Approximately 86% of 16,000 professionals worldwide surveyed by Adobe in 2025 reported actively using AI in their creative workflows. It’s no longer futuristic; it is the reality of our times. One would imagine that AI tools would free people from the difficulties of labour and prolonged work hours.

However, the opposite is happening. Instead of leisure, workers around the world are met with demands for unyielding speed and inhuman productivity.

We must decide whether this universal integration signifies genuine technological progress or whether it simply marks the moment human artistic labour becomes economically mandatory to execute at the pace dictated by Silicon Valley’s algorithms.

There is an immense economic pressure forcing creative professionals to comply or face immediate market obsolescence. The data confirms that AI is deeply integrated into creative workflows, yet this utility must not be mistaken for ethical merit or long-term soundness.

Creative professionals do see genuine, tantalising opportunities, with over half reporting that AI helps them explore new mediums and a remarkable 46% believing it helps them create higher-quality work.

This is the lure, the captivating promise of instantaneous enhancement and boundless efficiency, a promise designed to mask the underlying erosion of value and independence. The current analytical view of AI’s labour impact is dangerously complacent, focusing almost exclusively on macro-economic trends while entirely ignoring the microscopic, fundamental erosion occurring at the individual creator level.

Technophiles often point to recent analyses showing that the broader labour market has not experienced a discernible disruption since the public release of major generative AI systems, a finding that allegedly undercuts fears of immediate mass job losses across the entire economy.

This fact is often presented as reassurance, suggesting a measured, benign adoption trajectory, yet it hides a critical, predatory truth, namely that AI first displaces value and incentive long before it ever displaces employment.

Copyright and corporate capture

To understand the core immorality of the generative AI revolution, we must look no further than the fuel source that powers it, which is the massive, unprecedented datasets of human creative expression upon which these models are trained. These datasets, which developers use as a neutral shorthand for copyrighted works, are the products of millions of human lives, careers, and artistic struggles.

The training process, executed often without explicit permission, licensing, or any financial compensation, represents the original, defining sin of this entire industry, effectively turning the intellectual property and life’s work of millions of artists into free, disposable energy for a burgeoning multi-trillion-dollar technological complex.

The fear among creators is profoundly visceral and absolutely justified, because unlicensed training will fatally corrode the creative ecosystem, permitting AI-generated content to directly and unfairly compete in the marketplace with the very artists whose works were ingested and repurposed without consent.

The US legal system is currently caught in the paralysing gridlock of this crisis, embroiled in dozens of high-stakes lawsuits that specifically focus on the strained application of copyright’s fair use doctrine to the mass ingestion required for AI training.

These legal challenges have exposed the staggering scale of the alleged infringement, including claims against powerful entities like Meta for allegedly using its corporate IP addresses to download nearly 2,400 copyrighted adult movies via BitTorrent for the explicit purpose of training its AI systems, a transgression that puts the potential damages well over $350 million.

The stakes in these legal battles are existential, with some developers arguing that requiring formal licensing would irreparably throttle a transformative, world-changing technology, while creators fear, with equal passion, that allowing this unlicensed exploitation will mean the inevitable death of the human creative community. The public interest demands striking an effective balance, one that allows technological innovation to flourish without dismantling the thriving community of creators who feed it.

In terms of intellectual property protection, the American courts have established one clear and critical legal marker, confirming that human authorship is a foundational, bedrock requirement for copyright protection, thereby establishing a critical and necessary distinction between a human using a sophisticated tool and the tool itself attempting to claim the rights to its output.

This decision affirms the principle that intellectual property rights must apply to works generated by humans. The ruling addresses only the resulting output, leaving the foundational injustice of the mass, uncompensated training data capture entirely unresolved, a loophole large enough to drive a generative AI truck through.

Crowding out true innovation

The deployment of generative AI has led to a fundamental economic revaluation of creative labour, posing an existential threat to the long-term health of the artistic community. When AI provides sophisticated tools that enable individuals without traditional, hard-won artistic skills to produce high-quality, technically sound work in fields like illustration, design, or digital music, it fundamentally lowers the barrier to entering the market.

