As artificial intelligence continues to revolutionize industries, selecting the right AI model has become a crucial decision for businesses and individuals alike. With countless models available, each with unique strengths and limitations, it’s easy to feel overwhelmed by the options. Choosing the wrong model can lead to inefficiencies, increased costs, or suboptimal results, making it essential to approach the selection process thoughtfully.

This guide will walk you through key considerations to ensure the AI model you choose aligns with your specific needs and delivers the desired outcomes. From performance speed to the quality of outputs, these factors will help you make an informed decision.

The Model Must Perform Fast Enough

For many applications, speed is critical. Whether you’re using AI for real-time decision-making, chatbots, or automated trading systems, a model that lags can severely impact productivity and user experience. Consider the following when evaluating an AI model’s performance speed:

• Response Time: Measure how quickly the model can generate results under real-world conditions.
• Scalability: Ensure the model can maintain its speed as the workload increases.
• Task-Specific Demands: Identify whether your use case requires real-time outputs (e.g., customer service chatbots) or if slightly longer response times are acceptable (e.g., batch processing).

If a model’s speed doesn’t meet your requirements, no amount of accuracy or quality will compensate for the delay.

The Model Must Produce Useful Outputs

Accuracy and relevance are at the heart of any successful AI application. A fast model that delivers poor-quality results is unlikely to serve your needs effectively. To evaluate a model’s outputs, consider these criteria:

• Accuracy: Assess how well the model handles the data and provides precise, error-free outputs.
• Relevance: Determine whether the model’s results align with your goals and the context of the task.
• Adaptability: Check if the model can adjust to nuanced or evolving requirements.

Testing the model on a small sample of your data can provide insights into its quality and reliability before committing to full-scale deployment.

How Much Power and Memory Are Needed in Practice?

The resources required depend on the model size and complexity. Key factors include:

• Model size: Larger models typically require more memory (VRAM/RAM) and compute power.
• Task complexity: Simple tasks like basic text generation require less, while advanced tasks like real-time code completion or large dataset analysis need more.
• Hardware capabilities: For local use, GPU specifications (e.g., VRAM size) play a critical role. A powerful GPU with at least 8–12 GB of VRAM is often recommended for medium to large models.

How Can Performance Be Measured and Compared?

1. Latency: Measure the time it takes for the model to generate a response after input.
   • Tools: Benchmarking scripts or testing tools like Hugging Face’s inference pipeline.
2. Throughput: Evaluate how much data the model can process in a given time frame.
   • Useful for batch-processing scenarios.
3. Memory Usage: Monitor the memory consumption during the model’s execution.
   • Tools: GPU monitoring tools like NVIDIA’s nvidia-smi or system resource monitors.
4. Quality Metrics: Use evaluation metrics for the model’s output quality:
   • BLEU or ROUGE for text generation.
   • User feedback for practical usability in specific use cases.
5. Comparative Testing: Run the same task on multiple models and record performance, accuracy, and usability.
   • Example: Compare TabbyML, Continue, and CodeGPT for code completion in a controlled coding environment.

By balancing speed, quality, and resource usage, you can identify the model that best fits your requirements.

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Emotional Intelligence Benchmark for LLMs – This include failure rate

• https://dubesor.de/benchtable

Code Completion Leaderboard

• https://huggingface.co/spaces/bigcode/bigcodebench-leaderboard
• https://bigcode-bench.github.io/

LLM Capability as a Coding Assistant 

• How often do LLMs generate an acceptable answer
• https://prollm.toqan.ai/leaderboard/coding-assistant

Benchmarks for LLMs

• https://eqbench.com/

Comprehensive catalogue of the Large Language Model (LLM) evaluation frameworks

• https://github.com/aiverify-foundation/LLM-Evals-Catalogue

Benchmarks Collection

• https://huggingface.co/collections/open-llm-leaderboard/the-big-benchmarks-collection-64faca6335a7fc7d4ffe974a

LLM Leaderboard – Based on Task

https://www.vellum.ai/llm-leaderboard

When choosing a large language model (LLM) for a specific task, it’s essential to consider the model’s size, performance, and practicality. While larger models generally offer better performance, smaller and medium-sized models are increasingly competitive, depending on the use case. Here’s a breakdown:

Small Models

Despite their limited size, small models have shown surprising potential in generating coherent and contextually appropriate text. However, they still lag behind larger models in terms of sophistication and versatility. These models are best suited for lightweight tasks or scenarios with strict resource constraints, such as:

• Chatbots: For basic customer interactions or internal communication tools.
• Simple Summarization: Extracting key points from straightforward text documents.

