Reconstructing jobs Creating good jobs in the age of artificial intelligence

​Fears of AI-based automation forcing humans out of work or accelerating the creation of unstable jobs may be unfounded. AI thoughtfully deployed could instead help create meaningful work.

Creating good jobs

When it comes to work, workers, and jobs, much of the angst of the modern era boils down to the fear that we’re witnessing the automation endgame, and that there will be nowhere for humans to retreat as machines take over the last few tasks. The most recent wave of commentary on this front stems from the use of artificial intelligence (AI) to capture and automate tacit knowledge and tasks, which were previously thought to be too subtle and complex to be automated. Is there no area of human experience that can’t be quantified and mechanized? And if not, what is left for humans to do except the menial tasks involved in taking care of the machines?

At the core of this concern is our desire for good jobs—jobs that, without undue intensity or stress, make the most of workers’ natural attributes and abilities; where the work provides the worker with motivation, novelty, diversity, autonomy, and work/life balance; and where workers are duly compensated and consider the employment contract fair. Crucially, good jobs support workers in learning by doing—and, in so doing, deliver benefits on three levels: to the worker, who gains in personal development and job satisfaction; to the organization, which innovates as staff find new problems to solve and opportunities to pursue; and to the community as a whole, which reaps the economic benefits of hosting thriving organizations and workers. This is what makes good jobs productive and sustainable for the organization, as well as engaging and fulfilling for the worker. It is also what aligns good jobs with the larger community’s values and norms, since a community can hardly argue with having happier citizens and a higher standard of living.1

Does the relentless advance of AI threaten to automate away all the learning, creativity, and meaning that make a job a good job? Certainly, some have blamed technology for just such an outcome. Headlines today often express concern over technological innovation resulting in bad jobs for humans, or even the complete elimination of certain professions. Some fear that further technology advancement in the workplace will result in jobs that are little more than collections of loosely related tasks where employers respond to cost pressures by dividing work schedules into ever smaller slithers of time, and where employees are being asked to work for longer periods over more days. As the monotonic progress of technology has automated more and more of a firm’s function, managers have fallen into the habit of considering work as little more than a series of tasks, strung end-to-end into processes, to be accomplished as efficiently as possible, with human labor as a cost to be minimized. The result has been the creation of narrowly defined, monotonous, and unstable jobs, spanning knowledge work and procedural jobs in bureaucracies and service work in the emerging “gig economy.”2

The problem here isn’t the technology; rather, it’s the way the technology is used—and, more than that, the way people think about using it. True, AI can execute certain tasks that human beings have historically performed, and it can thereby replace the humans who were once responsible for those tasks. However, just because we can use AI in this manner doesn’t mean that we should. As we have previously argued, there is tantalizing evidence that using AI on a task-by-task basis may not be the most effective way to apply it.3 Conceptualizing work in terms of tasks and processes, and using technology to automate those tasks and processes, may have served us well in the industrial era, but just as AI differs from previous generations of technologies in its ability to mimic (some) human behaviors, so too should our view of work evolve so as to allow us to best put that ability to use.

In this essay, we argue that the thoughtful use of AI-based automation, far from making humans obsolete or relegating them to busywork, can open up vast possibilities for creating meaningful work that not only allows for, but requires, the uniquely human strengths of sense-making and contextual decisions. In fact, creating good jobs that play to our strengths as social creatures might be necessary if we’re to realize AI’s latent potential and break us out of the persistent period of low productivity growth that we’re experiencing today. But for AI to deliver on its promise, we must take a fundamentally different view of work and how work is organized—one that takes AI’s uniquely flexible capabilities into account, and that treats humans and intelligent machines as partners in search of solutions to a shared problem.

Problems rather than processes

Consider a chatbot—a computer program that a user can converse or chat with—typically used for product support or as a shopping assistant. The computer in the Enterprise from Star Trek is a chatbot, as is Microsoft’s Zo, and the virtual assistants that come with many smartphones. The use of AI allows a chatbot to deliver a range of responses to a range of stimuli, rather than limiting it to a single stereotyped response to a specific input. This flexibility in recognizing inputs and generating appropriate responses is the hallmark of AI-based automation, distinguishing it from automation using prior generations of technology. Because of this flexibility, AI-enabled systems can be said to display digital behaviors, actions that are driven by the recognition of what is required in a particular situation as a response to a particular stimulus.

We can consider a chatbot to embody a set of digital behaviors, how the bot responds to different utterances from the user. On the one hand, the chatbot’s ability to deliver different responses to different inputs gives it more utility and adaptability than a nonintelligent automated system. On the other hand, the behaviors that chatbots evince are fairly simple, constrained to canned responses in a conversation plan or limited by access to training data.4 More than that, chatbots are also constrained by their inability to leverage the social and cultural context they find themselves in. This is what makes chatbots—and AI-enabled systems generally—fundamentally different from humans, and an important reason that AI cannot “take over” all human jobs.

Humans rely on context to make sense of the world. The meaning of “let’s table the motion,” for example, depends on the context it’s uttered in. Our ability to refer to the context of a conversation is a significant contributor to our rich behaviors (as opposed to a chatbot’s simple ones). We can tune our response to verbal and nonverbal cues, past experience, knowledge of past or current events, anticipation of future events, knowledge of our counterparty, our empathy for the situation of others, or even cultural preferences (whether or not we’re consciously aware of them). The context of a conversation also evolves over time; we can infer new facts and come to new realizations. Indeed, the act of reaching a conclusion or realizing that there’s a better question to ask might even provide the stimulus required to trigger a different behavior.

Chatbots are limited in their ability to draw on context. They can only refer to external information that has been explicitly integrated into the solution. They don’t have general knowledge or a rich understanding of culture. Even the ability to refer back to earlier in a conversation is problematic, making it hard for earlier behaviors to influence later ones. Consequentially, a chatbot’s behaviors tend to be of the simpler, functional kind, such as providing information in response to an explicit request. Nor do these behaviors interact with each other, preventing more complex behaviors from emerging.

The way chatbots are typically used exemplifies what we would argue is a “wrong” way to use AI-based automation—to execute tasks typically performed by a human, who is then considered redundant and replaceable. By only automating the simple behaviors within the reach of technology, and then treating the chatbot as a replacement for humans, we’re eliminating richer, more complex social and cultural behaviors that make interactions valuable. A chatbot cannot recognize humor or sarcasm, interpret elliptical allusions, or engage in small talk—yet we have put them in situations where, being accustomed to human interaction, people expect all these elements and more. It’s not surprising that users find chatbots frustrating and chatbot adoption is failing.5

A more productive approach is to combine digital and human behaviors. Consider the challenge of helping people who, due to a series of unfortunate events, find themselves about to become homeless. Often these people are not in a position to use a task-based interface—a website or interactive voice response (IVR) system—to resolve their situation. They need the rich interaction of a behavior-based interface, one where interaction with another human will enable them to work through the issue, quantify the problem, explore possible options, and (hopefully) find a solution.

We would like to use technology to improve the performance of the contact center such a person might call in this emergency. Reducing the effort required to serve each client would enable the contact center to serve more clients. At the same time, we don’t want to reduce the quality of the service. Indeed, ideally, we would like to take some of the time saved and use it to improve the service’s value by empowering social workers to delve deeper into problems and find more suitable (ideally, longer-term) solutions. This might also enable the center to move away from break-fix operation, where a portion of demand is due to the center’s inability to resolve problems at the last time of contact. Clearly, if we can use technology appropriately then it might be possible to improve efficiency (more clients serviced), make the center more effective (more long-term solutions and less break-fix), and also increase the value of the outcome for the client (a better match between the underlying need and services provided).