While accessibility sounds like a profound social good, the immediate economic consequence is brutally clear: this widespread capability devalues the artistic skills honed over years of craft, study, and sacrifice, diminishing their perceived market worth and making the professional’s work less appreciated or undervalued.

This devaluation sets the stage for the most dangerous economic outcome, the widely observed “crowding out” effect. Generative AI excels at creating high-volume, low-variance, and highly formulaic work at nearly zero marginal cost, making these formulaic outputs significantly cheaper than traditional human creations.

The lower cost of this technically proficient content then acts as an economic steamroller, systematically forcing out the more costly, experimental, and risky human creations that are essential for driving long-term innovation and stylistic evolution in culture. This phenomenon is not theoretical; the marketplace is already providing clear warning signs, with consumers sometimes showing a direct taste for the influx of AI-generated images, selecting them over human-generated works, confirming that increased competition and variety for buyers come at the devastating cost of financially crippling the creators who fuel the market.

The ultimate psychological and financial violation faced by creators is the commodification of their unique artistic style. Creative professionals are acutely aware of this profound threat, which is why surveys indicate a significant majority express keen interest in being paid specifically to license their unique artistic style (58%) or getting paid for having the models trained on their specific body of work (55%).

Generative AI seeks to distil the most subjective, intangible, and unique element of an artist, his/her individual aesthetic footprint, into a fungible, replicable, and licensable commodity.

If a distinct style can be captured, licensed, and then replicated infinitely by a machine for a small fee, the intrinsic, irreplaceable value of the human hand, the individual struggle, and the unique history behind that style, everything, gets tragically erased.

Yet here lies the supreme, glaring irony, the self-defeating nature of the AI developers’ exploitation. The fundamental truth of machine learning is that the output of these complex models is fundamentally limited by the volume and, more importantly, the quality of the input, the human-generated works they ceaselessly ingest.

Suppose the economic displacement and devaluation of human creators continue unabated, and their financial incentives diminish to the point of collapse. In that case, the flow of new, high-quality, experimental, and challenging human work, the raw fuel of the entire system, will inevitably degrade. Machines are capable of regurgitation. They can modify existing work. But the true fuel of the creative economy is raw, high-quality human work. And this model ensures that there will be recycling and no innovation or radical experimentation in the field of creative arts. It demonstrates that a thriving and compensated creative community is necessary for technological advancement, not merely an optional luxury.

It’s important to recognise that not all creatives oppose technology. They are simply asking to be remunerated for the work they put in. A massive 83% of creative professionals think genuine transparency around whether artwork was created using generative AI is essential, and the same high percentage demands transparency about the specific data used to train the models.

This urgent need for verifiable provenance has spurred important initiatives, such as the Coalition for Content Provenance and Authenticity (C2PA), which now provides open technical standards for publishers, creators, and consumers to establish the origin and edits of digital content, thereby providing verifiable assertions about content origins and, most importantly, ensuring a necessary baseline of trust in this increasingly murky digital marketplace.

The advent of these transparency tools, which allow users to know the source of the information they are receiving, is the only possible path toward stabilising an ethical market where human and machine creations can coexist.

Ghost in the machine

AI can make skills slightly redundant. But true creativity and imagination come from intentionality and lived experiences. Human imperfection mixed with imagination is necessary for art. It can be mimicked, but machines cannot create anything new that is also relatable to the human psyche. We must draw a clear and forceful distinction between sophisticated computation and genuine, conscious creation.

Marvin Minsky, one of the foundational pioneers of AI, famously imagined machines capable of complex human reasoning. Yet the 21st-century generative AI has emerged primarily as the product of immense computational capacity and sophisticated algorithms, fundamentally departing from that initial, perhaps overly optimistic, vision.

The core difference remains immutable. Human creativity is intrinsically rooted in genuine vision derived from living within a specific physical world, from experiencing the emotional complexity of loss, the transformative power of joy, and navigating complex cultural nuances.

