• Cloud Computing
• Benefits
• Hyper-Virality
• Funding and Revenue Models
• Exploding Intangibles
• People
• The Caveat

Cloud Computing

Cloud computing is poorly defined. Offering computer resources as a service. Across a network. Probably the internet. I prefer Frank Gens’ description – a “grid-utility model”, that anyone can use. Grid computing is the use of lots of individual computers as one. Utility computing is paying for computing resources as you use them. So instead of investing in lots of private computing capacity, one simply buys computing resources from an internet-based cloud of (effectively infinite) computing resources, as they are needed.

Benefits

What’s the advantage?

Most private computing resources are woefully under-utilised. A typical buisness’s computers lie idle while employees are sleeping, and are probably only partly used when they are working. Most websites/services are hopelessly over-resourced to accomodate occasional peaks in traffic. And then promptly collapse when the peaks transpire to be bigger than anyone expected. Sharing huge clouds of computing resources between many users – each with their own peaks and troughs – logically allows the total current demand for computing resources to be met with far fewer computers. Utility charging provides the mechanism that translates “fewer computers” into “lower cost”.

But there’s far more to cloud computing than “making existing things more efficient”.

Hyper-Virality

I define hyper-virality as the near-instantaneous adoption of a service or product by consumers, distributed through a pyramid of social connections.

It is the theoretical end-point of many current social internet trends: That ideas, optimisations, and methods that enhance the efficiency or well-being of humanity, move around the world ever faster than before. The actions of a handful of dominant, successful, or somehow innovative “thought leaders” are copied by those they lead, who are in turn copied those they lead. Since all the people on the planet are connected to each other by a surprisingly small number of links (you know someone, who knows someone, who knows someone, who… not many more times to link to everyone), distributing something down to absolutely everyone is potentially easy.

In an extreme case, a service that really is, the greatest thing since sliced bread, could almost instantly be benefiting billions of people. Clearly this level of hyper-virality is theoretical. The whole notion of absolute hyper-virality may itself be nonsense: That any one idea can be so clearly the best thing to do, that almost everyone is immediately prepared to adopt it. But in a limited way, it is already happening:

The best current examples of the concept are probably Facebook applications. These are still far from hyper-viral, but do demonstrate basic behaviour: When a user starts using a service, that user automatically informs their “friends”. If the service is desirable, those friends start using it, and in turn, inform all their friends. It is already possible to grow from no users to millions of users in a matter of weeks.

With traditional computing services, it is very difficult to scale computing resources up fast enough to keep pace with that growth in demand. Even if you can find enough machines, you probably cannot organise and configure them. You would need to anticipate millions of users and plan sufficient computing resources beforehand. And probably anticipate the wrong number – either be left with a lot of unused, but costly resources, or be unable to meet demand.

Cloud computing solves this problem, because precisely the right amount of computer resources are available, with costs always in proportion to the amount of resources used.

(Well, actually it is not yet a complete panacea – some caveats are discussed later.)

In concept, this “agile deployment” of internet-based services to the cloud, enables true hyper-virality to occur, since it creates the ability to deliver the ultimate product or service to everyone, almost instantly. This becomes far more important if we assume that our economies are becoming highly intangible entities, which primarily function over communications/internet-type services (see a Socio-Economic Environment based on Nothing) – most economic activity will occur in this communications/internet-type environment. But even if we don’t accept that assumption, the cloud potentially changes how we finance internet-based services.

Funding and Revenue Models

In a hyper-viral environment conventional business plans need to be rethought. A wild guess at future usage won’t do: “Somewhere between 3 and 3 billion users, with growth occurring sometime between today and the next decade”. That’s already a real dilemma for anyone developing a mass-market internet-type application.

Instead one needs to answer the question: How do I rapidly scale to meet demand? At the very least covering marginal costs (operating costs).

Broadly, there are 2 solutions:

1. A revenue model that earns money in proportion to usage, with ultra-efficient cash-flow, that allows increasing revenue to immediately pay for increasing operating costs.
2. An ultra-efficient capital market, that will invest in proportion to growth in usage, pending the later establishment of revenue streams.

Both approaches differ from what we tend to do now: Convince someone to invest some money, and hope we’ve established enough growth/revenue/”something good” by the time we’ve burnt through the cash.

