AI Investment Insights

𝗔𝗜 𝘀𝗸𝗶𝗹𝗹𝘀 𝗮𝗿𝗲𝗻’𝘁 𝗷𝘂𝘀𝘁 𝗳𝗼𝗿 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀. 𝗧𝗵𝗲𝘆’𝗿𝗲 𝗳𝗼𝗿 𝗲𝘃𝗲𝗿𝘆𝗼𝗻𝗲. If your execs can’t articulate AI’s value — you’re stuck. If your experts can’t translate use cases — you’re stalled. If your employees don’t trust the tools — adoption fails. This is the AI literacy gap — and it’s killing transformation before it even begins. 𝗪𝗵𝘆 𝗔𝗜 𝗹𝗶𝘁𝗲𝗿𝗮𝗰𝘆 𝗺𝗮𝘁𝘁𝗲𝗿𝘀? It’s not just about new roles or flashy tools. It’s about enabling everyone to understand, trust, and challenge AI. Gartner calls AI literacy a major trend for 2026 — and here’s why: → It’s tied to regulation (like the EU AI Act) → It drives responsible, real-world adoption → It prevents the two biggest risks: blind trust and blind rejection The idea is simple: The more people understand AI, the better they use it. That includes non-technical teams too. AI literacy means: → Knowing where AI fails (hallucinations, misuse) → Navigating compliance, ethics, and governance → Cutting through hype to focus on business value Gartner’s framework breaks it down into four key level: 𝗟𝗲𝘃𝗲𝗹 𝟭 – 𝗡𝗼𝗻𝗲: → No clue how AI works. Still far too common. 𝗟𝗲𝘃𝗲𝗹 𝟮 – 𝗕𝗮𝘀𝗶𝗰: → Understands AI concepts. Can follow, not lead. 𝗟𝗲𝘃𝗲𝗹 𝟯 – 𝗜𝗻𝘁𝗲𝗿𝗺𝗲𝗱𝗶𝗮𝘁𝗲: → Applies AI meaningfully in their work. The SME sweet spot. 𝗟𝗲𝘃𝗲𝗹 𝟰 – 𝗦𝘁𝗿𝗼𝗻𝗴: → Leads AI strategy. Evaluates trade-offs. Connects models to mission-critical goals. There’s no one-size-fits-all training when it comes to AI literacy. A tailored approach is essential. Technical teams need different training than executives or middle management. So what’s needed? Targeted upskilling — by role, by depth, by design. Because AI success isn’t just about smarter models. It’s about smarter people. 𝗜 𝗲𝘅𝗽𝗹𝗼𝗿𝗲 𝘁𝗵𝗲𝘀𝗲 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁𝘀 — 𝗮𝗻𝗱 𝘄𝗵𝗮𝘁 𝘁𝗵𝗲𝘆 𝗺𝗲𝗮𝗻 𝗳𝗼𝗿 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀 — 𝗶𝗻 𝗺𝘆 𝘄𝗲𝗲𝗸𝗹𝘆 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿. 𝗬𝗼𝘂 𝗰𝗮𝗻 𝘀𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲 𝗵𝗲𝗿𝗲 𝗳𝗼𝗿 𝗳𝗿𝗲𝗲: https://lnkd.in/dbf74Y9E

Meta just hit Command + Zuck on its AI strategy - shredding the open-source playbook and replacing it with one that reads: Compute. Talent. Secrecy. The vibe is no longer “open source for all.” It’s “closed doors, infinite compute, elite team, existential stakes.” Let's break it down: (1) Compute: Zuck’s Manhattan Project Meta is building gigascale AI clusters. Prometheus comes online with 1 GW in 2026; Hyperion scales to 5 GW soon after. For context, Iceland’s total electricity consumption is ~2.4 GW, Cambodia is at ~4 GW. Meta’s Hyperion cluster alone could out-consume entire nations. These clusters are for training frontier models - GPT-4-class and beyond. In this new regime, FLOPS per researcher is the KPI, and Meta is going from GPU-starved to GPU-dripping. Each researcher now has more compute to play with than entire labs elsewhere. That’s not just good for performance, it's a hell of a recruiting pitch. (2) Secrecy: From Open Arms to Closed Labs Meta won developer love by open-sourcing its LLaMA models. But it also accidentally became the free R&D department for its own competitors. DeepSeek AI, for example, built on Meta's models and vaulted ahead. Now Meta is reportedly shelving its most powerful open model, Behemoth, due to both internal underperformance and external regret and shifting toward a closed frontier model, aligning more with OpenAI and Google. This is a massive philosophical reversal from “open wins” (as Yann LeCun would say) to “closed dominates.” (3) Talent: Just Buy Everyone Comp packages reportedly range from $200 million to $1 billion for AI leads. All AI efforts are now housed under a new unit, Superintelligence Labs, run by Alexandr Wang (ex-Scale AI). This elite team is small, only ~12 engineers, working in a separate, high-security building next to Zuckerberg himself. Forget beanbags and 10xers. This is a DARPA-style moonshot with a trillion-dollar company behind it. Zuckerberg has said, basically, “Look, we make a lot of money. We don’t need to ask anyone’s permission to spend it.” He’s not wrong. While OpenAI, Anthropic, and xAI rely on outside capital to fund their ambitions, Meta runs on a $165B/year ad engine. And unlike Google and Microsoft - who have boards, activist investors, and share classes that allow for dissent - Zuckerberg controls Meta, structurally and operationally. Meta’s unique dual-class share structure gives Zuckerberg over 50% of the voting power, even though he owns less than 15% of the company. He doesn’t need anyone’s approval, he can build whatever he wants. This makes Meta less like a public company and more like a founder-led sovereign AI lab - with Big Tech cash and startup flexibility. That governance structure is a strategic weapon, letting them place bold, long-term bets at breathtaking speed. Meta’s open-source era is over. This is the closed, compute-soaked, capital-fueled empire play. Less GitHub, more Los Alamos.

You're a #CTO. Your board asks: "What's our ROI on AI coding tools?" Your answer: "40% of our code is AI-generated!" They respond: "So what? Are we shipping faster? Are customers happier?" Most CTOs are measuring AI impact completely wrong. Here's what some are tracking: - Percentage of AI-generated code - Developer hours saved per week - Lines of code produced - AI tool adoption rates These metrics are like measuring how fast your assembly line workers attach parts while ignoring whether your cars actually start. Here's what you SHOULD measure instead: 1. Delivered business value 2. Customer cycle time 3. Development throughput 4. Quality and reliability 5. Total cost of delivery (not just development) 6. Team satisfaction Software development isn't a typing competition—it's a complex system. If AI makes your developers 30% faster but your deployment takes 2 weeks and QA adds another week, your customer delivery improves by maybe 7%. You've speed up the wrong part. The solution: A/B test your teams. Give half your teams AI tools, measure business outcomes over 2-3 release cycles. Track what customers actually experience, not how much developers produce. Companies that measure business impact from AI will pull ahead. Those measuring vanity metrics will wonder why their expensive tools aren't moving the needle. Stop measuring how much code AI generates. Start measuring how much faster you deliver value to customers. What are you actually measuring? And is it moving your business forward? -> Follow me for more about building great tech organizations at scale. More insights in my book "All Hands on Tech"

