Capacity development

Small business revival could hinge on AI-driven tools

If AI is to matter in the economy, it must first matter to small businesses. Firms employ over 61 million people, nearly half the private workforce, yet most run on outdated technology. While smartphones update monthly, many small businesses still use systems built a decade ago.

Search fund entrepreneurs bridge this gap by upgrading established firms with modern tech. One deal turned a 50-person roadside assistance firm into Asurion, now a global tech-care provider. Others have scaled compliance firms into nationwide SaaS platforms.

Generative AI now accelerates these transformations, cutting work times by over 60% across supply chains, compliance, and document processing functions. Complex tasks can now be completed in hours, unlocking double-digit productivity gains and allowing small businesses to focus on growth.

Search funds are not the only path forward. AI consulting firms, tech studios, and AI-powered roll-up strategies bring enterprise-grade tools to family-run firms. For communities that have relied on traditional playbooks, decades of growth can be compressed into months.

The cost of AI has never been lower, and the opportunity is wide open. Once deployed at scale, AI could power a wave of productivity on Main Street, helping small businesses compete and strengthening the economy for half of their workforce.

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AI and AFM deliver real-time macrophage phenotyping

Macrophages drive immune responses, including inflammation, tissue repair, and tumour growth. Identifying their polarisation states is key for diagnosis and immunotherapy, but current methods, such as RNA sequencing and flow cytometry, are expensive, slow, and unsuitable for real-time use.

Atomic force microscopy (AFM) has emerged as a powerful tool for decoding mechanobiological signatures of cells. Combined with AI, AFM data can be rapidly analysed, but macrophage phenotyping has been relatively underexplored using this approach.

Researchers led by Prof Li Yang at the Shenzhen Institutes of Advanced Technology have now developed a label-free, non-invasive method combining AFM with deep learning. The system accurately profiles human macrophage mechanophenotypes and identifies polarisation states in real-time.

The AI model was trained on well-characterised macrophage subtypes and validated using flow cytometry. Results showed that pseudovirus stimulation mainly produced M1 macrophages, with smaller populations of M2 and mixed phenotypes, closely matching the model’s predictions.

The study, published in Small Methods, offers a promising diagnostic tool that could be extended beyond macrophages to other cell types. It could support new approaches in cancer, fibrosis, and infectious disease diagnostics based on mechanophenotypes.

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NotebookLM turns notes into flashcards podcasts and quizzes

Google’s learning-focused AI tool NotebookLM has gained a major update, making studying and teaching more interactive.

Instead of offering only static summaries, it now generates flashcards that condense key information into easy-to-remember notes, helping users recall knowledge more effectively.

Reports can also be transformed into quizzes with customisable topics and difficulty, which can then be shared with friends or colleagues through a simple link.

The update extends to audio learning, where NotebookLM’s podcast-style Audio Overviews are evolving with new formats. Instead of a single style, users can now create Brief, Debate, or Critique episodes, giving greater flexibility in how material is explained or discussed.

Google is also strengthening its teaching tools. A new Blog Post format offers contextual suggestions such as strategy papers or explainers, while the ability to create custom report formats allows users to design study resources tailored to their needs.

The most significant addition, however, is the Learning Guide. Acting like a personal tutor, it promotes deeper understanding by asking open-ended questions, breaking problems into smaller steps, and adapting explanations to suit each learner.

With these features, NotebookLM is moving closer to becoming a comprehensive learning assistant, offering a mix of interactive study aids and adaptable teaching methods that go beyond simple note-taking.

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Google Cloud scraps transfer fees to win multicloud users

Google Cloud has rolled out a new Data Transfer Essentials plan, allowing customers to move workloads across multiple clouds without paying transfer fees. The move comes ahead of the EU Data Act on 12 September 2025, intended to strengthen competition across the cloud market.

Under the new rules, providers must charge data transfer fees only ‘at cost.’ While Microsoft and Amazon have already taken steps to comply, Google has gone further by removing the charge altogether.

The company said the change is aimed at supporting businesses with multicloud strategies, offering more flexibility, and reducing downtime in critical workloads.

The initiative also positions Google as more aligned with regulatory goals, particularly compared with Microsoft, which has faced scrutiny over restrictive licensing practices. Google said qualifying traffic will now be billed at zero cost, while other transfers remain charged at existing rates.

The announcement follows strong growth in Google Cloud’s business, especially from AI firms like OpenAI and Anthropic. Alphabet’s cloud contracts are valued at around $106 billion, with CEO Thomas Kurian projecting $58 billion in revenue conversion within two years.

Alphabet’s stock price rose 2.47% following the update, reaching $239.94, as investors responded positively to both growth prospects and regulatory positioning.

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Photonic chips open the path to sustainable AI by training with light

A team of international researchers has shown how training neural networks directly with light on photonic chips could make AI faster and more sustainable.

A breakthrough study, published in Nature, involved collaboration between the Politecnico di Milano, EPFL Lausanne, Stanford University, the University of Cambridge, and the Max Planck Institute.

The research highlights how physical neural networks, which use analogue circuits that exploit the laws of physics, can process information in new ways.

Photonic chips developed at the Politecnico di Milano perform mathematical operations such as addition and multiplication through light interference on silicon microchips only a few millimetres in size.

By eliminating the need to digitise information, these chips dramatically cut both processing time and energy use. Researchers have also pioneered an ‘in-situ’ training technique that enables photonic neural networks to learn tasks entirely through light signals, instead of relying on digital models.

The result is a training process that is faster, more efficient and more robust.