Some fairly basic revenue models fit the first method well, such as display advertising or subscription-only services. In others models revenue may lag behind growth slightly: Premium versions of services that don’t earn money until users have enjoyed a free trial, or methods that rely on first building user engagement or trust, such as micro-transactions.

There is a risk of bankruptcy due to slow cash-flow: If your advertising revenue is still written on a cheque, lost for weeks in the postal or banking systems, what do you pay the cloud computing bill with? Fortunately, both incoming revenue and outgoing costs tend to lag behind by about the same amount. Cloud computing tends to be billed after use, not before, so everything is delayed by the same arcane business payment practices. But there’s still an element of Russian Roulette to whether the credit or debit clears the system first.

Parts of the financial system have the potential to be ultra-efficient. For example, in minutes, stock markets can adjust to changes in relative demands for capital. Except that such markets are dealing in established businesses that investors have some understanding of. Even high-risk investors, such as venture capital or private equity, may be unable to respond fast enough to new hyper-viral investment opportunities.

Note that established organisations will face slightly different problems to new entrants. For example, a historic focus on capital expenditure and fixed budgets may require structural change to occur within the organisation.

Astute readers will note that I have only addressed funding after a product or service’s launch. Cloud computing and virality won’t make it any cheaper to actually develop that product/service to the point where it is ready to be used. I hope to return to the topic of iterative product development on the internet in a later article – I contend that less time should be spent trying to develop a “finished” product before launch, and instead design should gradually iterate improvements on a live, “permanent beta” product or service. A logical extension of agile development into the iterative specification of the product itself.

Exploding Intangibles

Corporate finance still seems to be biased towards physical assets. This is great for factories: You can add up the value of all the machinery, materials and stock – all the physical stuff – and discount it all year-to-year. But if the most expensive physical asset is the coffee machine, classic accountancy tends to struggle. Highly intangible companies are not new – plenty of businesses are built up around intangible assets, like brands or franchises. And methods have evolved to account for the value of these companies, even if the process is inherently less precise.

What’s not clear is whether a rapidly expanding, but totally intangible, business can gain access to any conventional forms of finance. Our hyper-viral, cloud-based internet business didn’t even exist last month, and (because of the hyper-virality of unknown potential competitors) might not exist next month. So what is additional investment secured against? The coffee machine? While it is easy to point towards the rise (and fall) of major technology startups that have grown to be stock market-listed within a few years, the ultimate hyper-viral business would want to make that leap almost instantly.

Can finance move as fast as hyper-viral businesses? Current practices suggest not. But that misunderstands the potential advantage in fast-paced hyper-virality: Everything can move faster, including the return on capital. Invest for a few months, instead of a few years. So perhaps the challenge shifts to creating “stuff” that better informs capital markets, which can then react appropriately to other sectors?

People

Does this work organisationally?

Is it naive to assume that one individual can ever develop a solution that can gain mass-appeal, without significant help? Just as at the start of the first internet bubble, many believed that “2 kids in a garage” were going to destroy established businesses. While small developers do sometimes come to dominate emerging markets, most historic business empires remain in tact. And if that help takes time to organise, won’t that human organisational factor limit the speed of virality, giving existing organisations significant advantages over lone individuals?

Perhaps there are just to many unanswered questions? Technology and capital is easy – people are far more complicated!

Curiously, if people both define the structure through which hyper-virality occurs, and are the reason why hyper-virality isn’t quite as fast as it might be, it will be people that naturally establish a happy equilibrium. It would be limited by people, not technology or capital.

The Caveat

A small caveat: Cloud computing cannot yet deliver all its promised potential.

For example, by consensus the cloud does not handle database-driven applications well, particularly where a lot of data is being written (presumably we need to radically re-think the way databases work). Techniques for scaling resources within the cloud, to meet rapidly changing computing demands, are still evolving. It is not yet as automated as it sounds. There are a plethora of concerns related to issues such as security (by consensus, more perception than actual problem), and standards (some commentators suggest inter-operability will be resolved through clever use of APIs, much as different database software were made to “talk” to each other).