If we’re not replacing the human, then perhaps we can augment the human by using a machine to automate some of the repetitive tasks. Consider oncology, a common example used to illustrate this human-augmentation strategy. Computers can already recognize cancer in a medical image more reliably than a human. We could simply pass responsibility for image analysis to machines, with the humans moving to more “complex” unautomated tasks, as we typically integrate human and machine by defining handoffs between tasks. However, the computer does not identify what is unusual with this particular tumor, or what it has in common with other unusual tumors, and launch into the process of discovering and developing new knowledge. We see a similar problem with our chatbot example, where removing the humans from the front line prevents social workers from understanding how the factors driving homelessness are changing, resulting in a system that can only service old demand, not new. If we break this link between doing and understanding, then our systems will become more precise over time (as machine operation improves) but they will not evolve outside their algorithmic box.

Our goal must be to construct work in such a way that digital behaviors are blended with human behaviors, increasing accuracy and effectiveness, while creating space for the humans to identify the unusual and build new knowledge, resulting in solutions that are superior to those that digital or human behaviors would create in isolation . Hence, if we’re to blend AI and human to achieve higher performance, then we need to find a way for human and digital behaviors to work together, rather than in sequence. To do this, we need to move away from thinking of work as a string of tasks comprising a process, to envisioning work as a set of complementary behaviors concentrated on addressing a problem. Behavior-based work can be conceptualized as a team standing around a shared whiteboard, each holding a marker, responding to new stimuli (text and other marks) appearing on the board, carrying out their action, and drawing their result on the same board. Contrast this with task-based work, which is more like a bucket brigade where the workers stand in a line and the “work” is passed from worker to worker on its way to a predetermined destination, with each worker carrying out his or her action as the work passes by. Task-based work enables us to create optimal solutions to specific problems in a static and unchanging environment. Behavior-based work, on the other hand, provides effective solutions to ill-defined problems in a complex and changing world.

If we’re to blend AI and human to achieve higher performance, then we need to find a way for human and digital behaviors to work together, rather than in sequence.

To facilitate behavior-based work, we need to create a shared context that captures what is known about the problem to be solved, and against which both human and digital behaviors can operate. The starting point in our contact center example might be a transcript of the conversation so far, transcribed via a speech-to-text behavior. A collection of “recognize-client behaviors” monitor the conversation to determine if the caller is a returning client. This might be via voice-print or speech-pattern recognition. The client could state their name clearly enough for the AI to understand. They may have even provided a case number or be calling from a known phone number. Or the social worker might step in if they recognize the caller before the AI does. Regardless, the client’s details are fetched from case management to populate our shared context, the shared digital whiteboard, with minimal intervention.

As the conversation unfolds, digital behaviors use natural language to identify key facts in the dialogue. A client mentions a dependent child, for example. These facts are highlighted for both the human and other digital behaviors to see, creating a summary of the conversation updated in real time. The social worker can choose to accept the highlighted facts, or cancel or modify them. Regardless, the human’s focus is on the conversation, and they only need to step in when captured facts need correcting, rather than being distracted by the need to navigate a case management system.

Digital behaviors can encode business rules or policies. If, for example, there is sufficient data to determine that the client qualifies for emergency housing, then a business-rule behavior could recognize this and assert it in the shared context. The assertion might trigger a set of “find emergency housing behaviors” that contact suitable services to determine availability, offering the social worker a set of potential solutions. Larger services might be contacted via B2B links or robotic process automation (if no B2B integration exists). Many emergency housing services are small operations, so the contact might be via a message (email or text) to the duty manager, rather than via a computer-to-computer connection. We might even automate empathy by using AI to determine the level of stress in the client’s voice, providing a simple graphical measure of stress to the social worker to help them determine if the client needs additional help, such as talking to an external service on the client’s behalf.

As this example illustrates, the superior value provided by structuring work around problems, rather than tasks, relies on our human ability to make sense of the world, to spot the unusual and the new, to discover what’s unique in this particular situation and create new knowledge. The line between human and machine cannot be delineated in terms of knowledge and skills unique to one or the other. The difference is that humans can participate in the social process of creating knowledge, while machines can only apply what has already been discovered.6

Good for workers, firms, and society

AI enables us to think differently about how we construct work. Rather than construct work from products and specialized tasks, we can choose to construct work from problems and behaviors. Individuals consulting financial advisors, for example, typically don’t want to purchase investment products as the end goal; what they really want is to secure a happy retirement. The problem can be defined as follows: What does a “happy retirement” look like; how much income is needed to support that lifestyle, how to balance spending and saving today to find the cash to invest and navigate and (financial) challenges that life puts in the road, and what investments give the client the best shot at getting from here to there? The financial advisor, client, and robo-advisor could collaborate around a common case file, a digital representation of their shared problem, incrementally defining what a “happy retirement” is and, consequently, the needed investment goals, income streams, and so on. This contrasts with treating the work as a process of “request investment parameters” (which the client doesn’t know) and then “recommend insurance” and “provide investment recommendations” (which the client doesn’t want, or only wants as a means to an end). The financial advisor’s job is to provide the rich human behaviors—educator to the investor’s student—to elucidate and establish the retirement goals (and, by extension, investment goals), while the robo-advisor provides simple algorithmic ones, responding to changes in the case file by updating it with an optimal investment strategy. Together, the human and robo-advisor can explore more options (thanks to the power and scope of digital behaviors) and develop a deeper understanding of the client’s needs (thanks to the human advisor’s questioning and contextual knowledge) than either could alone, creating more value as a result.

Rather than construct work from products and specialized tasks, we can choose to construct work from problems and behaviors.

If organizing work around problems and combining AI and human behaviors to help solve them can deliver greater value to customers, it similarly holds the potential to deliver greater value for businesses, as productivity is partly determined by how we construct jobs. The majority of the productivity benefits associated with a new technology don’t come from the initial invention and introduction of new production technology. They come from learning-by-doing:7 workers at the coalface identifying, sharing, and solving problems and improving techniques. Power looms are a particularly good example, with their introduction into production improving productivity by a factor of 2.5, but with a further factor of 20 provided by subsequent learning-by-doing.8

It’s important to maintain the connection between the humans—the creative problem identifiers—and the problems to be discovered. This is something that Toyota did when it realized that highly mechanized factories were efficient, but they didn’t improve. Humans were reintroduced and given roles in the production process to enable them to understand what the machines were doing, develop expertise, and consequently improve the production processes. The insights from these workers reduced waste in crankshaft production by 10 percent and helped shorten the production line. Others improved axel production and cut costs for chassis parts.9

This improvement was no coincidence. Jobs that are good for individuals—because they make the most of human sense-making nature—generally are also good for firms, because they improve productivity through learning by doing. As we will see below, they can also be good for society as a whole.

Consider bus drivers. With the development of autonomous vehicles in the foreseeable future, pundits are worried about what to do with all the soon to be unemployed bus drivers. However, rather than fearing that autonomous buses will make bus drivers redundant, we should acknowledge that they will find themselves in situations that only a human, and human behaviors, can deal with. Challenging weather (heavy rain or extreme glare) might require a driver to step in and take control. Unexpected events—accidents, road work, or an emergency—could require a human’s judgment to determine which road rule to break. (Is it permissible to edge into a red light while making space for an emergency vehicle?) Routes need to be adjusted due to anything from a temporarily moved stop to modifying routes due to roadwork. A human presence might be legally required to, for example, monitor underage children or represent the vehicle at an accident.

