UTAW partnered with the Data Workers Inquiry to produce a report on the lived experiences of data workers, particularly content moderators and trust and safety workers at TikTok in London. It focuses on how big tech companies treat data workers, documenting the context of exploitation, the systematic outsourcing to business process outsourcing companies (BPOs), the replacement of human labor with inadequate automated systems, and the reduction of workforce numbers without accountability.

It aims to expose the hidden realities of data work. It also highlights the emotional and psychological toll of moderating harmful content. The core message is that while data workers play a crucial role in keeping online platforms safe, they are often exploited, underpaid and left without mental health support. Their labor remains largely invisible, despite its importance.

This issue is important because data workers are responsible for protecting millions of users from harmful online risks including abuse, violence, and suicide-related materials, to mention a few. Despite this, they face significant psychological risks that are often underestimated or ignored.

Read the report:

Workers are organising at Meta with the UK’s largest tech union, citing attacks on their employment rights, redundancies and shocking violations of privacy against both Meta employees and users of their platforms.

“News like the reductions in UK employee insurance coverage and training AI on coworkers’ private data in spite of their objections, have left us feeling disrespected and unheard.

As individuals, we have little power against a company that signalled its intention to care about us less and less. Together, we can advocate for what’s right for each other, our users, and the long-term success of our company.

If we can build Meta, we can build our union at Meta too. It’s time to do something.

Join us!”

– a UTAW rep at Meta

If you work at Meta and want to join your colleagues to fight back against these decisions being imposed on you, join your colleagues and become a UTAW member, the only union run by and for tech workers.

Join us

A trade union is an organised group of workers who act together to improve their working conditions. Instead of each person negotiating alone — where the employer holds all the power — a union lets workers make collective demands with a unified voice. UK law gives this collective power a legal framework and protections: under Trade Union Law, workers have the legal right to organise, bargain together, and take action to back up their demands. You can join our union UTAW-CWU here.

Our Demands

Artificial intelligence holds immense power. As Google continues to push boundaries, we cannot allow that power to be used for harmful purposes such as military weapons, surveillance, or other unethical uses. Google’s technology must serve the greater good—advancing human rights, global security, and positive societal outcomes. By supporting this campaign, you are standing up for a future where tech companies, especially Google, operate in a way that prioritises ethical standards and respects fundamental human rights. We must ensure Google’s technologies are used for peace, safety, and equality, not for fueling conflict, oppression, or harm. Together, we can hold Google accountable to these principles, shaping the future of technology to protect rather than endanger humanity. We recognise our company’s complicity in the Gaza genocide and increasing involvement in unethical contracts. We therefore support our UCL colleagues’ campaign to boycott and divest from Google DeepMind.

1. Strengthen Google’s AI Principles

We call on Google to update its AI Principles to include a clear commitment that Google will not pursue or develop weapons or technologies whose primary purpose is to cause harm or injury to people, or to gather or use information for surveillance that violates internationally accepted human rights standards. Proposed New AI Principle: “We will not pursue weapons, technologies or contracts whose principal purpose, implementation or impact causes harm or directly facilitates injury to people, or gather or use information for surveillance violating internationally accepted norms.” This principle must be upheld, regardless of shifting geopolitical pressures or interests. The commitment to human rights and ethical standards should remain steadfast and unaffected by changing political climates.

2. Clear Monitoring and Enforcement Procedures

In order to hold the company accountable, we urge Google to establish robust monitoring and enforcement mechanisms for its AI Principles. This includes: Actionable Accountability: Google should clearly define the consequences for any violations of the AI Principles and ensure these consequences are enforceable across all levels of the company. Proportional Consequences: Violations should be met with significant, proportionate actions, and no employee, regardless of seniority, should be exempt from accountability.

3. Independent Oversight and Whistleblower Protections

We call for the establishment of a transparent independent oversight system to ensure compliance with these principles: Independent Ethics Board: Form a body of respected experts in AI ethics, international law, and human rights that can review projects, investigate potential breaches, and issue reports accessible by all FTEs within GDM.

4. Transparency and Employee Involvement in Decision-Making

Google must lay out the process for how it might change these principles in the future, and how employees and stakeholders can shape such changes: Clear Decision-Making Processes: The Responsibility Safety Council (RSC) should provide transparency regarding which projects have been approved or rejected and the rationale behind those decisions. Employee Rights: Employees must be empowered to take action if they notice any shift in the company’s ethical values or principles.

5. The Right to Abstain

We demand that Google grant all employees the explicit right to abstain from working on any project that violates their personal moral or ethical standards, without fear of retaliation.

6. Negotiation Over Automation of Our Roles

As Google accelerates the development and deployment of AI systems capable of automating tasks currently performed by workers, we demand the company commits to meaningful negotiation with our union representatives before implementing any changes that materially affect our roles, workloads, or job security. ####This must include: #####Prior Consultation: Google must notify and consult with union representatives at the earliest possible stage when AI automation is being considered for tasks performed by current employees — not after decisions have already been made.

Impact Assessments: Any proposed automation must be accompanied by a transparent assessment of its impact on affected workers, including changes to headcount, role scope, required skills, and working conditions.

No Unilateral Displacement: Google must not use AI automation as a pretext for unilateral redundancies or role restructuring without first exhausting retraining, redeployment, and negotiated transition options.

Ongoing Review: A joint worker-management oversight body must be established to continuously review the deployment of AI automation across Google’s workforce, with union representatives having genuine standing to challenge or delay harmful implementations.

Google workers build and maintain the AI that powers all of Google’s products. We must not be excluded from decisions about how those systems are turned on us. The future of work at Google must be negotiated — not imposed.

7. Clean Energy for AI

The explosive growth of AI is driving a surge in energy consumption across Google’s global infrastructure. We demand that Google takes concrete, verifiable action to ensure this expansion does not come at the cost of our planet. We call on Google to commit to the following: No AI Powered by Oil/ Gas. Google must publish a credible, public plan that includes: Round-the-Clock Renewable Power: All data centres must be powered by 100% additional, locally-sourced renewable energy on a 24/7 basis — not offset by credits purchased elsewhere or averaged across the year. End Contracts That Accelerate Fossil Fuel Extraction: Google must cease developing or providing custom AI solutions for oil and gas companies whose purpose is to identify new reserves or accelerate drilling operations. Building the tools of climate destruction is incompatible with any genuine commitment to sustainability. A Climate Roadmap: Google must publish a detailed glidepath for meeting its climate commitments — one grounded in peer-reviewed science, independently verified, and with enforceable milestones. Vague net-zero pledges are not enough. AI must cease to be an accelerant for the climate crisis. As workers at the heart of this technology, we refuse to be complicit in building systems that consume vast natural resources without accountability. Google has the means to lead on clean energy — we demand it uses them.

