No big intro, let’s get to it. This is how to build a startup…

1. www.theleanstartup.com learn to use less to create more
2. www.udacity.com/course/ep245 great quick visual course to give you the fundamentals of building a viable business model
3. www.rocketlawyer.com to register your business
4. www.irs.gov and apply for an EIN (tax) number
5. Go to a bank to set up your business account (with documents from the above)
6. www.waveaccounting.com for free (yes, really free) accounting and invoicing services
7. www.dreamhost.com for easy, fast web hosting with great customer service
8. www.google.com to set up email for your business
9. set up your social media. (we could write a whole article on this alone…)
10. Go make money…

There is a bot more too it than that, but these first steps will get well on your way.

There are many different types of startups that require different resources. This list covers the basics, the learning and the foundation of most businesses. If you want to build a startup that is pure technology, then your hosting needs will be different and your development team will a core element. If you are building a non-profit then the IRS process will be different. Use these steps as a guide and ask us questions if you want to find out more about your specific needs.

Business model canvas

We like the business model canvas (as talked about in the Udacity course above) as a way to track the evolution of our business model. Check it out.

Read more about the Lean Startup in the Harvard Business Review.
…rhubarb runs innovation workshops to help new companies, new projects and new ideas come to fruition. We work with you in immersive sessions to draw out your ideas, explore your strengths & weaknesses and drive your business forward.

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Event host Hermione Way of The Next Web said, “I’ve been seeing pitches all around the world and most are people are petrified, Americans can really pitch the hell out of anything!” Indeed the personalities were strong and for the most part confident and loud, but the content was not always what the judges (investors) needed to hear. The pitches included the basics of ‘outline the problem, provide the solution’, yet beyond that, left a lot of key information out.

When I was on the pitch side of the pitching table, I remember working for ages trying to create the perfect deck that said everything I wanted to say in as short a time as possible. Now that I’m listening from the other side of the table, I realise my error: a pitch should not be about what I want to say, but about what the people listening need to know.

Here are the questions which potential investors need answered from each pitch:

• What is the problem?
• What is your solution?
• Who are you? (ie Why should we listen to you about this?)
• How will your venture make money and how quickly? (Business model and unique selling point)
• How much money have you made so far, how much do you need, and for what?

Just like on the web, a pretty package is great but content is king.

StartupWorld allows three minute for each pitch; a number of pitches ended with the buzzer going off in mid-sentence. To ensure that thoroughly prepared content is heard, it’s integral for the enthusiastic and effusive entrepreneur to rehearse, rehearse and rehearse again.

Pitching prowess can be learnt but good ideas only come to those who think, and it was motivating to hear the great ideas there last night. Good luck to all the start ups and particularly to EnPlug, who gave the perfect pitch but were pipped to the post. Congratulations to the winner ArtKive, the online storage locker for children’s artwork – wishing you well pitching against the rest of the world in the final round of StartupWorld.

-cauri

cauri jaye: technophile, futurist, optimist.

Further reading:
Why every entrepreneur should learn how to become a great story-teller

The post How to pitch to make investors listen appeared first on rhubarb.

You may think this world a far and distant future, if you accept it at all. But it’s already well underway, transforming before our eyes and evident in the global unemployment figures.

In his prescient book ‘End of work’, in 1995 Jeremy Rifkin says “Worldwide, more than a billion jobs will have to be created over the next ten years to provide an income for all the new job entrants in both developing and developed nations. With new information and telecommunication technologies, robotics, and automation fast eliminating jobs in every industry and sector, the likelihood of finding enough work for the hundreds of millions of new job entrants appears slim.” While his timing may have been a bit off, this is the world we now live in.

From hands to machines

A look at historical trends shows just how quickly the rate of change can happen. Whether the history of technological change from prehistory to now is measured by energy used, information collected or specific inventions over time, it is clear that the rate of change is accelerating. How many of us remember what life was like without a computer? Now imagine it without the internet, a global credit card, a mobile phone or even a smartphone. And those are developments in just the last 20 years. The world is changing, faster and faster.

Look at history: 150,000 years ago humans used their bare hands, 140,000 years later they domesticated animals to work for them, followed by agriculture a mere 7,000 years later, which created sedentary culture and currency which allowed some people to amass wealth without producing anything but rather by controlling the people who did the work, or by throttling natural resources, or both. This created the so-called division of labour: blue and white collar work, or skilled and unskilled labour.

The industrial revolutions introduced tools, technology and machines which enabled fewer people to produce a lot more. There was a brief counter trend in the early twentieth century when Ford introduced the assembly line: the manufacturing process in which interchangeable parts are added to a product in a sequential manner by unskilled labour to create a finished product. This made production faster than with handcrafting-type methods. As this new method of production spread, there was an increase in demand for low-skilled labour. This trend continued, which lead to the relatively modern shift of manual labour from developed countries to the developing ones, epitomized by the infamous factories in countries like India, China and Taiwan. Perhaps best put by NASA in the 60s: “Man is the lowest-cost, 150-pound, nonlinear, all-purpose computer system which can be mass-produced by unskilled labor.” However, in the 1950s came the introduction of the industrial robot to the assembly line, which once again reduced the need for blue-collar labour.