As with chatbots, automating the simple behaviors and then eliminating the human will result in an undesirable outcome. A more productive approach is to discover the problems that bus drivers deal with, and then structure work and jobs around these problems and the kinds of behaviors needed to solve them. AI can be used to automate the simple behaviors, enabling the drivers to focus on more important ones, making the human-bus combination more productive as a result. The question is: Which problems and decision centers should we choose?

Let us assume that the simple behaviors required to drive a bus are automated. Our autonomous bus can steer, avoiding obstacles and holding its lane, maintain speed and separation with other vehicles, and obey the rules of the road. We can also assume that the bus will follow a route and schedule. If the service is frequent enough, then the collection of buses on a route might behave as a flock, adjusting speed to maintain separation and ensure that a bus arrives at each stop every five minutes or so, rather than attempting to arrive at a specific time.

As with the power loom, automating these simple behaviors means that drivers are not required to be constantly present for the bus (or loom) to operate. Rather than drive a single bus, they can now “drive” a flock of buses. The drivers monitor where each bus is, how it’s tracking to schedule, with the system suggesting interventions to overcome problems, such as a breakdown, congestion, or changed road conditions. The drivers can step in to pilot a particular bus should the conditions be too challenging (roadworks, perhaps, where markings and signaling are problematic), or to deal with an event that requires that human touch.

These buses could all be on the same route. A mobile driver might be responsible for four-to-five sequential buses on a route, zipping between them as needed to manage accidents or dealing with customer complaints (or disagreements between customers). Or the driver might be responsible for buses in a geographic area, on multiple routes. It’s even possible to split the work, creating a desk-bound “driver” responsible for drone operation of a larger number of buses, while mobile and stationary drivers restrict themselves to incidents requiring a physical presence. School or community buses, for example, might have remote video monitoring while in transit, complemented by a human presence at stops.

Breaking the requirement that each bus have its own driver will provide us with an immediate productivity gain. If 10 drivers can manage 25 autonomous buses, then we will see productivity increase by a factor of 2.5, as we did with power looms: good jobs for the firm, as workers are more productive. Doing this requires an astute division of labor between mobile, stationary, and remote drivers, creating three different “bus driver” jobs that meet different work preferences: good jobs for the worker and the firm. Ensuring that these jobs involve workers as stakeholders in improving the system enables us to tap into learning-by-doing, allowing workers to continue to work on their craft, and the subsequent productivity improvements that learning-by-doing provides, which is good for workers and the firm.

These jobs don’t require training in software development or AI. They do require many of the same skills as existing bus drivers: understanding traffic, managing customers, dealing with accidents, and other day-to-day challenges. Some new skills will also be required, such as training a bus where to park at a new bus stop (by doing it manually the first time), or managing a flock of buses remotely (by nudging routes and separations in response to incidents), though these skills are not a stretch. Drivers will require a higher level of numeracy and literacy than in the past though, as it is a document-driven world that we’re describing. Regardless, shifting from manual to autonomous buses does not imply making existing bus drivers redundant en masse. Many will make the transition on their own, others will require some help, and a few will require support to find new work.

The question then, is: What to do with the productivity dividend? We could simply cut the cost of a bus ticket, passing the benefit onto existing patrons. Some of the saving might also be returned to the community, as public transport services are often subsidized. Another choice is to transform public transport, creating a more inclusive and equitable public transport system.

Buses are seen as an unreliable form of transport—schedules are sparse with some buses only running hourly for part of the day, and not running at all otherwise; and route coverage is inadequate leaving many (less fortunate) members of society in public transport deserts (locations more than 800 m from high-frequency public transport). We could rework the bus network to provide a more frequent service, as well as extending service into under-serviced areas, eliminating public transport deserts. The result could be a fairer and more equitable service at a similar cost to the old, with the same number of jobs. This has the potential to transform lives. Reliable bus services might result in higher patronage, resulting in more bus routes being created, more frequent services on existing bus routes, and more bus “drivers” being hired. Indeed, this is the pattern we saw with power looms during the Industrial Revolution. Improved productivity resulted in lower prices for cloth, enabling a broader section of the community to buy higher quality clothing, which increased demand and created more jobs for weavers. Automation can result in jobs that are good for the worker, firm, and society as a whole.

Automation can result in jobs that are good for the worker, firm, and society as a whole.

How will we shape the jobs of the future?

There is no inevitability about the nature of work in the future. Clearly, the work will be different than it is today, though how it is different is an open question. Predictions of a jobless future, or a nirvana where we live a life of leisure, are most likely wrong. It’s true that the development of new technology has a significant effect on the shape society takes, though this is not a one-way street, as society’s preferences shape which technologies are pursued and which of their potential uses are socially acceptable. Melvin Kranzberg, a historian specializing in the history of technology, captured this in his fourth law: “Although technology might be a prime element in many public issues, nontechnical factors take precedence in technology-policy decisions.”10

The jobs first created by the development of the moving assembly line were clearly unacceptable by social standards of the time. The solution was for society to establish social norms for the employee-employer relationship—with the legislation of the eight-hour an example of this—and the development of the social institutions to support this new relationship. New “sharing economy” jobs and AI encroaching into the workplace suggest that we might be reaching a similar point, with many firms feeling that they have no option but to create bad jobs if they want to survive. These bad jobs can carry an economic cost, as they drag profitability down. In this essay, as well as our previous,11 we have argued that these bad jobs are also preventing us from capitalizing on the opportunity created by AI.

Our relationship with technology has changed, and how we conceive work needs to change as a consequence. Prior to the Industrial Revolution, work was predominantly craft-based; we had an instrumental relationship with technology; and social norms and institutions were designed to support craft-based work. After the Industrial Revolution, with the development of the moving production line as the tipping point, work was based on task-specialization, and a new set of social norms and institutions were developed to support work built around products, tasks, and the skills required to prosecute them. With the advent of AI, our relationship with technology is changing again, and this automation is better thought of as capturing behaviors rather than tasks. As we stated previously, if automation in the industrial era was the replication of tasks previously isolated and defined for humans, then in this post-industrial era automation might be the replication of isolated and well-defined behaviors that were previously unique to humans.12

There are many ways to package human and digital behaviors—of constructing the jobs of the future. We, as a community, get to determine what these jobs look like. This future will still require bus drivers, mining engineers and machinery operators, financial advisors, as well as social workers and those employed in the caring professions, as it is our human proclivity for noticing the new and unusual, of making sense of the world, that creates value. Few people want financial products for their retirement fund; what they really want is a happy retirement. In a world of robo-advisors, all the value is created in the human conversation between financial advisors and clients, where they work together to discover what the clients’ happy retirement is (and consequently, investment goals, incomes stream, etc.), not in the mechanical creation and implementation of an investment strategy based on predefined parameters. If we’re to make the most of AI, realize the productivity (and, consequently, quality of life) improvements it promises, and deliver the opportunities for operational efficiency, then we need to choose to create good jobs:

  • Jobs that make the most of our human nature as social problem identifiers and solvers
  • Jobs that are productive and sustainable for organizations
  • Jobs with an employee-employer relationship aligned with social norms
  • Jobs that support learning by doing, providing for the worker’s personal development, for the improvement of the organization, and for the wealth of the community as a whole.

The question, then, is: What do we want these jobs of the future to look like?

Saving Steve Jobs

Second Opinions Are Critical: Learn how Steve Jobs fought cancer with the right diagnosis, extending life expectancy when Apple needed him the most.