The AI Investment Boom: From Compute Gold Rush to Strategic Moats After sharing my perspective at the 1st Azerbaijan Investment Forum, I’m equally struck by how capital allocation in AI is reshaping the landscape in 2025. Investment trends this year are telling a powerful story, one that goes beyond hype: Capital Flows Are Redefining the AI Map ✔ Enterprise AI Spend surged 6×, from $2.3B in 2023 to $13.8B in 2024. Pilots turned into production, and AI is now embedded in P&L, not just PowerPoints. ✔ DeepSeek’s $6M Breakthrough showed that frontier-class models don’t always need 9-figure training budgets. This signals a new investment thesis: efficiency and ingenuity can compete with brute force. ✔ $1.5B Anthropic Settlement reminded investors that data rights and compliance aren’t “nice-to-haves” they’re billion-dollar risks. The smart money is flowing into AI compliance, security, and eval tooling. ✅The investment signals show us four durable moats: Data & Distribution: The moat isn’t just the model; it’s who controls data pipelines and user distribution. Infrastructure: Nvidia, AMD, TSMC, and hyperscalers still dominate, but efficiency challengers are emerging. Ecosystem Building: Talent + infrastructure = defensible advantage. The players creating robust agent ecosystems will own the platform layer. ✅Signals Investors Can’t Ignore: Thin Wrappers Still Work – Despite the hype cycle, lightweight apps built on top of foundation models keep winning niches. Even frontier labs are now funding or acquiring wrappers because distribution matters. Top Models Aren’t Everything – GPT-5 and Gemini dominate benchmarks, but they don’t dominate every use case. Open-source proved that agility and community adoption can be just as powerful. Talent Concentration Is Destiny – The biggest AI clusters (Bay Area, Beijing, Abu Dhabi) attract talent and capital, reinforcing themselves. Smart investors back ecosystems, not just single models. The Implication for Investors 👉 Not every “AI startup” will survive. Many will be commoditized. 👉 But data, distribution, compliance, and ecosystems are proving to be the real durable plays. 👉 The global map is also shifting: China (DeepSeek) is a credible challenger, while UAE and Singapore are positioning as investor-friendly AI hubs. Grateful to the hosts, ministers, and fellow panelists for the opportunity to exchange ideas together. The energy and insights from this group were truly inspiring: Samir Sharifov – Deputy Prime Minister Mikayil Jabbarov – Minister of Economy of the Republic of Azerbaijan simonida kordic – Minister of Tourism of Montenegro Jagoda Lazarevicć – Minister of Domestic and Foreign Trade, Serbia Nouriel Roubini, MAHDI ALADEL, Lisa Bodell and Fariz JAFAROV (Executive Director, 4SİM Azərbaycan / C4IR Azerbaijan). As I emphasized in Baku: this is the slowest AI will ever be. For investors and innovators alike, the time to place smart bets is now.

𝗔𝗜 𝘄𝗶𝗹𝗹 𝗺𝗮𝗸𝗲 𝗺𝗮𝗿𝗸𝗲𝘁𝘀 𝗮𝗰𝗰𝗲𝘀𝘀𝗶𝗯𝗹𝗲, 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁, 𝗮𝗻𝗱 𝗱𝗲𝗲𝗽𝗹𝘆 𝗽𝗲𝗿𝘀𝗼𝗻𝗮𝗹 — 𝗮𝗻𝗱 𝘁𝗵𝗮𝘁 𝗳𝘂𝘁𝘂𝗿𝗲 𝗶𝘀 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝗵𝗲𝗿𝗲. AI is reshaping how investors think, decide, and act. At Angel One, we see it not as a tool, but as a trusted companion that helps investors make more disciplined, data-driven choices. In the early days, the internet and mobile apps opened access to markets. Now, AI is unlocking something deeper: 𝗶𝗻𝘃𝗲𝘀𝘁𝗺𝗲𝗻𝘁 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲. It can recognise behavioural biases, analyse patterns, and help investors stay grounded when markets are unpredictable. At the Global FinTech Festival, one message stood out clearly: 𝗔𝗜’𝘀 𝗴𝗿𝗲𝗮𝘁𝗲𝘀𝘁 𝗽𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹 𝗹𝗶𝗲𝘀 𝗶𝗻 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗽𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗶𝘀𝗮𝘁𝗶𝗼𝗻. While institutions may gain efficiency, the 𝘵𝘳𝘶𝘦 benefit will go to individual investors through better understanding, consistency, and access. Here’s what I see the next wave of transformation: 𝟭. 