National AI Equity Fund: A Social Contract for the AI Age

Motion: An Artificial Intelligence Transition with No Jobless Growth
Gerald Giam (Aljunied GRC)
6 May 2026

I declare my interest as the owner and director of a company that provides software to training providers.

The Problem

Mr Speaker, we face a structural threat to our workforce. For decades, Singapore’s economic model has been built on the premise that a highly educated and skilled workforce would hold the keys to a prosperous future and be a buffer against economic storms. However, we are now in the midst of a paradigm shift where artificial intelligence (AI) is not only augmenting human capability, but in many ways, replacing it. Unlike past economic cycles, where such turbulence could be written off as an episode of creative destruction, AI promises to be a harbinger of a fundamental shift in our economic and social relationships. Taking this concept further, it would impact even the roles that the government plays in mediating between the individual and society.

Today, we must recognise that the very nature of labour’s economic power is changing. Failure to address this issue even as productivity soars will lead to an entrenched lower and middle class with a loss of economic agency.

​This concern is articulated by Jasmine Sun in an opinion piece for the New York Times, where she identifies the “San Francisco consensus”—a growing recognition that the hiring of young workers in highly AI-exposed occupations is already in decline. She reminds us of the risk of a resulting “permanent underclass,” where the gains of technology are concentrated in the hands of the very few.

Not all the evidence points toward catastrophe. A 2025 US National Bureau of Economic Research (NBER) working paper found that tasks with higher AI exposure do experience reduced labour demand. However, overall employment effects have so far been modest, as productivity gains offset some displacement.

Similarly, a study published in the Quarterly Journal of Economics by MIT’s Danielle Li and Stanford’s Erik Brynjolfsson found that generative AI tools boosted worker productivity by nearly 15%, with the greatest gains among less experienced workers — suggesting AI can be a ladder, not just a trap door.

It should be noted that these studies examined early, and controlled deployments. As agentic AI scales across entire industries simultaneously, the distributional consequences may be more severe and swifter than early productivity research would suggest.

We cannot be certain which trajectory Singapore is on. The asymmetry of risk demands that we prepare for the harder scenario, not the easier one.

This concern is shared by the very architects of the AI revolution. In 2021, OpenAI CEO Sam Altman predicted in his blog post, Moore’s Law for Everything, that AI would shift power from labour to capital, positing that if public policy does not adapt accordingly, most people will end up worse off than they are today. Crucially, Altman was not fatalistic — he argued that the proactive redistribution of AI-driven wealth, including giving citizens equity stakes in the economy, could make this a broadly prosperous transition.

Similarly, Dario Amodei, the CEO of Anthropic, has observed that the health of a democracy is premised on the average person having leverage through creating economic value — a view he expressed in his 2024 essay, Machines of Loving Grace. The erosion of that leverage is a deeply concerning prospect that requires a bold and structural policy response.

Singapore is uniquely positioned to lead this response — and to capture the genuine economic opportunities AI presents for our people. As a small, open economy with a highly educated workforce, strong institutions and well-capitalised sovereign wealth funds, we have the tools to act swiftly and structurally compared to many larger nations. But that window of opportunity will not remain open indefinitely.

Where cost arbitrage made offshoring attractive, AI could erode that advantage — not by bringing those jobs back, but by enabling small teams of skilled Singaporeans to do the work that once required hundreds of offshore workers. The opportunity is not in reshoring in the traditional sense, but the concentration of higher-value orchestration and oversight roles here at home, where trust, institutional quality and proximity to decision-makers matter.

And AI’s equalising potential extends beyond white-collar work. A blue-collar worker who struggles with English could dictate in their mother tongue and have AI render it as professional documentation in real-time — freeing them to focus on their craft rather than their grammar. AI should be an equaliser that elevates the technical master, not a wedge that stratifies our workforce.

AI tools can also power a new breed of local startups by enabling small, hyper-efficient teams to create immense value and scale, achieving global reach with minimal manpower.

Singapore must be at the forefront of this shift while ensuring the benefits accrue to all our citizens. This will require workers and entrepreneurs who are trained, skilled and adept at harnessing AI tools and innovations, and empowering their employees to do the same.

Our current efforts to reskill Singaporeans are often hampered by the trap of low-utility external training programmes which produce certifications that lack real-world currency in an AI-driven economy. These programmes enrich training providers while leaving workers with skills that have little economic value.