AI may function as a superb mimic and an incredibly fast learner, generating complex linguistic experimentation if prompted, but mimicry is not the same as true insight, and the resulting art risks lacking the genuine human depth that separates mere image generation from soulful expression.

Philosophical analysis strongly suggests that mass AI-generated artifacts cannot be legitimately defined as bona fide “art” because they fundamentally lack the sort of intentional control that is plausibly accepted as a necessary precondition for the label of “arthood.”

The aesthetic experiences created by mass-produced AI are often similar to those found in inorganic nature, relying solely on formal properties. Because the work is the result of statistical probability and algorithmic iteration rather than struggle, conscious choice, or personal commitment, it risks meaning nothing to the AI and consequently risks meaning substantially less to us, the audience. This absence of a discernible consciousness or intentional struggle creates an aesthetic void.

Imperative of human accountability

We stand at a profound cultural and economic precipice, facing an existential crisis that must be addressed with clarity and legislative courage. The problem isn’t the technology, which promises genuine improvements to people in all fields of life. As usual, the culprit is corporate greed and unchecked power that boardrooms wield.

These entities have ruthlessly leveraged this transformative capability to systematically dismantle existing legal and economic frameworks for their own profit, establishing an innovation structure that demands the consumption of past creativity while vehemently refusing to compensate the millions of creators whose labour and intellectual property fuel their systems.

The question we face today is fully comparable in its magnitude and complexity to the social shifts that accompanied the advent of the printing press centuries ago, demanding that society urgently debate and establish entirely new, robust frameworks for genuinely rewarding creativity and ensuring that information provenance is transparent and trustworthy.

We cannot possibly maintain a functioning, free creative ecosystem if the people in possession of the truth and the facts, the creators whose work defines our culture, are unable to win the necessary legal and rhetorical argument against powerful, highly capitalised corporate interests.

To effectively preserve the unique and irreplaceable value of human creativity and ensure a stable future for the arts, our political and regulatory response must be swift, comprehensive, and absolute, demanding three non-negotiable elements.

The first essential requirement is transparency and provenance, mandating the full, detailed disclosure of training data used by all generative models. Furthermore, we must implement verifiable authentication systems, such as the standards offered by C2PA, to provide immediate, verifiable confirmation of content origins, allowing both consumers and competitive creators to know exactly when the output is the result of a machine and statistical inference. This clarity is the minimum requirement for a fair market.

The second non-negotiable element is compensation and licensing, requiring an immediate end to the cynical reliance on tenuous fair use arguments for mass, systematic data ingestion. Governments must proactively establish robust collective licensing organisations or statutory compensation mechanisms that ensure genuine financial arrangements for all artists whose work is used to train these models. Creator participation must be predicated on appropriate financial arrangements, recognising that they hold the key intellectual assets that allow the algorithms to function.

The third critical element is the preservation of authorship, legally reinforcing the established principle that copyright ownership must belong only to human beings, recognising the inherent distinction between human creation and machine replication. This ensures that the unique human elements, including personal stories, genuine emotional resonance, and complex cultural nuance, remain the legally protected, recognised, and invaluable core of the creative economy, serving as the ultimate differentiator against the sea of machine-generated competence.

Not too long ago, we envisioned artificial intelligence handling the mundane tasks, like data entry, dishwashing, and manual labour, allowing us to focus on pursuits such as poetry, painting, and philosophy. However, the exact opposite has occurred. We are now automating creative endeavours like poetry and painting for profit, while humans are left to deal with the administrative remnants.

We are at risk of building a culture where the act of creation is viewed as an inefficiency to be solved. We thereby alienate ourselves from the process of creation. It becomes merely a product. A machine can generate a tear-jerking story, but it cannot know what it means to cry.

When we read a book or view a painting, we are unconsciously searching for the hand of the maker, seeking validation that our own joy, suffering, and confusion are shared by another living being. Without that shared resonance, we are simply staring into a mirror of statistical probabilities, profoundly alone. There is a need to fight for these protections to save jobs and to ensure that the future of human culture remains, quite literally, human.

The post The fight for creative rights appeared first on International Finance.