How Does It Work?

A union starts simply: a group of colleagues who share concerns about their workplace decide to organise together. They expand their membership, agree on shared demands, and build the collective power needed to make the employer engage. Once the union has sufficient support, it seeks recognition from the employer — the formal right to negotiate on workers’ behalf. With recognition secured, the union sits down with the employer at the bargaining table, where it can negotiate on pay, hours, holidays, and more. The employer is legally obliged to engage. If the employer agrees to what is being asked, a collective agreement is reached — and those improved terms apply to everyone in the workplace. If the employer refuses to bargain in good faith, the union can escalate — ultimately up to and including a strike ballot, where members vote on whether to take industrial action to back up their demands. Throughout all of this, the union operates democratically. Members decide the demands, members vote on any deal, and members have the final say on whether to escalate.

What is union recognition?

Recognition is what gives your union the legal right to negotiate with the employer on workers’ behalf. Without recognition, the employer can simply ignore the union. With recognition they are legally obliged to engage. By securing union recognition workers gain formal rights to collective bargaining, disclosure of internal company information, and mandatory consultation on major workplace changes. When a union applies for recognition it defines the bargaining unit, which is the group of workers that it seeks to represent. For example, it can be all of Google DeepMind, or a smaller unit.

Recognition can happen two ways:

Voluntary Recognition

The employer simply agrees to work with the union. UTAW officially asked for voluntary recognition on May the 5th 2026 – read The Guardian article. For voluntary recognition to happen, at least 10% of the bargaining unit must be members, and the majority of members should vote in favour of the recognition. The vote is internal to the union.

Statutory Recognition

If an employer refuses, a union can apply to the Central Arbitration Committee (CAC) to force recognition if they can prove sufficient support among the workforce. For statutory recognition there is a minimum membership threshold (10% of the bargaining unit, which GDM workers have passed) and all employees in the unit are called to vote (ballot). A majority vote in favour is sufficient for the recognition. The recognition process ends with a recognition agreement. A recognition agreement is a formal document which sets out who is covered by the union (the bargaining unit), what issues can be negotiated, how bargaining meetings work, what facilities and time off reps are entitled to, and how disputes are resolved if the two sides cannot agree. A recognition agreement is not set in stone. It can be expanded, updated, or renegotiated over time, and doing so is often one of the union’s most important long-term goals.

What happens if recognition fails? The union will not be able to apply for statutory recognition in the same or similar bargaining unit for 3 years after a failed process. It can apply after one year in a different bargaining unit. Voluntary recognition can happen at any time. What demands can be discussed at the bargaining table? Where a union gains recognition through the statutory process, it has the legal right to bargain collectively over pay, hours, and holiday entitlement. These are the minimum issues covered by the recognition agreement, but the union can negotiate on a wider range of matters. Under voluntary recognition, the scope can be broader. The employer’s willingness to negotiate beyond the statutory floor depends largely on the union’s strength, and strength comes from membership. An employer facing a well-organised, high-membership workforce is incentivised to agree to a broad recognition agreement and engage seriously at the table. An employer facing a small, fragmented union has little pressure to go beyond the minimum. How are demands agreed upon? Demands are set through a democratic process: they come from the membership. This is what gives them legitimacy, both within the union and in the eyes of the employer.

Consultation Members are asked about their priorities through meetings, surveys, or conversations with reps. This is where the issues that matter most to workers are identified and collected.

Drafting Reps collate the feedback and draft a set of demands that reflects the membership’s priorities.

Approval Members vote on or approve the demands before they are formally presented to the employer. After approval, collective bargaining begins. This is where the demands are negotiated with the employer until a deal is reached. Collective bargaining is an ongoing process, not a one-off event, and new demands can be raised at any time through the same democratic process. There is no legal limit on what can be raised, provided it falls within the agreed scope of the recognition agreement.

Can demands be changed? Yes. Demands can be amended at several points in the process through the same democratic process that set them in the first place. Any significant change during negotiations should go back to members for approval.

How does a bargaining process end?

A deal is reached If a deal is reached over a set of demands, members need to vote to approve it. They can also reject it and instruct reps to return to the table.

A deal is not reached members can be balloted on whether to escalate, including through industrial action. What are our current demands? Our demands focus on: ethics and transparency, job automation, clean energy. Read the full list of our demands.

FAQ

Can I stay anonymous?

Yes. Your employer does not have a legal right to know if you are a member of a trade union. Unions keep their membership lists strictly confidential. Under Data Protection (UK GDPR), trade union membership is classified as “special category data,” meaning it requires the highest level of privacy protection. If the union pursues statutory recognition through the UK Government’s Central Arbitration Committee (CAC), the CAC will need to verify the union has sufficient membership within the bargaining unit — typically at least 10% of workers. To do this, membership data may need to be submitted to the CAC, which is an independent governmental body. Critically, this data is not shared with Google. The CAC handles it confidentially, and the employer only sees the membership percentage. The law forbids employers from compiling lists of union members, see Blacklisting Regulations 2010, Reg. 3.

What legal protection are we entitled to?

Any questions?

Check also UTAW main FAQ page or contact us directly!

Google AI workers in pioneering union bid over Israel and U.S. military tech use

98% of Google DeepMind members vote YES.

Scores of Google AI workers have launched an unprecedented unionisation bid to end use of their technology by Israel and the US military.

The UK-based Google DeepMind employees - who aim to become the first frontier artificial intelligence lab worldwide to unionise - sent a letter on Tuesday requesting management recognise the Communication Workers Union (CWU) and Unite the Union as their official representatives.

“This is a really important moment where tech workers at Google’s frontier AI lab are connecting with some of the most oppressed people in communities around the world in meaningful ways, based on foundational values of solidarity and trade unionism.”

“By exercising their rights to collectivise they are in a strong position to demand their employer stop circling the ethical drain of military-industrial contracts, echoing the sentiment of many working people in the UK and elsewhere.”

John Chadfield, CWU National Officer for Tech

Google employees have for years protested the ethics of contracts like Project Nimbus, a joint programme with Amazon making cloud computing and AI tools available to Israel, including amid the Gaza genocide. Maven, a US government project from which Google withdrew in 2019 after staff uproar, has reportedly been used in targeting in the Iran war.

The unionising DeepMind workers are seeking an end to use of Google AI by Israel and the US military. Their demands also include restoring a scrapped commitment not to make AI weapons or surveillance tools, the creation of an independent ethics oversight body, and the individual right to refuse to contribute to projects on moral grounds.

“We don’t want our AI models to assist in violations of international law, but they already are aiding Israel’s genocide of Palestinians.”