This misalignment risks creating a two-speed economy, where capital owners and tech-integrated firms leave behind those stuck in the slow lane of traditional employment, leading to a fundamental erosion of social cohesion and increasing the risk of long-term structural unemployment.

The Proposal

To address this, I propose the establishment of the National AI Equity Fund. This fund is a necessary safeguard to maintain the integrity of our social contract. It is a strategic surplus transfer from enterprises which benefit immensely from AI back to Singaporeans to facilitate our collective stability.

I will elaborate on the precise financing mechanisms shortly, after I explain the uses of the Fund.

I propose that the fund be organised into two distinct pillars.

The first is a Social Dividend, where revenue is distributed as a direct payout to every adult Singapore citizen. I propose an initial annual dividend of $500 per adult citizen, scaling upward as Fund contributions grow. This is modest by design — it is not meant to replace income, but to provide a tangible signal that every Singaporean has ownership in our shared future. Based on our current citizen population, this would cost approximately $1.5 billion annually — or less than 10% of last year’s Budget surplus — and provide a meaningful return to every Singaporean household.

This would serve as a social floor, ensuring that the gains from national digital prosperity provide tangible peace of mind and dignity for all. This dividend will provide an additional cushion for families as the nature of work evolves. It also allows Singapore to reap the full productivity benefits of AI without overly exacerbating social inequality.

An argument could be made that the CDC vouchers already do this, but those are entirely discretionary. The Social Dividend I propose is a structural entitlement, a function of receipts rather than what the fiscal mood of the moment happens to be. That distinction matters enormously to a family planning its future.

​The other portion of the fund will be dedicated to a Mastery Fund, which will be an employer-led on-the-job training (OJT) model that moves training out of the classroom and onto every enterprise.

I propose that the Mastery Fund provide a Mastery Apprentice Wage, covering 50% of the gross salary, capped at the median wage, for six months for any Singapore citizen entering or transitioning into an AI-augmented role. This rewards the worker’s effort to adapt while lowering the barrier for firms to hire, train and retain talent in a volatile market.

​Recognising that many SMEs lack the capacity to design structured OJT, I propose that the fund also finance a pool of expert OJT consultants. These consultants, experienced in OJT design, will rotate between firms to structure OJT blueprints tailored to each firm’s specific needs. This would help SMEs fill in their talent gap while also addressing the need to create new steps in the ladder of training and apprenticeships for new entrants into the marketplace.

Furthermore, I suggest a Mentorship Credit be provided to employers to compensate senior staff for the time they spend on structured mentorship, turning our workplaces into true academies of mastery and ensuring that skills remain relevant to the actual needs of the economy.

The Mastery Fund should be made available to all business entities and societies that are founded and based in Singapore, including micro enterprises. The use of funds should be closely monitored to ensure that it genuinely contributes to AI mastery within each firm. I estimate the annual cost of the Mastery Fund to be approximately $1.42 billion.

Funding the Fund

Let me now set out the financing details.

The first source is a marginal increase of two percentage points in the Corporate Income Tax rate for firms with annual profits exceeding $100 million. By focusing on these companies, we capture the “automation surplus” from those best positioned to drive growth through AI rather than headcount. Whether global tech firms or traditional giants, these enterprises are at the forefront of decoupling revenue from labour. This tax increase would generate an estimated $1.5 billion annually, ensuring that gains from record-breaking efficiency are recycled back into the National AI Equity Fund for the benefit of all Singaporeans.

The second source is a targeted increase in the utilisation of our investment returns. I propose raising the maximum Net Investment Returns (NIR) taken into the budget from 50% to 52.5%, with this additional 2.5% flowing directly to the Fund. Based on current estimates, this would raise approximately $1.45 billion annually.

Our sovereign wealth entities, GIC and Temasek, have been early movers in the AI space, investing in foundational firms like Anthropic and committing billions to the AI Infrastructure Partnership alongside Microsoft, BlackRock, and Nvidia. As these global investments profit from the automation of labour worldwide, it is only right that we recycle a modest portion of those gains back to our own workforce. Reallocating 2.5% is not a radical request; it ensures our reserves provide more than just financial stability, but also the long-term economic agency of every Singaporean.