Running from February 20 to March 5 in New York, the auction featured modern artists like Refik Anadol, Vanessa Rosa, and Sougwen Chung as well as pieces by pioneers in artificial intelligence art including Harold Cohen.

The collection seeks to question conventional ideas of artistic authorship in a time when robots are progressively part of creative activities. Deeper concerns, meanwhile, lurk beyond the surface: Are artificial intelligence models using artists? Is this the dawn of a fresh creative renaissance or a deliberate action by digital interests to upset the art market?

Emergence of AI as an artistic agent

The rapid evolution of artificial intelligence-generated art in recent years can trace its roots back to the 1970s, when Harold Cohen’s AARON programme marked the first step toward machine-assisted creativity. Using neural networks and generative algorithms, artificial intelligence tools create amazing, occasionally frightening pieces of art today. The works at Christie’s span several digital, some physical, some both forms.

Alexander Reben’s “Untitled Robot Painting 2025,” one of the most divisive pieces, alters as bids come in with a robotic arm painting its ultimate shape. To guide robotic brushstrokes, some artists such as Sougwen Chung use motion tracking and EEG headgear.

Refik Anadol, whose “Machine Hallucations – ISS Dreams” is included in the collection, argues his data originates from publicly accessible NASA photos, therefore negating claims of intellectual property infringement.

The creative process is itself changing. By evaluating and reinterpreting historical works, photography, and abstract visual aspects, artificial intelligence algorithms let artists interact with data sets in fresh ways.

Some view artificial intelligence as a collaborative tool rather than a tool, pushing boundaries in generative design. For conventional artists, however, the whole concept of robots creating art feels like an existential challenge—one that would make human creativity extinct.

AI era art ownership

The opposition has been quick and relentless, though. Signed by hundreds of artists, the open letter claims that art created by artificial intelligence is based on pilfers. AI models such as Stable Diffusion, Midjourney, and DALL-E were trained on large datasets including copyrighted works—without permission or pay.

“These models, and the businesses behind them, exploit human artists, using their work without permission or payment to build commercial AI products that compete with them,” the letter read.

Artists worry that work produced by artificial intelligence is diluting their market and hence diminishing possibilities for human producers. Christie’s auction is seen by them as a risky precedent, supporting a system that lets technology companies profit off of unpaid labour.

For many, the issue goes beyond the competition artificial intelligence presents to include more general consequences for creative authenticity.

Could the part AI plays in art cause originality to fade? Years of creating original styles and approaches by artists find machine-generated work flooding galleries and internet venues. Many worry that without their knowledge or control, their artwork is being repacked under fresh names. The difference between human-made and machine-generated works is blurring as artificial intelligence-generated art rises in popularity.

Legal and ethical concerns

The legal scene is still in flux. Are works produced by artificial intelligence transforming inventions or are they derivative works violating copyright law? Courts haven’t decided unequivocally yet. Though some lawsuits are pending, there is no agreement on whether teaching artificial intelligence current art counts as fair use or infringement.

The AI sector contends that machine learning algorithms synthesis new outputs by evaluating patterns, not “copy” images in a conventional sense. But that difference seems intellectual to artists. What counts is that, although produced at a fraction of the time and expense, AI-generated works are sometimes exactly like those painstakingly made by people.

Intellectual property law has faltered in keeping pace. Some law academics contend that, given the user bears ethical responsibility, artificial intelligence models should be handled as any other instrument. Others contend that artificial intelligence firms ought to answer for teaching their models on copyrighted works without permission.

The ambiguity around AI-generated works has caused concern among galleries and collectors, many of whom are not sure how to classify these pieces regarding ownership rights.

Issues of cultural appropriation have also emerged. Indigenous artists withdrew from a Brisbane art competition in 2024 because of concern that works created by AI could replicate their forms without appreciating their cultural relevance. Is it theft if artificial intelligence systems can replicate brushstroke, voice, or trademark style of an artist?