“Even if our work is only used for administrative purposes, as leadership has repeatedly told us, it is still helping make genocide cheaper, faster, and more efficient. That must end immediately, as must harm to Iranians and human lives anywhere.”

a worker at Google DeepMind

Google recently agreed to let the US Department of Defense use its AI models for classified work, a move opposed by over 600 employees. Google staff worry how the technology will be used given the Pentagon imposed restrictions on competitor Anthropic over its refusal to permit use of AI system Claude in fully autonomous deadly weapons or domestic mass surveillance.

The British unionisation push is part of a wider campaign. DeepMind employees globally are considering in-person protests and “research strikes”, where they abstain from work expected to significantly improve core products such as the Gemini AI assistant.

The unionisation bid would secure representation on pay, hours, and holidays for at least 1,000 staff tied to Google DeepMind’s London office. The employees’ letter gave management 10 working days to voluntarily recognise the CWU and Unite, or take other steps like agreeing to mediated negotiations, before the matter is escalated to a formal legal process to force recognition.

“I hope that recourse to the statutory procedure will not prove necessary.”

“We look forward to working with you in a spirit of co-operation on behalf of the workforce.”

John Chadfield, CWU National Officer for Tech

“When artificial intelligence is developed without any accountability over its direction or purpose, it is bad for society as a whole - not for just the workers involved.”

“These conscientious workers demanding greater say over what they produce with their considerable skills are acting in the tradition of all those who have fought for workers’ rights before them.”

Dave Ward, CWU General Secretary

In the press

The Guardian: Google DeepMind in talks with UK unions amid staff concern over US and Israel's AI use ↗

auth: The Guardian date: 20-May-2026 cat: Press cmpgn: Google tags: Press

Novara Media: Google AI Workers in UK Vote to Unionise Amid Deal With US Military ↗

auth: Novara Media date: 06-May-2026 cat: Press cmpgn: Google tags: Press

WIRED: Google DeepMind Workers Vote to Unionize Over Military AI Deals ↗

auth: WIRED date: 05-May-2026 cat: Press cmpgn: Google tags: Press

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Your workplace needs a union, and your laptop needs stickers, so pickup a booster pack of our new stickers and get organising.

To celebrate May Day, AKA international workers day, our Merch Working Group is excited to present another batch of very limited edition handmade mugs, made by Comrade Dani. Find these and more at our merch store.

UTAW recently joined the Lift the Ban coalition, a group made up of trade unions, charities, and other organisations which seeks to have the government lift the ban on allowing asylum seekers to work in the UK while waiting for their asylum status.

We call on our members active in political parties and Trades Union Councils to encourage their parties and councils to join the coalition as well.

Asylum seekers are fleeing persecution and war and possess skills that would be an asset to the British economy. Despite this, they are forced to live on less than £7 a day (the current rate of support for asylum seekers) and are not allowed to contribute to the country they reside in. We call for this to end and for asylum seekers to have the right to work in this country.

Workers in Britain are facing a housing emergency. Average UK rent is rising faster than wages and inflation, and UK renters already spend more of their income on rent than almost anywhere else in Europe. When organised workers win increases in pay, these are being absorbed by landlords through ever greater rents. Despite the 1mn people on waiting lists, building of new council homes has stalled, all while the far right make hay, blaming working class migrants instead of government, developers and landlords.

Rent controls are urgently needed to shift the balance of power towards renters while we continue to campaign for what we really want - massive investment in council housing built on public land, run democratically and free from profit. We recognise that the housing crisis is a workers’ issue, and are supporting the National Housing Demo on 18th April at 1pm in Soho Square Gardens, London.

What is AI?

ok so verrrry broad terms about what AI is…

First, I have a short-ish version of what’s going on.

Then a longer version if you want a rough idea of how it’s actually working (explained by someone who scraped by in their undergrad degree lol)

the short version

“AI” is usually used to refer to “LLMs” - large language models. It’s a big mathematical model that takes in a large amount of text, converts it to “tokens”, and uses that to predict a result, also as text. (look there’s also images and now video butttt… it kinda does the same thing under the hood. it’s all “tokens”)

Each time an LLM is run, it works on a huge amount of calculations based on probabilities. These probabilities are worked out using training. This is a mostly automatic process where you have some input data which you know the result for, and you keep running the model over and over again and changing values until the results are close to what you expect.

Training and its energy impact

This training uses a lot of energy and data. Latest models are scouring the entire Internet, Wikipedia, textbooks, you name it, to find example data that they can use to train the models to guess the word that comes next. GPUs work quite well for this. So tech companies have been buying them all up…

Are they giving attribution or asking for permission…? Er… no. Usually not.

Running the model each time, even once the training is done, still requires loads of data, storage and energy. The environmental impact of the data usage means fossil fuel power plants are being brought back online. Tech companies are building miniature nuclear power plants just to keep up with the energy demand (small modular reactors. Data centers are stealing polluting water from surrounding land to keep their servers cool. We are going backwards on global emissions goals.

How many “r"s in “strawberry”?

Now remember I mentioned this is all based on probabilities? You betcha. This means that the output is never guaranteed. It makes mistakes, or “hallucinates”. The same input doesn’t always return the same thing. This means that LLM models can be unpredictable and work in unexpected ways. E.g., asking a model to add a list of numbers. Earlier models would convert the numbers to tokens, and because it sees something like “add these numbers” it will try to predict the output based on what it’s previously seen to come up next. This is why some models can’t even count the number of “r"s in “strawberry”.

An example. Asking GPT-4o to add up numbers.

Above: The correct answer, produced by Wolfram Alpha

Above: GPT-4o mini’s attempt.

Other models will predict that, say, there’s a list of numbers to add up, and then call upon a program to sum them before giving it back to the model to return as an answer, for instance. This is how a lot of modern LLMs now work, they are able to access a range of tools and programs to work in multiple steps to predict an answer. This is why, e.g. when you run Notion AI, you can see it print out a load of information as this process unfolds. It’s often known as “multi-stage reasoning”.

Prompts and prompt injection

A “prompt” is another word for the text input someone gives to an LLM. It can be a question, instructions and can get quite detailed.

Prompts can often be saved, used and shared as a way of encouraging an LLM to work in a certain way or provide a certain type of answer. You often hear these referred to as “skills”, “gems”, “custom GPTs”, that kind of thing. For instance, you might have one that says something like source:

Everything is fed as input into the LLM in the same way and converted into tokens the same. The chat history and the prompt used to try and control the behaviour of the model. It’s all treated the same, although by trying to separate separate bits of information like humans do in their writing (horizontal lines, headings, spaces) can try to influence these. Using strong, imperative language and even ALL CAPS can try to steer the model further into the behaviour the author wants.