​As we look toward this future, we cannot simply assume that displaced workers will transition smoothly into new roles as they have in previous technological revolutions. The steam engine did not replace human judgment, but AI may do just that.

That is precisely why passive reskilling is insufficient, and why the financial security of a Social Dividend is needed. Workers shifting toward less automatable roles — in entrepreneurship, care work, the skilled trades, sports and the arts — do not need just training, but time and security to make that leap. Certainly, new jobs will emerge that we cannot yet imagine, but we must build a system robust enough to support our people even if that emergence is slower or more unevenly distributed than we would otherwise hope.

The National AI Equity Fund provides the financial buffer for Singaporeans to make these transitions with confidence. During this year’s Committee of Supply debate, I proposed the Youth Wage Credit Scheme — a targeted wage subsidy for employers who hire younger Singaporean workers. The National AI Equity Fund extends that logic into a broader, longer-term framework for all Singaporeans navigating the AI transition and other future technological disruptions.

Conclusion

Mr Speaker, the National AI Equity Fund is a renewal of our social contract for the digital age.

​We cannot allow AI to become a wedge that fractures our society. Instead, we must use it to become the greatest equaliser our nation has ever known. By establishing the Social Dividend and the Mastery Fund, we give every Singaporean a direct stake in our digital prosperity and the resources to stay ahead of the curve.

​Let us make it our goal to ensure that as machines grow more capable, our people grow more secure. By acting now, we can ensure that technological progress serves the dignity and economic agency of every Singaporean.

​I support the motion.

Global leadership in AI

5 Feb 2024, Parliament

The government unveiled its National AI Strategy 2.0 report (NAIS 2.0) last December. The report outlines Singapore’s plans to harness artificial intelligence (AI) for the public good, focusing on enhancing AI capabilities, addressing potential risks and fostering a thriving AI ecosystem. 

I appreciate the hard work that many policymakers have put into writing this report, including their efforts to consult with industry. 

However, I believe that Singapore needs a more comprehensive AI industrial policy and clearer outcomes for each industry. We need a strategy that aims to make our nation the global leader in selected AI fields. 

Under NAIS 2.0, the government’s main role revolves around enabling an environment for AI to grow and enhancing the efficiency of public agencies. While these are important, the government certainly has the resources and capability to do much more. In this AI-driven era, we need the government to intercede more proactively to create a world-leading AI industry. Simply leaving things to the free market may not produce the desired results because of the constraints of our local private sector and our small domestic market. It will risk forfeiting some promising economic growth opportunities that AI can bring for our nation.

Singapore’s small population should not deter us from global AI leadership. Historical industrial successes have been built on strategic government interventions, such as Taiwan’s support of TSMC, which played a pivotal role in its journey to becoming a global semiconductor manufacturing juggernaut. 

Today, I will address NAIS 2.0’s AI labour policy and advocate for a strong AI industrial policy so that Singapore can aim for global leadership in selected AI domains. 

AI labour policy

Let’s begin with the AI labour policy. The goal in NAIS 2.0 to generate 15,000 AI jobs sparked a blend of enthusiasm and apprehension among Singaporeans. Concerns linger that, as has happened in the past, foreigners may dominate lucrative positions — including leadership positions — leaving Singaporeans with mainly the routine and lower-paying jobs. This could impede our citizens’ career advancement alongside the advancing AI landscape.

NAIS 2.0 has three labour planks. Scaling up AI-specific training programmes; scaling up technology and AI talent pipelines; and remaining open to global tech talent. 

Will the government commit to ensuring that a sizable majority of at least two-thirds of the 15,000 new “AI practitioner” jobs will go to Singaporeans? 

I acknowledge the importance of global AI talent. However, there must be a clear differentiation between exceptional global talent and the average foreign tech worker. We should welcome the former, but should avoid importing too many of the latter, as they may end up competing with Singaporeans who can do the job just as well. Any global talent that we bring in must be expected to transfer their skills to locals, not just use Singapore as a springboard for greater pursuits in other countries. This can be done through tying company grants to the achievement of knowledge transfer, or through limited-term foreign work passes tied to the training of Singaporean workers.

The AI playground is level, with a highly collaborative open-source community. The core techniques and frameworks are mature enough and reasonably accessible through papers and code. With the right training, mentorship and opportunities, Singaporean talent can deliver as well as anyone in the world.