The evolution of originality

The debate surrounding AI-generated art revolves around the fundamental question of what constitutes an artist, extending beyond mere copyright concerns. While some perceive AI as a tool akin to a paintbrush or camera, augmenting human creativity rather than replacing it, others view it as a threat to artistic integrity.

Proponents of AI in art emphasise the continued importance of human artists, who curate, refine, and guide AI’s output. As technology advances, the lines between artist and coder blur, with some artists embracing AI as a creative collaborator, using algorithms to shape their concepts. Conversely, others reject AI-generated art as a soulless imitation devoid of human intent.

Pindar Van Arman’s “Emerging Faces” exemplifies this shift. The artwork features two AI agents: one generating a face, the other disrupting the process of facial recognition. It prompts contemplation on whether machines can develop aesthetic sensibilities or if human involvement is essential for creativity. As AI’s capabilities expand, so do the philosophical questions surrounding artistic expression and authorship.

Reinvention or regulation?

Some advocate a middle ground whereby ethical rules, open data use, and financial models that pay artists whose work advances artificial intelligence training are established. One possible blueprint offered by the open-source software movement is where licencing systems let contributors share in group advantages.

The development of ethical artificial intelligence training guidelines, which demand businesses to acquire permission before downloading publicly available artworks for training uses, is one suggested fix. Another is financial pay for artists whose works are used to train artificial intelligence models, thereby establishing a royalty-based system guaranteeing creators share in the gains.

Big Tech has a mixed record on self-regulation, but businesses like IBM have pioneered more exacting methods for artificial intelligence ethics. Could the art scene match? Transparency in AI-generated work could be a positive start toward making sure collectors and viewers know how works were created.

The Christie’s auction is not just a sales event; it is a referendum on the future of creativity. AI is transforming the very concept of art, regardless of whether it is embraced or opposed. The issue extends beyond ownership of the final product to encompass control over the creative process itself.

For artists, the choice is clear: adapt or risk obsolescence. However, adaptation must not equate to capitulation. The art industry must demand legal clarity, hold AI developers accountable, and ensure that technology enhances rather than undermines human creativity.

The battle over AI art is fundamentally about safeguarding the essence of artistic expression, not merely about machines. This struggle is far from over. The Christie’s auction debate marks not the end but the beginning of a protracted and challenging dialogue between AI and human creativity—a dialogue that will shape the future of art.

The Christie’s auction is more than a high-profile sale; it is a defining moment in the intersection of artificial intelligence and artistic expression. The controversy surrounding the event reveals the depth of concern about AI’s role in creativity, intellectual property, and the broader art market. While AI-generated art presents exciting possibilities, it also raises fundamental ethical and legal questions that must be addressed before it is widely accepted.

A key takeaway from this debate is that AI-generated art is not inherently problematic—it is how AI is trained and how the results are used that sparks controversy. The unauthorised use of human-created works to train AI models is the crux of the issue. Artists, whose work has been scraped from the internet without their consent, are rightfully frustrated.

However, an outright rejection of AI in art may not be the solution. History has shown that new technologies—photography, digital tools, and even computer-generated imagery (CGI)—have faced initial resistance before being integrated into artistic practices.

Many artists are already finding ways to use AI as a collaborative tool rather than a replacement. The challenge now is to establish a framework that ensures ethical AI use while promoting innovation.

Legal clarity will play a crucial role in shaping the future of AI-generated art. Courts worldwide are currently grappling with copyright implications, and while no definitive rulings have emerged, it is clear that intellectual property laws must evolve to reflect this new reality.

The creation of licensing structures, similar to those used in music sampling, could be one approach. Artists whose works contribute to AI training datasets could receive royalties, ensuring that their labour is not exploited without compensation.

Beyond legal considerations, transparency is essential. Artists and collectors alike need to understand whether an artwork was created by a human, an AI, or a hybrid of both. Some platforms are already implementing AI disclosure policies, but industry-wide standards must be established.

Labelling AI-generated works and ensuring that datasets are ethically sourced will help maintain trust in the art market.

On a cultural level, the discussion about AI-generated art extends beyond ownership and legality—it touches on what it means to be creative. Art is not simply about output; it is about intent, interpretation, and the human experience.