𝗕𝗲𝗵𝗮𝘃𝗶𝗼𝘂𝗿𝗮𝗹 𝗙𝗶𝗻𝗮𝗻𝗰𝗲 𝗔𝗜 – Nearly 68% of investment decisions are emotion-driven. AI can act as a rational investing coach, recognising bias and guiding investors toward disciplined, long-term wealth creation. 𝟮. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗣𝗼𝗿𝘁𝗳𝗼𝗹𝗶𝗼 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 – AI-managed portfolios that auto-adjust with your goals and market shifts are becoming reality, bringing adaptive precision to everyday investing. 𝟯. 𝗔𝗜-𝗖𝘂𝗿𝗮𝘁𝗲𝗱 𝗧𝗵𝗲𝗺𝗮𝘁𝗶𝗰 𝗜𝗻𝘃𝗲𝘀𝘁𝗶𝗻𝗴 – From clean energy to quantum computing, AI’s pattern-recognition capability can spot emerging trends long before they go mainstream, giving Indian investors early access to global opportunities. 𝟰. 𝗙𝘂𝘁𝘂𝗿𝗲𝗽𝗿𝗼𝗼𝗳𝗶𝗻𝗴 𝗜𝗻𝘃𝗲𝘀𝘁𝗺𝗲𝗻𝘁𝘀 𝗶𝗻 𝗮 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗪𝗼𝗿𝗹𝗱 – In a world of rapid macroeconomic and geopolitical shifts, AI can help investors dynamically allocate across equities, bonds, gold, real estate, and digital assets, ensuring portfolios that remain resilient and growth-oriented across cycles. As India strengthens its AI capabilities through initiatives like the IndiaAI Mission, investor behaviour, data, and intelligence are converging to create a new investing paradigm that is personalised, adaptive, and globally aware, yet rooted in India’s long-term growth story. The next era of investing will be defined by precision and powered by trust. #FinTech #AI #Investing #ViksitBharat

Investors can’t get enough of AI companies but the signs of overvaluation in AI are flashing red. The AI sector’s near-euphoric investment surge is showing clear signs of extreme overvaluation and the data is striking: 1️⃣ $73 billion in global VC funding flowed into AI startups in Q1 2025 which is nearly 60% of all venture deals. 2️⃣ Public AI equities have jumped 46%, capturing one-third of $46 trillion in global market-cap gains over five years. 3️⃣ Many startups now trade at 20x–50x revenue multiples, far above traditional tech benchmarks. 𝗧𝗵𝗲 𝗥𝗶𝘀𝗲 𝗼𝗳 “𝗖𝗶𝗿𝗰𝘂𝗹𝗮𝗿 𝗜𝗻𝘃𝗲𝘀𝘁𝗶𝗻𝗴” 𝗶𝗻 𝗔𝗜 A new trend called circular investing is emerging where AI companies fund, supply, and buy from one another, creating financial feedback loops reminiscent of the dot-com bubble. 𝗥𝗲𝗰𝗲𝗻𝘁 𝗲𝘅𝗮𝗺𝗽𝗹𝗲𝘀: Nvidia ↔ OpenAI: Nvidia’s pledged $100 B investment mirrored by OpenAI’s multibillion-dollar chip orders. AMD ↔ OpenAI: Warrants grant OpenAI equity in exchange for purchase commitments. Oracle ↔ OpenAI: A $300 B compute deal tied to Nvidia’s investment cycle. 𝗩𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 𝗪𝗮𝗿𝗻𝗶𝗻𝗴𝘀 OpenAI’s reported $1 trillion valuation stretches investor imagination on future earnings. Analysts and institutions from Wall Street to the IMF warn that current valuations assume flawless execution with almost no margin for error. 𝗔 𝗕𝗮𝗹𝗮𝗻𝗰𝗲𝗱 𝗩𝗶𝗲𝘄 Mark A. Jamison (AEI) in Barrons argues the picture isn’t purely alarming: • AI’s profits remain concentrated in cash-rich incumbents with genuine innovation. • Circular deals can represent strategic risk-sharing, not mania. • Unlike the dot-com era, much of this boom is funded by free cash flow, not debt. Yet, the sector still faces headwinds like soaring energy needs, regulatory scrutiny, and uneven enterprise adoption. 𝗧𝗵𝗲 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆 AI promises a multi-decade technological supercycle, but we must be mindful of the risks inherent in overvaluation and circular financial structures. Prudent investment discipline, transparency, and realistic expectations will be crucial to avoiding bubble-like pitfalls and ensuring lasting value creation.