To raise a body of local AI talent, AI-training programme places and talent pipelines must be focused on Singaporeans. We need to plan ahead and start training all our students early in AI — not just students who are academically strong in the sciences and mathematics. 

For mid-career workers, hands-on interaction with AI tools is one of the best forms of training. The government should expand the scope of SkillsFuture Credits to cover expenses for subscriptions to AI assistants like ChatGPT Plus or Github Co-pilot to accelerate their productivity. This will level the playing field for Singaporeans with less means to pay for such subscriptions. Paid models like GPT-4 have been assessed to be significantly better than their free counterparts, and we should give our workers more opportunities to use the best models.

Moonshots in industry

Next, on industry. It was once unclear if it were possible for humans to reach the moon. But US President John F. Kennedy made a speech to Congress in 1961, rallying his nation to achieve the goal of landing a man on the moon and returning him safely to Earth before the end of that decade. And NASA’s Apollo 11 mission achieved it ahead of schedule on 20 July 1969.

A “moonshot” is an ambitious, exploratory and groundbreaking target that has the possibility of spurring breakout growth. We need moonshots in AI, which NAIS 2.0 appears to lack.

The government has the resources and capacity to take on more risks on a longer timescale to pursue high-reward moonshots. These could catalyse future engines of growth. But first, the government must catch the vision and have the determination to make our country number one in our chosen AI domains.

Achieving some of these AI moonshots has implications on our economic security and even our sovereignty. 

Advances in AI rely on large, high-quality data sets. We must ensure that foreign tech firms and governments do not end up extracting our data overseas to build AI products, which then get sold back to Singaporeans. This will allow such firms to profit immensely while local expertise flounders. 

Currently no global framework governs cross-border data flows and ownership. This allows predatory dynamics to continue between countries and companies. Once market dominance is achieved, network effects and the dynamics of chasing a moving target make it almost impossible for new entrants to catch up. 

If we are not careful, Singapore may become only a consumer of such platforms, while the economic benefits and the best jobs go overseas. Singapore should avoid this by proactively building comprehensive local data sets for homegrown AI development. 

Management of moonshots

We should pursue a few ambitious, publicly-funded moonshot projects. These projects must prioritise transparency and align their outcomes with the national interest, ensuring that economic gains directly benefit our citizens. 

A new government-owned AI startup will be needed to catalyse this moonshot, and I will refer to it as the AI Catalyst Corporation. It should be independently run with commercial dynamism, yet be ultimately answerable to Singaporeans.

What constitutes a well-chosen moonshot? I would like to propose five key principles:

First, its products or services must directly benefit Singapore and Singaporeans. Second, it should have export potential and become part of Singapore’s economic growth engine. 

Third, it needs to have “moats” — which are durable advantages to prevent it from being quickly outcompeted or swallowed up by global tech giants. 

Fourth, there must be a genuine unmet global market gap that Singapore has advantages in tackling. And fifth, the industry should be ripe for fundamental disruption, not just incremental improvements.

Healthcare AI as a moonshot

Healthcare AI could be a moonshot that Singapore can aim for. I will present the case for this, and answer the five questions in reverse order.

Is healthcare ripe for fundamental disruption? Yes. Healthcare systems worldwide are under strain due to ageing populations and chronic disease burdens. Healthcare institutions tend to treat diseases late in their course, when symptoms are severe and care is expensive. Yet major conditions like obesity, heart disease and cancer, are driven by shared underlying factors. This outdated care model no longer aligns well with scientific reality. Healthcare AI, supported by population-scale data, has the potential to predict risks and intervene early to significantly improve health outcomes.

This will require a reorganisation of healthcare delivery that focuses on early prevention and action. Singapore has started this journey through the Healthier SG programme. Let’s turbocharge it with healthcare AI.

Second, is there a genuine unmet global market gap? Yes. Electronic health record (EHR) systems remain fragmented worldwide. Even in the US, no dominant player exists in healthcare AI. The players are fragmented among various entities like EHR providers, tech firms, life sciences companies, insurers and hospitals. Those with vast healthcare data may not have efficient AI models, and vice-versa.