While AI can generate images, music, and even literature, can it truly create art in the way a human does? The answer to this question will shape how society chooses to engage with AI in artistic spaces.

Ultimately, AI-generated art represents both a challenge and an opportunity. The technology will continue to evolve, and artists, collectors, and institutions must decide how to navigate this shift. The Christie’s auction is not the final word in this debate, but rather the beginning of a larger conversation.

If handled responsibly, AI could become a powerful tool that enhances rather than diminishes human creativity. However, without clear ethical guidelines, it risks undermining the very foundation of artistic expression. The art world must now act to shape the future before AI does it for them.

The post Christie’s auction sparks debate on AI art appeared first on International Finance.

What’s fascinating is many of the mistakes we made back in 2008 are the same mistakes that many tech start-ups make time and time again. I know this because over the past decade we have represented hundreds of tech businesses – from start-ups to big businesses – and I find that time and time again the same issues crop up.

So, what are they?

1. Not having a strong shareholders agreement – or discussing, formulating and documenting the business plan with co-founders

I intended to found my business with three others, however, I soon realised that our goals and objectives were sadly not aligned; our work ethics were very different and our long-term motivations out of sync. Luckily, I had drafted a very good partnership agreement so managed to break free from what would have been a disastrous relationship. Luckily this enabled me to continue with A City Law Firm with just me at the helm, but not all businesses I have encountered can say the same.

Not only do you have to be careful to choose the right partners, but you need to clearly document your goals in a shareholder’s agreements. This means that as founders you can build upon the platform you have created together, but if it goes wrong you have a means to address the problem not just having to wind up the company. The key is choosing the right partners, talking candidly and asking the tough questions at the beginning.

2. Having poor contracts or no contracts – hitting your cash flow

You need to understand your marketplace, your competitors and what you need financially to be able to grow.

Cash is king. Be realistic with budgets and prices and ensure your contracts protect you – not only with clients but with employees, suppliers and contractors. It is fundamental that you closely monitor payment timescales with clients, especially if you are working on large projects. Corporate clients may expect 60 – 90-day payment terms but your sub-contractors will not.

It’s also important you pay yourself a reasonable salary, especially when you are seeking investment, otherwise, if the founder is distracted, the business is not going to progress. Any investor will want to see this factored into any business plans and financial models.

3. Not having good staff contracts or options to incentivise them

A large part of any company’s budget will be put towards recruitment, training and retention of its employees. Despite this, there is a real risk that those key people could walk out of the door leaving you without the requisite skill pool you need, but worse yet, there is a real possibility that they may also take all of their knowledge of your business and pass it to a competitor.

Many businesses focus on many things but staff retention and protection against staff competition is often neglected. This is especially key in the tech world as the opportunities for work are so great.

From a legal standpoint, it is important to:

• Have tight employment, contractor, consultancy and sub-contractor contracts in order to protect your IP and confidentiality. It is also important to have restrictive covenants to avoid staff taking your know-how in terms of clients, IP or staff to a competitor or setting up on their own.

• Consider EMI options as they can give staff the feeling of being part of the fabric of the business and as you succeed so do they in terms of profit sharing without actual cost in the short term to you. This also can attract more specialist experts to the team where cash is not readily available ;

Overall though the key is to find ways to incentivise and look after your team. If you can communicate your vision to the team so that they are working side by side with you, this inspires loyalty and dedication as you are all working from the same plan with the same goal.

4. Intellectual Property & the mistake of that ‘handshake deal’

IP ownership can only be granted or transferred (“assigned”) in writing. As such, if your freelance coder or developer has no contract with your business then they could actually own the IP that they have helped design, not you.

If there is a dispute, then they could hold this to ransom causing a costly dispute or loss of your code or design. You need to ensure you have checked these contracts carefully and that you actually have one carefully drafted in your favour. Many tech companies work with friends and often make arrangements based on goodwill, but when a dispute arises without a contract you are at the mercy of the designer.