Since the chat history and the original prompt are both treated the same way, does that mean the person sending messages to the customer support could use the same techniques to get the model to do what they want, even if the author didn’t want this?

…yeeeessss.

This is called prompt injection and it’s basically unfixable because of how these models operate (source). It’s been used to send malicious calendar invites to steal private information with Gemini. It’s an incredibly difficult problem!

Other bits

So there’s also the idea that because LLMs are trained on publicly available data, and more data online is being produced by LLMs, there’s this weird phenomenon where these models are training on themselves and converging towards “average” results. The Claude Opus 4.6 model has a reduced performance compared to 4.5. Generated images are increasingly becoming streaked with piss yellow because everyone in 2015 was using the X-Pro 2 filter on Instagram. “Model collapse” is something that some people believe to be an inevitability. I mean. People are sharing techniques for trying to get rid of the tint:

But also it means that when LLMs “hallucinate”, these can find their way into articles and blog posts. This finds its way into training data and now these fake statements become reinforced as “facts” according to future LLMs. It’s affecting journalism and resulted in an editor being fired. It’s alarming quite how much of the Internet is now being polluted with lies and slop.

The companies behind LLMs are morally bankrupt and are subsidised heavily by private equity. A recent Forbes article suggests Anthropic could be running up to $5,000 of computing power for a user spending $200… A lot of OpenAI’s funding is reliant on it “achieving AGI” (that’s Artificial General Intelligence, just a buzzword for a model that is smarter than a human) within a certain timeframe and it’s just not getting there. Ed Zitron has a fascinating and long article that dives into the OpenAI financials.

They are complicit in the current wars, despite the press releases. Claude has been used by the department of defence to help select targets for bombing. OpenAI seem to have no qualms about automated killbots (until they were caught). They are evil companies run by evil people.

Ok so that’s kinda about it off the top of my head. Below is a more in-depth explanation of how LLMs work if you’re interested.

(neural networks) what came first…

so like, in computing there’s a thing called a neural network. It’s essentially a program that takes some kind of inputs (like numbers!) and then does a bunch of calculations on them, combining and splitting results multiple times, to get an output (could be a number, a true/false, anything like that).

a lot of those calculations work on probabilities, so like, a certain calculation might have an 80% chance of returning a large value over a smaller one. stuff like that. these probabilities are worked out by taking a bunch of input data where we know the answer we want, and running this model over and over again, tweaking these probabilities each time to get the results closer and closer to those answers. this is what training is!

This happens on a huuuge scale. There’s many different calculations running (1000s to even millions!), a large number of inputs, and results being passed to further layers in the system. you could think of it as bit like this:

Where each of those circles is some kind of calculation (a neuron!) and each line is some kind of value being passed between it.

Even at a small scale like this, it can be really hard to unpick and work out why one set of inputs leads to an output. The idea is that it works in the same way a brain does. Lots of neurons firing and sending information around to return something that is correct, trained thousands or millions of times with learning data. (“reinforcement learning”).

You can use this for all sorts of cool things! If you imagine images as just a set of pixels with a number that represents its colour, a picture quickly just becomes a list of numbers. Standardise the data, feed it into a network, see if what comes out matches what you expect. Change the probabilities and run it again and see if it gets closer.

anndddd boom. You’ve just created something that can recognise text in images…

this powers pretty much everything in the world. tiktok algorithms, “dynamic pricing” in restaurants, shipping routes, speech to text on your phone. even on iphones, there’s a hidden model that runs on the keyboard that will grow and shrink the keys depending on what you type so you make fewer typus!

ok so now for AI

in the early 2010s, some computer scientists came up with this idea of a “transformer model”. it’s the same kind of thing except that it not only runs multiple networks at the same time, but can share results between them and look back and forwards across the data it receives. it’s now able to remember stuff. this is a “context”, which is a word that comes up so often in AI lol. but it means that one model is able to look at another model to figure out the “context” of what it’s currently looking at.

it makes training a lot easier since there’s no weird loops happening inside the model which for reasons I won’t go into, makes training faster. google dropped a paper at some point called “attention is all you need” and that was the hot shit at the time. this whole idea of being able to figure out the context of a bit of data meant that they got a lot better at being able to ‘understand’ words, language, things. it meant that a model that looks at images can figure out objects and shapes.

“AI” when its talked about at the moment usually refers to a “Large Language Model” or LLM. It’s a model operating on a huge amount of input data - human language. Transformer models are able to take a stream of text and break it down into words and phrases. Older neural networks would just treat it all as a big list of letters. This means that the model is able to simulate “reasoning” across sentences and paragraphs. The “memory” it keeps means that if a “cool cat” was mentioned at the start of the paragraph, it would be “remembered” later on when the LLM is processing a phrase about “the animal”.

This is really how a chatbot like ChatGPT or Gemini works in a basic level! When you send a message, your message and all of the messages in the chat history are turned into a big block of text and sent into an LLM. The LLM will then processes all of the text, turning it into tokens along the way, and then predicting the “most likely” answer.

Images, voice, it all works the same way. Everything is converted to tokens and run through a model and an answer is predicted.

This works okay for things like categorising data, converting speech to text, and picking out themes. It works to a slightly lesser extent for summarising large amounts of text, since its predictions are based on how similar sets of tokens (how similar pieces of writing) in its training were summarised. It can detect common themes or significant points, but can often struggle with prioritising what’s actually important. Because it’s not thinking, it’s predicting based on the data it’s been trained on.

This is why things like Gemini meeting summaries will often focus on small and insignificant things, like “the team discussed their morning commutes” over something like “the team raised concerns about the project’s ethics”. It’s all prediction, and it’s all based on probability. So there’s a chance it will do something unexpected and incorrect, and most often when you’re doing something new.

Get ready to march against the far right this Saturday!

We’ll be at the Together Against the Far Right national demonstration, joining our fellow CWU branches in the union bloc on Park Lane (closest address is 33 Park Lane). We’re assembling at 12pm and the march will leave at 1pm to arrive in Whitehall by 2pm. You can find us by searching out our UTAW banners, members are travelling from all over the country so no one will be marching alone!

It will be a busy march, so consider joining our Discord (link in your welcome email or message us for a new one) to chat with fellow attendees if you want to say hi beforehand.

Some UTAW members will be bringing along food and water, but don’t forget to bring your own stash just in case. Once at Whitehall, there will be performances to enjoy so get prepared for a day of joy, togetherness and community and to unite against the far right together.

You can find loads more information on togetheralliance.co.uk including a map, key information on the march route and previews of who is performing.

UTAW stands in solidarity with Iranian and Lebanese workers currently under bombardment by the United States of America and Israel.

