2004 32 Tesla $70mn IPO – 2010 $15mn (c)
November 2005 34 Game Trust $1mn M&A – Real Networks 2007 $1.5mn 12
September 2006 35 SolarCity $35mn IPO – 2012, then M&A to Tesla – 2016 123
January 2007 35 Mahalo/Inside $2mn 1
June 2007 36 OneRiot $2.5mn M&A – Walmart 2011 $2.5mn 12
August 2010 39 Halcyon Molecular $10mn Closed in 2012 $0mn 12
February 2011 39 DeepMind $1.65mn M&A – Google 2014 $92mn 1, (d)
March 2011 39 Stripe $10.2mn 1, (e)
March 2014 42 Vicarious $2mn 1
May 2015 43 NeuroVigil $500k 1
January 2017 45 Hyperloop TT $15mn (e)
TOTAL $266mn $328mn


1. When individual investments are not disclosed | assume that Musk invests at the round size divided by number of investors. If there is a defined lead investor, it invests a larger cheque size
2. SpaceX: I have assumed that Musk invested $100 million in 5 installments over 2002-6, Source
3. Tesla (2004 Series A: $6.35 million, 2005 Series B: $10 million, The rest is divided across future rounds (Crunchbase/Pitchbook sources) adding up to the rest of the $70 million he claimed to have invested, up to 2008. 2010 IPO: $15 million
4. DeepMind: As per Pitchbook data, Musk owned 14.16% of stock after its last funding round. Assuming exit proceeds shared equally pro-rata
5. Stripe (Series D) & Hyperloop: Pitchbook data

Lessons Learned

1. He goes all-in

Don’t cede control

I see Zip2 and PayPal as being vital lessons in Musk’s approach to management and control that he learned from and applied into his future ventures. The introduction of external venture capital and dilution of his own shares meant that Musk was powerless to resist being ousted as CEO of Zip2. Despite becoming a rich man from the successful sale, its outcome disappointed Musk, as he was unable to carve the company into his vision. Likewise at PayPal, he had significant largesse from his shares and status of founder of X.com, but again he was ousted as CEO. This happened while he was on holiday in Australia, over a dispute over whether to use Microsoft or Unix technology for the platform.

His disappointment with the outcomes comes across in some of the comments he has made about his early experiences. Commenting on Zip2, Musk stated“What they should have done is put me in charge,” he says. “That’s OK, but great things will never happen with VCs or professional managers. They have high drive, but they don’t have the creativity or the insight. Some do, but most don’t.”

In the next phases of his career, Musk took an iron grip over his investments and influence. He followed-on in Tesla’s funding rounds to maintain his percentage ownership. Also, during a dispute with Tesla, he wasn’t afraid to potentially sacrifice economics for the sake of control, when he converted $8 million of preference stock into common in order to oust CEO Martin Eberhard. Despite not technically founding Tesla, Musk’s hands-on approach and influence meant that he eventually assumed the role of CEO in 2008.

Cash light, but asset rich

Musk has continually plowed his net worth back into startup investing. Portfolio analysts would baulk at such a strategy (2% is recommended for venture capital within a normal portfolio allocation), but this has always been Musk’s mantra. It’s as if he takes the view that any net worth generated from startup sales is apportioned out from his other assets and reallocated into venture/startup investing. Immediately after Zip2 and PayPal, he was already founding and funding his next ventures.

This is a sign of a confident investor and one that gives rise to a view that Musk believes that he has an advantage investing in himself, or others as an angel. He likely feels that he has an element of control over the outcome of the deal, which would not be true for a passive strategy of investing in public equities or real estate. Achieving two successful outcomes from his two first ventures will have given Musk an unrivalled confidence in his ability to invest and operate, it just feels right to him.

The chart below shows an attempt at tracking Musk’s “Investing Account” over time. It shows his investments and proceeds (purely capital gains from exit events – not compensation) over the years. Post-PayPal, you can see how he largely spent all the proceeds over the following ten years. Also noteworthy is that he has not received significant exit returns from SpaceX, SolarCity, or Tesla. SpaceX is still a private company; at SolarCity’s IPO, Musk sold no stock, and at Tesla’s IPO, his proceeds amounted to $15 million. Even to this date (2017), he has never reduced his net share position in Tesla.

Chart 3: Elon Musk's "Investing Account" over Time, Showing Significant Exit Events

When news leaked from court filings of Musk’s divorce in 2010 it transpired that earlier in the year Musk had run out of cash. His voracious appetite to continue investing into his businesses resulted in that, despite significant paper wealth, his liquid assets were minimal. Back in 2008 during hard times at Tesla, he even cobbled together his next financing by immediately flipping proceeds from the sale of Everdream (one of his angel investments) directly into Tesla’s next round.

He sums up his philosophy as follows:“If I ask investors to put money in, then I feel morally I should put money in as well … I should not ask people to eat from the fruit bowl if I have not myself been willing to eat from the fruit bowl.”

He takes extreme, often personal, financial risk

As of March 2017, Musk had total personal borrowings of $624 million that have been used to fund his investments in Tesla. His borrowings are collateralized by his own shares in Tesla. Goldman Sachs and Morgan Stanley have been significant personal lenders to Musk and incidentally both have underwritten many of Tesla’s deals in the capital markets. The infographic below shows how Musk’s personal leverage has risen in recent years:

Chart 4: Elon Musk's Personal Borrowings from Investment Banks

One such example that shows how personal borrowing provides Musk with a powerful financing mechanism is from 2013. With the maturity of a loan from the government approaching, Tesla took to the capital markets to raise new equity to fund the principal repayment. Musk took out a $150 million personal loan from Goldman Sachs to fund buying new stock in the round. In an abstract way, he rolled a part of Tesla’s loan onto his personal balance sheet.

So aside from investing most of his net worth into his businesses, Musk also borrows to leverage more exposure. Because he takes an insignificant salary from his businesses, his goals are completely aligned towards increasing shareholder value. As a key human pillar in his businesses, investors also take comfort seeing him continually investing in them. His risks from this are such that, if Tesla stock starts to underperform, he may be required to pledge more and/or different types of collateral.

2: He creates ecosystems around himself

He invests in his network and reinforces it

Looking at Musk’s angel investing portfolio is again interesting, for his success rate, entrance patterns and sectoral strategies. He appears to be stage (and at one time sectorally) agnostic in his angel investing, ranging from deep Series D entrances, to seed round punts. Although Series A investing does seem to be his favored round.

Table 2: Elon Musk's Angel Investments at a Closer Look

Perusing through his angel investing timeline, it’s clearly apparent that his early investments were driven through connections to the idea and founders. In total I see 10 distinct angel investments made by Musk and his personal connections to the businesses are shown below:

2003 – Everdream – founded by Musk’s cousin, Lyndon Rive. He would later co-found SolarCity
2005 – Game Trust – founded by Adeo Ressi, Musk’s roommate from the University of Pennsylvania
2007 – Mahalo – founder Jason Calacanis met Musk through mutual friends, Adeo Ressi (UPenn and Game Trust) and David Sacks (PayPal)
2007 – OneRiot – Brother Kimbal Musk was its CEO
2010 – Halcyon Molecular – Co-invested with Peter Thiel (PayPal)
2011 – Stripe – Co-invested with Peter Thiel and later Max Levchin (PayPal)
2014 – Deepmind and Vicarious – there are various third degree connections, but Musk invests in AI to “keep an eye on what’s going on” 2015 – NeuroVigil – n/a, no apparent connection
2017 – Hyperloop Transportation Systems – Musk inspired this movement through releasing his famous white paper on the topic

Musk invests in what he knows and with people that he knows and trusts. The effect of this has been that, through this natural rapport, he can have more influence on his investments beyond the traditional board and control mechanisms offered to investors. The reciprocity works both ways, as his network has rallied to support him, either through investing in Musk’s companies or in 2010 when he was living on loans of $200,000 a month from his wealthy friends.

The sum is greater than the parts

The orbits of SpaceX, Tesla, and SolarCity cross regularly, with Musk being the sun at the center of this “Muskonomy”. Interactions between the three come in the form of shared personnel, investors, common goals, or actual business dealings. The figure below sums up some of these cross relationships on a personnel and investor level:

Figure 1: Map of Personal Connections between Musk's Companies

Musk took these relationships a step further by instigating the merger of Tesla and SolarCity. Benefits of synergies and economies of scale were touted behind the reason for the merger, with $150 million of cost savings slated for the first year. However, the genesis of the idea was bigger-picture, and aimed at vertically integrating their processes, sharing ideas and cross selling to their customers.

Tesla’s board of directors summarized its thesis as follows“We would be the world’s only vertically integrated energy company offering end-to-end clean energy products to our customers. This would start with the car that you drive and the energy that you use to charge it, and would extend to how everything else in your home or business is powered”

After working directly or indirectly in three distinct businesses for the majority of the new millennium, merging two of them together would formalize what was already standard operating procedure for Musk. Considering that both companies operate within nascent markets, a merger together doubled down the risk and potential reward for Musk, the eternal gambler. Daniel Gross describes his control mechanisms as that of a Japanese keiretsu.

“Musk’s companies function in some ways like a Japanese keiretsu, a group of allied companies with interlocking business relationships”

SpaceX has purchased over $255 million of SolarCity issued Solar Bonds as corporate investments. SolarCity’s business model requires cash up front to fund its activities, while SpaceX largely has chunky cash balances from its funding rounds, or pre-paid contracts. In this arrangement, SpaceX has been able to earn an attractive yield and SolarCity has secured vital financing cash flows. Musk himself has also personally invested $65 million into the same bonds. It should also be noted that this program is an innovative financing solution within itself. SolarCity purchased fintech firm Common Assets in January 2014 to build an online platform to allow for retail investors to buy Solar Bonds.

The Boring Company is Musk’s most recent new venture, describing the tunneling operation as a “hobby.” However it is a venture that could theoretically provide benefit to Musk’s existing businesses alongside standalone tunneling projects. It also links into his Hyperloop idea, in 2017, Musk tweeted that he has verbal approval to dig tunnels for Hyperloop projects.

3: He uses creative financing methods

Government support

As of the Summer of 2015, the LA Times calculated that Tesla, SpaceX, and SolarCity had received $4.9 billion in government support. With the largest contributions arriving that same year. In 2015 alone, the Nevada government pledged support via tax breaks of $1.3 billion for a Tesla Gigafactory in its state. The New York government engaged in similar support of $750 million for a SolarCity factory in Buffalo.

While these headline figures suggest that Musk has received a significant leg-up from the government, reading into them tells a more nuanced story. With SpaceX and SolarCity being significant players in renewable energy, government assistance is to be expected. Electrek’s deeper look into the LA Times figures also suggest that this was by no means free money and the Nevada and New York factories are tied to significant performance targets and spending pledges, and they contain punitive clawbacks.

The chart below shows the LA Times’ $4.9 billion figure apportioned out by the end-beneficiary of this alleged government support. The breakdown of this quoted amount shows that consumers actually were the beneficiary of 30% of these subsidies via tax breaks and rebates, to encourage renewable adoption. Sure, Musk’s companies also benefit from these initiatives, in that they position their products in a more cost-attractive way to consumers, but it is in no way free money.