If you are bringing your designing or coding in-house, then it is especially key to convince an investor you have secured long-term staff and that the IP ownership will effectively transfer to you.

Many businesses fail to check that their proposed company name or branding is free to trademark. This should be carefully checked before a large budget (or large budget relative to the size of your business) is set aside for branding and marketing as otherwise, you may find yourself having to start all over again.

5. Rushing to Investment and giving up equity in the company

I managed to self-finance my business throughout without taking in partners or investors. I did consider these at points along the way and even had offers of mergers and partners coming into the business but having carried out checks into these entities, I often found hidden skeletons and things I was too anxious to continue to explore.

If you are seeking investment, which is often a necessity for tech companies scaling up, it is vital that you carry out your due diligence on what’s available, what the risks are and who the investor actually is.

– Do they understand your sector?

– Have they got the resources to add more money at a later date if that’s what you need?

– Can you approach them if things go wrong?

– Do they have competing interests in the marketplace?

– What is your exit plan for them?

– Have you also explored grants available for tech, innovation offerings, R & D credits and other means of funding?

Many people are often dazzled by the cheque and sign a contract… but that’s just the start of the journey.

It is important that you consider whether you want to get involved unless you are certain you have aligned goals, exit plan and can handle a crisis together.

6. Not being investor ready

When start-ups do find the right investor, a common pitfall is they are not investor ready because they haven’t got their house in order.

For example, they have not allocated and issued shares correctly. Their articles do not reflect the workings of the company. If an investor picks this up, it can make tech founders look careless and could scare off the investor.

More broadly other things that put investors off include inaccurate statements that have been put in writing… such as:

-“This is unique to the market, no one else is doing this”. This is often a bold statement that just isn’t upheld or accurate;

– “I don’t need a salary for 1-2 years; I can use 100% investment on the business”; wrong! No one will invest in someone who can’t eat and pay their bills!

– “My business is valued at £10 million because it’s going to be worth that in two years when we build our technology”. Can you support that with figures and market research? Be realistic and able to evidence all assertions.

7. Not understanding how markets are regulated

Many businesses, especially those in disruptive markets, need to be regulated or are covered by additional regulations or laws.

Many fintech or ICO companies need to be regulated and choose to risk investment or token raises prior to taking proper advice or considering the proper process exposing you to an FCA investigation.

This is not an issue which only affects those in financial markets but includes among many others those in advertising, legal services/legal tech, recruitment, packaged holidays etc. Knowing your marketplace, sector and taking advice is essential prior to any public offering.

8. Not taking experienced advice and creating an ecosystem

Tech developers are necessarily geared to be financial directors or HR managers yet running a business these roles become fundamental. Not getting good advisors on board early enough is a common mistake. A good lawyer, accountant and tax advisor saves you money and pain at a later stage, especially if they can secure you EIS or another favourable structuring. A good FD helps secure investment and cash flow by managing the budgets and financial forecasts, they also add the commercial know-how into your passionate pitch deck. Downloading templates; googling advice I appreciate happens because of the costs involved, but if you want your business to succeed you need tailored, personal advice and support. I know this is something I have benefited from greatly as I brought in consultants to help me and train me in my areas of weakness. Admitting these gaps in my knowledge and bringing experts in has helped me scale up.

9. Not having skin in the game & asking too little

If you are seeking investment for your tech business you need to start with securing some capital yourself or through your contacts. This shows investors you have faith in your offering, which then means they are more likely to match. This is something I hear frequently from equity investors, so try friends and family first. Another common mistake is asking for too little which cannot be sustained and then you have to go back to the platform or investor for money which could result in them losing faith in your financial model. You need to forecast and present realistic figures so you don’t ask for too much or too little.

10. Don’t let the cat out of the bag

If you don’t have a signed NDA and if you discuss a potential or pending patent you could lose the rights. Discuss the details of your tech, design or offering in as much detail as you can to secure an investor or client, but where possible secure an NDA to protect your confidential trade secrets or ideas or Patents. They may be hard to enforce, often a concern of many so they don’t bother, but it’s a deterrent; it protects you Patents and it’s a good starting point for an injunction if someone tries to reproduce your tech.