#AI disruption sent shockwaves through the $3.5 trillion private credit market this week, as investors in some of the largest private credit funds asked for their money back and were told no. Stay with me for two minutes to understand what happened - and how this might affect your 401(k): Private credit rarely makes headlines. The sector emerged after 2008, when banks pulled back from lending to mid-sized companies, and new funds filled that gap by raising capital and lending it to private businesses. The appeal was simple: higher returns than traditional bonds, but your capital is harder to withdraw. For the last five years, the steadiest revenue in these portfolios has come from software companies. If your company pays for Salesforce or Workday, you probably renew every year because you've built your operations around it, and switching is expensive. That recurring, hard-to-cancel revenue is ideal collateral, and software grew to 29% of these loan portfolios. AI is now eroding the collateral those loans were built on. Enterprise software stocks are down 25-30% from their highs as AI reduces the headcount that needs licenses and makes it possible for companies to build internal tools that replace off-the-shelf software. That is AI disruption showing up not as a headline about the future, but as a balance sheet problem today. Investors realized this and sought their money back, with mixed results reported widely: Cliffwater's $33 billion fund capped withdrawals at 7% after investors requested 14%. Blackstone, BlackRock, and Blue Owl all faced record requests this quarter, and several froze redemptions entirely. I lead a large global foundation and oversee billions in endowments as a board director on investment committees and I've also spent a decade studying how AI changes economic structures. In recent years, those worlds have converged. The question of what happens to software-backed lending when AI erodes the revenue underneath has been building. UBS estimates that 25-35% of these portfolios face elevated AI disruption risk. This quarter, the convergence became visible: redemption caps, loan markdowns, and fund managers telling investors no. Until last year, private credit was restricted to institutional investors and the wealthy. Then, Executive Order 14330 opened 401(k) plans to the asset class for the first time, meaning 90 million Americans can now enter a market that institutional investors are trying to exit. Retirement savings are increasingly tied to financial vehicles where capital moves faster than our ability to measure what AI is doing to the assets inside them. Everyone is debating how AI will reshape products and jobs. The debt markets are ahead of that conversation, repricing the entire premise of enterprise software. This might be the first clear signal of bigger disruptions to come. David Ramage Sophia Tsai Karen Gill John A. Barker The Patrick J. McGovern Foundation

“If it’s not AI, I don’t want it” – a VC headed to Monaco for summer Q2'25 data* shows AI companies are securing significantly larger rounds across sectors, with median deal sizes hitting $4.6M – over $1M above the broader market. In Q2’25, the AI premium was strongest in Auto Tech which saw AI companies securing deals $20.6M larger than traditional peers (lead by Applied Intuition's $600M Series F at $15B valuation), followed by Robotics and Cybersecurity with median deal premiums of $10.7M and $6.4M respectively. The AI premium extends beyond funding to company performance and trajectory metrics. AI companies consistently score higher on our Mosaic Score (success probability) and Commercial Maturity (ability to compete and partner) metrics, proving their fundamentals justify investor confidence. Why are AI companies commanding these premiums? 1) Capital-intensive development cycles AI companies often require dramatically more upfront investment for compute infrastructure, data acquisition, and model training before achieving product-market fit, necessitating larger initial rounds to reach meaningful milestones. 2) Longer runway to defensibility Unlike traditional SaaS where competitive advantages emerge quickly, AI companies need 12-18 months of continuous model refinement and data collection to build meaningful moats, requiring sustained funding through extended R&D phases. 3) Premium for hybrid expertise The most successful AI companies combine rare AI/ML talent with deep domain expertise (like automotive engineers for autonomous driving), creating interdisciplinary teams that command higher compensation. 4) Infrastructure-first business models AI companies often build foundational platforms (like simulation environments or data processing pipelines) that require significant upfront investment but can later support multiple product lines and customer segments. The AI premium continues to reflect investors' "go big or go home" approach; making concentrated bets on AI teams they believe can capture outsized market share. The AI premium signals more than just funding enthusiasm – it's recognition that AI-first companies are simultaneously disrupting the last two decades of companies and building the infrastructure for tomorrow's economy. *Data from CB Insights’ State of Venture Q2’25 report. Explore the latest data on what happened last quarter across the startup ecosystem at the link in the comments.

