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How Venture Capital Can Help Create Startup Fraud

Fraud Is Often A System Problem, Not Just A Founder Problem

A new report from Imperial College London and Emlyon Business School examines how venture capital-backed founders commit fraud and how investors can unintentionally create the conditions for it.

Published in June, the study draws on cases pursued by the U.S. Securities and Exchange Commission and the Department of Justice between 2000 and 2023. Its central conclusion is that fraud is not solely a founder problem, but can also stem from the incentives, expectations and governance structures surrounding startups.

High Expectations, Higher Risks

Several high-profile cases, including Charlie Javice of Frank, Gökçe Güven of Kalder, Do Kwon of Terraform Labs, and Alexander and Valerie Lau Beckman of GameOn, have intensified debate over where ambitious fundraising ends and fraud begins.

“Fraud is much more common and normalized in the startup world than we are ready to admit and accept,” Tim Weiss, one of the report’s authors, told TechCrunch.

Weiss also cited a University of Toronto study covering 654 fraud cases involving U.S. venture-backed startups between 2000 and 2023. Although fraud remained relatively rare, venture-backed companies were more likely to face fraud charges than non-VC-backed firms, while startups launched during overheated investment markets were 19% more likely to commit fraud later.

According to Weiss, pressure from investors and boards to deliver rapid growth can encourage misconduct, particularly in fast-moving sectors such as artificial intelligence.

The Three Stages Of “Façading”

The report, co-authored by Weiss and Nevena Radoynovska, identifies a three-stage process the authors call “façading.”

Surface façading begins with exaggerated claims about a company’s progress or traction. Reinforced façading involves creating evidence to support those claims, including fabricated contracts, invoices or revenue records. Deep façading extends the deception to the product itself through fake demonstrations and staged proof points.

Rather than beginning with a single act of fraud, the report argues that misconduct often develops gradually as founders attempt to sustain increasingly unrealistic expectations.

Investors Also Shape The Conditions For Fraud

One of the report’s central arguments is that investors are not always passive victims of founder misconduct. In some cases, they help create the conditions in which fraud becomes more likely.

According to the researchers, venture capital can “co-create fraud” by continuing to back founders who have previously been accused of misconduct, signaling that such behavior carries few long-term consequences. A separate University of Toronto study found little evidence that founders accused of fraud struggle to raise funding for new ventures, even when earlier cases attracted significant media attention.

“New investors and the broader VC market do not penalize past misconduct,” the report said, linking that pattern to Silicon Valley’s long-standing tolerance for failure.

Governance Plays A Critical Role

The University of Toronto study also identified governance as a key factor. Startups with founder-controlled boards were twice as likely to commit fraud as companies with investor-controlled or shared-control boards.

It also found that venture-backed companies going public were more likely to face securities class-action lawsuits within two years than private equity-backed firms. As startups remain private for longer while raising larger funding rounds, Weiss argues that governance has not kept pace with their growing scale.

“Founders do not have a professional body or association that could govern or enforce rules of entrepreneurial and investor conduct on how to be a good founder and what reasonable growth expectations are,” he said.

Calls For Stronger Oversight

Weiss argues that regulators should take a more proactive approach by introducing routine investigations and formal audits once startups reach significant funding thresholds, rather than waiting for whistleblower complaints or investor lawsuits.

The report also calls on investors to accept greater responsibility when aggressive growth targets contribute to governance failures. According to the authors, stronger oversight by both regulators and investors would help reduce the conditions in which fraud can develop.

Apple Ties Its Mac Strategy To The AI Boom With New Mac Mini And Mac Studio Models

Apple has updated its Mac Mini and Mac Studio desktops with new processors and higher AI performance as developers increasingly use Macs for local AI workloads. The new models are scheduled to ship on Sept. 22, weeks before the company is expected to introduce its next iPhone generation.

Macs Target Local AI Development

Developers and researchers are increasingly using Apple computers to run AI models locally, reducing reliance on cloud infrastructure. Mac Mini systems can support AI agent software, while Mac Studio machines are designed for more demanding model training and deployment workloads.

Apple said its processors combine Neural Engines for machine learning with unified memory architecture designed to reduce performance bottlenecks. The company says the combination allows users to run and fine-tune larger AI models directly on their devices.

Mac Mini Gets First M6 Generation Chip

The updated Mac Mini can be configured with Apple’s M6 and M5 Pro processors, making it the company’s first computer with an M6-generation chip. The M6 is manufactured by Taiwan Semiconductor Manufacturing Co. (TSMC) using a 2-nanometer process.

The previous Mac Mini lineup offered M4, M4 Pro and M4 Max processors. Apple said the M5 Pro version of the new model can process large language model prompts 8.5 times faster than earlier Mac Mini Pro configurations.

Pricing has also increased. The new Mac Mini starts at $899, $100 more than the previous model, after Apple raised the price from $599 earlier this summer, citing higher memory costs.

Mac Studio Targets Larger AI Workloads

Mac Studio remains Apple’s highest-performance desktop without an integrated display, following the discontinuation of the Mac Pro earlier this year. New configurations include the M5 Max, which Apple says can run large language models nearly four times faster than the previous generation.

The M5 Ultra is available for users with heavier computing requirements. Apple says multiple Mac Studio systems using the Ultra chip can be connected to pool memory and run models with up to a trillion parameters.

Mac Studio with the M5 Max starts at $2,499, unchanged from the previous generation. The M5 Ultra configuration starts at $5,499, compared with at least $5,299 for the previous model using the M3 Ultra.

Apple Expands Its Local AI Hardware

The new desktops give developers and researchers more computing capacity for running AI models locally. Apple is also increasing the role of its custom processors and unified memory architecture in handling AI workloads without relying entirely on cloud-based computing.

Both Mac Mini and Mac Studio models are available for presale and are scheduled to begin shipping on Sept. 22.

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