A recent US federal court case involved a former marketing agency employee who used stolen trade secrets to train an AI agent, aiming to replicate his employer’s expertise and compete directly. The court granted a temporary restraining order and preliminary injunction, calling the AI agent an existential threat to the business. The defence’s claim that no harm occurred because files were placed in a “quarantine folder” was rejected.

The case shows that AI can be used to misappropriate trade secrets in ways that are difficult to detect or reverse. Even if stolen files are removed, the AI model may still be influenced by the data, making it hard for defendants to prove otherwise. The risk extends to inadvertent violations, as employees using personal devices with AI agents could expose confidential information without realising it.

For regulated companies, standard digital discovery is no longer enough. Full forensic imaging and rapid legal action are now minimum requirements. AI also makes it easier to reverse-engineer products, increasing uncertainty in IP litigation. Firms should act immediately if data misuse is suspected and update security and legal policies to address these new risks.

Asia's startup funding maintained its momentum in the third quarter, reaching the second-highest level in recent years despite a slight dip from the previous quarter. Investors collectively funneled $38.5 billion into seed through growth-stage rounds across the region, with more than half directed toward AI-driven companies, reflecting a global pattern. The concentration of funding remained steady, indicating a sustained focus on favored startups rather than a widening distribution.

China dominated the funding landscape, attracting around $27 billion, which accounted for 70% of the region's venture capital. India and Singapore followed distantly with $2.9 billion and $2.5 billion respectively. Late-stage investments took the lead with roughly $20 billion, nearly doubling year-over-year figures, propelled by various large financing rounds such as Moonshot AI's $3.5 billion Series F.

Early-stage funding dipped by 28% from the prior quarter but was still considerably higher than last year, totaling $13.6 billion. Seed and angel investments hit a multi-year high of $4.9 billion, buoyed by investor enthusiasm for AI startups. Other markets like India and Singapore saw declines, while China’s robust funding echoed its growing innovation dominance.

This trend highlights Asia's pivotal role in AI advancement and startup growth, with particular emphasis on giant rounds from companies such as Moonshot AI and generative video startup Kling AI. The data comes from Crunchbase as of October 2026, reflecting reported funding rounds and investment flows within the region.

In the third quarter of 2026, cybersecurity and privacy startups worldwide attracted $6.8 billion in funding, marking a 50% increase from the previous quarter and tying recent records. This growth was largely due to bigger investment rounds rather than an increase in the number of deals. AI-focused cybersecurity companies, especially those specializing in identity security, dominated the funding landscape. Keyfactor led with a $1 billion round, while Island and Cyera each secured $400 million deals with valuations of $6.4 billion and $12 billion respectively. The quarter also saw major acquisitions like Visa's $2.4 billion purchase of BioCatch and Cyera's $1 billion acquisition of Oasis Security. Despite a slow IPO market, cybersecurity remains a hot sector for investment due to rising concerns over AI-driven security risks. The trend signals continued strong support for startups protecting against emerging digital threats.

By Nnamdi Okike

Tosin Eniolorunda may not be a household name, but he and his team have transformed Moniepoint into the backbone of financial transactions for over 6 million Nigerian businesses, handling more than $300 billion annually — nearly 40% of Nigeria’s digital payments volume. As a Nigerian American, I've witnessed firsthand the challenges of the traditional, costly payment systems, and Moniepoint is revolutionizing that experience.

Moniepoint is part of a wave of super apps like Sea Limited, Grab, Kaspi, Yassir, and Nubank — giants in emerging markets that integrate banking, commerce, ride-hailing, and bill payments into a single trusted platform. Unlike the U.S., where trust in financial institutions is high and 96% of households are already banked, emerging markets offer unique conditions for super apps due to high mobile penetration, weak competition from incumbents, poor financial services, and financial crises that erode trust in traditional institutions.

Countries like Brazil, Kazakhstan, and Southeast Asian nations faced severe financial upheavals that left openings for these apps to thrive by rebuilding trust. For example, Nubank emerged from Brazil’s banking turmoil, and Kaspi capitalized on Kazakhstan’s banking crisis. Even smaller populations, like Kazakhstan’s 20 million, can support massive super apps when customers engage deeply—Kaspi’s users transact about 77 times monthly.

Emerging markets such as Bangladesh, Egypt, Vietnam, and Pakistan exhibit the conditions ripe for new super apps: severe but contained financial crises delegitimizing incumbents, high mobile use, and governments enabling easier onboarding. The growth potential is enormous, as demonstrated by historic investments in Tencent and Nubank.

The next generation of super apps will leverage innovations like stablecoins and AI, driven by visionary founders in cities like Abidjan, Buenos Aires, Karachi, and Lagos. These platforms will play a crucial role in shaping emerging market economies.

Nnamdi Okike is co-founder and managing partner of 645 Ventures, focusing on fintech and enterprise technology investments. He has extensive experience investing in technology companies and sees super apps as a transformative force in emerging economies.

Illustration: Dom Guzman

Full disclosure: The author holds shares in Kaspi, Sea Limited, and Nubank, although these are not part of 645 Ventures’ portfolio.

Some of the fastest-growing AI startups are increasingly becoming serial acquirers, purchasing smaller companies to close product gaps, enter new markets, and onboard specialized teams quickly, according to Crunchbase data. OpenAI leads with the highest number of acquisitions, but well-funded startups in sectors like legal tech, customer service, and software development are also actively expanding through M&A. From January through September 29, AI startup acquisitions reached 195—a 14% rise over last year—primarily driven by repeat buyers who are leveraging acquisitions to accelerate their growth and broaden their product offerings. Legal tech startups, for example, are acquiring specialized tools to build more comprehensive platforms, while others are expanding geographically or into adjacent markets. Notable deals include Nscale's $1.65 billion acquisition of Anyscale and Cyera's $1 billion takeover of Oasis Security. Industry leaders emphasize speed and strategic fit in acquisitions to stay competitive and rapidly scale, with many startups using equity to finance deals at minimal dilution. This trend signals a shift to M&A as a central growth strategy among AI startups, with more activity expected going forward.