The trouble with misfits, as Steve Jobs would say, was that they refused to comply. As an entrepreneur who pioneered successive revolutions in personal computing & portable devices, Jobs will always be remembered as an aggressive creator and visionary innovator, who never settled for what the world believed to be a norm.

Of course – we know him as the man who famously put a thousand songs in your pocket and gave the world exceptionally efficient and beautiful gadgets; we have admired his ideas on life and debated endlessly about his arguments. We have smiled at his antics and shed tears at his interpretation of Gandhi. But there is another story – one that runs parallel to his tales of entrepreneurial excellence, and sadly, the one that gets misrepresented the most.

As is the case with volatile urban legends, it is widely speculated that Steve Jobs outlived the general life expectancy of a terminal pancreatic cancer patient. He was diagnosed, after all, in 2003!

Myth: Steve Jobs Had Pancreatic Cancer.

It is common knowledge that Jobs was never too vocal about personal issues. At the helm of a publicly traded computing giant however, he was answerable to his board, shareholders, and members of the Apple tribe. Even so, Steve usually refrained from focusing on the specifics of his diagnosis, which led many to believe that he suffered from pancreatic cancer.

However, there is a general consensus within the medical fraternity that pancreatic cancer (Adenocarcinoma) would have translated into an expedited death, shortly after his diagnosis was confirmed in 2003. What really allowed Jobs to live reasonably well for the next 8 years, was an accurate diagnosis.

Pancreatic Cancer vs Neuroendocrine Cancer

Among a very few instances where Jobs decided to throw some light to his diagnosis, he referred to his affliction as a “hormonal imbalance” as opposed to your regular, run-of-the-mill Pancreatic Cancer.

In a convocation speech at Stanford University in 2005 (now popular, thanks to YouTube), Jobs reflected back on the discovery of a tumor in his pancreas in 2003, and the initial reaction of his doctors who were almost certain at the time, that it was an ‘incurable’ type of cancer, giving him a probable life expectancy of 3 to 6 months.

I lived with that diagnosis all day. Later that evening I had a biopsy, where they stuck an endoscope down my throat, through my stomach and into my intestines, put a needle into my pancreas and got a few cells from the tumor. I was sedated, but my wife, who was there, told me that when they viewed the cells under a microscope the doctors started crying because it turned out to be a very rare form of cancer that is curable with surgery. I had the surgery, and I’m fine now.

-Steve Jobs, Stanford Convocation, 2005

It is confirmed today, that Jobs suffered from Neuroendocrine Cancer. Due to a lack of clear public understanding and widespread awareness about the disease (It is reported that as few as 10 cancer specialists in the world fully understood Neuroendocrine Cancer in 2001), its symptoms were often mistaken for Pancreatic Cancer, Irritable Bowel Syndrome or Crohn’s Disease.

While most forms of pancreatic cancer arise from pancreatic cells, neuroendocrine tumors arise from hormone-producing islet cells that happen to be in the pancreas. Unlike regular pancreatic cancer, where patients are likely to die within weeks or months after diagnosis, neuroendocrine cancer grows slow, and can be controlled and contained with an early, accurate diagnosis.

Fact: Steve Jobs Had Access To Specialists.

Following his surgery, Jobs lived for 8 more years, and during this time, administrative responsibilities at Apple were gradually handed down to the right personnel.

It isn’t hard to understand that a man of Jobs’ stature had access to the absolute best that the medicare industry had to offer at the time, and that his diagnosis and subsequent surgery were accelerated by the availability of dedicated on-call specialists whenever required. In other words, Jobs didn’t have to worry about the credibility of the treatment he was receiving.

Sadly, though – misdiagnosis and incorrect treatments result in a huge number of deaths around the world today.

Not everyone can afford to deploy medical resources like Steve Jobs did, and yet – as many as 1,000 patients are diagnosed with Neuroendocrine cancer each year in the United States alone.

With the right push and timely access to specialists, they can be treated well, treated right, and allowed the same life expectancy extensions as Jobs enjoyed, if not more.

Question: Doesn’t limited access make you vulnerable to an incorrect diagnosis?

Plagued by the deplorable condition of state-sponsored and privately-distributed health insurance providers and non-availability of surplus funds, most cancer patients in the world today have their options severely limited to standard-issue procedures for diagnosis and treatment. While rare cases such as Neuroendocrine tumors require special analysis, it is still extremely common to find misinterpretations of its symptoms.

The general population still doesn’t have enough access to specialists. Even if they go to general physicians and hospitals, they get referred to standard procedures such as chemotherapy and radiation therapy. In fact, credible and qualified second opinions were really hard to source, until a new wave of internet-enabled services made it possible to connect patients with specialists and multidisciplinary panels of oncologists.

The Advantage

At, we offer single consultations with domestic & international oncologists, as well as a tumor-board review for advanced cases, for patients who wish to have their ongoing cancer treatment reviewed. In a reference that Jobs would have probably humored, we are trying to intervene as an ‘Autocorrect’ service for cancer treatments around the world, with the availability of an unbiased consultation/treatment being our top priority.

Any patient, irrespective of the stage of their diagnosis/treatment, can send us their existing medical data and receive an accurate analysis of their treatment, and we will revert with any necessary course corrections, as well as suggestions about possible clinical trial engagements that can really make a difference. Not being affiliated to any hospital or treatment centre allows us to be focused only on the right advice for cancer patients, and lets us push forward into a world where everyone has access to the the diagnostic advantages that helped Steve Jobs live for 8 more years.

References & Bibliography

  1. Reference: Neuroendocrine Tumor Research Foundation
  2. Reference: An article that first appeared in Charlotte Observer
  3. Reference: National Cancer Institute – PDQ on Islet Cell Tumors

Steve Jobs Knew How to Write an Email. Here’s How He Did It

Undoubtedly, Apple co-founder and visionary Steve Jobs wrote several thousands of emails throughout his life. Relatively few of them have been shared with the public, and most of those are short responses to customer complaints.

But there are a few trails out there that show the skillful way Jobs used written communication. Let’s take a look at just one example and see what lessons we can glean from it.

[Note: This email was made part of public record when it was used as evidence in a U.S. lawsuit against Apple accusing the company of conspiring to raise the price of Ebooks in violation of antitrust laws. Apple was found guilty, although the company denied it had done anything wrong and fought the decision through the appeal process. The U.S. Supreme Court eventually declined to hear Apple’s appeal, meaning the company was required to pay a $450 million settlement.]

The context.

In 2010, Jobs and Apple were preparing to release the iPad. A key feature would be the tablet’s ability to function as an e-reader, similar to Amazon’s Kindle (which had already been out for a few years). Of course, the more publishers willing to contribute books to Apple’s iTunes store, the more appeal the iPad would hold.

Four major publishers had already signed on, but another, HarperCollins, was holding out.

Negotiations eventually centered around a key conversation between Jobs and James Murdoch, an executive at News Corp. (HarperCollins’ parent company). Murdoch wasn’t convinced his company (and its partners) could agree to the terms Apple was offering, especially regarding the “ceding of pricing to Apple.”

Jobs proceeded to write an email to try to convince HC to join.

Here’s what it said:


Our proposal does set the upper limit for ebook retail pricing based on the hardcover price of each book. The reason we are doing this is that, with our experience selling a lot of content online, we simply don’t think the ebook market can be successful with pricing higher than $12.99 or $14.99. Heck, Amazon is selling these books at $9.99, and who knows, maybe they are right and we will fail even at $12.99. But we’re willing to try at the prices we’ve proposed. We are not willing to try at higher prices because we are pretty sure we’ll all fail.