The Iranian people deserve self determination, to choose their government by democratic means, not through an external regime change war intended to destabilise the country and cement U.S. control over the region and its resources. We condemn the military action, and call for an immediate ceasefire.

We also demand that the British government withdraw its troops, arms and intelligence from the region.

22.5% across the board

£8000 per member

UTAW-CWU members at Mitie have won a historic pay rise.

In 2025, the remote engineering team, based in Northern Ireland, secured union recognition with UTAW-CWU. Following successful negotiations on their first pay claim, members have now accepted a huge, inflation-busting pay deal. When workers unionise, pay goes up.

United Tech & Allied Workers is the UK’s first trade union run for and by tech workers, and is a national branch of the Communication Workers Union.

Sapphire Tech Workers Win Union Recognition in Just Six Months

In a stunning show of solidarity, the managed services team at Sapphire have won union recognition in just six months without even needing a ballot.

From the very beginning, workers built their campaign on conversations, trust, and a shared determination to change the way things were done at Sapphire. Within half a year, they had already secured an overwhelming majority of colleagues in support of UTAW union recognition there. The message to management is crystal clear: when workers stand together, recognition isn’t optional, it’s inevitable.

“Winning recognition this fast shows what’s possible when people stick together, through simply starting a conversation with a few colleagues about the challenges we faced, we found out these issues were team-wide” said one member involved in the campaign. “We are proof that in tech - just like anywhere else - when workers are united, we can win and have a voice in the workplace.”

Recognition is more than a legal milestone. It means Sapphire workers now have a formal voice at work, the ability to negotiate on pay and conditions, and the power to hold management accountable. For an industry often marked by precarious contracts, high turnover, and opaque decision-making, this win is a landmark step forward.

The victory also adds to a growing wave of organising across our sector, where workers are increasingly standing up for better pay, fair treatment, and ethical accountability in the industry.

For Sapphire workers, this is only the beginning. With recognition secured, these workers are now preparing for the next phase: negotiating a strong first pay deal and continuing to build their organisation.

Their success sends a clear message across the industry: organising works and tech workers are no exception.

We are proud to have supported Sapphire workers in their fight for union recognition. We look forward to more wins from them in the future!

If you’re reading this wondering how you can win a voice at work - become a UTAW member.

Strike by Digital Taxi Drivers on April 29–30: A Rebellious Step against Exploitation

By Ezra Otieno, PTWU

Between April 29 and April 30, 2025, virtual taxi drivers in Kenya organized a robust two-day strike that shut down activities on high-profile ride-hailing services like Uber, Bolt and Little Cab. Led by the Progressive Tech Workers Union (PTWU), the strike was a historic escalation in the battle for justice and fair treatment of workers in the gig economy.

Drivers across Nairobi shut down their apps, disabled their vehicles, and joined a synchronized call for greater pay, transparency, and rights in the law. The strike was something more than protest against discriminatory commissions and arbitrary deactivations it was a public declaration that digital workers are ready to fight.

The Build-Up: Piling Up Anger

For years now, Kenya’s ride-hailing drivers have labored under opaque terms. Though referred to as “partners” by apps, drivers face algorithm-controlled terms and have little say over prices, commissions, or the settlement of disputes. Companies like Uber and Bolt earn commissions of 25–30%, while drivers cover all the costs of operation fuel, maintenance, insurance, and loan fees.

It all came to a head in early April 2025 when several drivers were unexpectedly removed from the Bolt platform without notice or explanation and without a process to appeal the removal. Around the same time, new commission structures were implemented that continued to devastate drivers’ revenues. With living costs in orbit and fuel prices still as volatile as ever, frustration reached a breaking point.

PTWU, which has been mobilizing drivers primarily in Nairobi, moved swiftly to stage a strike. Organizers through WhatsApp groups, Telegram channels, and physical congregations demanded an unqualified digital shutdown from April 29.

The demand was straightforward:

• To limit commissions to 15%

• To reinstate unfairly suspended drivers

• Complete enforcement of NTSA regulations

• Legal status of drivers as employees, not contractors

• Drivers’ representation in all regulatory proceedings

By early morning on April 29, Nairobi’s central business district and its outskirts were unusually quiet. Thousands of drivers had logged off, and commuters were left stranded or faced exorbitant surge prices as the platforms scrambled to fill the vacuum.

Drivers met in locations like Lang’ata, Kasarani, and Umoja in makeshift meetings, trading experience and strategy. Some even made placards, on which were written messages like “We Are Not Robots” and “No Justice, No Rides.” Strategic locations like outside Bolt offices in Westlands saw small pickets held, clearly establishing that this was not going to be just a one-day protest.

Mainstream media picked up the story by mid-day. Local outlets interviewed frustrated passengers, but more importantly, they gave drivers a platform to speak out. Charles, a Nairobi-based Bolt driver and PTWU field organizer, told Citizen TV, “We are tired of being treated like disposable tools. If the platforms can’t respect us, we’ll shut them down.”

Although initially planned as a one-day protest, the sheer solidarity and quietness of the platforms led PTWU to extend the action to April 30. Things gained momentum, and the pressure mounted on ride-hailing companies.

By the second day, action had intensified within Nairobi. Drivers continued to protest peacefully, including outside Uber’s local office, demanding a conversation. Instead of listening, the authorities escalated.

Three PTWU-affiliated organizers two driver leaders and a union mobilizer were arrested in the afternoon near Waiyaki Way for allegedly “causing public disturbance.” Eyewitnesses said they were peacefully talking to other drivers and members of the media when the police arrived. PTWU denounced the arrests immediately as acts of intimidation and stifling of due worker protest.

Union lawyers worked through the night to secure their release, and on Labour Day morning itself, May 1, all three were granted police bond and released. In a media release issued soon after their release, PTWU declared:

“The arrests are a clear sign that the state and digital capital are in cahoots to silence workers. But we will not be silenced. The strike has taught us what is possible when workers organise together.”

Platform Reactions: Denial and Silence

Bolt initially asserted on Twitter that operations were “largely unaffected” — something ridiculed by users who shared screenshots of app unavailability and extortionate prices. Uber, on the other hand, said nothing but was said to be providing SMS inducements to bring drivers back online.

Neither platform addressed the root problems: exploitative charges, lack of recourse for deactivation, or outright lack of driver representation in company policy. Their silence only did more to reaffirm what most drivers already knew these platforms are more concerned with making a profit than with people.

Public Support and Political Implications

What surprised most was the amount of sympathy that existed in the public. Despite the inconveniences to commuters, the majority of Kenyans—especially young people who know the gig economy—were rallying around the strike. Both #DigitalStrike254 and #RespectDrivers trended on X (formerly Twitter) throughout both days.