In 1965, Intel co-founder, Gordon Moore, observed that every 18 months the number of transistors on a computer chip doubles. This became known as Moore’s law: every 18 months computers chips would double in speed, and halve in cost. So the idea that humans will always be the cheapest form of labour will be challenged. In accordance with Moore’s law, the cost of machines continually drops – and yet workforce rights enforcement and the rising global standards of living are increasing the cost of offshore labour. The ultimate culmination of these trends is that, not too long from now, machines will build machines, and they will be cheaper than humans to both produce and run.

From secretaries to iPads

In parallel to the replacement of blue-collar labour by skilled machines, the latter half of the twentieth century saw the rise of the computer. From the 1970s to today, white collar jobs have been replaced by smarter and smarter computer systems. Gone are the days of secretaries, typists and printing services: a single person can build and run a business with global sales on an iPad; and do so with greater reach, product diversity and consumer loyalty than most global corporations could have dreamed of just 30 years ago.

Similarly, customer service is creeping away from being human to software-based. Since the late 50s, call centres were run by humans on the phone. In the 90s, these centres were completely revamped with software to help diagnose problems and respond faster. In the early 2000s, companies moved these centres offshore to capitalize on cheap labour using phone and internet technologies. In the last five years, increasingly the software has collected information and presented solutions to the consumer directly, with more sophisticated voice recognition handling a large percentage of issues with no human interaction at all.

From human to software

This shift from human-only to software-assisted to software-only, is representative of many areas of business and industry, and sits at the core of the jobs issue. This trend will only continue. As systems become smarter and manufacturing becomes cheaper and more automated, both unskilled labour and managerial labour will be replaced by machines. As economists John Fernald and Susanto Basu put it: “Computers and networks bring an ever-expanding set of opportunities to companies. Digitisation, in other words, is not a single project providing one-time benefits. Instead, it’s an ongoing process of creative destruction; innovators use both new and established technologies to make deep changes at the level of the task, the job, the process, even the organisation itself. And these changes build and feed on each other so that the possibilities offered really are constantly expanding.”

In the last year, the idea of a digital assistant has re-surfaced. Apple introduced Siri, its 24/7 virtual assistant to listen to your needs and try to attend to them. IBM is working to squeeze all the power of their Jeopardy-winning computer Watson into smartphones. Google has introduced Google Now, a service which goes a step further by using your habits, your friends’ habits and global trends to anticipate what you need before you even ask for it. This use of software analysing global patterns and trends to help identify individual needs is at the centre of the current revolution that will alter the job market forever.

Sensors and big data

Here’s what this trend towards automation looks like in real terms: at the core of the issue sits an ever-increasing number of detectors gathering more and more info.

Detectors or sensors refers to the different ways that computers are gathering and generating data in the modern world. A few very public examples in the last 15 years include geo-location sensors in mobile devices gathering positioning data, citywide CCTV cameras tracking human activity, smartphone cameras connected to social media capturing video, images and opinions for online publication, RFID chips on products generating sales data, and even software sensors like browser cookies gathering website traffic data.

‘Big data’ is a catch-all phrase that refers to the rapid increase in the velocity, variety and volume of data. The increase in the number of sensors and the different types of sensors delivers the increased quantity of data. Breaking this data down into usable information is all about pattern recognition: categorizing information into overlapping, logical groups. Given a limited set of data, human beings excel at this (you recognise the face of your best friend immediately, whereas computers still have problems with this), however given a massive set of data, computers far outstrip humans (a computer looking for a specific trend in 40,000 pages of documents will find the desired information much quicker than you can). Take the example of 17-year-old Brittany Wenger who programmed a pattern-matching neural net to diagnose breast cancer. So industries based on pattern recognition in large data sets are the first being affected: market research, law, medicine and science.

Computers learn exponentially. They get faster and more efficient with each iteration. They will get better and better at pattern recognition on smaller and smaller sets of data until they will rival and eventually surpass the human ability to recognise patterns on small datasets (such as recognising a face, as the much-debated Facebook algorithm now does). This process of pattern recognition on small, multi-dimensional data sets is at the core of what we call intuition, which is the basis of understanding, insight and discovery.

A large part of the labour force of the Fortune 500 companies today is made up of analysts of various types: sales analysts, marketing analysts, business analysts, research analysts and so on. Even senior managers are really multi-skilled, high-level analysts looking for trends, usually in their specialist subject. Company staff numbers will be decimated as the necessary analytical skills are gradually done better by computers. Suddenly the line between quantitative and qualitative research will be further blurred as computer systems see these as completely compatible inputs to the same pattern matching system. Business teams’ numbers will be significantly reduced as the primary pass at the data will be handled by computers, and even the secondary refinement, the testing of those refinements and eventually the insights and the application of those insights themselves will be controlled in an automated fashion.