As I see it, HC has the following choices:

1. Throw in with Apple and see if we can all make a go of this to create a real mainstream ebooks market at $12.99 and $14.99.

2. Keep going with Amazon at $9.99. You will make a bit more money in the short term, but in the medium term Amazon will tell you they will be paying you 70 percent of $9.99. They have shareholders too.

3. Hold back your books from Amazon. Without a way for customers to buy your ebooks, they will steal them. This will be the start of piracy and once started there will be no stopping it. Trust me, I’ve seen this happen with my own eyes.

Maybe I’m missing something, but I don’t see any other alternatives. Do you?


This email contains a number of invaluable lessons. Let’s break them down.

He uses the recipient’s name.

This email is only a part of a larger thread that had begun at least two days earlier. There was no need for Jobs to address Murdoch by name. So, why did he?

Of course, we can’t read Jobs’s mind. But using a person’s first name reestablishes connection and helps build trust. It says: Look, I know you. You know me. We’re on the same side here.

Takeaway: I’m not suggesting you begin every email with the person’s name, especially after correspondence has already begun. But if you’re trying to make a point or reestablish a common ground, remember the famous words of Dale Carnegie:

“A person’s name is to that person the sweetest and most important sound in any language.”

It’s well-thought out.

I don’t know how long Jobs took to compose this email, but we can assume it was more than a few minutes. It clearly explains his position, in simple, understandable terms.

“Heck, Amazon is selling these books at $9.99, and who knows, maybe they are right and we will fail even at $12.99,” Jobs writes. “But we’re willing to try at the prices we’ve proposed. We are not willing to try at higher prices because we are pretty sure we’ll all fail.”

This language is conversational, vulnerable, and paints the picture of Apple giving it their best shot, pursuing bias for action, and preparing to learn from any mistakes.

Jobs goes on to clearly spell out HarperCollins’ three options. In enumerating these, he further simplifies a complex issue in efforts to get Murdoch to make a decision.

Takeaway: Take your time when crafting an email. View it as an opportunity to really affect another person–whether it’s an attempt to persuade, influence, or further build the relationship. If it’s an important email, write a draft and then leave it alone. Come back to it later to re-read and edit. Try to read the email through the other person’s eyes.

Ask yourself:

  • Is it clear and logical? Fair and balanced?
  • Is it easy to read? (Using numbers or bullets like Jobs did can help.)
  • Am I sure I won’t later regret something I’ve written here?
  • Was I careful not to write too much?

Repeat the process a few times, until you can confidently answer yes to each question.

Additionally, there’s no need to try to impress or use extensive jargon when writing your emails. People, even your superiors–and especially your partners–appreciate dealing with a real person on the other side.

Make sure you sound like one.

It’s well-written.

Conversational and real doesn’t have to mean sloppy. Clear thinking leads to clearer writing, and vice versa.

For example, you might notice that throughout the email Jobs uses proper:

  • Capitalization
  • Punctuation
  • Spelling
  • Grammar
  • Syntax

Did that come by chance, on the first try? Don’t bet on it.

Takeaway: I’m amazed at how sloppy most emails are nowadays. If you pay attention to your writing, it will be easier to understand and carry the full weight of your thoughts. By showing attention to detail, you’ll stand out among peers and leave a better impression.

It’s emotionally intelligent.

No one wants to feel like they’re being pressured into a decision, or that power is being taken away from them.

That’s why the final line in this email the most powerful:

“Maybe I’m missing something, but I don’t see any other alternatives. Do you?”

With two simple sentences, 13 words, Jobs simultaneously communicates confidence and humility. He throws the ball back into Murdoch’s court, giving him an opportunity to push back or offer solutions.

Within two days HarperCollins would agree to Apple’s terms.

Takeaway: Make sure your communication partner feels they’re a part of the process, not just a pawn in your game. Be willing to listen to others, to consider their concerns, and acknowledge mistakes.

By doing so, you’ll instill trust. Not only will you move matters along more quickly, you’ll do so efficiently–by addressing problems and concerns head-on.

It uses a sign-off.

You might not think much of the way Jobs ends his message. Actually, in an email like this one, where the conversation is expected to continue, Jobs could have ended with his question. But remember that the sign-off is the last thing the recipient reads–so it can be the “cherry on top,” so to speak. That quick little “regards,” along with his first name, keeps things personal and respectful.

Takeaway: You’re trying to build (or maintain) a relationship with the people you email. Just as you wouldn’t normally end a spoken conversation without saying goodbye, you shouldn’t do it with email, barring a few exceptions. (If you’re looking for ideas on how to effectively end your email, check out these suggestions.)

Well thought out. Clearly written. Easy to understand. Short and sweet.

And most of all: emotionally intelligent.

That’s how you write a great email.

THIS is Why Genius Minds Always Wear The Same Clothes.

A human being is capable of processing about 70 gigabytes of information daily.Shocking huh?! Intelligent people who can use a higher percentage of their brain are known to consume a lot of information, which ultimately causes Option Fatigue. They get tired, and it hampers their decision-making power.

Steve Jobs

And it’s the main reason why the majority of intelligent and genius individuals – who are known to create history in the world through their many smart inventions – wear the same types of clothes every day.


A neuroscientist and a cognitive psychologist, Daniel Levitin, shares that information overload takes place when humans process way too much information than their brain’s potential to consume.

Steve Jobs Wears Same Clothes

He further says that most humans think they are capable of paying attention simultaneously to nine things.

But this is not right. The conscious mind is capable of focusing on three things at a stretch. And when we start handling more than three things at once, we tend to deprive our mental prowess. This is the reason behind the geniuses of the world wearing the same clothes every single day – from Albert Einstein, Mark Zuckerberg to Steve Jobs and Barack Obama.

Steve Jobs wore the black turtleneck, Albert Einstein his gray suit and Mark Zuckerberg wears a gray t-shirt.


In one interview – Mark Zuckerberg – said that he organizes his life so that his decision-making power is reliable. Thus, he wears the same type of clothes every day, so that he does not have to worry about social issues or obligations. In a Vanity Fair interview in 2012 Barack Obama said that he wears either blue or gray suits.

Mark Zuckerberg

It helps him to reduce his decision-making tasks. He also shared that he does not want to worry about making decisions related to the things he eats or what he wears – as he has way too many important decisions to make in life as the President of America.

Albert Einstein


Our mind makes use of nutrients and energy in the same proximity as it uses to make vital decisions in life.

Most of the time, we make our mind worry about things that do not make any difference in our life. And when we have to make little decisions, our brain is too tired to do so. Therefore, it is necessary to make smart use of our brain.


Every human being has the same type of brain with similar strength and potential. However, only a few make smart use of their brain by focusing their energy on things that matter. So, wearing the same clothes deduces decisions and allows us to put our focus on things in life that will make a difference and help us to grow – professionally and personally!


25 Inspirational Steve Jobs Quotes That’ll Help You Reach Your Goals .

Fact: Steve Jobs didn’t become successful overnight.

Steve Jobs

It took years of hard work, determination, and perseverance to build Apple into the company that it is today. When you take a step back from your MacBook (and put down your iPhone), and really think about all that he accomplished, it’s beyond remarkable. He changed the way we live.

Thanks to his many lectures and speeches, we have a glimpse into his day-to-day work ethic and how he managed to do as much as he did. And, in order to help you reach your career goals, we’ve rounded up 25 of his best quotes. Read them, be inspired by them, and then get out there and make your dreams come true.

My favorite things in life don’t cost any money. It’s really clear that the most precious resource we all have is time.

I’m as proud of many of the things we haven’t done as the things we have done. Innovation is saying no to a thousand things.