Civic organizations, university campuses, and even boda boda organizations issued solidarity messages. The Kenya Human Rights Commission condemned the arrests and called on the government to assist in negotiating dialogue between platforms and drivers.

Several MPs, especially those from Nairobi, also weighed in, urging the Ministry of Labor and the NTSA to take action. As of May 2, PTWU had submitted a formal petition before both agencies outlining the drivers’ requirements and demanding changes in regulation.

A Turning Point for Gig Worker Organizing

The April 29–30 strike was more than a disruption. It was the coming of age of gig worker mobilization in Kenya. It was the first time digital taxi drivers demonstrated the ability to organize, display discipline, and stand firm even in the face of intimidation.

It also spoke to the relevance and demand for such unions as PTWU in the digital age. Unlike traditional unions, PTWU understands the temporary, app-based character of work today and is building a model of bottom-up organization that accounts for those realities.

Initiatives are now underway to:

• Create a national digital drivers’ council within PTWU

• Launch a cooperative transport app operated by drivers

• Organize a national gig economy conference in July

• Embark on a constitutional petition to protest the current designation of drivers as “independent contractors”

According to one driver at a May 2 debrief meeting: “The apps treat us like numbers, but this strike proved we are people organized, angry, and ready to take control.”

Conclusion: The Road Ahead

The April 29–30 strike was a landmark moment for Kenya’s gig workers. It highlighted the underlying brutalities of the ease of ride-hailing and demonstrated the huge potential of organizing among gig workers.

As the businesses try to return to normal, something fundamental has changed. Drivers are no longer solitary figures - they’re conversing, planning, and coordinating. And the message is clear and loud: the gig economy must be rebuilt to serve the people who power it.

The path ahead will not be without difficulties. But to the thousands who drove off, marched, and risked arrest this was merely the beginning.

A Note from UTAW

We commend the Progressive Tech Workers Union and their members for their incredible courage, determination, discipline and resilience. Long may it continue and we wish you success and solidarity in the next steps you take!

There is much to be learned from the Kenyan Taxi Driver’s strike and we thank Ezra for sharing this report with us. Support their fundraiser here.

As workers continue to face assaults on our dignity from multiple fronts, it is paramount we stick together and learn how to fight back. We build international links as a priority.

…or, how big is the carbon footprint of your AI endeavours?

The AI hype machine rumbles ever onward, engulfing the UK government and spawning a thousand experts along the way. But it’s not only good for contributing to the zombie-fication of the internet, AI is also quickly playing a starring role in climate breakdown too! There’s a growing movement flagging the significant environmental costs of developing, training, and running models, particularly the incredible resources required to maintain the data centres associated with the largest models.

The first step for most organisations or individuals concerned about the impact of their forays into AI is to understand how bad it currently is. Baselines then provide a benchmark for looking at how effective their strategies for reducing it are. With this in mind, we’ve rounded up some of the available carbon footprint calculators below, to help you take that first step.

Most calculators are based broadly speaking on the type of hardware you’re using, how long you’ll be using it for, and **where the compute power is located that you’ll be using. There are variations on this theme across all the different tools.

A quick note: the tools below all share a limitation that they are predominantly focused on the release of carbon dioxide into earths atmosphere. While this is a key greenhouse gas driving global warming, there are other emissions associated with global warming that are not included. The tools also don’t cover the water usage of data centers, another significant climate impact which will only be compounded as the climate warms further. More on the water usage of AI here

We also see a split in enterprise vs individual project level estimates: project-level estimates typically calculate the one off emissions of training a model, while enterprise tools allow ongoing monitoring of the impacts of using a model as well.

Enterprise solutions for cloud compute carbon impact

You might be surprised to know that the three major cloud service providers offer a variety of different tools to estimate the carbon emissions of customers

Microsoft Emissions Impact Dashboard for Azure

Designed not just for your machine learning models, this PowerBI app calculates the carbon footprint of your cloud-based computing in Azure.

It’s based on a Microsoft methodology validated by Standford in 2018 and includes emissions from Microsoft vendors and suppliers, as well as regional variations in fuel usage, “in line with ISOs”. Although this is Microsoft marking it’s own homework, and it only provides estimates, if you’re already using Azure, this tool should help you track and potentially reduce, the emissions associated with cloud usage.

Link: https://appsource.microsoft.com/en-us/product/power-bi/coi-sustainability.emissions_impact_dashboard

Google Cloud Carbon Footprint Dashboard

Similar to Microsoft, Google will provide you with a lovely visual dashboard for tracking how your IT cloud operations are contributing to climate breakdown. Perfect for management of multiple projects across an organisation!

The biggest surface difference between the two is that Google claims to adhere to the Greenhouse Gas Protocol for emissions reporting instead of an unknown ISO. The documentation for the methodology is also easily accessible online, vs the random PDF file from Microsoft.

Amazon Web Services Carbon Dashboard

Not to be outdone, Amazon claims to meet Greenhouse Gas Protocol AND ISO14064 with it’s carbon footprint estimates for Amazon Web Service customers. Notably however, it only includes on-site fossil fuels, and Amazon Web Service products electricity use in it’s calculations. Google, in contrast incorporates a third category (the terminology used in the documentation is “scopes”) which includes upstream emissions from data center equipment and buildings. So, while AWS might look like a lower carbon option, that’s probably not the full picture.

Essentially though, which of the above you use depends on the enterprise cloud provider your organisation has gone with. They each have their benefits and drawbacks but most of them will give you some kind of overview that can be used to make management decisions about project cloud resources and use. If they’re implmented before your company dives head first into the shallow end that is coporate AI adoption, you could also use them to demonstrate further what a bad idea that is climatically.

Individual Project Tools

Looking for tools at a different scale to the enterprise dashboards? There are a variety of different tools out there, mostly with some flavour of open license, that will integrate into your python project to provide tailored estimates

ML Co2 Impact

An open source (MIT license) project with a quick and easy website for calculating an estimate of carbon impact across cloud providers and hardwares - just select from the drop down options and hit the big red button. The data used to support all the calculations is on GitHub in case you’d like to interrogate it or add to it yourself, and the assumptions used to build the calculations are laid out clearly. There’s also a python package to install if you’d like to integrate carbon estimations in your own coding workflow (say, alongside PyTorch, for example).

Bonus points for incorporating a LateX template for the researchers looking to include calculations and providing some tips on how to reduce the carbon impact of machine learning. Downsides include difficulty in monitoring emissions over time and at a scale larger than individual projects or work.

