Nedbank Targets Small South Africa Firms With Ikhokha Purchase
The Johannesburg-based lender will pay 1.65 billion rand ($94 million) to acquire all of iKhokha, the company said in a statement Wednesday. The transaction is subject to regulatory approvals and is expected to conclude in the coming months, the bank said.
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Forbes
17 minutes ago
- Forbes
Cutting AI Costs: Smart Strategies for Small Business Savings
If you run a small business, you might already feel the AI pinch: your customer support runs on ChatGPT, your marketing automation uses Claude, and you're paying for Grok's research capabilities and real-time updates. For the average company (or user), those subscriptions can easily hit $300 a month, especially if you're integrating multiple tools into your workflow. That's a serious line item for what's supposed to be affordable technology. What most don't realize is that these ballooning costs have more to do with the hardware on the backend than they do the software running their workflows. Every time an AI model responds, it triggers a process called inference: the act of generating output from a trained model. Unlike training—which costs a fortune but only happens once—inference occurs billions of times each day and scales with usage. It has become one of the largest ongoing expenses in AI, driving massive, sustained energy demand that fuels the industry's growing power crisis. For individuals and small business owners, this hidden cost means AI remains incredibly expensive. But that might be about to change. A new cohort of hardware startups—including Positron AI, Groq, Cerebras Systems, and Sambanova Systems—are racing to make inference radically cheaper. If they succeed, AI tools could drop from $300-a-month luxuries to accessible everyday infrastructure for freelancers, educators, retailers, and entrepreneurs. If Positron and its peers succeed, the $300-a-month AI stack could shrink to $30. It could also be replaced entirely by tools you run yourself, privately and affordably. And that changes who gets to participate in the future of AI. Among these, Positron has emerged as a favorite choice by some of the world's dominant neocloud providers, gaining investor attention for its unique approach. 'The early benefits of AI are coming at a very high cost – it is expensive and energy-intensive to train AI models and to deliver curated results, or inference, to end users.' DFJ Growth co-founder Randy Glein said. 'Improving the cost and energy efficiency of AI inference is where the greatest market opportunity lies, and this is where Positron is focused.' Inference Is The New Electricity Bill In the world of AI economics, inference is like your utility bill: it grows as you grow, and it's never just a one-time fee. Whether you're sending AI-generated emails or running a support chatbot, inference is what keeps the lights on—and right now, that light is powered by Nvidia's premium-priced GPUs. 'Nvidia GPUs have become the backbone of AI infrastructure today according, powering nearly every major inference workload at every major cloud provider. The downside to this, beyond having one $4 trillion company They own the entire inference market, is that they weren't designed with efficiency in mind. They're built for flexibility and optimized for training complex models that require general-purpose chips for multifaceted tasks. And yet, the majority of inference today still runs on Nvidia hardware, leaving the industry with high power usage, steep cloud bills, and limited options for smaller players.'said Mitesh Agrawl CEO of Positron The Race To Make AI Affordable Those are exactly the problems Positron, Groq, Cerebras, and Sambanova are solving by building alternatives to the Nvidia tax. And while they all share a common goal—deliver inference infrastructure that slashes energy consumption, improves performance-per-dollar, and gives developers more control—Positron is arguably the most technically ambitious and commercially mature contender in this race. Founded by systems engineer Thomas Sohmers and compiler expert Edward Kmett, Positron has taken a radically different path from its peers. Instead of building application-specific chips or chasing general-purpose GPUs, Positron bet on field-programmable gate array (FPGAs)—reconfigurable chips optimized for memory efficiency—and used them to build Atlas, an inference-first system designed from the ground up for performance and energy savings. Atlas delivers 93 percent memory bandwidth utilization (vs. about 30 percent for GPUs), uses 66 percent less energy, and offers 3.5 times better performance per dollar—all while supporting seamless deployment with no code changes. That kind of out-of-the-box compatibility makes it a practical swap for existing cloud or local systems without forcing teams to rewrite their infrastructure from scratch. These gains have landed it major enterprise deployments with Cloudflare, Crusoe, and Parasail. The company recently raised a $51.6 million Series A led by Valor Equity Partners, Atreides, and DFJ Growth—the very firms that bankrolled SpaceX, Tesla, X, and xAI, some of the world's largest buyers of AI hardware. Positron is already working on its next-generation system, Titan, built on custom 'Asimov' silicon, which is expected to support models up to 16 trillion parameters with two terabytes of memory per chip—all while running on standard air-cooled racks. That could make high-throughput inference viable in a wider range of environments, from enterprise data centers to sovereign cloud infrastructure. While others in the field are exploring niche optimizations, Positron is staking a claim to general-purpose inference acceleration—solving for cost and compatibility at scale. But it's not alone. Other Challengers Redefining The Stack While Positron is focused on general-purpose inference acceleration, other challengers are tackling different bottlenecks. Groq is optimizing ultra-low-latency inference for large language models (LLM). Its Tensor Streaming Processor (TSP) delivers consistent, repeatable latency, with sub-millisecond response times—enabling a new class of AI tools that respond instantly—without incurring massive cloud costs—and laying the groundwork for local, responsive AI that could eventually be accessible to small businesses. Cerebras brings an edge-native, security-first perspective. Its modular AI appliances can run powerful models entirely on-site—ideal for defense, critical infrastructure, or industries where cloud deployment isn't an option. Cerebras makes it possible for organizations to deploy advanced AI with a small footprint—something previously only achievable by hyperscalers. Sambanova is taking a full-stack approach, combining hardware and software to deliver vertically optimized AI systems. Rather than asking businesses to build training pipelines and inference clusters