Singapore, on the other hand, has a unique opportunity to build a population-scale healthcare data ecosystem tailored for AI. We can more easily overcome the coordination challenges that may oblige larger ecosystems to build healthcare AI components in a piecemeal fashion. A platform called MOH TRUST already aggregates multiple healthcare research datasets, and the National Electronic Health Records (NEHR) system aggregates clinical data across Singapore. So the government is already collecting and coordinating healthcare data. What is missing is the impetus to use this data to drive the future of AI through an industrial policy.

Third, are there “moats” against global tech giants? Yes. Healthcare AI depends a lot on having local healthcare teams and physical sensors to collect and manage clinical data. AI can serve as an assistant to local healthcare workers and give Singapore an edge over others. Singapore has already signalled its commitment to generating comprehensive data, like in the SG100K genome study. Healthcare also has more durable data moats than other AI spheres like linguistics, where SEA-LION’s defences against global tech giants in low-resource languages is uncertain.

Fourth, does healthcare have export potential? Yes, as a public good, healthcare AI can benefit other countries while facing fewer sovereignty concerns. Debates are taking place globally about where large AI systems are trained and deployed. However AI, when used for healthcare, which can potentially benefit everyone, is less likely to attract controversies or nationalist and protectionist tendencies. 

By assuming a leadership role in this field, Singapore can export our healthcare AI innovations, generate much international goodwill and even use this to advance our foreign policy.

And finally, does it benefit Singaporeans? Yes, through improved public health, economic growth and global technological leadership.

Singapore has other comparative advantages in the race for global leadership in healthcare AI. We have a robust healthcare system, the SingPass digital ID, cross-domain talent and a history of government investments and interventions in specific industries. 

Singapore’s demographic diversity provides rich healthcare data across ethnicities and ages. Healthcare AI can catalyse the development of adjacent fields like computational genomics and precision medicine.

Singapore has the ingredients for a breakthrough in Healthcare AI. To succeed, the government needs to take the lead in putting these ingredients together. 

What should we do in Healthcare AI?

To realise a healthcare AI moonshot, Singapore must combine existing ingredients into a coherent strategy. We should aim to export specialised services and medical diagnoses, not raw data. 

We can create an advanced AI model trained on genetics, protein biomarkers, histology and electronic health records. We should aim to radically improve our ability to prevent disease, intervene and make causal inferences.

A multi-modal healthcare AI foundation model moves beyond narrowly specified “point-solutions”. By having a single foundation model for, say, both chest X-rays and retinal image interpretation, we can overcome data fragmentation across medical specialties and make it increasingly possible to uncover foundational principles of diseases.

As for electronic health records, the NEHR system is valuable, but it needs to be AI-ready in order to consolidate complex datasets such as histology imaging, genetic data or protein data. We should create comprehensive longitudinal patient histories spanning years. Existing data needs to be sufficiently standardised to serve AI analysis without a massive amount of preprocessing.

An AI Healthcare Company under the AI Catalyst Corporation could drive this moonshot. This AI Healthcare Company needs its own versatile multi-modal foundation model integrated with the NEHR. This will enable large-scale analysis to identify at-risk groups, conduct preventative screening and perform early treatment. It will also enable high-quality acute care, as a simultaneous expert in genomics, biology, general medicine and the specialties.

The AI Healthcare Company could build the world’s best multi-modal healthcare AI and healthcare dataset. This could establish an unmatched resource — built in Singapore, for the world. 

Local AI startups can also benefit from this foundation model to build their own applications to sell to the world.

Just like how OpenAI’s access to ChatGPT queries provides unmatched data for improving their future large language models, the first company to create a versatile multi-modal healthcare foundation model would likely find itself at the frontier of healthcare AI.

Conclusion

Mr Speaker, in conclusion, a healthcare AI moonshot strategy will position Singapore as a global leader in AI by leveraging our unique capabilities in consolidating biomedical and healthcare data. It is a national approach designed to secure data sovereignty, navigate data-privacy concerns, ensure Singapore captures the benefits of AI, and maximise public by-in. This isn’t about picking winners; it is a proactive strategy to ensure Singapore thrives in the AI-driven future to benefit all Singaporeans.

I have presented just one example of a moonshot that Singapore could pursue. There may be other moonshots of equal or greater merit. I welcome open debate on selecting moonshots, but we cannot pull our punches if AI is truly the new industrial revolution. 

Singapore possesses the talent, resources and infrastructure needed to compete for the top spot in selected AI fields. Achieving it requires political will, a readiness to embrace risks and proactive intervention by the government. We can do it, and we must do it, for the benefit of Singapore and Singaporeans.