Chart 5: End Beneficiary of Government Support Received by Tesla, SpaceX, and SolarCity (2015, $mn)

10% of these subsidies have also arrived with the help of Tesla’s competitors, in the form of Zero Emission Vehicle Credits in California. According to the LA Times’ data, at the time of writing Tesla had banked $517.2 million through selling its own ZEV allowances. With its entire fleet being electric, this is a savvy and no-brainer strategy for Tesla, which on a strategic level also allows them to take money directly from the pockets of competitors via the sales.

Tesla has followed patterns of banking up the ZEV credits and selling them en masse. The effect of which has allowed it to boost revenue (and cashflow) during certain opportune periods and enhance margins via their no-cost basis. The charts below show the influence of ZEV (and other smaller) credits over a four-year period versus Tesla’s revenue and earnings:

Chart 6: Tesla's ZEV Credits as % of Revenue; and Chart 7: Tesla GAAP Income and Loss before and after Green Credits

SpaceX has received scant assistance in comparison to SolarCity and Tesla, but its largest customers are governments. A contract from NASA for $1.6 billion essentially saved SpaceX from falling out of existence in 2008. Maintaining relationships with federal agencies is critical for SpaceX’s growth, as these lumpy contracts provide the financial backbone that funds the operation.

Outsourcing Investment

In recent years, Musk has followed a policy of open-sourcing his research idea. Because of how widely followed and listened to Musk is, I see this as a clever way of him taking his hands off the wheel and outsourcing R&D in a cheaper and potentially faster manner.

In August 2013 Tesla released a white paper called Hyperloop Alpha, detailing initial research and concepts into a potentially revolutionary mode of transport. It was a no strings attached invitation to other entrepreneurs to go away and try to build upon the idea. This benevolent gesture captured the imagination of the public and a number of emergent Hyperloop startups then emerged. One way Tesla supports this initiative is via its Hyperloop Pod Competition.

This was a smart way of an already stretched Musk to outsource the initial formation of the Hyperloop ecosystem. A clean method of transport that could conceivably harness Tesla and/or SolarCity services when in operation. Also in January 2017 Musk invested in Hyperloop Transportation Technologies (Pitchbook), which now provides him exposure to the venture without having done the initial heavy lifting. Waiting for upstarts to emerge also gave him optionality to wait and see about who to invest in.

Following the Hyperloop gesture, in 2014 Tesla open-sourced all of its patents. Again this is an indirect invitation for others to undertake R&D that ultimately will assist Tesla and save it money, via long-term improvements to its ecosystem. The 2014 announcement came a number of weeks after Toyota announced plans for hydrogen fuel-cell cars, a movement that could spark a format war. Musk open-sourcing his patents could be perceived as a way to accelerate growth within his chosen format of Lithium-ion technologies and find customers for Tesla’s new $5 billion battery “gigafactory.”

A dilettante, or a brave maverick?

Musk’s heavy handed tactics and attitude have received criticisms over the years. Corporate governance and conflicts of interest are two issues that Musk walks a tightrope on. The track record and respect that Musk holds offers him an element of deference from shareholders. In April 2017, public investors in Tesla wrote to Musk raising their concerns about corporate governance at Tesla. Directors in Tesla were only being elected every three years, were largely unchanged from pre IPO days and had a number of links to Musk’s other companies. There have also been arguments that his transactions with SolarCity bonds for himself and SpaceX smacks of double dealing.

SpaceX’s dealings with SolarCity have also attracted attention in Capitol Hill, with lawmakers raising concernsthat federal contracts for SpaceX’s services could be surreptitiously propping up SolarCity.

Various other commentators, obviously with their own agendas, have labelled Musk as a dilettante and his associated businesses as Hype Machines and Ponzi Schemes. As a man who has been described as the best “CEO of Social Media,” his public and candid persona endears him to followers. His commentaries about Tesla’s share price have been called strangely accurate, which again raises concerns about undue influence. It also transpired that his comments about The Boring Company being commissioned to dig Hyperloop tunnels may not have been as truthful as he had implied.

In my opinion, Musk is a victim of running a company that is listed on the public markets. Tesla is an automotive company that is priced and treated as a high-growth technology company. Musk’s maneuvers point to the actions of a private company CEO who will hustle and do anything to ensure the financial survival of his businesses. His plans to only IPO SpaceX when there are regular flights to Mars are telling, in that he doesn’t want the distractions of being a public CEO, he just wants to grow his businesses.

This article is originally posted at Toptal

How Artificial Intelligence Is Disrupting Finance

Key Highlights

Artificial Intelligence (AI) Is Exploding
Risk Management
AI Trading
Insurance Underwriting and Claims

General purpose technology is a term economists reserve for technologies that spur protracted economic growth and societal advancements, revolutionizing the operations of households and corporations alike. A sample general purpose technology is electricity. Electricity spawned a multitude of products and sectors, including refrigerators, washing machines, trains and, of course, computers. The advent of electricity radically transformed the world.

A recent Harvard Business Review article designates artificial intelligence (AI) as the most important general purpose technology of our era. We’re familiar with the power of AI. It manifests in the form of a robot defeating a world-renowned chess player. A car that can parallel park itself. Devices that respond with tomorrow’s weather when we ask. But much of our contact with—and understanding of—AI revolves around products that affect our everyday lives as consumers. At the organizational level, there’s a larger question around how AI will disrupt industries, and specifically, how financial services will harness AI.

The following article will define artificial intelligence, the sphere of its related technologies, the size of the overall AI industry, and the applications of artificial intelligence in finance. This piece is not intended to provide a normative judgment on AI development; rather, it will focus on how AI is disrupting finance.

Artificial Intelligence: What Is AI?

Artificial intelligence is an area of computer science focused on creating intelligent machines that function like humans. AI computers are designed to perform human functions including learning, decision making, planning, and speech recognition.

Artificial intelligence enables machines to continually improve their performance without humans providing prescriptive instructions for how to do so. This is significant for a couple reasons. First, humans know more than we are capable of telling. That is, humans may be able to recognize a face or execute a smart strategy in a game of chess. However, prior to advanced artificial intelligence technology, humans’ inabilities to articulate our knowledge meant that we couldn’t automate many tasks. Second, AI technology is superhuman in execution, operating more quickly and often with more accuracy than humans.

Artificial Intelligence Technologies

Artificial intelligence encompasses a multitude of capabilities and technologies. Consulting firm PWC reinforces that AI is “not a monolithic subject area. It comprises a number of things that all add to our notion of what it means to be ‘intelligent.’” Below are a few of the most popular areas of AI:

  • Machine learning is a method of data analysis that automates analytical model building. Using algorithms that iteratively learn from data, machine learning enables computers to find hidden insights without being explicitly programmed where to look.
  • Deep learning is a subset of machine learning. It has facilitated object recognition in images, video labeling, and activity recognition, and is making progress in perception (including audio and speech). For example, Facebook’s deep learning application DeepFace has been trained to recognize people in photos. Many draw the comparison between deep learning technology and biology, but experts generally agree that while inspired by the human brain, it’s not necessarily modeled after it.
  • Natural language processing is the ability of a computer program to understand human speech in real time. Research and development is shifting towards systems capable of interacting with people through dialog, not just reacting to stylized requests.
  • Internet of things (IoT) is devoted to the idea that a wide array of devices, including appliances, vehicles, and buildings can be interconnected. For example, if your alarm rings at 7:00 a.m., it could automatically notify your coffee maker to start brewing coffee for you. Wearable technologies that act as sensors when worn are also part of this larger trend.

Of course, this list is not comprehensive. See below for a wider range of artificial intelligence topics and technologies.

Figure 1: Topic Areas within Artificial Intelligence (Non-exhaustive)

Artificial Intelligence Market Size

The aforementioned Harvard Business Review article predicts that “The effects of AI will be magnified in the coming decade, as manufacturing, retailing, transportation, finance, health care, law, advertising, insurance, entertainment, education, and virtually every other industry transform their core processes and business models to take advantage of machine learning. The bottleneck is in management, implementation, and business imagination.”

The widespread adoption of AI across industries is predicted to drive global revenues of $12.5 billion in 2017 and $47 billion in 2020 with a compound annual growth rate (CAGR) of 55.1% from 2016 to 2020. Specifically, the industries that will invest the most in the technology are banking and retail, followed by healthcare and manufacturing. In aggregate, these four industries will comprise over half of global AI revenues in 2016, with the banking and retail sectors each delivering nearly $1.5 billion.

Across industries, the greatest AI investments in 2017 will be in areas such as automated customer service agents, automated threat intelligence, and fraud analysis (see chart below). According to Jessica Goepfert, program director at market research firm IDC, “Near-term opportunities for cognitive systems are in industries such as banking, securities and investments, and manufacturing. In these segments, we find a wealth of unstructured data, a desire to harness insights from this information, and an openness to innovative technologies.” The next section of this article will delve into the various use cases for artificial intelligence in the financial services industry.

Chart 1: Top Use Cases for AI Base on 2017 Market Share

Present and Future Applications of Artificial Intelligence in Finance

Artificial intelligence could drive operational efficiencies in areas ranging from risk management and trading to underwriting and claims. While some applications are more relevant to specific sectors within financial services, others can be leveraged across the board.

Risk Management

Artificial intelligence has proven extremely valuable when it comes to security and fraud detection. Traditional methods of fraud detection include computers analyzing structured data against a set of rules. For example, a given payments company might set a threshold for wire transfers at $15,000 so that any transaction exceeding that amount would be flagged for further investigation. However, this type of analysis produces many false positives and requires a lot of additional effort. Perhaps even more significantly, cybercrime fraudsters frequently change their tactics. Therefore, the most effective systems must continually become smarter.

With advanced learning algorithms, such as those from deep learning, new features can be added to the system for dynamic adjustment. According to Samir Hans, an advisory principal at Deloitte Transactions and Business Analytics LLP, “With cognitive analytics, fraud detection models can become more robust and accurate. If a cognitive system kicks out something that it determines as potential fraud and a human determines it’s not fraud because of X, Y, and Z, the computer learns from those human insights, and next time it won’t send a similar detection your way. The computer is getting smarter and smarter.”

PayPal’s Success with Artificial Intelligence and Fraud Detection

Take payment giant PayPal and its advanced fraud protocols, for example. Due to its scale and visibility, PayPal “has a huge target on its back.” It processed $235 billion in 2015 from four million transactions by its 170 million customers. However, PayPal has been able to boost security by leveraging deep learning technology. In fact, PayPal’s fraud is relatively low at 0.32% of revenue, a figure far better than the 1.32% average that merchants see.

In the past, PayPal used simple, linear models. Today, its algorithms mine data from a customer’s purchase history and reviews patterns of likely fraud stored in its growing databases. While a linear model can consume 20-30 variables, deep-learning technology can command thousands of data points. These enhanced capabilities help PayPal distinguish innocent transactions from suspect ones. According to Hui Wang, PayPal’s Senior Director of Global Risk Sciences, “What we enjoy from more modern, advanced machine learning is its ability to consume a lot more data, handle layers and layers of abstraction and be able to ‘see’ things […] even human beings might not be able to see.”

Figure 2: Some of PayPal's Fraud Management Options for Developers

Artificial Intelligence Trading

Transition from Human-Constructed Models to True AI

For years, investment management companies have relied on computers to make trades. Around 1,360 hedge funds, representing 9% of all funds, rely on large statistical models built by data scientists often holding mathematics PhDs (otherwise known as “quants”). However, these models only utilize historical data, are often static, require human intervention, and don’t perform as well when the market changes. Consequently, funds are increasingly migrating towards true artificial intelligence models that can not only analyze large volumes of data, but also continue to improve themselves.