The post Ten Mistakes Made By Startups appeared first on International Finance.

Suparna Goswami Bhattacharya

September 28, 2016: Slowing economy and tough government regulations notwithstanding, China is all set to become the world’s first trillion dollar aviation market by 2035. According to manufacturers Airbus and Boeing, the Chinese aviation market looks one of the most promising to make adequate investments.

Although the slowdown in China’s GDP growth attracted much notice in the media, air travel continued to perform well. The reason: Chinese consumer sectors, which drive travel behaviour, remained strong.

“There is a very strong aspiration of people around the world to travel by air. Air transport growth overpasses GDP growth and especially here in China, where the annual air traffic increase is still over 10%,” states an Airbus spokesperson.

The world’s most populous country is expected to add 6,810 new aircraft to its commercial fleet, worth $1.025 trillion by 2035, said Boeing in its annual China Current Market Outlook. “As China transitions to a more consumer-based economy, aviation will play a key role in its economic development,” said Randy Tinseth, vice-president of marketing, Boeing Commercial Airplanes. “Because travel and transportation are key services, we expect to see passenger traffic grow 6.4 per cent annually in China over the next 20 years.”

Even from the demand side, there are ample reasons to believe in China’s aviation growth story. “For one, only 1/3rd of China’s population holds a passport. Hence, the market size and opportunity is huge. Additionally, I feel China has a great opportunity to be a hub for other destinations, just like Dubai and Singapore, given its location,” feels Prof. David Yu of IBA, a leading aviation consultancy.

China’s growing e-commerce business also drives demand for cargo planes, with 180 new freighters and 410 converted freighters to be added over the next two decades, forecasts Boeing. Boeing and Airbus are competing with each other to get the upper hand in the China market. While Airbus has a manufacturing unit in Tianjin, Boeing has reportedly submitted to the Chinese government plans for a factory in the eastern province of Zhejiang.

“There is a final assembly line in China for the A320 family with a present production rate of 50 aircraft per year. Our project for next year is to adapt it to our new SA aircraft — the A320 NEO — and to extend this cooperation to a completion and delivery centre for the A330. This will be the first time we’ll have such a cooperation on WB aircraft outside Europe and this will be a good way of satisfying the growing demand for larger aircraft,” the Airbus spokesperson said.

Not an easy ride

The ride, though, is not going to be easy. Both manufacturers are facing increasing competition from state-backed local players. Commercial Aircraft Corp of China (COMAC) produces China’s largest domestically produced passenger plane C919 and is competing with Boeing’s 737 and Airbus’s A320. Experts expect to see more local players gaining traction as demand increases.

However, Airbus and Boeing are not worried about China government’s history of supporting local players. “We have always been welcome in China and we have been able to develop many activities in the country with solid industrial and commercial partners… It’s a win-win partnership and we are confident of the willingness of these partners to keep on extending our cooperation,” states Airbus.

Addison Schonland, founder and partner, AirInsight, a commercial aviation consultancy, says Boeing and Airbus currently account for 94% of the market share. “The pertinent question is can China ever win back this share? Maybe, they can reduce it, but China does not have the capacity to ever win the main share. There are too few aerospace firms in China and they don’t have the products. The ARJ-21 is awful. The C919 is already outclassed. We should be looking out 50 years and then see whether they can actually pose any competition for Airbus and Boeing. Even then, my bet is Airbus and Boeing will own the market,” remarks Schonland.

Other players weary

Manufacturers from France, Italy, Russia and Brazil are also showing keen interest in China. However, they are weary of the way China operates. In China, any foreign firm must have a local partner and that partner must share its IP. Though every aerospace OEM wants to get into China, this comes at a high cost. No OEM wants to spend a fortune developing IP and then give it away. “China copies everything it can – the local copy of the Sukhoi SU-27 is a prime example. There are others. Investing in China is a two-edged sword and firms like Embraer and Bombardier tread very warily. Airbus and Boeing are so dominant that they have less to fear,” says Schonland.

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