Ownership and privacy is a potentially important, but poorly explored issue: Logically a handful of huge cloud computing providers could gain considerable control and influence over the creativity of individual developers (much as early-modern “publishers” historically came to dominate people that worked as printers).

Understanding the Odds

During 6 and a half years at Garage Technology Ventures:

• 97,500 entrepreneurs submitted information via a proforma.
• 17,000 of these were invited to submit executive summaries.
• 1,200 of these got a face-to-face interview.
• 100 of these got funded.

(And Bill Joos didn’t say it, but likely only 10-20 of those that got funded will develop into successful businesses.)

In such a fiercely competitive environment, with such long odds, perfecting a business plan and pitch is clearly important. So learn from the 10 top mistakes:

1. “Too darn Long”

Venture capitalists have Attention Deficit Disorder. Everything needs to be as short and to the point as possible. Short text is harder to write than long text.

Some benchmarks:

• The “elevator” pitch: 1 minute, at a leisurely pace.
• Executive summary: 1 page. The aim is to get a meeting.
• Initial meeting slides: “The dirty dozen” (certainly less than 20 slides). Aim to keep 40 minutes of a 60 minute meeting for discussion, not presentation. Save detail for later meetings.

2. “Poor Positioning”

A “solution looking for a problem” is not a strong case. While there are exceptions, most funding is for “painkillers” – and for those to sell, someone needs to be in pain. Don’t forget to consider who the customer actually is.

Benefits need to be put into context – given a frame of reference. It is unlikely that those listening to pitches will be experts in the field. Bill cited Molly the elephant: Is a “7,000 pound” elephant over or under weight? Is that good or bad?

3. “Lack of Tight Focus”

Avoid “Swiss army knife plans”: Business plans that attempt to enter every possible market immediately: It is hard to succeed in just one market. Justify a focus on the market segment initially, with an “encore strategy” for developing into other markets over time.

4. “Not Enough Real World Market Analysis”

There are 2 approaches when researching markets:

1. Top down: Citing overall market research, and then claiming an arbitrary percentage of that market as a target.
2. Bottom up: Detailed research within a small part of the market.

Bottom up approaches show greater understanding and depth, however top down can still be used to validate bottom up.

Don’t just quote the total available market – understand what is reachable. Oh, and do not state the bleedin’ obvious: “Mobile phones are a growth market in developing countries…”

5. “What are the 3 Drivers of your Business?”

What are the key performance metrics that will determine whether the business operates effectively? A simple question, that many cannot answer.

6. “Unclear Business Model”

How will you make money? Or a profit? Particular care is needed where adoption is likely to be slow or dependant on other companies. For example, large corporation (Fortune 500 businesses) have very long sell cycles – don’t expect to sell to them in the first year. Similarly if customers need to be found from among a handful of businesses, exploratory negotiation with those companies is worth doing beforehand.

How well will sales scale? The first few sales are often the easiest, because they will be made to customers that are already well know to the entrepreneur and their team. Subsequent sales might be much harder.

Finally, don’t quote revenue expectations without explaining the assumptions made.

7. “Poor or Incomplete Competitive Analysis”

The startup needs to be distinguishable from the competition. But it must also acknowledge that competition exists and position itself in the market accordingly. Not disclosing the competition either marks one out as naive or a liar, neither of which is positive.

It is common not to understand the power of the “status quo” in a market. This is a particular problem when entering an existing market, because sales need to displace an existing product or service. Displacement sales are the hardest to do.

8. “Weak Team Information”

Admit to weaknesses in the team (such gaps are often a reason for seeking support from a venture capitalist). Know the function of the first 3 hires. Generally venture capitalists will “bet on the jockey – because they pick the best horses” (although I suspect that varies).

9. “Poorly Defined or Weak Go To Market Plans”

Who is going to sell this stuff anyway? Who will have budgetary authority? And who besides the entrepreneur has a vested interest in success – for example, a partner company?

10. “Goofy Fundamentals that Distract”

Startups are initially an exercise in survival. Sometimes an apparently rational decision can backfire: For example, giving an employee a stake in the business accidentally formalises the value of the company, and makes it harder for investors to realise the true value of the business. So get some “adult supervision”: Establishing a board of advisers shows willingness to take advice, and depending on the people involved, reduces the risk of the venture.