Website: https://mlco2.github.io/impact/ GitHub: https://github.com/mlco2/impact

CarbonTracker

Another open source (MIT license) python package developed by students in Denmark, this tool will estimate the carbon emissions for training your machine learning model, and even gives you the option to end training cycles early if the carbon emission predictions are too high. It’ll automatically detect your location based on IP, then use that for part of the calculations of emissions.

CarbonTracker has been widely cited in academic papers (400+ times and counting) but as with codecarbon, it’s not clear how well it plays with enterprise perspectives and demands. This is understandable, since it was developed by PhD students, but it’s worth bearing in mind that you’ll need to do some aggregation and parsing if you want a nice dashboard for your manager to point at in a meeting. The documentation also isn’t great.

Website: https://carbontracker.info/ GitHub: https://github.com/lfwa/carbontracker

Want to get really nerdy about the tools mentioned above? “How to estimate carbon footprint when training deep learning models? A guide and review” (Bouza, Aurelie, & Lannelongue, 2023) is for you! It looks at seven different tools available in depth, so if the two mentioned above aren’t quite what you were looking for, this paper probably has one that’s a better fit. DOI: 10.1088/2515-7620/acf81b

Other options

For the non-technical folks out there, Deloitte has hopped on the AI bandwaggon with it’s very user friendly AI Carbon Footprint Calculator. Is there a rigorous methodology described? Don’t be silly, it’s a big 4 consulting firm! It uses the latest weights and metrics, they promise. What it does come with are some limited multiple choice options in an easy to read format and a rating out of 10 at the end. This writer couldn’t manage to get it give them a gold star as well, but you might have better luck. You also have the chance to enter their marketing funnel by downloading a personalised report with “recommendations”.

This is honestly, probably best for senior managers or people trying to convince senior managers that using live streamed cloud data to develop autonomous flying cars with natural language processing might be a bad idea.

So once you’ve identified the carbon impact of your project, organisation, or idea to do weather prediction with chatGPT, what to do next? We’ll cover this in a future article (Step 1: STOP PUTTING AI IN EVERYTHING), but in the meantime you can send your thoughts to contact@utaw.tech.

At UTAW, members are are organising and campaigning on issues ranging from tech complicity in genocide, employee surveillance and workplace discrimination, to climate breakdown. Join today and you too can get stuck into pushing for change across tech industry.

Revolution, against all the odds.

This year, I had the honour of visiting Cuba with the Cuba Solidarity Campaign’s Young Trade Unionist’s 2025 May Day Brigade as UTAW’s first ever delegate. I was alongside comrades from all across the trade union movement, with members from CWU, Unite, ASLEF, RMT, TSSA, POA - more acronyms than you can shake a stick at.

Our trip lasted two weeks, and in that time we heard and saw lots of different things about Cuba and how society is organised. There was a varying level of pre-existing knowledge amongst our brigade, some had deep specialist knowledge before coming, and some knew very little. I’d put myself in the latter camp, I knew relatively little about Cuba before my visit, and a lot of what I did know was through smashing articles and documentaries in the weeks leading up to the trip. One thing I noticed a lot was the Western propaganda spin on pretty much everything, spinning everything Cuba has done into a negative - stuff like “Cuba had one of the greatest and fastest literacy campaigns ever, and this is bad because…”, that’s not a direct quote from anything in particular, but the overall vibe I got whilst reading stuff. Over the past few years, in the face of Israel’s genocide, many people have got used to the lengths that our media will go to in the name of Western propaganda (think things like “young Gazans die” instead of “Palestinian children murdered”) but it’s still always jarring to identify and read such blatant ideological propaganda from our “trusted” media sources. For a small period of time, the Western media machine began to paint Cuba in a more positive light, as a result of Obama’s effort to warm relations with Cuba. Famously, the Kardashian’s had visited in 2016, televised in the episode ‘Havana Good Day’. Their visit was met with mixed reviews, and illustrations of Kim as Che (god bless the days before AI slop, where real artists made mashups of celebrity influencers and revolutionary commanders).

During our visit to Cuba, we learned how a state so boldly manages to organise their society in an alternative way to capitalism, despite having so few resources, being so physically close to the centre of the capitalist world, and the enduring blockade intended to bring about “hunger, desperation, and overthrow of government”. It’s very easy to have an ideologically romantic image of the revolution and the society it organised in place of the previous corruption and US interference, but the trip highlighted the importance of not just holding the ideology of Cuban society in a romantic, idealised capacity, but also recognising and understanding the damage caused by decades of the US’ illegal blockade. By recognising the damage caused by the US, and understanding how longstanding US policy chokes the possibility of Cuban prosperity, we become better comrades who can advocate for practical solidarity and change. It’s vital that all comrades who attend Cuba take what they learn home and share it, advocate for the Cuban people, and fight for better. Particularly those comrades visiting from the imperialist core where the greatest opportunity for meaningful change lies.

In 2025, Cuba are experiencing massive fuel shortages and enduring rolling blackouts. The camp we stayed at was experiencing a blackout both when we arrived, when we left, and at frequent points in between. In March 2024, protests began in response to food shortages and blackouts. The blackouts peaked in intensity in 2024, when Cuba suffered a national blackout from 18-22 October. The illegal blockade has increased in intensity since 2021 when Trump added Cuba to the arbitrary ‘State Sponsors of Terrorists’ list. Many people had hoped Biden would reverse this decision but he instead completely avoided the issue until his last few days in office, when he finally removed Cuba from the list, only for Trump to immediately return Cuba to the list when he returned to office.

Despite everything the Cubans endure, we experienced nothing but hospitality and kindness from everyone we met. We were met with total kindness and willingness to answer all of our questions about life in Cuba.

When we arrived at Camp, it was almost dusk, and there was a power cut - so we were thrown into the deep end. That being said, the staff and other delegations handled the cuts so well that often you couldn’t tell there even was a power cut. The dorms we stayed in were basic but had everything we needed. It’s all very communal, you share your rooms with seven other people, and you share the toilets and showers with your delegation. The toilet doors were very low, like a full head’s height lower than an average height person, so going to the toilet became a social experience. The toilets themselves didn’t flush and so you had to flush away your own waste with a bucket of water. All of this became second nature quite quickly and in all honesty, is preferable to the average British music festival. There were often frogs in the showers and toilets, they were pretty cute and I looked forward to seeing them. I actually kind of miss them now.