Although small models may not yet match the capabilities of larger ones, their rapid improvement suggests they may become more viable for general use within the next year.

Medium-Sized Models

Medium-sized models strike a balance between performance and resource requirements. They deliver highly competitive results compared to larger models while demanding less computational power, making them an attractive option for many applications. Examples of their use include:

• Advanced Text Generation: Writing blog posts, emails, or other complex documents.
• Data Analysis: Extracting insights from structured or semi-structured data.

If you have access to robust computing resources, such as a high-end graphics card, medium-sized models can be an excellent choice. They offer significant versatility without the heavy costs and infrastructure requirements of large models.

Local Language Models

The progress in local language models (LLMs) has been remarkable, with constant advancements in model quality and techniques. These models offer the advantage of running locally, ensuring better data privacy and control. Key areas where local LLMs are excelling include:

• Offline Applications: For users requiring secure, on-device AI processing.
• Domain-Specific Tasks: Tailored solutions for specific industries or fields.

The rapid pace of innovation in this area suggests that local LLMs will continue to close the gap with their cloud-based counterparts.

Visual Studio Code Plugins

For developers, Visual Studio Code plugins powered by LLMs are becoming indispensable tools. These plugins are in a highly developed stage, offering intelligent suggestions, code completion, and debugging support. Some popular options include:

• Continue: A plugin that enables seamless collaboration with LLMs for in-editor assistance.
• Twinny: Provides real-time suggestions and guidance for complex coding tasks.
• TabbyML: Specializes in code completion and auto-generation of boilerplate code.
• Code GPT: Enhances development workflows by integrating GPT capabilities directly into VS Code.
• Qodo: Focuses on improving productivity by suggesting optimized code snippets and refactoring options.

These plugins are rapidly evolving, providing developers with powerful tools to streamline workflows and enhance productivity, especially when paired with medium or large-sized models.

The situation with local encoding language models has seen significant growth over the past year. A year ago, there were approximately 300,000 models available, but that number has now surged to over 1,294,616 models, according to Hugging Face’s model repository.

To run these models locally, programs like LLaMA and Code GPT can be utilized effectively. These tools provide powerful options for leveraging advanced language models in local environments.

The advancements in Artificial Intelligence technologies has been exciting, but nevertheless frightening to many. Media outlets often portray AI as a looming threat—something that might overtake human jobs, or worse, turn against us. But these portrayals are grounded in myth rather than reality.

According to Pew Research Center 52% of Americans are more concerned than excited about AI in daily life, compared with just 10% who say they are more excited than concerned; 36% feel a mix of excitement and concern.

Let’s make this clear, AI is a smart, powerful, intelligent tool, but it is not “smart”. It is crucial to understand that AI, despite its impressive knowledge, operates on fundamentally different principles than human intelligence.

The University of Cincinnati’s Anthony Chemero, a professor of philosophy and psychology in the UC College of Arts and Sciences, states “LLMs [large language models which AI is] generate impressive text, but often make things up whole cloth, they learn to produce grammatical sentences, but require much, much more training than humans get. They don’t actually know what the things they say mean,” he says. “LLMs differ from human cognition because they are not embodied.”

Source

This chart shows how most Americans think AI will (or already has) become more intelligent than people. Let’s dive into this and why AI isn’t this enemy or higher power people claim it to be and instead look at AI through a positive light.

Understanding Machine Learning: A Closer Look

Machine Learning, in simple terms, uses algorithms that learn from data to make predictions. At its core, machine learning is a field of computer science that involves the development of algorithms that allow computers to learn from data without being explicitly programmed. These algorithms, often inspired by statistical methods, identify patterns within vast datasets and use these patterns to make predictions or decisions. 

You may hear the term “learning” and assume a human-like process, although it is nothing like that. AI doesn’t possess the ability to understand or reason in the same way that humans do. Instead, it relies on statistical correlations and mathematical models to process information.

The Illusion of Learning

The misconception that AI can “learn” stems from the fact that these algorithms can improve their performance over time as they are exposed to more data. However, this improvement is not like human learning, where we actively acquire knowledge and apply it to new situations. AI systems, on the other hand, simply become better at recognizing patterns within the specific domain they were trained on.