These new technologies utilize complex techniques including deep learning, a form of machine learning called Bayesian networks, and evolutionary computation, which is inspired by genetics. AI trading software can absorb enormous volumes of data to learn about the world and make predictions about the financial market. To understand global trends, they can consume everything from books, tweets, news reports, financial data, earnings numbers, and international monetary policy to Saturday Night Live sketches.

To be clear, the above is distinct from high-frequency trading (HFT), which allows traders to execute millions of orders and scan multiple markets in a matter of seconds, responding to opportunities in ways humans simply cannot. The AI-driven platforms discussed above are seeking the best trades in the longer-term, and machines—not humans—are dictating the strategy.

Some of these AI trading systems are developed by startups. For example, Hong Kong-based Aidiya is a fully autonomous hedge fund that makes all of its stock trades using artificial intelligence (AI). “If we all die,” says co-founder Ben Goertzel, “it would keep trading.” Traditional institutions are also interested in AI trading technology. In 2014, Goldman Sachs led the Series A funding round of and began installing an AI trading platform called Kensho. For Kensho’s Series B round, in addition to S&P Global, Wall Street’s biggest six banks (Goldman Sachs, JPMorgan Chase, Bank of America Merrill Lynch, Morgan Stanley, Citigroup, and Wells Fargo) also participated.

Trading Performance Comparison

A recent study performed by investment research firm Eurekahedge tracked the performance of 23 hedge funds utilizing AI from 2010-2016, finding that they outperformed those managed by more traditional quants and generalized hedge funds.

Chart 2: AI/Machine Learning Hedge Funds Index vs. Quants and Traditional Hedge Funds

Implications for Traders and Quants

It will be interesting to observe how AI will impact the trading labor market. Its effects are already apparent at some major banking institutions. In 2000, Goldman Sach’s U.S. cash equities trading desk in its New York headquarters employed 600 traders buying and selling stock. Today, it has two equity traders, with machines doing the rest. Daniel Nadler, CEO of Kensho, declares, “In 10 years, Goldman Sachs will be significantly smaller by headcount than it is today.” And as for the quants, they may find that their skills are in less demand from investment management companies.

Currently, about a third of graduates from top business programs feed into finance. Where would some of the nation’s best talent move to? Mark Minevich, senior adviser to the U.S. Council on Competitiveness, believes that “Some of these smart people will move into tech startups, or will help develop more AI platforms, or autonomous cars, or energy technology […] New York might compete with Silicon Valley in tech.”


What Is a Robo-Advisor and How Does It Work?

Robo-advisors are digital platforms that provide automated, algorithm-driven financial planning services with minimal human supervision. While human financial managers have been utilizing automated portfolio allocation since the early 2000s, investors had to employ advisors to benefit from the technology. Today, robo-advisors allow customers direct access to the service. Unlike their human counterparts, robo-advisors monitor the markets non-stop and are available 24/7. Robo-advisors can also offer investors up to 70% in cost savings and typically require lower or no minimums to participate.

Today, robo-advisors can help with the more repetitive tasks such as account opening and asset transferring. The process typically involves clients answering simple questionnaires about risk appetite or liquidity factors, which robo-advisors then translate into investment logic. The majority of current robo-advisors aim to allocate their clients to managed ETF portfolios based on their preferences. It is expected that capabilities in the future will evolve into more advanced offerings such as automatic asset shifts and expanded coverage across alternative asset classes like real estate.

Robo-advisory can have a major impact on the personal finance and wealth management sectors. While current robo-advisor total assets under management (AUM) only represent $10 billion of the wealth management industry’s $4 trillion (less than 1% of all managed account assets), a Business Insider studyestimates that this figure will rise to 10% by 2020. This equates to around $8 trillion AUM.

Chart 3: Current and Future Robo-advice Capabilities

Industry Adoption of Robo-Advice

Industry players have adopted varied approaches to robo-advisory. Smaller wealth management firms are adding algorithmic components to automate their investment management, reduce costs/fees, and compete with robo-advisors. On the other hand, established investment firms are buying existing robo-advisors, such as Invesco’s acquisition of Jemstep or creating their own robo-advisor solutions, such as FidelityGo and Schwab’s Intelligent Advisory.

Figure 3: Approaches to Robo-advice Capabilities

Robo-Advisors vs. Financial Advisors: Will Humans Be Replaced?

The general consensus among experts is that humans will remain indispensable. The human touch will remain critical, as advisors will still need to reassure customers during difficult financial times and persuade them with helpful solutions. A study performed by consulting firm Accenture revealed that 77% of wealth management clients trust their financial advisors while 81% indicate that face-to-face interaction is important. For clients with complex investment decisions, the hybrid advisory model, which couples computerized services with human advisors, is gaining traction.

While financial advisors will remain central, robo-advisors may cause shifts in their job responsibilities. With AI managing repetitive tasks, investment managers might take on the responsibilities of a data scientist or engineer, such as maintaining the system. Humans may also focus more on client relationship-building and explaining the decisions the machine has made.

Artificial Intelligence in Insurance Underwriting and Claims

Insurance relies on the balance of risk amongst pools of people; insurers group similar people together, and some people will require payouts while others won’t. The industry is built around risk assessment; insurance companies are no strangers to data analysis. However, AI can expand the amount of data analyzed as well as the ways it can be utilized, resulting in more accurate pricing and other operational efficiencies.

Startups are at the forefront of pushing the industry forward. According to Henrik Naujoks, a partner at Bain & Co, “The start-ups are showing what is possible and what can be done. A lot of incumbent executives are looking at it — they don’t really understand it but they want to get involved.” Investors have also caught onto this trend (see below). In 2016, AI was one of the most popular themes for insurance tech investment.

Chart 4: Investor Interest in Insurance Technology Is Increasing

Artificial Intelligence and Underwriting

PWC report predicts that AI will automate a considerable amount of underwriting by 2020, especially in mature markets where data is available. Currently, an insurance underwriter, with the help of computer software and actuarial models, evaluates the risk and exposures of potential clients, how much coverage they should receive, and how much they should be charged for it. In the short term, AI can help automate large volumes of underwriting in auto, home, commercial, life and group insurance. In the future, AI will enhance modeling, highlighting key considerations for human decision-makers that may otherwise have gone unnoticed. It’s also predicted that advanced AI will enable personalized underwriting by company or individual, taking into account unique behaviors and circumstances.

Enhanced underwriting may leverage not only machine learning for data mining, but also wearable technology and deep learning facial analyzers. For example, Lapetus, a startup, wants to utilize selfies to accurately predict life expectancy. In their proposed model, customers will email their self-portraits, which computers will then scan and analyze—analyzing thousands of regions of the face. The analysis would consider everything from basic demographics to how quickly the person will age, their body mass index, and whether they smoke. In addition, wearable technology could make the underwriting process more collaborative. Instead of relying on lengthy medical checks and complicated contract processes, wearables can provide real-time insights into policyholder health and behavior.

These types of nuanced, real-time risk analyses will enable not only more accurate customer pricing, but also early detection of health risks and an opportunity for insurance companies to invest in prevention. Instead of eventually paying for costly treatments for the patient, insurance companies can proactively try to lower the probability of damages and associated costs.

In a 2013 Oxford study analyzing over 700 professions to determine which were most susceptible to computerization, insurance underwriters were included in the top five most susceptible. Even where AI does not completely replace an underwriter, AI automation can alter an underwriter’s responsibilities. AI can free up an underwriter’s time for higher value-add, such as assessing and pricing risks in less data-rich emerging markets, providing more risk management and product development feedback.

Artificial Intelligence and Insurance Claims

Insurance claims are formal requests for payment sent to insurance companies. Insurance companies then review the claim for validity and pay out to the insured once approved. Here’s how artificial intelligence can enhance the process:

Improved customer data accuracy. The claims process is fairly manual: Human agents manually log customer information and incident details. According to an Experian report, data quality can suffer: incomplete data accounts for 55% of data errors, while typos comprise 32%. AI can improve accuracy by reducing manual input. In addition, claims processes often require insurance agents to match customer information with numerous databases. AI can be used to do this more efficiently.

Faster payout recommendations. According to a J.D. Power & Associates property claims satisfaction study, slow claims cycle times are one of the largest contributors to customer dissatisfaction. AI can help to reduce turnaround times by first validating the policy, then making determinations on the claims and whether to automate payment. This is because AI has the ability to analyze not only structured data, but also unstructured data like handwritten forms and certificates.

This article is originally posted at Toptal


How to Hire the Right M&A Advisor to Help Sell Your Business

It can take around five months to sell a business, which makes it a significant commitment. One too with many risks, in terms of juggling attention between ongoing operations and negotiating the sale. For that, enlisting the help of a professional M&A consultant will help a founder to manage time, maintain their business, and maximize the economic outcome of the deal.

To help select an M&A consultant to work with during this process, we will outline the general steps of selling a business and, for each, outline the beneficial characteristics that a consultant can provide.

This article is written within the context of trying to sell a business (outbound), as opposed to fielding an unsolicited request to buy a business (inbound)

Prelude: Does the Founder Want to Sell?

The reasoning behind looking to sell a business will first be defined by the personal situation of its owner and their desires, such as the following:

Figure 1: Personal reasons for wanting to sell a company

The combination of these, alongside the current competitive state of the business, will play an important part in the narrative of the sale process, in terms of buyer demand and valuation. For example, a founder that has personal liquidity issues operating a distressed company will be following a different path from a “plateaued” founder running a successful operation.

An M&A consultant is particularly useful during this part for initially providing an unaffiliated but informed opinion on the founder’s situation and what route to take. A great advisor will be someone that doesn’t just push towards getting a mandate, with empty promises of a successful sale, but someone who is not afraid to bring pertinent and difficult issues to the fore and manage the expectations and enthusiasm of the founder.

This is also the part where fees will be discussed, which typically will be divided between a retainer (i.e., a base salary) and a success fee (a stepped bonus) for completed sales. The founder must pay attention to the weightings of these fees as it is in the best interests of the founder to incentivize the M&A consultant to work towards a successful sale. Too much weighting towards a retainer may not align the interests of both parties.

Characteristics of the consultant:
Honesty and transparency (especially with fees)
Strategic mentality

1. Valuing the Business

After being hired and undergoing a general assessment of the operation, the M&A consultant will need to independently value the business. This will in turn provide the owner with price guidance and help the advisor to gain a deeper understanding of the company by poring over its numbers. Valuing a private company is a subjective topic and one that needs to be grounded in clearly justified assumptions. Public companies have visible asset values attached to their traded stocks, while private companies often have to be valued on their book valuecomparable analysis to other companies, or via a discounted cashflow valuation.

Figure 2: Example of a Comparable Company Analysis Valuation

Figure 3: Example of a Discounted Cash Flow Analysis Valuation

Beyond the valuation, the advisor must prepare for a range of operating scenarios, such as best or worst cases for future projections of business performance. Understanding what dynamics affect the business on a macro and microeconoic level will help both the founder and advisor to field difficult questions that will inevitably arise during the negotiation stage.

An M&A consultant that has worked in a variety of fields will have more exposure to esoteric valuations, in terms of what specific methods are most appropriate or what multiple benchmarks are followed. They will be expected to consult with all areas of the business to deduce the nuances of it, so never accept someone that just says “give me the numbers and I’ll get back to you in a week’s time.”