Your work is going to fill a large part of your life, and the only way to be truly satisfied is to do what you believe is great work. And the only way to do great work is to love what you do. If you haven’t found it yet, keep looking. Don’t settle. As with all matters of the heart, you’ll know when you find it.

Have the courage to follow your heart and intuition. They somehow know what you truly want to become.

I think if you do something and it turns out pretty good, then you should go do something else wonderful, not dwell on it for too long. Just figure out what’s next.

Sometimes when you innovate, you make mistakes. It is best to admit them quickly, and get on with improving your other innovations.

When you’re a carpenter making a beautiful chest of drawers, you’re not going to use a piece of plywood on the back, even though it faces the wall and nobody will see it. You’ll know it’s there, so you’re going to use a beautiful piece of wood on the back. For you to sleep well at night, the aesthetic, the quality, has to be carried all the way through.

That’s been one of my mantras—focus and simplicity. Simple can be harder than complex; you have to work hard to get your thinking clean to make it simple.

Quality is more important than quantity. One home run is much better than two doubles.

Being the richest man in the cemetery doesn’t matter to me. Going to bed at night saying we’ve done something wonderful…that’s what matters to me.

The people who are crazy enough to think they can change the world are the ones who do.

Your time is limited, so don’t waste it living someone else’s life. Don’t be trapped by dogma—which is living with the results of other people’s thinking. Don’t let the noise of others’ opinions drown out your own inner voice. And most important, have the courage to follow your heart and intuition.

We’re just enthusiastic about what we do.

You can’t connect the dots looking forward; you can only connect them looking backwards. So you have to trust that the dots will somehow connect in your future. You have to trust in something—your gut, destiny, life, karma, whatever. This approach has never let me down, and it has made all the difference in my life.

Be a yardstick of quality. Some people aren’t used to an environment where excellence is expected.

For the past 33 years, I have looked in the mirror every morning and asked myself: ‘If today were the last day of my life, would I want to do what I am about to do today?’ And whenever the answer has been ‘No’ for too many days in a row, I know I need to change something.

I’m convinced that about half of what separates successful entrepreneurs from the non-successful ones is pure perseverance.

What is Apple, after all? Apple is about people who think ‘outside the box,’ people who want to use computers to help them change the world, to help them create things that make a difference, and not just to get a job done.

Things don’t have to change the world to be important.

Technology is nothing. What’s important is that you have a faith in people, that they’re basically good and smart, and if you give them tools, they’ll do wonderful things with them.

I’ve always been attracted to the more revolutionary changes. I don’t know why. Because they’re harder. They’re much more stressful emotionally. And you usually go through a period where everybody tells you that you’ve completely failed.

Bottom line is, I didn’t return to Apple to make a fortune. I’ve been very lucky in my life and already have one. When I was 25, my net worth was $100 million or so. I decided then that I wasn’t going to let it ruin my life. There’s no way you could ever spend it all, and I don’t view wealth as something that validates my intelligence.

My model for business is The Beatles: They were four guys that kept each others’ negative tendencies in check; they balanced each other. And the total was greater than the sum of the parts.

Remembering that you are going to die is the best way I know to avoid the trap of thinking you have something to lose. You are already naked. There is no reason not to follow your heart.

Getting fired from Apple was the best thing that could have ever happened to me. The heaviness of being successful was replaced by the lightness of being a beginner again. It freed me to enter one of the most creative periods of my life.

Why Steve Jobs Didn’t Let His Kids Use iPads (And Why You Shouldn’t Either)

If you fall within the Gen-Y era like us, chances are you’ve given a bunch of thought as to how you would raise your own children in this day and age (assuming you don’t have children already). Especially with technology, so much has changed since our childhoods in the 90s. Here’s one question: Would you introduce the technological wonder/heroin that is the iPod and iPad to your kids?

Why Steve Jobs Didn’t Let His Kids Use iPads (And Why You Shouldn’t Either)
Steve Jobs wouldn’t, and for good reason too.

In a Sunday article, New York Times reporter Nick Bilton said he once assumingly asked Jobs, “So your kids must love the iPad?”

Jobs responded: “They haven’t used it. We limit how much technology our kids use at home.”

Especially in Silicon Valley, there is actually a trend of tech execs and engineers who shield their kids from technology. They even send their kids to non-tech schools like the Waldorf School in Los Altos, where computers aren’t found anywhere because they only focus on hands-on learning.

There is a quote that was highlighted in The Times by Chris Anderson, CEO of 3D Robotics and a father of five. He explains what drives those who work in tech to keep it from their kids.

“My kids accuse me and my wife of being fascists and overly concerned about tech, and they say that none of their friends have the same rules… That’s because we have seen the dangers of technology firsthand. I’ve seen it in myself, I don’t want to see that happen to my kids.”

If our current addictions to our iPhones and other tech is any indication, we may be setting up our children for incomplete, handicapped lives devoid of imagination, creativity and wonder when we hook them onto technology at an early age. We were the last generation to play outside precisely because we didn’t have smartphones and laptops. We learned from movement, hands-on interaction, and we absorbed information through books and socialization with other humans as opposed to a Google search.

Learning in different ways has helped us become more well-rounded individuals — so, should we be more worried that we are robbing our children of the ability to Snapchat and play “Candy Crush” all day if we don’t hand them a smartphone, or should we more worried that we would be robbing them of a healthier, less dependent development if we do hand them a smartphone? I think Steve Jobs had it right in regard to his kids.

So the next time you think about how you will raise your kids, you may want to (highly) consider not giving them whatever fancy tech we’ll have while they are growing up. Play outside with them and surround them with nature; they might hate you, but they will absolutely thank you for it later, because I’m willing to bet that’s exactly how many of us feel about it now that we are older.

How to Listen to Your Heart, Even If Your Mind Disagrees.

“Your time is limited, so don’t waste it living someone else’s life. Don’t be trapped by dogma – which is living with the results of other people’s thinking. Don’t let the noise of others’ opinions drown out your own inner voice. And most important, have the courage to follow your heart and intuition.” ~Steve Jobs

We’ve all been there. Stuck in the middle of our decision making mode. Our heart tells us one thing, while our mind tries to keep us safe. Two totally different directions. One feels right, while the other is the most logical option.

What have you been following in the past? Do your decisions sound right or feel right? Take a look at where you are right now. Your life might be filled with logical and safe decisions. Which is great, but it’s leaving a lot of unused potential on the table.


You would love to be free flowing, in love with your decisions and place you’re at in your life. For some reason you’re not there yet. You’re close, but always feel on the fringe.

You’ve tasted the times in your life when you’ve been fully immersed in your decisions. Engaging with the uncomfortableness of not having a plan, but at least it felt right. If only you could be here more often.

You Haven’t Given Yourself Time to Develop Heart-Centered Confidence

Living in tune with your heart can be a totally new concept. Today we’re so wrapped up in making decisions based on endless pro and con lists, that we never allow any space for new opportunities or potentials. The notion of living in a “cause and effect” mechanistic world pervades our every thought.

Even though we’re notoriously bad at predicting the future, we pretend as if we have the insight of Merlin’s crystal ball. Not bad, but we can only predict so far. I’m guessing most of your decisions come in the form of receiving an innate feeling you know you should see out, but that’s usually overridden because it doesn’t seem possible.

As humans we love falling back on routine. Our same thoughts and habits pervade our everyday existence. We can either let these thoughts and beliefs, based on our past, direct our lives or we can inject new life into them.

The decision is up to you.