The Limitations of AI

AI systems are constrained by several key limitations:

• Data Dependency: AI models heavily rely on the quality and quantity of training data. Biased or incomplete data can lead to biased or inaccurate outputs.Patrick Tucker, the science and technology editor of the national defense news website Defense One wrote in January, “When … researchers put the statements to ChatGPT-3, the generative AI tool ‘agreed with incorrect statements between 4.8 percent and 26 percent of the time, depending on the statement category.’” An error rate approaching 25 percent can be particularly troublesome for any discipline. 
• Lack of Adaptability: AI systems are generally not capable of adapting to new situations or unforeseen circumstances without additional training or human intervention.
• No Consciousness or Awareness: AI lacks consciousness, emotions, and awareness, which are fundamental to human intelligence. It cannot experience the world in the same way that humans do.

The Role of Human Oversight

Despite their limitations, AI systems can be powerful tools when used responsibly and ethically. Human guidance and oversight are essential throughout the entire AI development and deployment process to ensure ethical and accurate information. 

Humans play a critical role in:

• Data Collection and Preparation: Ensuring that data is accurate, unbiased, and relevant.
• Algorithm Development and Training: Designing and fine-tuning algorithms to achieve specific goals.
• Model Evaluation and Improvement: Assessing the performance of AI models and identifying areas for improvement.
• Ethical Considerations: Addressing potential biases, privacy concerns, and societal impacts.

The Future of AI: A Balanced Perspective

The subject of artificial intelligence often evokes fear that its rapid advancements could surpass human capabilities, displacing workers, eroding human agency, and reshaping society in ways that challenge traditional roles and values. A stretch, but many worry about this reality. As AI continues to evolve, it’s important to maintain a clear understanding of its capabilities and limitations. While AI can automate tasks, enhance decision-making, and drive innovation, it cannot replace human creativity, empathy, and critical thinking.

AI is a technology like no other, and when viewed in a positive light, it is a powerful tool for all because…

1. Efficiency and Innovation: AI automates routine tasks, freeing up time for humans to focus on creative, high-impact work, driving innovation across industries.
2. Solving Complex Problems: AI helps tackle global challenges like climate change, healthcare, and poverty by analyzing data and offering insights that are beyond human capabilities.
3. Accessibility: AI-powered tools make education, healthcare, and services more accessible, particularly in underserved or remote areas.
4. Safety and Precision: In fields like medicine and manufacturing, AI can improve safety and precision, reducing human error and enhancing outcomes.
5. Sustainability: AI can optimize resource use, reduce waste, and create solutions for more sustainable energy production and consumption.

By recognizing the true nature of AI and embracing a balanced perspective, we can harness its potential while mitigating its risks and ensuring a future where humans and AI work together harmoniously. 

Artificial Intelligence has revolutionized countless industries, and Search Engine Optimization (SEO) is no exception. As AI technologies continue to advance, they are reshaping the way search engines work and how businesses approach their online marketing strategies. 

18.7% of SEO specialists believe that AI and machine learning will lead to dynamic shifts and industry changes in SEO.

Navigating the SEO Evolution

The term Search Engine Optimization was first used by Webstep Marketing Agency in 1997 in their marketing materials. The term was later incorporated as a service to help clients optimize their content to rank higher in search engine results. Little did they know the important role SEO would play in web practices. 

Since 1997, Google has taken the reins as the most dominant force in SEO.

Source: Rellify

As we see the SEO landscape evolve as technology continues advancing, will Google have to step down as the SEO empire as AI takes its place?

AI allows for more personalized, specific results. However, it’s unlikely that Google will simply step down rather it may only benefit them. They may integrate AI more deeply into their algorithms and services, adapting to the changes in user behavior and expectations.

Emerging platforms and technologies may challenge Google’s supremacy, but Google’s extensive data, resources, and continuous innovation make it a resilient player. They have been in the game for too long to be taken down, if anything AI will keep them at the top. 

As for the future, who knows what could happen? But for now, Google will likely remain a major force in the SEO space with the integration of AI to its benefit.

How Does AI impact SEO?

Go search Google with a question right now and look at the first result. AI Overview has taken that first search engine result. By analyzing vast amounts of data, AI Overview is able to answer your question precisely and in a simple summary format. This is great, although now you don’t need to dive into a website to find your answers. 