Once a valuation is in place, the advisor must be able to justify it clearly and explain it in layman’s terms to the founder. This is particularly important in terms of managing expectations around whether the founder actually does want to sell the business. A good and ethical advisor will show a “warts and all” valuation, which may at that point possibly diminish the founder’s interest in selling the business.

If an advisor were to dress the numbers in an attractive way to the founder to solicit the sale, it may result in an ugly, expensive, and time wasting process after which, when offers are eventually received, they range widely from what was originally suggested to the founder.

An advisor must also be able to explain the economic process of selling the company, because it is never just a simple process of a handshake and money being transferred into a bank account. There will be scenarios of earn-outs and vesting tied to the purchase. Situations that may tie the founder(s) in a physical or economic manner to the entity post sale. Similarly, if there are current investors in the business with varying levels of participation and preference attached to their stock, the economic proceeds to the founders may not tie up exactly with their ownership percentage.

When assessing an M&A consultant for this role, one must also pay attention to their technical skill profile. A quality advisor will possess the personal qualities to both build connections and negotiate as well as the financial brain to crunch numbers and understand the fundamentals of how the business operates. Those that have a classical training within a banking or professional services institution will have had a rigorous apprenticeship in this regard and the requisite letters after their name, for example: ACACFA, or CPA.

Characteristics of the consultant:
Technical skills/qualifications
Proven track record
Forensic approach

2. Getting the House in Order

As they are assessing the business, the M&A consultant will begin to organize document records that will assist with the due diligence process to be undertaken by potential buyers. This will be an opportunity for some administrative spring cleaning inside the business. These actions will be well worth the effort down the line, as they can speed up processes and demonstrate professionalism and consideration towards the needs of prospective buyers.

Data room

Expect the advisor to create a data room containing electronic records that buyers can audit during their initial appraisals. A cloud storage service such as Dropbox can be used for this and will keep everything organized and transparent for the buyers. It may also provide the company with a feedback mechanism during the sale process, as it may be possible to see what documents or areas are being looked over the most.

Below is a list of high level items that should be included in a data room. It’s not exhaustive and the M&A consultant should be relied upon to provide more clarity on what to include:

  1. Certificate of Incorporation and any other relevant documents
  2. Lists of any subsidiaries owned
  3. Tax returns dating back four years
  4. Audited financial statements
  5. Details of any loan and/or lease agreements
  6. Patents and IP
  7. Headcounts and org charts
  8. Signed board minutes
  9. Cap tables and stock option plan details

For marketing materials to show prospective buyers, expect a combination of the following:

  1. One-page teasers to solicit initial interest without “showing too much”
  2. A range of pitch decks tailored to the type of buyers, a document that will evolve as feedback suggests areas to revise
  3. A summary financial model projecting future revenues
Characteristics of the consultant:
Organizational skills
Creativity (For the Marketing Materials)
Clear communication and presentation

3. Finding buyers

With the valuation completed and a solid strategic understanding of the business, the M&A consultant will then be expected to start seeking out buyers. With a rational financial valuation fleshed out, they should then devise a list of suitable buyers, which will be correlated with the strategic benefits that they could gain from buying the business. For example:

  • Acqui-hire benefits: Do some competitors lack talent that the seller has?
  • Increased footprint: Could buying the business allow for new markets to be seized?
  • All-round improvements: Is the company being sold more efficient and innovative?
  • Complementary synergies: Would buying this company save time and potential money from fixing weak areas or expediting R&D efforts?

Using the power of their rolodex, earned knowledge, or old fashioned database research, the advisor will then create a wishlist of potential acquirers. This should be made and managed in a transparent manner that can be updated to show progress amongst the leads. A CRM is a particularly serious way of showing this funnel, but a shared spreadsheet would be more than sufficient. The founder should have equal input into this process and must reflect back on previous years over all the interactions made with potential buyers for their business.

With the marketing documents in hand, the advisor will then attempt to contact buyers, provide them with initial information, and filter through the interest. Once interest is deemed to be beyond initial curiosity, the advisor will formally introduce the founder to the potential buyer.

For characteristics of an advisor, this is the point where their personal qualities really shine through. A confident advisor who is unafraid of picking up the phone and being rejected is invaluable here. For independent advisors, this is where their experience outside of large companies will count. Within a bank, for example, there are both the resources and brand name to help advisors knock on doors and contact buyers. When they are independent and representing a private company, these luxuries are not so apparent and, for that, during the initial assessment of the M&A consultant, they must be tested for their “hustle” and malleability to operate in unique situations.

Characteristics of the consultant:
Strength of network

4. Handling offers

Assuming all goes well, eventually an offer will be received, or even multiple ones. Firstly, this may take some time, and secondly, it does not yet mean that the business is sold. The offer will be signalled by the delivery of a letter of intent or a term sheet; both are formal methods of indicating that a buyer wishes to proceed with formal talks. Prior to this offer, a request for more information about the business may be instigated by a buyer. In this case, the advisor should present an NDA to them to permit them looking further into the business without a formal offer.

The receipt of an offer can create a signalling effect to other prospective buyers, showing that the business is indeed a validated and attractive prospect. This creates urgency and potential FOMO and may result in tires being kicked again by previously dormant leads. A skilled M&A consultant will be adept managing this dance, firstly by doing it respectfully, but also by tailoring the message to each prospect based on their personalities.

An offer will contain an expiration date to agree to begin formal talks and lay out the next steps. Serious offers contain this because a) time is a precious commodity to everyone and b) the buyer will want a decision quickly to minimize the seller shopping theirself around for counteroffers. An aggressive offer may come in the form of one that explodes, in which a seller’s response is required very quickly or else the deal is off, as opposed to a softer expiration deadline. The M&A consultant should provide counsel on whether any of these terms are unfair and if more time is required.

At the other end of the spectrum can be deceptive offers that offer vague timelines and promises. These are not serious offers and can either result in lowball prices intended to capitalize upon frustration and lack of alternatives, or time wasted for the buyer’s benefit of undertaking a fact finding exercise. If the offer is coming from an individual investor, they should be pre-vetted to check for solvency.

Assuming that they have undertaken a thorough vetting process, the M&A consultant should be able to shield the founder from these offers. Or, if they are received, they will be swiftly rejected and/or sent back for revision.

An offer has been made.

The offer will contain an array of financial terms and conditions that will not be black and white. At this point, the advisor must be able to communicate clearly the permutations of accepting the offer, in terms of timing for payouts, implications if they are tied to stock, and the physical commitments required post-sale. If it is such that a large portion of the economic consideration is tied to the fortunes of the acquirer, this is the point where the founder must then make a financial assessment of their prospects. The advisor would be expected to have a clear top-down view of the buyer’s business and its competitive landscape.

Here are some high level points that will be laid out in the initial offer document:

  1. Purchase price
  2. Nature of purchase: cash, stock
  3. Non-solicitation ‘no-shop clauses
  4. Non-compete clauses on the founder to prevent future competitive scenarios
  5. Conditions required on the part of the buyer ie: financing, government approval
  6. An initial due diligence process map

Signing a term sheet is the point when the founder moves from thinking about selling their company to actually going through with the process. If there are no-shop clauses that lock the buyer into exclusive negotiations with the seller, then this is another critical junction for the founder to take stock and think again.

Signing an offer is vital, because after this point, the prerogative moves to the buyer. Whereby, assuming that everything ticks the legal boxes in the offer, the seller will be obliged to sell the business. At worst, the buyer can still back out, either due to discrepancies during due diligence, or for getting cold feet. At the start of the article, it was referenced that the role of an advisor is to prevent the founder from being distracted during the sale process. This point is critical, because if the business has suffered operationally during the distractions of the selling process, it could be an excuse that proffers an easy get-out clause for the buyer.

Characteristics of the consultant:
Conciliatory negotiator
Pressure management

5. Due Diligence and Closing

Lawyers now enter the fray and will drive the negotiation process of the sale. It is also the point where due diligence moves beyond just perusing a data room; it will involve physical assessment of assets and operations. Lawyers and accountants will both be prevalent actors on both sides of the transaction at this point.

This is a particularly nervous period for a founder as it is a time period that is largely full of inertia. The M&A consultant will again have to rely on their personal qualities during this stage, helping to organize the steps and acting as a conduit between the buyer and seller.

Having documentation and data access all ready in advance will assist with the due diligence process. The founder will also need to be open and available to employees who may feel distracted and/or worried by these developments, which should be coming as a surprise for them.

In all, this step should take no longer than 60 days. If there are other roadblocks that are out of the control of the seller (for example the buyer’s financing capital) then the transaction may take longer to close. But assuming that all goes well, then the deal will finally close and the buyer and seller can exchange.

Characteristics of the consultant:
Attention to detail
Time management
Expectation management


In describing this process, it is evident that an M&A consultant is a very visible and important actor inside the transaction of selling a company. Aside from the tangible characteristics that will be straightforward to asses—track record, qualifications, brand names on their CV, a key attribute to assess is rapport—this will be an advisor that the founder will be going into battle with. There will be many late nights and tough conversations. Assessing this and their integrity and flexibility will ensure that a prospective business seller has a skilled operator on their side.

Originally appeared on Toptal Finance blog

Experts’ Corner: Pitch Deck Tips for Fundraising Success

Experts’ Corner is a series of articles sharing practical tips and solutions that our experts have gained over years of on-the-job experience. It aims to elevate our readers’ day-to-day execution and performance.

Fundraising, for companies at any stage, is undoubtedly a challenging process. According to a recent study, an average series seed raise requires contact with 58 investors, 40 investor meetings and over 12 weeks to close a round. Even for seasoned entrepreneurs and startups already with market traction, a compelling pitch and accompanying pitch deck are still necessary. Despite variance around stylistic delivery and aesthetics, you might be relieved to hear that the infamous pitch deck boils down to a formula. In fact, there are a number of topics and slides that investors actually expect—all of which will be discussed in this article.

The following piece is meant to serve as a guide for creating effective, successful investor decks. It focuses on the creation of the deck itself, instead of the delivery of the pitch. While there is no ultimate one-size-fits-all format for investor decks, we will share a set of widely-accepted guidelines along with corresponding pitch deck examples.

Pitch Deck Best Practices

Treat your pitch like a story

Weave a compelling narrative about a problem in the world, what the inevitable solution is, how your product is that solution, and why your company will succeed. Your story should lead to your product being the logical response to the problem you’ve identified. Rather than adding bells and whistles, focus on crafting a story that is both streamlined and coherent. Be passionate. Make it personal.

This task can sometimes prove tricky for founders who have lived and breathed their business for months or years. Take a step back and imagine that you were learning about your business for the first time. As venture capitalist Tomasz Tunguz suggests, “The most successful pitches argue the market will unfold inexorably in the way the founders envision on a relevant time scale. And, that this startup in particular will dominate share in that new world. There is no prescriptive way I can recommend to consistently argue inevitability. Some founders use data. Others use logic. Still others use emotion and passion to do it. But in the end, these exceptional storytellers make you want to believe, suspend doubt, and disregard the great risks that all startups face all along their journey, and get involved with the business.” If you need a bit of inspiration, you can borrow from the famous Hero’s Journey narrative.