from scratch, they offer a turnkey platform with pre-trained models—essentially packaging AI as an appliance for organizations without a dedicated machine learning (ML) team. All of these players are on a mission to unlock high-performance inference that doesn't require hyperscaler infrastructure or ballooning cloud costs—opening the door to entirely new economic possibilities. Why This Matters For Your Mottom Line If inference becomes cheaper, everything changes. A Shopify seller could train and run a private AI model locally—without relying on costly cloud infrastructure. A solopreneur could fine-tune a sales assistant on years of customer emails, then run it on a $10 chip instead of a $30,000 graphic processing unit (GPU). A tutoring platform could deploy personalized lesson-plan generators without needing a full-time infrastructure team. This is already happening. Smaller teams are building domain-specific copilots that live inside their own companies' firewalls. Independent consultants are running multi-agent AI workflows from their laptops. This shows that inference costs are a technical problem, but more importantly, they're the gatekeeper to who gets to build with AI. If Positron and its peers succeed, the $300-a-month AI stack could shrink to $30. It could also be replaced entirely by tools you run yourself, privately and affordably. And that changes who gets to participate in the future of AI. Nvidia's Grip Might Finally Be Loosening Today, Nvidia holds a near-monopoly over AI infrastructure—and by extension, who gets to play. Its chips power the vast majority of generative AI systems worldwide, and its ecosystem (CUDA, TensorRT, etc.) makes switching difficult. The result is a pay-to-play system where cost determines access. But that grip may not hold if companies like Positron, Groq, Cerebras, and Sambanova continue to gain traction and change the economics of AI. By lowering the cost of inference, they're making it possible for smaller teams and individual users to run powerful models without relying on expensive cloud infrastructure. This shift could have broad implications. Instead of paying hundreds of dollars a month for AI-powered tools, users may soon be able to run custom assistants, automations, and workflows locally—on hardware they control. For small businesses, freelancers, educators, and startups, that means more control, more customization, and a lower barrier to entry—for a fraction of today's cost. If inference becomes affordable, innovation stops being a privilege and starts becoming infrastructure. Because when you democratize cost, you decentralize control. The next chapter of AI won't be written by whoever builds the biggest model, but by whoever makes it cheap enough to run—and that's how you break a $4 trillion monopoly.
Bloomberg
2 hours ago
- Bloomberg
Centerbridge Taps $1.4 Billion of Private Debt for MeridianLink
A group of private credit firms are providing nearly $1.4 billion of loans to support Centerbridge Partners ' acquisition of financial software platform MeridianLink Inc., according to people with knowledge of the matter. Goldman Sachs Asset Management, alongside Blackstone Inc., Ares Management Corp., Blue Owl Capital Inc. and Antares Capital are participating in the financing, said the people, who asked not to be identified discussing private information. The loan is set to be split equally among lenders, they said.
Yahoo
2 hours ago
- Yahoo
Axius Water® Expands Nutrient Removal Capabilities with the Acquisition of S&N Airoflo, Inc.
POCASSET, Mass., August 14, 2025--(BUSINESS WIRE)--Axius Water ("Axius"), a leading provider of nutrient management solutions for municipal and industrial wastewater treatment facilities, announced today that it has acquired S&N Airoflo, Inc., a leading supplier of surface aeration systems, based in Greenwood, MS. This acquisition enables Axius to further expand its nutrient removal solutions for water and wastewater, while optimizing efficiency across the ecosystem. S&N Airoflo specializes in oxidation ditches and lagoons for municipal and industrial wastewater treatment. Their proprietary floating brush rotors, continuously refined since 1989, deliver superior oxygen transfer and mixing while reducing power consumption by 30 to 60 percent. The innovative blade design and floating platform architecture maximize treatment efficiency while significantly minimizing energy requirements. "S&N Airoflo comes to us with a reputation for exceptional customer support combined with simple, reliable solutions that both minimize downtime during installation and also offer long-term cost-efficient operation," said Chris McIntire, Axius Water CEO. "Their demonstrated expertise establishes them as an optimal strategic alignment for Axius's long-term objectives." Todd Nester, Operations Director of S&N Airoflo, added, "Founded over 25 years ago, S&N Airoflo delivers high-performance, efficient aeration systems. We take pride in our rapid installation technology and our simple, reliable operations. We are thrilled to begin a new chapter as part of Axius and look forward to collaborating with them to support our shared mission of reducing nutrient pollution." About Axius Water® Axius Water was founded in 2019 by KKR's Global Impact Fund, in partnership with XPV Water Partners. Axius' differentiated products and services improve the effectiveness of the water and wastewater treatment process, thereby measurably improving the quality of treated water. Axius is expanding globally as it builds a diverse portfolio of leading solutions that improve the overall wastewater management processes. For additional information, please visit Axius Water Companies: Aero-Mod® – Operator-approved, end-to-end scalable mechanical plants improving wastewater in North American communities since 1981. ATAC Solutions™ – Leading U.K. wastewater design and process equipment contractor specializing in capital, hire, and turnkey applications. EDI® (Environmental Dynamics International®) – Global wastewater solutions provider with advanced aeration design, equipment, and aftermarket services since 1975. EOSi® (Environmental Operating Solutions, Inc.) – Experts in biological nutrient removal solutions, premium non-hazardous carbon sources and advanced process control. MITA Water Technologies® (MITA WT) – Global supplier of energy-efficient systems for wastewater biological treatment and tertiary pile cloth filtration since 1971. Napier-Reid® – Engineered water and wastewater systems with 3,000+ installations in 40+ countries since 1950. Nexom® – Proven filtration, biological and lagoon-based wastewater solutions to reduce key nutrients that contribute to pollution. Triplepoint® – Preserving small-town lagoon systems by engineering solutions that allow them to meet modern effluent standards. View source version on Contacts Media Contact: Laura Brockway: Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