Efficient and convenient, yes. But this completely defeats a website’s powerful SEO strategy to get your search to the top.

Let’s continue to evaluate how AI has impacted SEO. Here are 4 Ways AI has impacted SEO:

Search Algorithms

AI Machine learning has significantly influenced search algorithms. Analyzing large amounts of data, these algorithms can better understand user intent, context, and behavior. This leads to more accurate and relevant search results, such as the AI Overview insights we looked at before. 

Some of the key ways AI has changed search algorithms include:

• Semantic Search: AI enables search engines to understand the meaning behind queries, rather than just matching keywords. This allows for more contextually relevant results. In simple terms, instead of connecting the words in your question to words on sites, AI can comprehend your intention in your question allowing for a more precise, correct result. 
• RankBrain: Google’s RankBrain algorithm uses machine learning to interpret the meaning of search queries and rank web pages based on their relevance and quality. It analyzes how users interact with results to improve their future searches. 
• Natural Language Processing (NLP): NLP allows search engines to understand and process human language, enabling them to better interpret complex queries and provide more accurate results.

AI has taken search algorithms to the next level. 

Content Creation and Optimization:

AI tools have become invaluable assets in content creation and optimization. 

• Content Idea Generation: AI can generate content ideas based off of trend analysis, keywords, and user behavior. It is a great assistant in keeping content relevant and creative. 
• Write Content: While AI cannot fully replace human creativity, it can assist in writing drafts, generating outlines, and even creating entire articles. 
• Optimize Content: AI can analyze content for keyword density, readability, and SEO best practices, making it easier to optimize for search engines.
• Personalize Content: AI can tailor content to specific users based on their preferences, demographics, and browsing history.

AI is a great help in helping content strategy be more efficient as the demand for it increases on all platforms. Although AI can not replace human creativity, rather it can be used as a tool.

User Experience Focus:

In the age of AI, user experience has become a paramount factor in SEO. AI can identify performance issues to enhance page speed, analyze mobile site performance for better usability, and help optimize content for voice search by targeting long-tail keywords and adopting a conversational tone. 

Automating these tasks streamlines the process and frees up time for more strategic work.

Voice Search and Conversational AI:

As voice search continues to rise, businesses must adapt by focusing on long-tail keywords and optimizing for local searches, as voice queries often emphasize local information.

• Optimize for Long-Tail Keywords: Voice search queries are often longer and more conversational, so businesses need to focus on long-tail keywords.
• Create Natural Language Content: Content should be written in a natural, conversational tone to match how people speak.
• Optimize for Local Search: Voice search is often used for local searches, so businesses need to optimize their local listings and content for voice queries.

The Dark Side of AI in SEO: Potential Negative Impacts

While AI has revolutionized SEO with data-driven insights and personalized experiences, it’s not without its risks. Companies that fail to manage AI-driven strategies properly could face significant setbacks in their SEO performance.

Content Quality and Authenticity Issues

AI-powered tools like chatbots and content generators make it easy to churn out massive amounts of content quickly. However, this can lead to a focus on quantity over quality, resulting in:

• Thin content: AI-generated content may lack depth or relevance, offering little value to users. Google’s Helpful Content Update penalizes low-quality content, reducing site rankings.
• Duplicate content: AI models sometimes reuse similar phrasing, unintentionally creating duplicate or near-duplicate content that search engines frown upon.
• Loss of brand voice: Automated content often lacks the unique tone and perspective that resonate with target audiences, harming engagement.

A study by BrightEdge found that over 50% of online content gets little to no traffic, highlighting the importance of high-quality, human-centric content.

Over-Optimization and Penalties

AI tools optimize metadata, keywords, and backlinks with precision, but misuse can lead to:

• Keyword stuffing: Overloading pages with keywords to game search engines risks penalties.
• Black-hat tactics: Some AI-powered strategies may unintentionally engage in manipulative practices like link farming, resulting in search engine penalties.
• Algorithm misalignment: AI often follows patterns, but search engines like Google continuously evolve, making rigid AI-based strategies obsolete or misaligned with updates.

To harness AI effectively, businesses must ensure human oversight, prioritizing quality and ethical practices while leveraging AI’s capabilities.

What does this mean for your business?