In the below example from marketing software company SEOmoz founders Rand Fishkin and his mother Gillian Muessig were not shy about sharing their trials and tribulations. In their opening, they position their story as “How a tiny Mom + Son consultancy became the world leader in SEO software.” Being open and utilizing a timeline makes their narrative something people can understand and root for.

Continually refresh your pitch deck

According to Finance Expert Jeff Fidelman, who has executed over $500 million in transactions across industries, “A pitch deck should be viewed as a dynamic, living document that evolves over time.” As an entrepreneur continues to pitch multiple audiences, they will notice that they are often asked the same questions time and time again. They should take note of these questions, incorporating and addressing them in the presentation.

In addition, the market data and company traction data (e.g., number of users, traffic to your website, sales numbers) should also be continually updated. You never want your data to seem outdated, as you want to minimize the opportunities to call into question your knowledge or credibility.

Understand the context

Understand and evaluate the context in which your audience will be reading or listening to your pitch. How far along are you in conversations with them? Are they familiar with you and your company already? Are they familiar with the market and technology in question? You should even consider whether investors will be reviewing the deck in soft copy form or in-person with a printed copy.

Toptal Finance Expert Kelly Sickles, who raised $100 million for Boxed’s Series C, explains, “Make several decks. Except for the lucky few, pitching is a process more akin to dating than an arranged marriage: You may have to share a deck with investors that you’ve never met. The goal of that deck is to simply entice them to take a meeting. Once interested, you’ll need a deck for your initial meeting. The goal of that deck is to enable a meaningful conversation about your business and your roadmap – again, not to overshare details so that the investors stare at a PowerPoint throughout the meeting.”

Anticipate potential questions and incorporate answers into the deck

An oft-overlooked step in the pitch deck creation is the anticipation of investor questions. You must critically examine your company and your presentation to identify the potential gaps. Instead of being caught off-guard by the investor, think about what investors might ask and create supplemental slides to provide visual support for your answers. If you are delivering an in-person pitch, it could even be helpful to include these supplemental slides in the appendix section of the investor deck.

Keep the aesthetic polished and consistent

The pitch deck should be aesthetically pleasing and professional—this can help form an investor’s first impression of you and your company. You can simply use the colors and design from your product as a theme. Or, it can be beneficial to hire a professional for the deck design. In any case, the pitch deck should reflect your company’s personality and align with the branding on your site and product.

In designing the investor pitch deck, keep in mind that not all investors have the same goals and investment approach. Toptal Finance Expert Zachary Elfman, who has served as a buy-side advisor for a multimillion dollar Series A funding round, underscores the importance of this: “If you are pitching a young investor new to VC, maybe having cashed out after selling their own startup, a racier or trendier look and feel might be appropriate. Whereas with more veteran investors, a more to-the-point presentation would provide more credibility. Stay high-level and avoid pictures, small print, and any font that has been invented in the past decade with these investors who have seen both sides of the cycle. There are surely a lot of gimmicky decks flying around at this very moment that more seasoned investors are tossing immediately into the wastebasket. DOA.”

In the below investor pitch deck from Tealet, an online farmers’ market for tea, the aesthetic matches their product and website. It’s also clean and easy to read.

Pitch Deck Mistakes to Avoid

Don’t make it too long; 15-20 slides should suffice

Investors are people too. This means that they have limited attention spans. According to the aforementioned study, VCs spent an average of three minutes and 44 seconds reviewing soft copies of series seed raise pitch decks. Continually remind yourself that you are building a pitch deck, not a comprehensive business plan. Include the necessary and most compelling components; don’t try to cram all the details in. If you are overly ambitious, chances are that the excessive information will divert attention away from the story you’re trying to communicate. Should you need to include more information such as growth details, complex technical explanations or financials, opt for creating separate documents.

Finance Expert Aleksey Krylov, who has advised over 50 clients on raising a collective $1.6 billion, suggests abiding by the acronym KISS: “Keep it simple and short. I advise my clients to keep their decks under 15 slides and move non-essential details to the appendix. Investors review numerous pitch decks and business plans a day. All else equal, shorter pitch decks are likely to produce a more positive response from investors.”

Don’t include too much information or text on a slide

Similar to the preceding point, investors won’t have the time or energy to actually read the fine print or all the extra details on the slides. You’ll want to focus on communicating a few key takeaways per slide, making it as easy as possible for your audience to understand and remember these points.

In addition, be sure to include relevant graphs, charts, images, or other media. Utilizing supplemental visuals can be a powerful tool for reinforcing or communicating your message. However, be judicious when deciding which to add; each visual should add real value.

Don’t use overly-complicated jargon

Even if your product is particularly technical or detailed, don’t fall into this common trap. Be exceedingly clear about how you present your company and your story, and be willing to simplify. If you pitch your product as a “B2C scalable cloud-based social media platform for the next gen,” it’ll remain unclear whether it’s a website, an app, and what exactly it does. If the jargon would annoy a tech journalist, it probably won’t impress investors either.

Don’t belittle your competition

While acknowledging your company’s position relative to your competition is necessary, as we’ll discuss in the upcoming section, it’s important not to underestimate or belittle your competition. This could backfire, reflect poorly on you, or potentially be off-putting for investors.

Imperative Slides to Include

The following section will detail the topics that investors will expect and look for in pitch decks. Still, there is some flexibility in terms of topic ordering, as long as you are crafting the narrative in a logical manner.

Company Overview

The Company Overview slide, which can serve as the opening of the deck, should set the stage for investors so they have an idea of what’s to come. It should include a handful of points (between three to eight) about what problem your product solves, background on the management team, and any key traction that your company has seen. The information you include on this slide should be straightforward, exciting, and easily understandable. You don’t want to confuse anybody or get them stuck early on in the deck.

Below is an example from social news, media and entertainment company Buzzfeed. In just six bullets, the slide includes key highlights communicating traction, press, headcount, and high-level financial information.

Company Mission, Vision, or Purpose

This slide is meant to convey your company’s ultimate goals, whether you choose to include its purpose, mission, or vision. According to Harvard Business School, here’s how to define these terms:

  • Mission statement: Describes what business the organization is in both now and in the future. Its aim is to provide focus for an organization’s employees.

    Example: The mission for a consulting firm could be, “We’re in the business of providing high-standard assistance on performance assessment for middle to senior managers in medium to large firms in the finance industry.”

  • Vision statement: Says what the organization wishes to be like, and takes the thinking beyond day-to-day activity in a memorable way.

    Example: Ericsson’s (a global provider of communications equipment, software, and services): “the prime driver in an all-communicating world.”

  • Purpose: Summarizes what the company is doing for its customers, connecting “the heart with the head.” Greg Ellis, former CEO and managing director of REA Group, refers to his company’s purpose as its “philosophical heartbeat.”

    Example: Insurance company IAG: “To help people manage risk and recover from the hardship of unexpected loss”

When describing your business and its goals, aim to convey the originality of your idea. Many entrepreneurs describe their business by utilizing comparisons to other well-known tech companies such as “We are the Airbnb for cooks.” However, this approach is not recommended because it can diminish the uniqueness of your idea. According to Amy Webb, Professor at NYU’s Stern School of Business, “On one site alone—AngelList, where startups can court angel investors and employees—526 companies included “Uber for” in their listings. As a judge for various emerging technology startup competitions, I saw “Uber for” so many times that at some point, I developed perceptual blindness.”

Below is an example of a Company Purpose slide from video-sharing company YouTube:

The Team

Startup investing, particularly for early stage startups, is often focused on human capital. At the end of the day, investors base their decisions on whether they can trust in you, your capabilities, and your persistence.

Given the importance of the team in shaping investors decisions, make sure you highlight both the range of skillsets and the necessary experience that the founding team possesses. Toptal Finance Expert Samir Chaibireinforces this concept: “You can show me that you can execute in two ways: traction and team; a combination of both is, of course, best. I would happily back a founding team of a fashion marketplace that can show me that they know that industry very well, that they have worked with global fashion retailers and brands, and the skills of the team as a whole represent the right mix to take on the industry incumbents. In that case, a mix of experience in eCommerce, operations, and marketing/merchandising would be best. If you are raising your seed round, your team won’t be complete and you will lack some key competencies; your pitch should address that through a detailed hiring plan.”

This slide typically includes images and accompanying titles for each key team member and brief summaries of team members’ educational attainment and prior employment. It also highlights relevant expertise. It should also list out advisors, consultants, and board members, if any.

There’s varying advice around whether to introduce the team earlier or later on in the deck. Finance Expert Grant Blevins, venture capitalist who has has helped raised over $20 million in early stage capital in the past year alone, recommends putting the team early on: “Put Team first, at the very beginning of your slides. Bias investors from the beginning with your credentials. I can tell you some stories of average pitches where I was not that interested in the product until I found out how accomplished and educated the team was.”

Below is an example from content marketing software company Contently. It exhibits the key team members, including images and impressive credentials from each (education and work experience). It also includes other investors, which can stoke the FOMO (fear of missing out). Y Combinator founder Paul Graham talks about this herd mentality in one of his blog posts: “The biggest component in most investors’ opinion of you is the opinion of other investors. Which is of course a recipe for exponential growth. When one investor wants to invest in you, that makes other investors want to, which makes others want to, and so on.”

Current Traction or Progress

Regardless of what stage your company is in, including compelling traction statistics will be received positively and can help build credibility among investors. Typically, “traction” include sales, traffic, downloads, or any other growth metrics to indicate scale and adoption. On this slide you can also include any strategic partnerships, large accounts or clients won, client testimonials, or accolades achieved. Feel free to include the logos of any clients or large customers.

Finance Expert Aleksey Krylov contends that entrepreneurs should “Always highlight milestones. A startup’s milestones can be associated with technology, product, regulatory, market or other developmental goals. Market testing and customer traction milestones tend to be of particular interest to investors.”

Below is an example from map creation platform Mapme, which includes press and user statistics.

Total Addressable Market (TAM)

The Total Addressable Market slide is meant to demonstrate to investors that the opportunity at hand is part of a larger market shift or trend. The TAM figure is meant to indicate the underlying revenue opportunity for a given product or service. As such, entrepreneurs often include the largest, most impressive statistics they can find from various research reports.

According to Reid Hoffman, founder of LinkedIn, however, the issue with most TAM slides is that they often quote huge numbers from research companies or reports that are incentivized to inflate those numbers. Therefore, Hoffman recommends that the TAM calculation utilize a “bottoms-up” approach—an approach focused on revenue and traction. If you do not use your own calculations, be sure to cite sources that are objective and independent. In general, Hoffman suggests that if you are to include a TAM slide, not to linger on it too long during the actual pitch.

Below is an example from personal finance app Mint. It details the assumptions used to calculate the total addressable market, an approach that can lend more credibility than simply showing the number itself.

The Pain Point

You’ve probably heard this advice before: you must spell out the “problem” you are looking to solve with your company, product, or service. It is particularly important to frame the problem in a way that people can easily relate to. You should view this slide as an opportunity to add a personal touch and connect with your audience.

According to Samir Chaibi, who has raised a $1.5 million seed round and prepared a $10 million Series A fundraising for multiple startups, “Too many times I see founders attacking a vertical without a clear-cut understanding of the problems users are experiencing that are not already solved by the incumbents. When you develop a pitch, be mindful about the pain points you want to focus on from the start. It is fine to show a growth path towards a broader set of offerings that will turn a startup into a category leader but your pitch deck should start with the specific problem you are solving, always.”