The moment your eyes open in the morning you’re faced with decisions. This route or that route to work. Take the new job, or move across the country. Most decisions aren’t life changing, but still add up to our overall life experience. By adding more space and deeper feeling into your decision making process you bring more possibility into your life. It’s time to start learning how to navigate these new waters.

All you need to do is balance two aspects of your being, the heart and mind.

Logic and Analytic Thought Dominate Our Culture

Logic and analytic thought saturate our world, there’s no way around it. Since the start of the scientific revolution we’ve been on a binge of rationality. A well thought out piece of writing is truly a beautiful thing, but when rational thinking dominates the spectrum of your life, you’re leaving spontaneity and the potential for unseen growth on the table.

Rationality isn’t inherently bad, but since we’re imbalanced we end up playing life with half of the chips. We can see the dominance of rationality, fear and control throughout the world. From global issues such as global warming, to the governments of repression. Worldwide issues can give us a glimpse into where our inner worlds have gone wrong.

In this case, an imbalance of logic over the subtleties of an intuition based language. Instead of following our heart and operating with trust at the forefront we place a higher degree of value on conforming and what makes the most sense. The biggest issue here is our individual nature is lost in fear of rebellion from the whole. We’ve created a cultural footprint that’s almost impossible to step out of.

Rebellion is met with resistance, and a lot of times that resistance wins.

We can only forecast our lives based upon the information we have at the present. By strictly living in the realm of rationality we cut off contact to the deeper source of life and the random events that change us and the course of history. The freewheeling nature of a heart-centered decision reaches farther than the contents of our mind can follow.

It’s time to change course and start navigating the deeper waters.

Bring the Power Home and Awaken the Heart

In utilizing your heart you open an entirely new stream of possibility into your life. By making decisions with your heart wide open you develop the trust muscle. In doing this a new source of self-love and trust emerges where there was only emptiness before.

Big changes and shifts in your life seem a little less scary as you begin to become familiar with the presence of uncertainty in your life. By living in tune with the part of yourself that always has your greatest interests in mind you’ll bring more of what you’re looking for into your life. This isn’t woo woo law of attraction imaginings, but instead, a way of viewing and feeling through the world instead of judging and analyzing yourself into a box.

When you first begin to make decisions from the deeper part of yourself you’ll feel massive resistance. The feeling of uncertainty is simply the mind trying to grapple with your decision. The amount of evidence currently in your palm doesn’t compute with the path you’re about to take. Your decision might go against the grain of your peers and family, but if the decision feels right then it’s what you have to do.

Obviously, this is easier said than done. The process of building your inner trust muscle takes time and can only blossom through action. Just as an iron sword is forged in the heat of a fire. Your life’s path can only unfold through coming into contact with the realities of life. In bringing your heart to life you awaken a fire within that has more force than all of the willpower you could ever muster.

How do I begin to unravel the hidden yearnings of my heart?

Start to Lay the New Decision Making Foundation

A house won’t last very long without a proper foundation, especially if you’re building a cabin to withstand the elements. A gust from a big bad wolf will knock it down in an instant. If you want your new decision making power to last longer than the first gale force wind thrown at you then it’s time to get to work.

The following steps will start to build a momentum of their own if you engage with them daily. For some that means building routines, while for others that means setting aside some time or space, or even setting a reminder on your phone to step back into your new decision making mechanism.

1. A proper foundation takes time

Trying to make any lasting change takes time. Especially, if it’s worthwhile. We all wish for habit changes to be as simple as turning on a light switch, but sadly this is never the case. If it was we wouldn’t value it as much.

Think about it, what holds more value, a handcrafted good, every stitch made with love, or a mass produced burlap sack? I’ll leave that judgement up to you.

If you were to start weight lifting or any kind of training, it would be impossible to start lifting heavy or training intensely right away. You need time for lasting growth.

Set aside some time every day and commit to it. You can’t build momentum by rolling a ball once. Every day push it a little farther. The first few times you’re priming your heart and it will feel awkward, so be ready for this.

Start the process by continuously asking yourself the following questions:

Where do I feel this decision?

Am I doing this because I feel it’s what I “should” do?

Is this in tune with the best version of myself?

How do I feel moving forward?

By consciously playing in the realm of the heart you’ll start to be able to see patterns and actually see if you’re living in tune with your highest self. By asking these questions you start to allow the mind and heart to play together nicely. You enable the mind to take a back seat through asking questions laced with deeper purpose and feeling.

 2. Think of it as learning a new language

If you’ve ever tried to learn a foreign language you know firsthand how confusing the process can be. Or maybe you’ve even experienced being dropped into a country where you don’t speak the language. Definitely, a sink or swim moment!

Think of this process along the same vein. If you really want to become fluent you have to immerse yourself as frequently as possible. You must cultivate drive, persistence, and inner-trust, soon it will become easier to flow through life and your decision making process.

Instead of having a decision come in the form of a weighing of good and bad, it will show up with a feeling. You need the courage to let this deep feeling impulse direct you. Make sure to watch out for the emotional swings we all feel. You have to overcome these and realize these won’t lead you where you want to go.

You’ve gotta’ go deep, my friend. You can’t assume your hunger pains or fatigue are telling you to quit your job and grab a burger. The deeper current is where you want to swim.

When you have a deep feeling you’ll know it, it feels like love, lightness, intense fear, deep unknowing, or nervousness you’ve never felt before. For everyone it shows up differently, I can’t give you the details of your inner experience.

That’s where the trust muscle comes in. Feel it and run with it.

3.  Start small and develop a toolkit of feeling

As you continue to ask yourself questions on a daily basis certain patterns might start to show up. Try to take notice of these. Maybe when you immerse yourself in writing, time dissolves and you’re left feeling refreshed after the experience. This wont happen every time, but if it happens more often than not, then it’s where you need to be.

Life is a grand experiment anyways, so you might as well conduct your life in the same manner. In doing a series of mini-experiments you’ll learn to distinguish your fleeting impulses from your heart-centered callings. In this case action is key.

You can either act based upon these or let them float by. The choice is always in your hand, when you let your mind override these feelings. Discounting them as silly, childish, or impossible, you’re really not valuing your own innate potential and value as a human being.

Before you start to rationalize why you shouldn’t take action, do yourself a favor and take a baby step. Try recognizing your deeper feeling current and act from it. See what happens. As you take action, confidence in your ability to trust the greater workings of the universe will begin to arise.

You can never trace your steps going forward, only after you’ve taken action will patterns begin to emerge.

4. Reflect on the direction and ask questions

We’ve all had the feeling of falling off, doing things not in alignment with who we truly are. This can happen even when we’re attempting to follow our hearts if we never check in and see how far we’ve come. Our mind is a tricky beast and has the ability to allow us to diverge from where we truly want to be. All while thinking we’re still on track.

You must make time to reflect, on the process, on your life, and on your new path of learning. By following the process of engaging with deep questions, seeing when deep feelings arise, taking aligned action and taking notes you’ll be well on your way to developing the ability to listen to your heart.

The strength of this way of feeling through life will allow you to override your mind. You’ll be able to lean on yourself and trust your decisions, even if your mind says they’re irrational. You’ve learned to trust and navigate the deeper current.

Reflect on your path as often as possible. Your decisions may look like a smattering of stars dotting the sky, but after a while you’ll be able to build constellations out of your own life.

Computer programming: Why we should all learn to hack.

Owning a computer once went hand in hand with understanding exactly how it worked. That may have changed but Tom Chatfield says it’s time to reclaim the past.


There is an old joke amongst computer programmers: “There are only 10 types of people in the world: those who understand binary, and those who don’t.”