AI is constantly evolving, leading to exciting possibilities for the future of SEO. Upcoming trends include highly personalized search results based on individual user preferences and behavior, as well as AI-powered predictive analytics that can forecast future trends and user actions, enabling businesses to stay ahead of the curve. 

Visual search tools will also emerge, allowing users to search using images rather than text. However, with these opportunities come challenges. 

To remain competitive in the age of AI, businesses must embrace continuous learning to keep up with the ever-changing SEO landscape, invest in AI tools to automate tasks and improve efficiency, prioritize user experience, and adapt their content and websites for voice search. Embrace AI and use it to your advantage rather than steer away or fear it. 

AI is not the enemy. 

In fact, 40% of marketers have seen a 6-10% increase in revenue after implementing AI in their SEO practices. So not only is it increasing efficiency, it is also increasing revenues. 

By understanding AI’s impact on SEO and embracing these trends, businesses can position themselves for success in the digital age. 

How are you or your company adapting your SEO strategy to the rise of AI?

In today’s rapidly evolving technological landscape, Artificial Intelligence (AI) has emerged as a game-changer for businesses of all sizes. While large corporations have been quick to adopt and benefit from AI, small and medium-sized businesses (SMBs) face unique challenges in implementing these cutting-edge technologies. Let’s explore the hurdles SMBs encounter on their AI journey and find solutions on overcoming them.

The AI Adoption Landscape for SMBs

Before diving into the challenges, let’s look at some statistics that highlight the current state of AI adoption among SMBs:

– According to a survey by Vistage, 29.5% of respondents say they believe AI is among the technologies that will have the greatest impact on their business in the next year

– Global News Wire shares that the global AI market size is projected to grow from $58.3 billion in 2021 to $309.6 billion by 2026, with SMBs playing a crucial role in this expansion.

Source

A study from Deloitte and Stanford University, found that 25% of small businesses are currently using AI in some form or another. With the use of AI in SMB’s increasing, the opportunities are arising. Machine learning enhancements, natural language processing, and computer vision, AI software is becoming more accessible for small business and in fact 72% of small business leaders believe that AI technology can offer a competitive advantage in the market. Although SMBs are gaining more access to AI, it comes with plenty of challenges that are stunting the exponential growth projections of AI in SMBs.

Challenge 1: Limited Resources and Budget Constraints

One of the most significant hurdles for SMBs in implementing AI is the perceived high cost and resource requirements.

• In a poll done by the Bipartisan Policy Center, the majority reported that the most significant barrier to the adoption of AI-enabled tools was cost (55%).
• The upfront cost to implement an AI system ranges from $5,000-$30,000 on average. This includes expenses like software licenses, cloud computing fees, hardware, integration, testing, and custom development. Also adding monthly maintenance and support costs of around $1,000 – $5,000 month.

The initial investment in AI is costly and can steer SMBs away, but considering the long-term benefits and potential ROI it may be a risk these businesses should be willing to take. Increasing efficiency and productivity in the workplace pays back over time.

Start small with pilot projects or consider AI-as-a-Service (AIaaS) solutions to minimize upfront costs.

Challenge 2: Lack of Technical Expertise

Many SMBs struggle with implementing AI due to a shortage of in-house technical expertise. AI is new and complex and it takes someone with a good understanding of it to keep it running smoothly. Also for the benefit of the company, having an expert in-house can allow for the AI to be used to its full potential.

According to the U.S. Chamber of Commerce, 77% of small businesses cited either insufficient understanding of AI or uncertainty regarding its benefits as the main reasons for not integrating the technology into their operations.

SMBs can bridge this gap by investing in training programs for existing staff, partnering with AI consultants, or leveraging user-friendly AI platforms designed for non-technical users.

Challenge 3: Data Quality and Availability

AI systems require large amounts of high-quality data to function effectively, which can be a challenge for SMBs with limited data collection and management practices.

SMBs should focus on improving their data collection and management practices. Start by identifying key data sources, implementing data governance policies, and leveraging cloud-based storage solutions for better data accessibility.

Challenge 4: Integration with Existing Systems

Integrating AI solutions with legacy systems and existing business processes can be complex and time-consuming for SMBs. SMB’s time is precious, and implementing AI could temporarily disrupt efficiency. Although once integrated, it will enhance capabilities and efficiency.