Below is example from data platform Mattermark:

Solution: Your Product or Service

You’ll want to immediately follow your “Problem” slide with the natural solution: your company’s product or service. On the same slide or on a separate slide, you’ll also need to spell out exactly what your company’s product or service involves, and why it is distinct from what already exists on the market. Showing your product is far more powerful than simply describing its functionality in words. If your company has a product ready to be demo-ed, this would be a natural point to introduce it to investors, either live or in a video. Otherwise, you can also include screenshots and powerful images or visuals. If you haven’t completed the product itself, include a mockup so investors have a visual aide. You should highlight your product’s key features, target user, product milestones and roadmaps, and key differentiating features.

Below is an example from online housing marketplace Airbnb, which chose to separate the Solution slide (Slide 3) from the Product slide (Slide 6).

Business or Revenue Model

In order to build investor confidence, you will want to address your company’s planned revenue model, pricing strategy, average account size, or sales and distribution model. To indicate that you are not simply making empty promises, you can include not just current revenue model plans, but also future ones. You can also include specific dates to demonstrate decisiveness. Of course, investors expect that your company will need some flexibility to iterate, so being decisive does not necessarily mean making decisions that are set in stone either.

Below is an example from personal finance app Mint, which details both current and future revenue models.

Marketing & Growth Strategy

This slide is meant to provide investors with insight into how you plan to growth and market your product or service. It’s also a slide with a lot of latitude. You can include anything from the various market channels you’ll use to promote your product (paid search, social media, email marketing, etc.) to hiring plans.

Below is an example from Mixpanel:


Investors will want to understand the company’s current financial situation and future burn rate, the rate at which a new company is spending its financing before generating positive cash flow from operations. Separate from the pitch deck, you should have your financials ready in Excel if investors are interested in more detail. This will allow you to present a simpler summary in the deck itself.

Below is an example from mobile payment service Square, which actually exhibits “best,” “base,” and “worst” case financial scenarios. This approach can help investors understand the general range of the projections, and can appeal to conservative and optimistic investors alike.

Competitive Landscape

It’s all relative. Even if you’ve built the most comprehensive product on the market, perhaps it’s a low barrier-to-entry industry, or there are already a couple giant competitors to contend with. Therefore, your “Competitive Landscape” slide should address the following questions: Who are your company’s competitors? How is your company different from them, and what are your competitive advantages? Including more details helps to build investor confidence.

Below is an example from business social networking service LinkedIn, which not only includes the company’s competitors, but also indicates their relative positions in the market.

Investment “Ask”

Be clear about what you are asking for. Demonstrate that you have thought about your “ask” carefully and haven’t just plucked the amount you’re looking to raise out of thin air. Toptal Finance Expert Aleksey Krylovrecommends specificity: “I recommend my clients be very specific about their target number as opposed to using a range. They should be conservative in their estimates, as a lower targeted raise is more likely to close quickly.”

Krylov also advises including information on how you will use the proceeds, adding, “In my experience, it is important for building trust. It should highlight that the capital sought is not going to be used to maintain the founder’s lifestyle. The slide also needs to communicate that the money raised will bridge the company through the next value creation milestone(s). Investors like stories where step up in valuation is highly likely from the current round to the next.” You should show your 18-24 month plan and how much you’ll need to execute on that strategy.

Below is an example from marketing software company Intercom:


If you are interested in downloading or utilizing pitch deck templates, the following have been provided by venture capital funds: Google VenturesSequoia CapitalNextView Ventures.


As a parting thought, it is worth keeping in mind the following: In today’s world where startup fundraising has been glamorized to such an extent that raising capital is portrayed as a key success metric, one must remember to think about whether your “ask” is what your company actually needs. As Chris Dixon of Andreessen Horowitz says, “The best thing is to either never need to raise money or to raise money after you have a product, users, or customers.” Equity is the most expensive form of capital, so be sure to have truthfully assessed your startup’s needs, as opposed to feel like you have to fundraise just because it is the prescribed path for technology startups.

And if you do decide that you really need to fundraise, be clear about how much you need to raise, and what exactly it will be used for. The following two blog posts from legendary investor Fred Wilson can get you started: What Seed Finance Is For and How Much Money to Raise.

The art of fundraising is one that, if mastered, can often mean survival for your newly-established startup. In fact, you’ll also need to master the art of persuasion through Aristotle’s three modes of persuasion. The web is awash with advice on these matters, but much of the material out there is, frankly, not high quality. Should you need it, at Toptal, we have an array of experienced fundraising experts and pitch deck consultants who can help you craft successful pitches and fundraising strategies.


What is a pitch deck?

A pitch deck is a brief PowerPoint presentation used by companies seeking external funding. The presentation includes key highlights from the company, used by entrepreneurs as a persuasive tool and used by investors to learn more about the investment opportunity.

This article is originally posted in Toptal.

Three Core Principles of Venture Capital Portfolio Strategy

Key Highlights

  • Growth in startups worldwide has seen an influx of new professionals into venture capital. $3.8bn across 32 first-time manager funds was raised in 2016, continuing the trend over the last 5-7 years.
  • Returns for the asset class as a whole continue to be lackluster. VC returns haven’t significantly outperformed the public market since the late 1990s, and, since 1997, less cash has been returned to investors than has been invested into VC.
  • A driver of these returns is the decreasing barrier to entry for the industry and the basic mistakes made by many new entrants.
  • VC is a game of home runs, not averages. Strikeouts are extremely common. 65% of venture deals return less than the capital invested in them. But strikeouts don’t matter. The best performing funds actually have more strikeouts than mediocre funds.
  • The vast majority of a venture fund’s returns come from a few home run investments. For the best performing funds, less than 20% of their deals generate 90% of the returns.
  • Not only do the best funds have more home runs, they have bigger, better home runs.
  • Home runs are also extremely rare; the chances of hitting one are in the 0.5-2% range.
  • Some funds (e.g., 500 Startups) have gone for a strategy of maximizing at-bats. However, larger portfolios come at the detriment of quality. The ratio of accelerator-funded startups receiving follow-on investments is 18%, significantly below the market average of c. 50%.
  • Top US VC funds tend to do 1-20 investments per year, with the larger funds focused on the lower end of this range. Within a 4-5 year investment period, this implies a portfolio size of less than 50. Conventional wisdom in the VC space seems to be for there to be 20-40 companies in a given portfolio.
  • Many newcomers to the space fail to reserve sufficient capital to follow-on in the companies they have invested in. Andreessen Horowitz’ 2010 investment in Instagram of $250k, for instance, returned 312x in less than two years. Whilst being an incredible return on a deal basis, at a fund level Andreessen would have had to invest in 19 other companies on the same terms and with the same incredible performance to break even on the fund as a whole, illustrating the importance of following-on in the home run investments in a portfolio.

VC: The En Vogue Asset Class

From humble beginnings, the venture capital (VC) industry has exploded into one of the most significant, and certainly best-known, asset classes within the private equity space. Venture-backed startups are some of the most disruptive and influential companies of our generation. The venture capitalists backing them have also taken their spot in the limelight, with the likes of Marc Andreessen, Peter Thiel, and Bill Gurley gaining recognition far beyond the confines of Sand Hill Road. You could compare this cult of personality to that of “corporate raider” era of the 1980s, when Michael Milken et al catalyzed the start of the LBO and junk-bond boom.

Partly as a result of this, the venture capital space has seen an influx of participants and professionals. First-time fund managers continue to raise new VC funds at healthy clips (Chart 1), and the once clear lines separating venture capital from private equity, growth equity, and other private asset classes have begun to blur (Figure 1). Corporates have also shifted into the space, creating venture arms and participating in startup funding at ever increasing levels. And perhaps the greatest sign of the times, celebrities such as Kobe Bryant, Jay Z, and Derek Jeter have all thrown their hats into the startup-investment ring. As John McDuling puts it, “Venture capital has become [one of] the most glamorous and exciting corners of finance. Rich heirs used to open record labels or try their hand at producing films, now they invest in start-ups.”

Chart 1: VC First-time Fundraising Activity; and Figure 1: VC Raised vs. VC Contributed ($bn)

Succeeding in venture capital is not easy. In fact, while data assessing the asset class as a whole is scarce (and data on individual fund performance is even harder to come by), what is clear is that the asset class has not always lived up to expectations. As CB Insights points out, “VC returns haven’t significantly outperformed the public market since the late 1990s and, since 1997, less cash has been returned to investors than has been invested in VC.” Even the most well-known venture funds have come under scrutiny for their results: At the end of last year, leaked data showed that results for Andreessen Horowitz’ first three funds are less than spectacular.

The reasons for this lackluster performance are of course varied and complicated. Some believe that we may be in a bubble, which, if true, could explain the less-than-satisfying results of many funds (inflated values slowing the rush towards exits and offering a higher risk of valuation down rounds). Others argue that current fund structures are not properly set up to incentivize good performance. Scott Kupor’s response to the leaked results of Andreesen Horowitz also gives a defensive angle to this narrative, in that the lack of a wider understanding of the performance of the VC asset class drives the negative rhetoric.

But while all of this may or may not be true, another potential reason for lackluster performance amongst many funds is that they’re not following some of the fundamental principles of VC investing. As former bankers and consultants reinvent themselves as venture capitalists, they fail to assimilate some of the key differences that separate more established financial and investment activities from the more distinct form of venture investing.

To be clear, I am firmly within this camp. As someone who made the transition from the more traditional realms of finance into the world of venture investing, I have witnessed firsthand the differences between these activities. I am not in any way annointing myself as a venture capital guru, but through continual learning, I acknowledge and respect some of the important nuances that distinguish venture capital from other investing activities. The purpose of this article is therefore to highlight three of what I believe to be the most important venture capital portfolio tactics that many participants in the space fail to internalize.

1. Venture Capital Is a Game of Home Runs, Not Averages

The first, and arguably most important, concept that one has to understand is that venture capital is a game of home runs, not averages. By this, we mean that when thinking about assembling a venture capital portfolio, it is absolutely critical to understand that the vast majority of a fund’s return will be generated by a very few select companies in the portfolio. This has two very important implications to one’s day-to-day activities as a venture investor:

  1. Failed investments don’t matter.
  2. Every investment you make needs to have the potential to be a home run.

To many, particularly those from traditional finance backgrounds, this way of thinking is puzzling and counterintuitive. Conventional financial portfolio strategy assumes that asset returns are normally distributedfollowing the Efficient-market Hypothesis, and that because of this, the bulk of the portfolio generates its returns evenly across the board. A 2014 10-year analysis of 1-day returns from the S&P 500 in fact conforms to this bell curve effect, where the mode of the portfolio was more or less its mean (Chart 2).

Chart 2: S&P 500, 1-Day Returns over the Past 10 Years (2,500 Observations)

Turning away from the more liquid public markets, investment strategies in private markets also strongly emphasize the need to balance a portfolio carefully and manage the downside risks. In a recent interview with Bloomberg, legendary private equity investor Henry Kravis said this:

“When I was in my early 30s at Bear Stearns, I’d have drinks after work with a friend of my father’s who was an entrepreneur and owned a bunch of companies. “Never worry about what you might earn on the upside,” he’d say. “Always worry about what you might lose on the downside.” And it was a great lesson for me, because I was young. All I worried about was trying to get a deal done, for my investors and hopefully for myself. But you know, when you’re young, oftentimes you don’t worry about something going wrong. I guess as you get older you worry about that, because you’ve had a lot of things go wrong.”