💡 Why Blackstone’s recent investment is important for you? Blackstone has made a groundbreaking $300M investment in DDN, valuing the California-based AI data company at $5B. This move highlights the private equity giant’s growing focus on artificial intelligence and its supporting infrastructure. 📊 Key Highlights: 📌 DDN's Role in AI Growth: DDN provides tools to manage and analyze massive datasets, a crucial element in AI model training and deployment. They power some of the largest AI projects, including Elon Musk’s Colossus supercomputer. 📌 Blackstone’s Broader AI Push: Beyond DDN, Blackstone has invested heavily in data centers and chip-supporting companies, including a $7B deal with Digital Realty and a $16B acquisition of Asian data center operator AirTrunk. 📌 Strategic Rationale: With the explosion of AI applications, efficient data handling is non-negotiable. DDN’s solutions aim to make AI deployments more cost-effective and scalable, positioning it as a leader in the AI ecosystem. 📈 What This Means for the Broader Market: 📌 AI Infrastructure is Booming: From data centers to AI chips, the backbone of AI growth is becoming an attractive investment theme. IPO Potential: DDN’s growth trajectory suggests it could go public soon, offering new opportunities for investors. 📌 Sector Evolution: This underscores a shift toward strategic investments in AI-enabling technologies, highlighting the symbiotic relationship between private equity and tech innovation. 💡 Investor Takeaways: 📌Diversify into AI Infrastructure: AI isn’t just about software—consider the enabling technologies like data centers and hardware. 📌Long-Term Growth Opportunity: With AI adoption accelerating, companies that support its infrastructure are poised for exponential growth. 📌Stay Informed: The competitive landscape is heating up, making it crucial to follow developments in the AI ecosystem. 💬 Are you positioning your portfolio to capture the AI wave? #AI 

At Mayfield Fund, we have a network of fortune 500 CXOs and the topic everyone wants to talk about is Gen AI & LLMs. Here are some notes from recent conversations: Enterprise GenAI Today 1. AI's evolution in the enterprise sector mirrors internet -> mobile -> LLMs. 2. GenAI will lead to innovation in software development. 3. Companies must embed GenAI in products for competitive edge; starting preparations now is crucial. 4. Investments in GenAI are risky; short-term contracts recommended. 5. Collaboration with major cloud platforms is valuable for early exploration. 6. Understanding and explaining AI model decisions is a significant challenge. 7. Data quality, not just technology, determines AI's impact; data's origin and validation is vital, especially in regulated sectors. 8. Chief Data Officer roles will transition to Chief AI Officer. 9. AI startups should adhere to enterprise standards and ensure transparency. 10. Focus on AI's outcomes and outputs, not just inputs; true production applications may take 12-20 months. Generative AI & Security 1. Data security is paramount, especially with third parties. 2. As AI's threat vectors evolve, smarter security solutions are necessary. Leadership 1. Consistent AI education for teams, boards, customers, and regulators is critical. 2. Establish internal AI usage guidelines and encourage innovation within set boundaries. Effectiveness of Early GenAI Use Cases 1. Opportunities exist, but challenges with data, security, and deployment hinder progress. 2. Industry type determines caution level; regulatory scrutiny might delay deployments. Promising Early Use Cases 1. Processes like RPA, where success metrics are known, are the strongest contenders. 2. Areas of potential include RFP automation, customer service, content generation, forecasting, and internal security. AI Council Best Practices 1. Council should comprise diverse roles, including Data/AI, Product, Legal, and Engineering. 2. Needs should be aggregated and streamlined, with a singular team leading initiatives for unified progress. Buy or Build Decision 1. A hybrid approach or buying and then training is ideal. 2. Fine-tuning generic models is recommended; however, cost-efficiency is crucial.