To overcome integration challenges, SMBs should conduct thorough assessments of their current systems, prioritize API-driven solutions, and consider working with AI vendors that offer seamless integration capabilities.

Challenge 5: Opportunity Cost

It’s estimated that small business owners spend Roughly 68 percent of their time on operational needs (day-to-day-management, overcoming problems etc.) and 32 percent of time working on growing the business.

Between the time needed to operate and grow a small company, creating time to research, invest and build AI tools is a significant hurdle for most operators.

The challenge becomes more difficult with businesses struggling to determine ROI of AI. In a recent Gartner survey of 700 IT leaders at organizations that have adopted or plan to adopt AI, 50% stated that AI’s value was in question.

Small businesses will find it hard to justify a large investment of time and staffing resources with returns being a moving target, choosing instead to allocate their time on operations and growth.

Challenge 6: Ethical and Regulatory Concerns

As AI becomes more prevalent, SMBs must navigate the complex landscape of ethical considerations and regulatory compliance.

Ascend2 partnered with Constant Contact and conducted a survey, surveying 486 small business owners and decision makers who work at U.S. organizations operating in both B2B and B2C. They found that 12% don’t think AI is ethical and 44% were concerned about their data. Along with other findings, it can be observed that people are not fully educated on AI and its purpose.

SMBs should stay informed about AI ethics and regulations in their industry. Develop clear AI governance policies, ensure transparency in AI decision-making processes, and regularly audit AI systems for potential biases.

Conclusion: Embracing AI Despite the Challenges

While the challenges of implementing AI for SMBs are significant, they are not insurmountable. By addressing these hurdles head-on and taking a strategic approach to AI adoption, SMBs can unlock new opportunities for growth, efficiency, and innovation.

Remember:

1. Start small and scale gradually
2. Invest in education and partnerships
3. Prioritize data quality and management
4. Focus on seamless integration
5. Stay committed to ethical AI practices

By embracing AI technologies thoughtfully and strategically, SMBs can level the playing field and compete effectively in an increasingly AI-driven business landscape.

At J. Arthur, we are deploying multiple custom AI solutions with enterprise Gemini & GPT API’s. If you need an AI

As Artificial Intelligence continues to captivate industries worldwide, a new reality emerges: beneath its potential lies a significant and overlooked cost, the cost to power it. It brings up the question if AI is truly a sustainable and financially sound investment for businesses. 

The costly electricity consumption required to power AI systems should be considered before AI is praised as a tool of the future. Companies already are paying for equipment, payroll, and other fixed costs; implementing AI into companies is going to be the next major cost consideration. Is it worth the large capex investment?

SERVER & ELECTRIC COSTS

To implement AI into your company, a built in software solution can range from $5,000 to  $150,000 to start up with maintenance costs considered after. AI consumes a very large amount of electricity, which requires a large amount of money to fund. As datacenters incorporate AI and many planning to in the U.S., more power is demanded.  

Source: S & P Global

Depending on many factors, AI’s energy costs are unpredictable and many are unsure of the true cost of AI in terms of money with so many variables to consider. 

With the excitement of the versatility of Artificial Intelligence, research on its cons are limited and many are not fully understood yet due to it just recently becoming a trend. People attest how it’s going to obstruct human interaction or take jobs, but the real downside of AI is its energy cost. 

Data Scientist Alex de Vries, has recognized the obscene amount of energy being used to power AI and has taken time to research. De Vries states:

“By 2027 worldwide AI-related electricity consumption could increase by 85.4–134.0TWh of annual electricity consumption from newly manufactured servers.”

He compares this to the annual electricity consumption of countries such as the Netherlands, Argentina and Sweden.

Market size and revenue comparison for artificial intelligence worldwide from 2018 to 2030 (in billion U.S. dollars)

Source: Statista

This chart examines the expense of AI and its rapid growth expected in the coming years. 

With large amounts of energy use, comes large costs on electric bills.

REVENUE VS. VALUATION: ARE AI COMPANIES OVERVALUED?

AI startups have caught the attention of investors. According to Crunchbase, more than a fifth of all venture funding in February went to AI companies with $4.7 billion invested towards the sector. Crunchbase states:

“There are many concerns in venture capital circles that AI is the next bubble, as companies in this sector continue to raise massive rounds in quick succession and at huge valuations.” 

Are AI companies overvalued?