And putting aside what we are taught from financial theory altogether, Chris Dixon mentions how the adversity to losses may be an in-built human mechanism:

“Behavioral economists have famously demonstrated that people feel a lot worse about losses of a given size than they feel good about gains of the same size. Losing money feels bad, even if it is part of an investment strategy that succeeds in aggregate.”

But the crux of the point with venture capital investing is that the above way of thinking is completely wrong and counterproductive. Let’s run through why that is.

Strike-outs Don’t Matter

Most new companies die out. Whether we like it or not, it happens frequently. And unfortunately, there is ample data to support this. The US Department of Labor, for instance, estimates that the survival rate for all small businesses after five years is roughly 50%, and falls dramatically to a low of 20% as more time passes. When it comes to startup investments by venture capital funds, the data is bleaker. A Correlation Ventures study of 21,640 financings spanning the years 2004-2013 showed that 65% of venture capital deals return less than the capital that was invested in them (Chart 3), a finding corroborated by a similar set of data from Horsley Bridge, a significant LP in several US VC funds which looked at 7,000 of its investments over the course of 1975-2014 (Chart 4).

Chart 3: Realized Multiple Range by % of Total; Chart 4: US Venture Investments by Return Range

Attentive readers may of course point out that the failure rate of startup investments may simply be upward-skewed by a number of bad funds who invested poorly. And they’d be forgiven for thinking that. But the fascinating outcome of the Horsley Bridge data is that this is in fact not correct. Quite the opposite, the best funds had more strikeouts than mediocre funds (Chart 5). And even weighted by amount invested per deal, the picture is unchanged.

Chart 5: Money-losing Investments as % of Total by Fund Return Range;

In other words, the data shows that the number of failed investments you make does not seem to be associated with the fund’s overall returns. It actually suggests that the two are may be inversely correlated. But if that’s the case, then what does drive a venture fund’s performance?

What Matters Are the Home Runs

What matters is other side of the coin: the home runs. And overwhelmingly so. Returning to the Horsley Bridge data, one can clearly note how returns for its best performing funds are overwhelmingly derived from a few select investments that end up producing outsized results (Chart 6). For funds who had returns above 5x, less than 20% of deals produced roughly 90% of the funds’ returns. This provides a tangible example of the Pareto Principle 80/20 law existing within VC.

Chart 6: Deal Distribution by Share of Fund

But it goes further than this: Not only do better funds have more home runs (and as we’ve seen above, more strike-outs too), but they have even bigger home runs (Chart 7). As Chris Dixon puts it, “Great funds not only have more home runs, they have home runs of greater magnitude,” or as Ben Evans summarizes, “The best VC funds don’t just have more failures and more big wins—they have bigger big wins.”

Chart 7: Gross Fund Returns, Horsley Bridge

Whichever way one chooses to word it, the takeaway is clear. Venture capital fund returns are extremely heavily skewed towards the returns of a few stand-out successful investments. The investments end up accounting for the majority of the fund’s overall performance. Perhaps the best way to summarize all this comes from Bill Gurley, one of the most successful venture capitalists around. He stated, “Venture capital is not even a home run business. It’s a grand slam business.”

The Babe Ruth Effect in Startup Investing

The above has led to what is commonly referred to in the venture capital space as the “Babe Ruth effect” to startup investing. For those unfamiliar with Babe Ruth, he is widely considered to be one of the greatest baseball players of all time, and was elected into the Baseball Hall of Fame as one of its “first five” inaugural members. In particular, what made him so famous, and such a crowd-drawer, was his batting ability. Babe Ruth set multiple batting records, including “career home runs (714), runs batted in (RBIs) (2,213), bases on balls (2,062), slugging percentage (.6897), and on-base plus slugging (OPS) (1.164)”.

But what is surprising, and less well-known, is that Babe Ruth was also a prolific misser of the ball. In other words, he struck out. A lot. His nickname for many years was the King of Strikeouts. But how could the two things be reconciled? The answer lies in Ruth’s batting style. In his own words:

“How to hit home runs: I swing as hard as I can, and I try to swing right through the ball […] The harder you grip the bat, the more you can swing it through the ball, and the farther the ball will go. I swing big, with everything I’ve got. I hit big or I miss big. I like to live as big as I can.”

The reason why Babe Ruth has this abstract association with venture capital portfolio strategy is that the same principals behind Ruth’s batting style can, and should, be applied to startup investing. If strikeouts (failed investments) don’t matter, and if most of the returns are driven by a few home runs (successful investments that produce outsized results), then a successful venture capitalist should look to invest in those companies that display the potential for truly outsized results, and to not worry if they fail. To contradict Henry Kravis’ thoughts on private equity investing, in VC one shouldn’t worry about the downside, but just focus on the upside.

Jeff Bezos takes this analogy even further, contrasting the ceiling of a 4-run baseball grand slam to the infinite possibilities of a successful financial deal:

“The difference between baseball and business, however, is that baseball has a truncated outcome distribution. When you swing, no matter how well you connect with the ball, the most runs you can get is four. In business, every once in a while, when you step up to the plate, you can score 1,000 runs.”

2. How to Maximize Your Chances of Hitting a Home Run

Given all of the above, the logical follow-on question should be how one can maximize their chances of finding a home run investment. This is a contentious topic to answer and I am going to frame it across two areas that are worth looking into. The first is how one can assess each investment opportunity to ascertain its potential of being a home run, and the second relates to more general portfolio strategy and number of investments in order to maximize the chances of having a home run in your VC portfolio. I look at these in turn, starting with the latter:

More At-Bats = More Home Runs?

If we follow the probabilities laid out above regarding the percentage of hitting a home run, we will note that no matter what data set is chosen, the probabilities are very low. The Correlation Ventures data shows that less than 5% of investments return above 10x, and of those, only a tiny fraction are in the 50x+ category. Similarly, the Horsley Bridge data shows that only 6% of deals return more than 10x.

Following this logic, a reasonable conclusion might be the following: In order to maximize your chances of hitting a home run, you need to have more at-bats.

Several VCs have taken this path. The most notable, and outspoken proponent of this investment strategy is Dave McClure, founder of 500 Startups. In a now famous blog post, McClure outlines his thesis clearly:

“Most VC funds are far too concentrated in a small number (<20–40) of companies. The industry would be better served by doubling or tripling the average [number] of investments in a portfolio, particularly for early-stage investors where startup attrition is even greater. If unicorns happen only 1–2% of the time, it logically follows that portfolio size should include a minimum of 50-100+ companies in order to have a reasonable shot at capturing these elusive and mythical creatures.”

His thesis is backed by a few illustrative portfolio examples, which he uses to display the importance of portfolio size, and which we’ve reproduced below (Tables 1, 2, and 3)

Table 1: Highly Concentrated Portfolio; Table 2: Modestly-Concentrated Portfolio; Table 3: Highly Diversified Portfolio

His numbers rely heavily on an arguably overlooked concept when it comes to portfolio strategies: the law of rounding. He is of course right, in that you cannot have a fraction of a startup. Which means that, assuming the probabilities he uses are correct (and in fairness they seem aligned with other data sets we’ve seen), if you really want to be “sure” of landing on a unicorn, you need to invest in at least 50 startups for that to happen (given that he assumed a 2% change of investment turning into a unicorn.)

McClure’s overall point is an interesting one. It resembles the “moneyball-style” investment tactics that have emerged successfully in many other areas of finance. And as mentioned, several other funds have taken a similar approach. In a sense, this is a fundamental philosophy behind all accelerators and incubators.

And yet, most venture capital funds do not follow this strategy. While information on fund size is hard to find, we charted data from Entrepreneur.com’s 2014 VC rankings and, showing a 3-way cross reference of number of deals (x) vs. average deal size (y) vs. fund assets under management (z), an interesting segmentation of the market emerges (Chart 8).

Chart 8: 2014 Data for 100 Largest VCs in the World. Number of Investments per Year, by Fund Size and Average Ticket

One can see in the chart above that the bulk of funds tend to do 1-20 investments per year, with larger funds (aside from a few outliers) focused on the lower end of the range. Within the context of a 4-5 year investment period, this leads to an implied portfolio size which is smaller than McClure’s suggested number. What is clear from the above is that the strategy of investing in many companies rather than fewer is not the norm. But if McClure’s analysis is correct, then why haven’t the majority of VC funds followed this approach? In McClure’s own words:

“My guess is it’s due to the mistaken belief by traditional VCs that they need to serve on boards directly, rather than simply securing the necessary voting rights and control they want that usually come with board seats. Or maybe they think they’re just better than the rest of us who aren’t tall, white, male, or didn’t go to the right schools. Or who don’t wear khakis. Or maybe it’s due to all those tee times, I’m not quite sure.”

It’s a colorful argument that has credibility from his experiences, but it is of course subjective and difficult to assess. Unfortunately, a data-driven approach at evaluating the true “value-add” that VCs bring to startups is near impossible. Nevertheless there are a few data points out there that seem to contradict McClure’s theory. Data from CB Insights, for instance, shows that the success rate of accelerator-funded companies to achieve a follow-on funding round are significantly lower than the market average (Chart 9). And if Forbes columnist Brian Solomon is correct in saying that “Only 2% of companies emerging from the top 20 accelerators have a successful exit yet” then that would again imply below-average results.

Chart 9: Follow-on Conversion for US Accelerator Portfolio Companies

Piecing this all together shows that there probably is a tradeoff between portfolio size and quality. While there has been a huge increase in startup activity in recent years (meaning that the sample to choose from has grown a lot), it’s hard to believe that shooting for 100+ companies in a portfolio allows for a maintenance of quality standards. But the truth will ultimately come out in due course, as data becomes more publicly available and time is called on portfolios that have emerged in the last decade.

Picking the Winners Effectively

If one rejects the moneyball-style approach and instead embraces the more traditional doctrine which holds that VC firms should pick fewer companies and “cultivate” them to succeed, then an important question becomes: How can you pick your investments wisely in order to maximize the chances of landing on a home run?

This is of course an extremely difficult question to answer, and one that differentiates the successful venture capital investors from the rest. After all, if it were so easy, then the returns of the asset class would be far superior to what they really are. The practice of choosing which startups to invest in is more of an art than a science, and as such no definitive playbook can be laid out. Nevertheless, there are a few general points that emerge from scanning the writings of the best investors.


In an investment decision, two factors are being assessed: the idea and the people behind it. More emphasis should be applied to assessing the team. Back the jockey, not the horse, so to speak. In the words of Apple and Intel early investor Arthur Rock:

“I invest in people, not ideas […] If you can find good people, if they are wrong about the product, they’ll make a switch, so what good is it to understand the product that they are making in the first place?”

Ideas are more malleable than people. Someone’s personality is far harder to change than executing a product pivot. The vision and talent of a founder is the drive behind everything in the company and, in these days of celebrity founders, it is also a branding exercise.

Empirical data is now being released that supports this theory. A study by by professors Shai Bernstein and Arthur Korteweg with Kevin Laws of AngelList found that on the latter’s platform, teaser emails about new Angel Deals that featured more prominent information about the founding team increased click rates by 14%.