Preventing disclosure of confidential data through ChatGPT

In April 2023, reports emerged that engineers from Samsung Electronics accidentally leaked internal source code. They had uploaded it to ChatGPT, presumably as part of their input prompt to the large language model (LLM). In response to this incident, Samsung took swift action, banning employees from using popular generative artificial intelligence (GenAI) tools like ChatGPT. Additionally, the company urged employees who utilised ChatGPT and similar tools on personal devices to refrain from submitting any company-related information or personal data that could potentially unveil its intellectual property.

Furthermore, a research report released in June 2023, titled “Revealing the True GenAI Data Exposure Risk” by LayerX Security, highlighted a troubling trend. It revealed that 6% of employees had pasted sensitive data, including source code, internal business information and personal identifiable information, into GenAI tools. This concerning behaviour could inadvertently result in organisations unknowingly sharing their plans, product details, and customer data with competitors and potential attackers.

While this research primarily focused on private sector employees, I was concerned about the possibility of inadvertent sharing of sensitive official secrets with GenAI tools by civil servants and government contractors.

In light of these concerns, I raised several questions in Parliament regarding the government’s use of large language models (LLMs) owned by private or foreign companies:

a) How does the government ensure that confidential data is not disclosed in the input prompts for LLMs?

b) Whether the government has signed any non-disclosure agreements (NDAs) with these companies?

c) What are the companies that the government has signed NDAs with?

d) How does the government monitor compliance with such NDAs by these companies?

In response, Mrs. Josephine Teo, the Minister for Information and Communications, provided an explanation of the government’s approach. She assured Parliament that highly sensitive applications and data remain shielded from exposure on the Internet. For instances involving LLMs and sensitive data, open-source models may be customised for use but are strictly deployed on government servers and computers.

For less sensitive data use cases, AI models may be owned and managed by commercial and private companies. The government’s contracts with these companies include clauses pertaining to data handling and security. These clauses encompass non-retention of data and restrictions on data usage for training other products or models. She did not reveal which companies the government has signed NDAs with. She said that the government has implemented a range of technical, visual, and governance measures to ensure data security and enforce compliance. The Minister emphasised the government’s commitment to continuously reassessing the adequacy of these measures as technology evolves.

Here are the original questions raised and answers on 9 January 2024 in Parliament:

REGULATIONS ON INPUT PROMPTS FOR LARGE LANGUAGE MODELS TO PREVENT DISCLOSURE OF CONFIDENTIAL DATA

Dr Tan Wu Meng asked the Minister for Communications and Information whether the Government has plans to develop in-house artificial intelligence capabilities to ensure that input prompts for large language models need not be processed by private firms not under the purview of the Government, or by cloud computing units located in foreign territories or under foreign jurisdiction or control.

Mr Gerald Giam Yean Song asked the Minister for Communications and Information (a) when using large language models owned by private or foreign companies, how does the Government ensure that confidential data is not disclosed in the input prompts; (b) whether the Government has signed any non-disclosure agreements (NDAs) with these companies; (c) what are the companies that the Government has signed NDAs with; and (d) how does the Government monitor compliance with such NDAs by these companies.

Mrs Josephine Teo: Large language models (LLMs), such as those powering ChatGPT, have the potential to enhance the delivery of public services and the productivity of public officers. We adopt a risk-managed approach for LLMs, consistent with the existing public sector framework for the handling of classified information when using technologies such as Internet-based applications and the commercial cloud.

Highly sensitive applications and data are not exposed to the Internet. Where use cases involve sensitive data, open-source models may be finetuned for use but must be deployed on Government servers and computers.

For use cases involving less sensitive data, the artificial intelligence (AI) models may be owned and managed by commercial and private companies. Our contracts with these companies are governed by service agreements which include clauses on data handling and security, such as the non-retention of data, and limitations on the use of data to train other products or models. Beyond contractual safeguards, the Government has also implemented technical measures to screen sensitive data, visual cues to remind users on data security practices, and governance measures to enforce compliance.

We continuously re-assess the adequacy of our measures as the technology evolves.

Source: Singapore Parliament Reports (Hansard)

#ChatGPT #AI #Parliament #WorkersParty #MakingYourVoteCount