Not funny to everyone, but it makes a neat point. We now live in a world divided between those who understand the inner workings of our computer-centric society and those who don’t. This is not something that happened overnight, but it is something that has profound consequences for our future.

Rewind to computing’s earliest decades and being a “hacker” was a term of praise rather than disgrace. It meant you were someone who could literally hack code down to size and get it to do new things – or stop it from doing old things wrong. You were someone who could see through the system and, perhaps, engineer something better, bolder and smarter.

In the early 1970s, Steve Jobs and his co-founder at Apple, Steve Wozniak, worked out how to “hack” the American phone system by using high-pitched tones, so that they could make prank calls to people such as the Pope (he was asleep at the time). It was a mild kind of mischief by modern standards – and a sign of a time in which the once-impenetrable realms of mainframe computers and institutional communications systems were beginning to be opened up by brilliant amateurs.

As you might expect, the phone system has become considerably harder to hack since the 1970s, and the divide between those who use computers and those who program them has also widened as the software and machines have become more complex. Having started out as outposts of do-it-yourself home computing, companies like Apple have become pioneers of seamless user experience, creating apps and interfaces that don’t even demand anything as technical as the use of a keyboard or mouse, let alone insights into the inner workings of the technology involved.

Year of code

This relentless drive towards technology that blends seamlessly into our lives leaves us in an increasingly bifurcated world. Information technology is a trillion-dollar global industry, with legions of skilled workers creating its products. Outside of their ranks, however, the average user’s ability to understand and adapt the tools they are using has steadily declined. It is a situation that is unlikely to change overnight – but there are movements aimed at bridging this gap.

In the coming weeks, a UK foundation will launch the Raspberry Pi – a £16 “computer” aimed largely at schoolchildren. Unlike your tablet or laptop, however, this computer is not a glossy, finished piece of kit, and deliberately so. The credit card-sized, bare bones circuit board is more akin to the early DIY machines that the likes of Jobs and Wozniak created and played with in the earliest days of computing. It demands to be tinkered with or “hacked” – and that is the whole point. It encourages people to better understand the hardware at their fingertips.

Across the Atlantic, meanwhile, a young organisation called Code Academy has made the increasing of people’s understanding of the code that runs on their machines into its mission. With over half a million users registering just during its first month of operation in 2011, Code Academyis a rapidly-expanding service aimed at imparting the basics of coding to anyone wishing to learn, free of charge. Its initial focus is the web language JavaScript, and it is inviting users to make 2012 their “code year” by sending out emailed prompts to complete one interactive coding lesson every Monday.

In professional terms, it’s easy to see why knowing how to put together a program is a valuable skill: more and more jobs require some technical know-how, and the most skilled students have glittering prospects ahead of them. But with only a fraction of those signing up for free lessons ever likely to reach even a semi-professional level of skill, are movements like Code Academy able to offer more than good intentions?

The answer, I believe, is a resounding yes. Because learning about coding doesn’t just mean being able to make or fix a particular program; it also means learning how to think about the world in a certain way – as a series of problems ripe for reasoned, systematic solution. And while expertise and fluency may be hard-won commodities, simply learning to think like someone coding a solution to a problem can mean realising how the reasoned, systematic approaches someone else took might not be perfect – or, perhaps, neither reasonable nor systematic at all.

‘No magical safeguards’

Like Neo’s moment of revelation in the first Matrix movie, learning to picture the code behind the digital services you are using means realising that what you are looking at is not an immutable part of the universe; it is simply a conditional, contingent something cooked up by other human coders. And this is the divide that matters more than any other between coding insiders and outsiders: realising that the system you are using is only a system; that it can be changed and criticised; and that, even if you do not personally have the skills to rip it apart and report on the results, someone else probably does and already has done.

This last point – the ability to benefit from others’ expertise, and to know how to begin searching it out – is an especially important one. From cynical corporations to shadowy spam-mailers, there are plenty of people who would like nothing more than a digital citizenship ill-equipped to ask what lies beneath the surface. Thinking differently does not demand coding mastery. It simply requires recognition that even the most elegant digital service has its limitations and encoded human biases – and that it is possible for more troubling cargoes to be encoded, too.

In 2010, for example, an FBI investigation revealed that one suburban Philadelphia school district had included malicious software on laptopsgiven out to pupils that allowed the computers to be used for covert surveillance via their cameras and network connections. The software in question would have been undetectable to all but the most devotedly expert of investigators. Since the case emerged, however, the widespread documentation and discussion it provoked has left those alert to such possibilities far better prepared to defend against them in future.

Code Academy and its ilk have no magical safeguards to offer or instant paths to understanding. For many people, though, signing up will be a first step towards asking a better class of question about their online world – and searching a little longer and harder for better answers within it.

And in case you are still wondering – 10 is the binary for two.



When Teammates Don’t See Eye to Eye.

It can be very frustrating when people who work together and share common goals can’t see eye to eye on how to reach them. The problem, more often than not, is that you don’t actually think about those goals in the same way.

Some of us have what psychologists call a promotion focus, meaning that they see their goals as opportunities to gain if they succeed, while others have more of a prevention focus, seeing goals in terms of what they stand to lose if they don’t. The promotion-focused person is all about achievement, aspirations, reaching for the stars and being their best, while the prevention-focused person cares about fulfilling obligations, avoiding danger and mistakes, and being the kind of person others can count on to keep things running smoothly.

Your dominant focus shapes your identity as a worker and even a life partner. If you’re promotion-focused, you are probably an optimist, motivated by confidence and praise. You work quickly and creatively, and you take chances.  You might make mistakes, but you’re willing to go for the big win.

If you are prevention-focused, you are probably more realistic, even a little pessimisticCriticism, not praise, motivates and energizes you. There’s nothing like the possibility of failure to get your motivational juices flowing.  You work deliberately, carefully, and accurately. You plan ahead, and rarely procrastinate.  You may not seize every opportunity, but you are very good at avoiding disaster.

What happens when we find ourselves paired with someone who has a different focus? One of you wants to innovate, the other is worried about the perils of uncharted territory. One of you thinks you need to cultivate new leads, the other thinks you need to strengthen ties to the customers you already have. One of you wants to rush your exciting new product to market ahead of your competitors, the other thinks it needs a lot more testing. Even though you all want what’s best for your team and your company, differently-focused colleagues often waste a lot of energy, and create a lot of unnecessary animosity in the workplace, arguing over which person is seeing things the “right” way.

It’s not easy, but once you accept that you and your colleagues simply see your goals differently, you will stop arguing over who is right. You will recognize the value of your colleague’s viewpoint, and start speaking in one another’s motivational language. That means telling promotion-focused Betty that your ideas are about “innovation and advancement” (i.e., a gain), but framing them to prevention-focused Bob as “a means to keep from falling behind” (i.e., avoiding a loss).

The very best partnerships – like the visionary, innovative Steve Jobs and the analytical, detail-oriented Steve Wozniak – strike a balance between promotion and prevention, since both are necessary for the success of any organization. And if you and your spouse are differently-focused, rest assured that between the two of you, you’ll make sure that your family has adventures and new experiences, while also making sure the kids have clean underwear and the bills get paid.

About the Author: Heidi Grant Halvorson is a social psychologist and Associate Director of the Motivation Science Center at Columbia Business School. She is the author of Focus: Use Different Ways of Seeing The World for Success and Influence (Hudson Street Press, 2013), Nine Things Successful People Do Differently(Harvard Business Press, 2011) and Succeed: How We Can Reach Our Goals

Source: WSJ



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