Such advances could lead to more powerful AI models capable of running directly on devices instead of being dependent on energy-hungry data centres.

An approach that paves the way for technologies such as autonomous vehicles, portable intelligent sensors and real-time data processing systems that are both greener and quicker.

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South Korea to grant crypto firms venture company status

South Korea will grant cryptocurrency firms ‘venture company’ status from 16 September, giving them access to tax breaks, financing support, and other incentives. The move follows a partial revision of the Venture Business Act removing restrictions on crypto trading and brokerage.

The regulatory change aims to stimulate growth in the crypto and blockchain sectors. Advantages include research and development grants, credit guarantees, and the ability for existing venture companies to expand into crypto without losing their status.

Minister Han Seong-sook said the measure will help create a transparent and responsible ecosystem for venture capital and innovation.

Crypto firms were first restricted in October 2018, and the ban’s removal reflects industry maturity and better user protections. Officials expect the change to accelerate growth in virtual asset trading, blockchain technologies, smart contracts, and cybersecurity.

South Korea’s crypto industry has already benefited from a friendlier environment under President Lee Jae-myung. The market is projected to reach $1.1 billion in revenue in 2025 and $1.3 billion by 2026, with exchange users surpassing 16 million, over 30% of the population.

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Oracle and OpenAI drive record $300B investment in cloud for AI

OpenAI has finalised a record $300 billion deal with Oracle to secure vast computing infrastructure over five years, marking one of the most significant cloud contracts in history. The agreement is part of Project Stargate, OpenAI’s plan to build massive data centre capacity in the US and abroad.

The two companies will develop 4.5 gigawatts of computing power, equivalent to the energy consumed by millions of homes.

Backed by SoftBank and other partners, the Stargate initiative aims to surpass $500 billion in investment, with construction already underway in Texas. Additional plans include a large-scale data centre project in the United Arab Emirates, supported by Emirati firm G42.

The scale of the deal highlights the fierce race among tech giants to dominate AI infrastructure. Amazon, Microsoft, Google and Meta are also pledging hundreds of billions of dollars towards data centres, while OpenAI faces mounting financial pressure.

The company currently generates around $10 billion in revenue but is expected to spend far more than that annually to support its expansion.

Oracle is betting heavily on OpenAI as a future growth driver, although the risk is high given OpenAI’s lack of profitability and Oracle’s growing debt burden.

A gamble that rests on the assumption that ChatGPT and related AI technologies will continue to grow at an unprecedented pace, despite intense competition from Google, Anthropic and others.

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Harvard develops AI to identify life-changing gene-drug combinations

Researchers at Harvard Medical School unveiled an AI designed to match genes and drugs to combat disease in cells. The system, called PDGrapher, aims to tackle conditions ranging from Parkinson’s and Alzheimer’s to rare disorders like X-linked Dystonia-Parkinsonism.

Unlike traditional tools that only detect correlations, PDGrapher forecasts which gene-drug pairings can restore healthy cellular function and explains their mechanisms. It may speed up research, lower expenses, and point to novel treatments.

Early tests suggest that PDGrapher can identify known effective combinations and propose new ones that have yet to be validated. If validated in trials, the technology could move medicine towards personalised treatments.

The debut of PDGrapher reflects a broader trend of AI transforming biotechnology. Innovations in AI are accelerating research by mapping biological systems with unprecedented speed, showing how machine learning can decode complex biological systems faster than ever before.

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Ransomware 3.0 raises alarm over AI-generated cyber threats

Researchers at NYU’s Tandon School of Engineering have demonstrated how large language models can be utilised to execute ransomware campaigns autonomously. Their prototype, dubbed Ransomware 3.0, simulated every stage of an attack, from intrusion to the generation of a ransom note.

The system briefly raised an alarm after cybersecurity firm ESET discovered its files on VirusTotal, mistakenly identifying them as live malware. The proof-of-concept was designed only for controlled laboratory use and posed no risk outside testing environments.

Instead of pre-written code, the prototype embedded text instructions that triggered AI models to generate tailored attack scripts. Each execution created unique code, evading traditional detection methods and running across Windows, Linux, and Raspberry Pi systems.

The researchers found that the system identified up to 96% of sensitive files and could generate personalised extortion notes, raising psychological pressure on victims. With costs as low as $0.70 per attack using commercial AI services, such methods could lower barriers for criminals.

The team stressed that the work was conducted ethically and aims to help defenders prepare countermeasures. They recommend monitoring file access patterns, limiting outbound AI connections, and developing defences against AI-generated attack behaviours.

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Pressure mounts as Apple prepares AI search push with Google ties

Apple’s struggles in the AI race have been hard to miss. Its Apple Intelligence launch was disappointing, and its reliance on ChatGPT appeared to be a concession to rivals.

Bloomberg’s Mark Gurman now reports that Apple plans to introduce its AI-powered web search tool in spring 2026. The move would position it against OpenAI and Perplexity, while renewing pressure on Google.

The speculation comes after news that Google may integrate its Gemini AI into Apple devices. During an antitrust trial in April, Google CEO Sundar Pichai confirmed plans to roll out updates later this year.

According to Gurman, Apple and Google finalised an agreement for Apple to test a Google-developed AI model to boost its voice assistant. The partnership reflects Apple’s mixed strategy of dependence and rivalry with Google.

With a strong record for accurate Apple forecasts, Gurman suggests the company hopes the move will narrow its competitive gap. Whether it can outpace Google, especially given Pixel’s strong AI features, remains an open question.

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