Companies don’t want to be left out of the Artificial Intelligence investing frenzy. Amazon took the bite and invested $2.75 billion into the AI startup Anthropic being their largest venture deal ever. Anthropic is forecasted revenue of more than $850 million next year. 

Large amounts of money are being poured into AI, but the value is prevalent. Companies have benefited immensely from AI, the biggest example of all is Nvidia, an AI chip making company, powering the AI revolution. 

“Accelerated computing and generative AI have hit the tipping point. Demand is surging worldwide across companies, industries and nations,” Jensen Huang, founder and CEO of Nvidia, said in a statement,  “Vertical industries — led by auto, financial services and healthcare — are now at a multibillion-dollar level,” he added. 

Nvidia is now trading with a market cap above $2.5 Trillion, making it larger than Tesla and Amazon combined. A company that only five years ago had a market cap of $100 billion. Despite its exponential trajectory, will it plateau? 

Source: Statista 

AI investments are bold considering many of the AI technology is not yet to be proven successful. For example, SoundHound uses AI for conversational experiences such as in drive-thrus to make the ordering process more efficient, the risk of this being a success or not is unpredictable. Soundhound has a market cap of $1.65 billion with an enterprise value of $1.55 billion. They had a significant net loss of $89M with a $46M revenue so it is being reconsidered by investors. 

With only $11 million in revenue, Stability AI, the company behind the open-source AI image generator Stable Diffusion, was valued at $1 billion in 2022. The startup raised at least $101 million from investors like Lightspeed Venture Partners and Coatue Management in a seed round in July 2022. However, Stability AI has been struggling to raise funding at a $4 billion valuation in 2023. 

AI Startup, Hugging Face, said on Thursday it was valued at $4.5 billion in a $235-million funding round. They have $50-70 million in annual revenue. Hugging Face’s co- founder and CEO Clément Delangue recognizes the demand is high and their growth is increasing rapidly. Delangue said:

“In five years, every tech company will be an AI company.”

Given its substantial costs and significant investments, Artificial Intelligence is an unpredictable yet undeniably lucrative technological pursuit.

UNDEFINED PATH TO RETURN ON INVESTMENT

Major companies have had to consider the great capital expense that comes with the implementation of AI. 

“According to analysts at Raymond James, “AI inflation” is driving up capital expenditures (capex) for big tech, with Amazon (AMZN), Meta, Microsoft, Alphabet and Oracle (ORCL) expected to spend $199 billion on capex this year, about 28% more than in 2023.” 

Companies like Microsoft, according to the Washington Post, have spent $14 billion in the most recent quarter with larger investments on the horizon. Many companies alike are spending the same, if not more. 

Investors are being careful on what companies to trust when it comes to the ROI of AI systems being installed because the investments are in the billions. 

With Mark Zuckerberg officially showing interest in bringing Artificial Intelligence to Meta, investors fear a similar case to when Zuckerburg introduced the metaverse. The metaverse was far from successful, losing the company $46.5 billion according to Fortune. Meta is already showing signs that the AI installments are costing more than originally anticipated. 

According to Business Insider and Meta’s first-quarter report, Meta originally expected to spend roughly $30 billion and $37 billion, but with new large investments towards research and development, that number is now around $35 to $40 billion for each quarter. 

With all of these companies pouring dollar signs into AI, will there truly be any return on investment? 

The true cost is not known yet. When Zuckerburg introduced the metaverse he believed it would pay off, when it certainly did not, so we can only be so sure. 

The initial excitement and competitive edge AI brings to the table is instigating companies to invest and start now. With billions going into these systems it is a shot in the dark. Although AI softwares, like the free to use AI ChatGPT, have already exceeded the 100 million user mark. 

Source: Venture Beat

From a business perspective, the electric bill alone to power AI is something to be considered when implementing it. It brings efficiency to the workplace and the ability for employees to get work done faster. Even with pre-built AI softwares, which is accessible to the public either for free or at a low cost, the energy behind the AI is being charged somewhere either in bill form or in terms of impacting the environment.

Conclusively, when thinking of bringing AI into your business, many factors must be considered before taking on this electric powerhouse. Between the electric bill, the software, and the energy costs, Artificial Intelligence has a great impact on the world of business and its operations. Optimistically in the coming years as alternative energy sources develop, perhaps a world will exist where AI can be an effective business tool without the big dollar sign and major environmental consequences.