Knowledge and the end of the job era

Examples of computers catching up to humans and outstripping us abound. Take Google’s brain, which learnt on its own and of its own volition how to recognise pictures of cats in YouTube videos. And David Cope, a professor emeritus at UC Santa Cruz, who has developed software that creates classical music imitating masters like Bach. Ashutosh Saxena, at Cornell University created a robot that can arrange your room for you. Soon avatars will replace humans at NYC airports. And Google’s driverless cars, now legal in Nevada and Florida, will become the norm.

These examples of recent developments show the important leap that technology is making into the previously ‘human’ domain: knowledge. Analysis is about the filter through which data is perceived. In the past, it took humans to translate this into useful thought or insight by looking at it through the lens of knowledge and experience. With the new systems emerging that use the massive stores of information available through big data and the semantic web to recognise the patterns in the data at a higher level of abstraction, computers can begin to do what the brain does and transform facts into knowledge. It will look at concepts, objects and actions in context. Like East Asian languages which have a relatively holistic cognitive orientation, emphasizing relationships and connectedness, the language does not express a “cup” as an object like westerners do, it describes “cup” as a “vessel for confining liquid from which we drink”, reflecting its relationship with other objects, people and substances. Similarly computer systems now perceive a verb, an interaction, an interconnected, codependent, symbiotic relationship between an object or concept and everything else. This advance is not trivial. It is the difference between a child’s brain and an adult’s one. It is the difference between knowing and understanding. It is the difference between a human doing the job, or not.

As these trends continue, with manufacturing being dominated by robotic-based systems and management being driven by pattern-recognition systems, the number of jobs for humans will dwindle rapidly. So the question arises: how will the world operate when there are no jobs as we know them left for humans to do? How will the economy change? How will education change? How will the nation-states change? What will the role of multinational corporations become?

There are some who say humans will begin to work side by side with these smarter computers, the symbiosis creating better output than human or computer alone. Google Glass, released earlier this year, is the perfect example: an internet-connected monocle that directly overlays data on the world that you see around you giving you the benefit of the wealth of info on the web all the time. Others take this even farther and think that we will start to put more implants in humans and create more organic computers so that human-computer hybrids will do the work. There is some conversation about technology taking us to unlimited resources, through better energy-sources and nanotechnology, making work as we know it completely redundant. There are some more theories about what comes next but the conversation is neither happening broadly enough nor fast enough.

This is not a question for the future, it is a question for now. It is not a problem for economists and we cannot leave the discussion to the writers of Star Trek. It is a problem for us all to address. Governments will not solve this issue through debate nor companies through meetings; the general population must be part of the conversation. This is not an issue for our children, it will happen in most of our lifetimes. So I ask again, how will the world operate when there are no jobs as we know them left?

cauri jaye: technophile, futurist, optimist.

Read more:

Race against the Machines by Erik Brynjolfsson and Andrew McAfee

Culture, Control and Perception of Relationships in the Environment by Li-Jun Ji

The history of the call centre by Jonty Pearce

Guns, Germs and Steel by Jared Diamond

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Not convinced it applies to you? Just look at the trends. The change from tech-free to tech-dependant is visible in so many industries; even an utterly human activity such as dating is now a booming industry, almost entirely technology-driven. And if you’d asked a match-making service a decade ago, they would have said there was no place for technology in what they offer.

The challenge businesses now face is not whether to invest in technology, but how and for what benefit. SMBs usually don’t have the budget for in-house, full-time technology expertise, and external advice usually comes with the ulterior motive of landing a supply contract, rather than meeting overall business needs. Or worse, advice comes from John, the guy you met at a dinner party the other evening who knew a bit about tech, or your grad student nephew.

Here’s where …rhubarb can help, offering executive level technology expertise, tailored to your business needs, and translated in language you can understand. Just as you wouldn’t plan your financial investments without a financial adviser, ensure you make future business plans with a technology expert to advise you on what your options are, and what kind of world you are likely to be operating in.

Rebecca McLauchlan, CEO, says: ‘Technology and digital communications are moving so fast that many small-medium businesses are overwhelmed: before they’ve even got a handle on what’s available, it’s changed, and so has their business. …rhubarb specialises in finding out what challenges your business faces, then finding the solutions to help you flourish. If you want to maintain your competitive edge, you need to know what’s on the horizon – and have a map to help you get there.’

…rhubarb technology communications consultancy is set up by two Brits who specialise in embedding digital solutions into business planning. They bring a wealth of experience from one of the world’s most active online consumers nations: the UK. They have a particular talent for converting ‘digital dinosaurs’ – often cynical or just plain scared – to tech-enabled entrepreneurs.

To schedule your free discovery meeting, email …rhubarb or call Rebecca on 909-30-rhbrb (74272). Find out more about …rhubarb’s services at www.rhbrb.com.

Rebecca McLauchlan: motivate, facilitate, make it happen.

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