Work

business & working

working for capitalists

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The automation of human work is an ongoing objective of our capitalist systems. Our accounting practices amortize machines while listing people as costs, which keeps the power of labour down. The machines do not even have to be as good as a person, due to our bookkeeping systems that treat labour and capital differently. Labour is a cost while capital is an investment. Indeed, automation + capitalism = a perfect storm.

Recently, The Verge reported that the CEO of Shopify, an online commerce platform, told employees — ‘Before asking for more Headcount and resources, teams must demonstrate why they cannot get what they want done using AI.’ The underlying, completely misinformed assumption being that large language models and generative pre-trained transformers are as effective at thinking and working as humans.

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making time

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In the past year many workers in the tech sector have lost their jobs, often replaced by the vision of what generative AI can do instead. I know of lay-offs in bio-tech as well and now we are seeing massive firings in the US civil service. One consequence of all of these job losses is that fewer people will have to do more work. My observations of medium to large organizations has been that most people are busy, most of the time. Back to back meetings are not uncommon as well as overflowing email in-boxes.

This is a challenge for performance improvement, learning, and knowledge management initiatives. Any new attempts to improve these will be seen as extra work on top of a demanding work load. While those of us in the field of organizational performance improvement know the long-term value of better knowledge sharing, collaboration, and cooperation, getting over the short-term pain can be insurmountable. I have learned that it’s important to first find and make more time and space for knowledge workers.

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assistive technology

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Donald Clark has posted about how many people are using AI as assistive technology.

Time and time again, someone with dyslexia, or with a son or daughter with dyslexia, came up to me to discuss how AI had helped them. They describe the troubles they had in an educational system that is obsessed with text. Honestly, I can’t tell you how often I’ve had these conversations. —Plan B: 2024-08-15

Donald goes on to cite several types of assistive technology.

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work and learning 2024

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Work is constantly evolving but technological and social changes are accelerating certain aspects of work. Working from anywhere has exploded since the beginning of the coronavirus pandemic and does not look like it will disappear. The digital workplace requires unique skills in collaborating in distributed teams and cooperating in knowledge networks.

The most recent technology to influence how work gets done is artificial intelligence — specifically generative large language multi-modal models (GLM). The rate at which these new technologies are being integrated requires agile sensemaking from workers adapting to the changing human-machine work interface. It is highly likely that the pace of change will continue and even accelerate.

While we cannot predict the future of work or know how GLMs will develop, we can assess what human meta-skills are necessary to individually and collectively understand working with smart machines. There are three meta-skills that can help us adapt to a future of work with smart machines.

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stepping aside

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In Only Humans Need Apply, the authors identify five ways that people can adapt to automation and intelligent machines. They call it ‘stepping’. I have added in parentheses the main attributes I think are needed for each option.

  • Step-up: directing the machine-augmented world (creativity)
  • Step-in: using machines to augment work (deep thinking)
  • Step-aside: doing human work that machines are not suited for (empathy)
  • Step narrowly: specializing narrowly in a field too small for augmentation (passion)
  • Step forward: developing new augmentation systems (curiosity)

There is a lot of talk and media coverage about stepping-up, stepping-in, and stepping-forward. I have previously discussed stepping-in and concluded that anyone affected by these technologies [AI, GPT, LLM] needs to understand their basic functions and their underlying models. These tools will be thrust into our workplaces very soon. So let’s step-in to working with machine learning but with a clear understanding of who needs to be in charge — humans. I stand by this position today.

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meaning-making

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“The ignorance of how to use new knowledge stockpiles exponentially.” —Marshall McLuhan

For the past decade I have promoted the idea that a job is not the same as meaningful work. Most jobs are refillable and replaceable. One worker leaves, another one fills the job position. Our work can help to define us, but our jobs should never define us.

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communities are the new conference

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Are communities the new conference?

I asked this question in our monthly video call of the perpetual beta coffee club [PBCC] which I facilitate. There was almost universal agreement that people prefer to engage in communities, both online and in-person, rather than a conference, particularly ones that have a lot of vendors. The PBCC was a significant sanity check for many of us during the lock-downs of the early stages of the SARS-2 pandemic. For the first few months we switched to weekly video calls so we could stay in touch and find out what was happening around the world.

Asynchronous, continuous online communities like ours provide something that most conference do not — time for reflection and deep conversations.

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nothing time is for deep work

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In slow media for the great reset I noted that one nice thing about blogs is that there are few trolls because it takes more time write a comment on a blog post and often there is an approval process. Plus, anyone can easily delete crap comments from their own blogs. If more people engage in longer form writing and share through blogging, we may collectively address some of the challenges we face with the misinformation and disinformation on consumer social media. Perhaps ‘slow media’ can slow the reversal effects of digital platforms which create a mono-culture of noise without meaning and meme wars. Or, in the words of Marshall McLuhan, “The ignorance of how to use new knowledge stockpiles exponentially”.

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learning from failure

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In 2019 I noted in hybrid sailors that the US Navy was piloting a new way of manning its Littoral Combat class ships, which are modular by design. The crew are all multi-purpose, with several roles onboard and always learning new tasks. They operate with one-fifth the crew size of a regular ship. Specialization is a thing of the past for these crews. One reason for this is that specialized knowledge has an increasingly shorter lifespan, so generalists who are good learners can make for a more flexible, or agile, crew. This approach also has its downsides, such as fewer redundant positions onboard to mitigate combat losses, and lack of deep knowledge for some complex problems.

I concluded that organizations should start testing out new models now. Learn from the Navy and others who are trying new ways of organizing work. For individuals, the ability to ‘flexibly shift’ may become a critical work skill.

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The future of work?

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There is lots of talk and writing about the future of work. I follow the #FutureOfWork hashtag on Mastodon. A recent report produced for Unilever — The Future of Work is Flexible — featured three ideas:

  • Embrace the ‘pixelated’ workforce.
  • The rise of the internal talent marketplace
  • Is the ‘skills passport’ the future of recruitment?

The report features several drivers of change, such as how AI can decompose [pixelate] jobs into smaller pieces for employees and contractors to compete for work. Fractional hiring then blurs the lines between full-time and contract work, which leads to an internal marketplace for work. This can lead to more precarious work but as the report notes, it can also result in ex-employees getting called back for contract work at their convenience. Re-skilling is a major theme of the report stating that many skills degrade after 2.5 years.

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