Addressable Market Size

If each investment made needs to have the potential for outsized returns, then an obvious facet of these companies is that they have a large addressable market size. Total Addressable Market slides are now a mainstay of pitch decks (and equally so, a source of derision when they all contain the now-seemingly-obligatory $1 trillion market opportunity).

A deeper understanding of the dynamics of the market being tackled is necessary in order to understand how truly addressable this market is. This example from Lee Howler sums up this fallacy quite well:

“There’s $100bn+ spent each year on plane flights, hotels, and rental cars in the US […] but if you’re an upstart online travel service, you’re not competing for those dollars unless you actually own a fleet of planes, rental cars, and a bunch of hotels”

Investors want to see entrepreneurs that have a profound understanding of the value chains and competitive dynamics of the market that they are tackling. In addition, a startup needs to show a clear roadmap and USP of how they can carve an initial niche within this and grow, or move into horizontal verticals.

Scaleability/High Operating Leverage

Good venture investors are looking for startups that grow exponentially with diminishing marginal costs, wherein the costs of producing additional units continually shrink. The operating leverage effects of this allows companies to scale quicker, more customers can be taken on for little to no operational change, and the increased cashflows can be harvested back into investing for even more growth. How would an investor asses this at Day 0? Steve Blank provides a strong definition of a scalable startup:

“A scalable startup is designed by intent from day one to become a large company. The founders believe they have a big idea—one that can grow to $100 million or more in annual revenue—by either disrupting an existing market and taking customers from existing companies or creating a new market. Scalable startups aim to provide an obscene return to their founders and investors using all available outside resources”

Consider Tesla open sourcing its patents. This was not intended as a solely benevolent gesture by Elon Musk; instead, it was an attempt by him to accelerate innovation within the electric car space by encouraging external parties to innovate in his arena. More efforts to produce better technology (i.e., longer life batteries) will ultimately help Tesla to reduce its marginal costs faster.

The importance of operating leverage is one of the main reasons, amongst others, why venture capitalists often focus on technology companies. These tend to scale faster and more easily than companies who do not rely on technology.

An “Unfair” Advantage

Startups face up to deeper pocketed and more experienced incumbents with a goal to usurp them. To do this, they have to employ unconventional tactics that are not easily replicated by incumbents. An investor must look to what innovative strategies the startup is using to tackle larger competitors. Aaron Levie of Box sums this up in three forms of unfair advantage: via product, business model, and culture. Lets consider three examples of this.

An unfair product: Waze turns geo-mapping on its head by deploying its actual users to generate its maps for free. Exponentially quicker and making a mockery of the sunk costs incurred by incumbents like TomTom.

An unfair business model: Dollar Shave Club realizes that the majority of shavers care very little that Roger Federer uses Gillette and creates a lean, viral marketing campaign that delivers quality razors for a fraction of the price. It was impossible for incumbents to respond to this without cannibalizing their existing lines.

An unfair culture: The two former points will be driven by a culture in the startup that is more laser focused than an incumbent. Consider this example of Dashlane, which built a unified culture from eschewing traditional startup perks and using innovative video technology to bring its French and American offices together.


Through looking at the reasons for success across a range of startups, Bill Gross of Idealab concluded that timing accounted for 42% of the difference between success and failure (Chart 10). This was the most critical element from his study, which also accounted for team, idea, business model, and funding.

Chart 10: Top 5 Factors in Success across More Than 200 Companies

To give an example of how he defined this, he referred to Airbnb during his TED Talk:

“[Airbnb was] famously passed on by many smart investors because people thought, “No one’s going to rent out a space in their home to a stranger.” Of course, people proved that wrong. But one of the reasons it succeeded, aside from a good business model, a good idea, great execution, is the timing.”

Using the 2009 recession at the time to frame this:

“[This was at a time] when people really needed extra money, and that maybe helped people overcome their objection to rent out their own home to a stranger.”

A venture capital investor will look at the timing of startups as part of their investment process. Is the deal arriving at the optimal time and is this business model riding a macroeconomic or cultural wave? The investors in Airbnb will have had the vision to frame this investment away from the prevailing biases of the time and view it as a unique opportunity arriving at the opportune moment.

3. Follow-on Strategies: Doubling Down on the Winners

The final venture capital portfolio strategy that I want to highlight, and one that many newcomers to venture investing fail to account for, relates to follow-on strategy. By follow-on, I mean the ability and disposition to invest further capital into future fundraising rounds of the companies that are already in the portfolio.

The importance of follow-ons was illustrated by Peter Thiel in his book, Zero to One. In it, he gives the following example:

“Andreessen Horowitz invested $250,000 in Instagram in 2010. When Facebook bought Instagram just two years later for $1 billion, Andreessen netted $78 million—a 312x return in less than two years. That’s a phenomenal return, befitting the firm’s reputation as one of the Valley’s best. But in a weird way it’s not nearly enough, because Andreessen Horowitz has a $1.5 billion fund: if they only wrote $250,000 checks, they would need to find 19 Instagrams just to break even. This is why investors typically put a lot more money into any company worth funding. (And to be fair, Andreessen would have invested more in Instagram’s later rounds had it not been conflicted out by a previous investment). VCs must find the handful of companies that will successfully go from 0 to 1 and then back them with every resource.”

The example above demonstrates vividly the importance of follow-ons. If only a few investments end up being home runs, then a successful fund will identify that and double down on its winners to maximize the returns of the fund.

The actual decision of when to double down is, however, not as simple as it may seem. At a high level, the chart below shows how a venture investor should choose their follow-on targets, using the analogy of doubling down at the “elbow.” As the slide behind this chart explains: “1) Invest at “The Flat” when prices are low, 2) Double-down if/when you detect “The Elbow” (if valuation isn’t crazy), and 3) Don’t invest at “The Wall” unless capital is infinite—if valuation starts running away, you usually can’t buy any meaningful ownership relative to existing.””

Chart 11: The Flat, The Elbow, The Wall

Nevertheless, in real life, being able to distinguish between Startup W, Startup K, and Startup L is not that easy. Particularly between Startup W and Startup K. Mark Suster wrote a helpful post outlining his way of thinking about this issue, but the fact remains that the decision is not always a clear-cut one. But that is, of course, where, again, the best VCs will differentiate themselves from the also-rans. And as in the previous section, this is more of an art than a science. Successful following-on is a strong test of a venture manager’s chops, where they are presented with the sunk cost fallacy decision, of pouring more money into a loser in the hope it turns around, or letting the investment die.

The importance of follow-ons to a fund’s overall returns stands out in the publicly available data. Union Square Ventures’ 2010 Opportunity Fund had a calculated IRR of 60.59% (Pitchbook), making it an extremely successful VC fund. If we look at follow-on trends (CB Insights) for USV after this period, we can see the majority of their funding choices were going as follow-ons into their winners. They were doubling down and the fund result shows that this was indeed a profitable strategy.

Chart 12: USV % of New vs. Follow-on Investments

This post has been about highlighting certain often overlooked venture capital portfolio strategies that serve to maximize performance. And this last point around follow-ons should not be considered least. Fred Wilson of USV sums it up:

“One of the most common mistakes I see new “emerging VC managers” make is that they don’t sufficiently reserve for follow-on investments. They don’t go back for a new fund until they have invested 70 to 80% of their first fund and then they run out of money and can’t participate in follow-on rounds. They put too many companies into a portfolio and they can’t support them all. That hurts them because they get diluted by those rounds they can’t participate in. But it also hurts their portfolio companies because the founder and/or CEO has to explain why some of their VC investors aren’t participating in the financing round.

Most people think that VC is all about the initial portfolio construction, selecting the companies to invest in. But the truth is that is only half of it. What happens with the portfolio after you have selected it is the other half. That includes actively managing the portfolio (board work, adding value, etc.) and it includes allocating capital to the portfolio in follow-on rounds, and it includes working to get exits. And it is that second part that is the harder part to learn how to do. The best VC firms do it incredibly well and they benefit enormously from it.”

At the start of this section, I said following-on was an overlooked part of VC. This is because the initial investments and their associated glamor of pitch decks and coffee meetings are the tip of the iceberg. The home runs are followed out of the park with the 66% of the fund that is reserved for follow-ons.

Optimizing for the Power Law

At the beginning of this article, we mentioned how the venture capital industry, as an asset class, has posted generally unsatisfactory returns. A fascinating report by the Kauffman Foundation shed further light on the issue and produced some interesting results. In the report, called We Have Met the Enemy and He is Us, the Foundation found that when looking at a collection of venture capital funds, only a few were responsible for most of the returns for the asset class as a whole (Chart 13).

Chart 13: A Small Number of Funds Generate Big VC Returns

In many ways, the performance of funds is analogous to the performance of venture deals: a few home runs and a lot of strikeouts. The shape of fund level returns follows a similar pattern to the distribution of single deal returns shown in the Correlation Ventures study in Chart 3 at the beginning of this article in which the 50x deals constitute a tiny portion of the sample, but with a significant magnitude of absolute returns.

The implication of the above is very significant. Readers will recall how returns of public stocks seemingly follow a normal distribution. What we hope to have conveyed in this article is that returns in the venture capital space, both at a deal level as well as at a fund level, do not follow a normal distribution. Rather, they seem to follow a power law distribution, a long-tail curve where the vast bulk of the returns are concentrated within a small number of funds. Figure 2 below illustrates the difference between a power law distribution and the more common normal distribution.

Figure 2: Power Law Distribution vs. Normal Distribution

The concept of the VC industry conforming to a power law distribution was rendered popular by Peter Thiel in Zero to One. In it, he said:

“The power law becomes visible when you follow the money: in venture capital, where investors try to profit from exponential growth in early-stage companies, a few companies attain exponentially greater value than all others. […] We don’t live in a normal world, we live under a power law.”

On an empirical level, evidence is arising to support this claim. Dario Prencipe of the European Investment Fund performed a detailed and fascinating statistical analysis of the fund’s returns from VC, which showed preliminary evidence supporting this power law principal. Investor Jerry Neumann also offers an in-depth look into the concept of power law existing in venture capital.

All of this implies that investors looking to succeed in the venture capital space must internalize the concepts and implications of the power law. Whether it is empirically and mathematically correct that the VC industry’s returns are distributed according to a power law is perhaps still a question, but conceptually, it is very clear that the venture capital space is very much an “outlier-driven” industry.

Not only this, but once one has internalized the concepts underlying the power law, one then needs to think about how to tactically use this to one’s advantage. The concepts we’ve outlined above regarding the number of at-bats and the importance of follow-ons are some of the more important ways one can do it.

The proliferation of startup “culture” and venture capital investing worldwide is arguably a positive phenomenon for the world. Paraphrasing Peter Relan, “[The world] needs new ideas, and citizens can’t expect the government to foster tomorrow’s disruption […] [Startups] have become a pathway to achieve this approach; they give people an opportunity to make their dreams come true. And even if most of these ideas fail, they will still create innovations that can be reflected in the product technology in other spaces.”

So the influx of new professionals into the venture capital space is a good thing. But for this all to continue and succeed, LPs need to see positive results for their investments. If only a few venture capital funds really know what they’re doing, and drive most of the returns for the asset class, then perhaps the solution would be for there to be fewer venture capital funds. But following on the above, that could be detrimental to society. Instead, we’d like to think that the solution should be the other way around: More venture capital funds should know what they’re doing. Hopefully this article can, even in a small way, be helpful in that regard.

This article is originally posted in Toptal.