Tech industry layoffs in 2026 are climbing faster than in 2025, with sharp spikes rather than a steady flow, per Crunchbase’s Tech Layoff Tracker. From January to August, over 94,000 jobs were cut, marking a 16.8% increase from the previous year. This rise aligns with companies redirecting budgets towards AI and restructuring to reduce costs. The start of the year saw significant layoffs, particularly in May, which saw the biggest monthly spike, including Meta’s 8,000 job cuts. However, layoffs slowed in the summer months, hinting at a potential easing in the trend. AI becomes a major factor in these decisions, cited in a third of this year’s layoffs, as companies invest heavily in AI to boost productivity with smaller teams. Top tech giants like Amazon and Meta led the cuts, accounting for most job losses. AI's impact is twofold: automating tasks and shifting focus that causes layoffs in some areas, while increasing hiring in AI-related roles. The tech sector leads all industries in layoffs this year, reflecting a broader shift in workforce strategies tied to AI adoption. Some workers might even find new opportunities in emerging AI fields, suggesting the changes bring both challenges and potential growth opportunities. A few companies are already seeking to rehire former employees, indicating the evolving nature of tech employment needs.

This year marks a surge in hefty Series A funding rounds for top startups, with over 114 rounds exceeding $100 million globally, according to Crunchbase. Collectively, these startups have raised around $33 billion, including 12 rounds worth $500 million or more. The spike is largely driven by AI-focused ventures, making up over 70% of these mega Series A rounds. Notable deals include River AI's $1.2 billion raise and Xpeng Robotics' $900 million financing. US startups dominate half of these large rounds, tallying $15 billion and setting new records in 2026. Investors with large reserves are aggressively backing promising sectors and teams early, betting big on potential winners.

Baselayer, an AI-driven startup specialized in helping financial institutions verify business identities and mitigate fraud, has raised $35 million in Series A funding led by M13. The San Francisco-based company has developed technology that integrates business identity, credit, and fraud data to assist banks and fintech firms in evaluating prospective customers. Over 2,000 financial institutions use Baselayer's platform, which has helped prevent more than $1 billion in fraud losses since its founding in early 2023.

With its new funding, Baselayer is expanding to address the challenge of verifying AI agents acting on behalf of individuals or businesses. Their newly launched Agentic Identity Suite aims to establish authorized credentials for AI agents to prove legitimacy, helping merchants and financial institutions make informed decisions on transactions involving such agents. Baselayer is collaborating with industry players like FIS, Prove, and Socure in this effort.

The rise of AI agents creates both opportunities and risks, as fraudsters can now exploit automation to scale fraudulent activities more rapidly. Baselayer's platform seeks to provide a framework to identify and trust AI agents, a necessary evolution in a world where AI increasingly performs economic and transactional roles.

In 2026, U.S. venture-backed tech companies have raised nearly $90 billion through public offerings, ranking as one of the highest on record. However, the vast majority of this capital was secured by just two heavyweights: SpaceX, capturing 83% of the funds, and Cerebras Systems, with 6%. The rest of the tech IPO landscape remains modest, with only 21 other sizable offerings including a mix of traditional IPOs and SPACs totaling less than $10 billion. Notably absent this year are enterprise software IPOs—a sector traditionally represented among tech offerings—which may be shifting focus towards AI-driven innovations instead. Meanwhile, sectors like energy, defense, aerospace, medical devices, and consumer products have seen relatively strong activity. This year saw energy startups, especially in geothermal and nuclear technologies, leading the IPO count, quantum computing and defense tech making solid entries, and consumer mobility platforms like Lime debuting despite valuation challenges. The uneven distribution highlights a winner-takes-almost-all dynamic in tech IPOs, with big players dominating returns and smaller companies holding back. Pipeline IPOs similarly are dominated by giants like Anthropic and OpenAI, leaving enterprise software offerings largely absent from the immediate horizon.

By SC Moatti

Leading with “we use AI” no longer sets a product apart; AI is now basic infrastructure. According to data from 576 venture-backed AI B2B companies raising over $50 million since 2025, true competitive moats today are those that AI models alone cannot replicate. Using Hamilton Helmer’s 7 Powers framework and insights from Products That Count, two moats stand out:

1. Counter-positioning: Building a business model so distinct that incumbents won’t copy it because doing so would undermine their own business economics. Examples include AI insurers who bypass traditional brokers and AI-native revenue management that competes against consulting revenue. Only 5% of companies employ this, commanding the highest valuation multiples. Key question: Would it cost an incumbent more to copy you than it would cost you to build?

2. Network economies: Value grows as more users or companies join, creating a powerful self-reinforcing cycle. Seen in platforms connecting brands, factories, advertisers, and audiences, where data accumulates and becomes harder to replicate. Also used by 5% of companies, offering strong capital efficiency.

Other assumed moats like proprietary data and switching costs are often weaker than they appear due to model erosion and high capital needs. Scale economies aren’t achievable for most startups outside major players like OpenAI.

Ultimately, the moat is structural—embedded in business model or network design—not just AI tech. Founders must define what aspect of their business would survive a better-funded competitor launching tomorrow. Those who can't answer are merely building products, not enduring powers.

SC Moatti is Founding Managing Partner at Mighty Capital and Chair at Products That Count. She is recognized for pioneering investments and product leadership, with deep experience building award-winning products at Meta and Siebel Systems.