We were fed well, especially given the context of the US’ illegal blockade making it hard to import food, and fuel shortages making it harder to grow food. Lots of modern farm equipment runs on fuel, so a lack of fuel means moving back to older forms of farming, like manual tools, and using Oxen instead of vehicles to plough. All of this is slower, and leads to a lower yield, therefore making it harder to feed everyone. Despite this, we ate three solid meals, three times a day. We ate rice, meat, potatoes, and eggs largely. There was always tomatoes and cucumber available with lunch and dinner, and the tomatoes in Cuba had much more flavour than any of the tomatoes in the UK. The delegation had been briefed to bring condiments and seasoning for the food, and while having the condiments did add to the meals, there was no time that the additional condiments were absolutely necessary. And there were many meals I ate without using the condiments I’d brought (particularly when we’d had a long day and I didn’t want to walk back to my room and search through my bag.) My condiment of choice was Laoganma Crispy Chilli Oil - which makes everything taste better, especially rice (which you can expect to eat twice a day).

Throughout the trip we had a packed agenda that helped us to see many aspects of Cuban life, we visited primary care facilities (polyclinics) and listened to the importance of free healthcare to the Cuban approach to healthcare. For example, every town has a GP and every family has a family doctor. The polyclinics also have small wards and ICUs, they seemed to operate most comparably to a walk-in centre. As we toured the polyclinic though, we saw that the facilities were dated and resources were scarce. It’s no secret that particularly right now, Cuba is struggling with even the most basic of medical supplies. As an act of practical solidarity, the entire brigade brought medical supplies (painkillers, sterilisation equipment, contraception etc.) to be distributed to healthcare and community facilities. These gifts were appreciated but we were under no illusions that our gifts were a drop in the ocean compared to what the Cuban people need and deserve.

We also heard about the importance of healthcare in Cuba’s internationalism. For example, how they sponsor Palestinian students to attend the Latin American School of Medicine, and how they’ve sent brigades of Doctors to other countries, like to Italy during COVID when it became the centre of the pandemic. Healthcare is a huge point of pride for the Cuban people, and we saw that there’s a big focus on primary care and preventative approaches. For example, Cuban healthcare professionals take a community and primary care based approach to treating HIV.

Cuba also has the most progressive policies in Latin America for Trans and gender affirming healthcare which is fully provided by the state free of charge. A person must have lived in their ‘new’ gender for two years before they can access some types of gender affirming healthcare. Reform of the family code, and introducing incredibly progressive policies on LGBT+ issues like healthcare and marriage are a recent development in Cuba society. It wasn’t lost on me throughout the trip that this wasn’t always the case, and that during the early stages of the revolution - gays weren’t treated well, often being placed in labour camps and being labelled as counter-revolutionaries. In his later years, Castro acknowledged this as a mistake and took the blame.

The centrepiece of the trip was undoubtedly the May Day parade, an annual parade of workers through the heart of Plaza de la Revolución in Havana in celebration of International Workers’ Day. Hundreds of thousands of workers march along the street in support and celebration of the revolution. Traditionally, Fidel would’ve attended. Raúl and Miguel Díaz-Canel now typically attend. I’d never seen such a large march in my life before, and definitely not to celebrate something.

On farms, we saw the impacts of reduced access to fertiliser, tractors, and other important farm equipment, because of the blockade. In response to this, Cuba has adopted largely organic and manual farming techniques, largely through necessity - but also because Cuba understands its place in the environment and considers their existence in relation to the environment. There’s a conscious effort to farm in an ecological way. Cuba is acutely aware that as an island in the Caribbean, the impacts of the ongoing climate crisis will not be kind to them.

Our visit to Cuba was nothing short of life-changing. It challenged everything we thought we knew about Cuba, and about the world by extension. It showed us that it is indeed possible to fight back, even in the most challenging conditions, and that it’s possible to win. Each one of us must continue to fight for a Cuba free from the pressures of imperialism, we must fight to end the illegal blockade. We must defend the right of a state to exist freely and without US influence.

UTAW Members at Cambridge Apple Store ask for union recognition

Grand Arcade joins a growing number of Apple Retail Stores globally that are organising, including White City and Southampton, where workers have also requested voluntary recognition through UTAW and faced considerable pushback and union-busting from management.

Apple’s response has followed a familiar and predictable pattern seen internationally. Managers have conducted one-on-one meetings with staff to interrogate them about union involvement, while offering extra hours as an incentive. All-hands “captive audience” meetings have been held to spread misinformation about unions, and a small number of anti-union workers are encouraged to speak out regularly.

Apple attempts to portray UTAW as a meddling external organisation disrupting the so-called “family” dynamic between employees. They frame unions as “businesses” trying to exploit Apple’s workforce for profit. A union could lead to losing out on pay or benefits. A union might negotiate you a worse deal. A union might lead to you receiving pay increases and benefits later than other employees. A union can’t actually affect all the things they want to change.

In 2022, Apple hired the notorious “union-avoidance” firm Littler Mendelson in the US — a law firm previously involved in union-busting at Starbucks and McDonald’s. The Verge reported on this move, which signalled how seriously Apple was preparing to resist worker organising. In 2024, the US National Labor Relations Board (NLRB) awarded a settlement to Apple retail workers represented by our sister union, the Communication Workers of America, after a manager unlawfully interrogated a worker about union support. Reuters covered the ruling. The behaviours ruled on by the NLRB represent more than a passing resemblance to the playbook Apple have deployed in the UK.

Apple’s day-to-day union-busting tactics in the UK aim to slow things to a crawl — delaying meetings with union reps, creating the illusion that nothing is happening. Phrases like “do your own research” are used to sow doubt and delay decisions, echoing the rhetoric of conspiracy theorists, pseudoscience promoters including the followers of the far-right QAnon conspiracy. Apple also flaunts its “Solicitation and Distribution” policy to discourage workers from talking about the union or distributing materials at work. While legally they cannot enforce this against protected union activity, the implication alone is intended as a threat to deter activists.

For all the effort Apple has put in to union-busting, it continues to refuse to engage with the real issues workers want to raise. Retail staff routinely face disciplinary action for disability-related absence, often caused by the company’s failure to implement reasonable adjustments. Managers are poorly trained, leading to unfair disciplinary procedures and harsh sanctions. Apple use an employee forum called the ERT as a sham union proxy to consult and inform workers on changes to roles without providing those reps with an agenda prior to meetings or the opportunity to raise concerns. And chiefly amongst all these issues, Apple doesn’t recognise the huge contribution that individual retail workers add, who receive bog-standard retail wages in exchange for individually contributing hundreds of thousands of pounds each in revenue every year.

All of this amounts to a hostile, toxic work culture for workers, emblematic of the issues that workers want to resolve in the first place. All of this is abundantly obvious to workers at Apple Grand Arcade who have prepared to face these tactics. They routinely identify and share the actions that Apple is taking. Blatant union-busting like this wont work. If anything it strengthens the resolve of our members and only further demonstrates the need for a real worker voice in the form of a worker-led trade union. That’s what we’re building at Apple, and we won’t back down.

– The reps at Apple, Cambridge