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AI & Tech

Harvard Adds AI Avatars to a $699 Founder Bootcamp

Nic Reeve3 min read
Harvard Adds AI Avatars to a $699 Founder Bootcamp

Harvard Business School has put an artificial-intelligence twist on entrepreneurship training, rolling out a new eight-week startup bootcamp that pairs live instruction with AI-generated instructor avatars. The program, known as HBS Foundry, costs $699 and is designed to give aspiring founders more personalized feedback during pitch practice and simulated board meetings.

According to reporting from TechCrunch, the course uses avatars built by AI video platform HeyGen to mimic instructors and respond during exercises such as practice pitches and boardroom scenarios. The weekly live sessions are still led by real teachers, but the AI avatars are intended to extend the feedback students receive between those classes.

The idea reflects a broader push by business schools and online learning platforms to make startup education more scalable without losing the feel of individualized coaching. In this case, Harvard is trying to blend human-led teaching with digital replicas that can deliver critiques in a format similar to a live conversation.

The bootcamp is part of Harvard Business School’s effort to reach founders beyond its traditional degree programs. HBS Foundry is aimed at entrepreneurs who want practical guidance on shaping and testing ideas, refining their pitches and preparing for investor-style questioning. By using AI avatars, the program attempts to offer more frequent and accessible feedback than a standard classroom model might allow.

CryptoRank’s coverage highlighted the novelty of the setup, and the story quickly spread across tech and startup media because of the contrast between Harvard’s elite brand and the mass-market feel of a $699 online bootcamp. The price point is notably lower than the cost of many executive education offerings, which makes the course more accessible to early-stage founders and operators.

The use of AI avatars also raises questions that are now common across education and corporate training: how well can synthetic instructors capture the nuance of a seasoned mentor, and where is the line between useful automation and imitation? In this program, the avatars are not replacing live faculty altogether, but they are taking on a role that traditionally depends on one-on-one human interaction.

HeyGen, the company behind the avatars, has been positioning itself as a tool for AI-powered video creation and presentation workflows. In Harvard’s bootcamp, its technology is being used for a more specific purpose: simulating instructor feedback in a startup-training environment. That makes the course one of the more visible examples so far of generative AI moving from demo use cases into formal education.

The broader significance lies in what Harvard is signaling about the future of teaching entrepreneurship. If AI avatars can reliably provide structured feedback on pitches, board meetings and founder communication, similar systems could be adopted by other schools, accelerators and corporate training programs. If they fall short, the experiment will still serve as a useful test of how far AI can go in roles that depend on judgment, tone and mentorship.

For now, HBS Foundry stands out less as a replacement for professors than as a hybrid model that uses software to extend their reach. That combination of live instruction and AI-driven personalization is likely to draw attention from founders looking for practical training — and from educators watching how far the technology can be pushed.

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Beyond Model Launches: The Metrics That Reveal How Fast Frontier AI Is Moving
AI & Tech

Beyond Model Launches: The Metrics That Reveal How Fast Frontier AI Is Moving

Frontier laboratories are moving too quickly for a single score or release date to explain the pace of AI development, according to recent research and reporting. The most useful picture combines release cadence, benchmark gains, task duration, computing resources, reliability and safety results. That broader measurement problem is now central to AI news as companies move from occasional model launches toward continuous improvement. What happened to the release cycle? Model launches have become more frequent in 2026, but product announcements alone cannot show how much a system has improved. A release may represent a new model, a tuned variant, a safety update or a lower-cost version. Counting releases remains useful, but only when paired with consistent tests and dates. According to The Register , published September 23, 2026, Anthropic’s release cadence accelerated from roughly quarterly launches in 2025 to nearly monthly releases in 2026. According to Tech Insider’s September 5, 2026 report, four frontier laboratories shipped major models within a 72-hour period at the start of September. According to the same report, major updates that arrived once or twice per quarter early in 2026 were increasingly appearing monthly or faster. A faster cycle can signal stronger engineering and deployment capacity. It can also reflect smaller updates, product packaging or parallel versions rather than a comparable jump in general capability. Researchers therefore need release logs that record model size, intended use, evaluation date and whether the system is a preview or a production release. Which benchmarks show genuine progress? Capability benchmarks remain the clearest way to compare systems, yet tests lose value when frontier models reach the ceiling. A useful measurement system tracks both the score and the remaining headroom. It should also include unfamiliar tasks, because performance on one benchmark does not guarantee reliable performance elsewhere. According to Stanford University’s 2026 AI Index, published in September 2026, nearly all leading frontier-model developers report capability results, while reporting on responsible-AI benchmarks remains inconsistent. According to the Stanford AI Index figures cited in recent reporting, performance on SWE-bench Verified rose from about 60% to nearly 100% over one year. According to Stanford’s report, documented AI incidents reached 362, compared with 233 in 2024. The earlier figure is historical background, not a current-year count. Benchmark saturation creates a practical problem. A test designed to remain difficult for years can become easy within months. The next generation of evaluations must measure open-ended research, software work, scientific reasoning, factual accuracy and resistance to manipulation under conditions that resemble real use. Why is task duration becoming a key measure? Task duration measures how long a model can work successfully before errors become likely. Instead of asking whether a model answers one question correctly, evaluators test whether it can complete a multi-step job that a skilled human would normally perform over a defined period. The measure captures autonomy more directly than a single accuracy score. Recent frontier-model tracking has used task-horizon evaluations, in which researchers estimate the human time required for tasks and identify the point where a model succeeds about half the time. This approach can distinguish a system that solves five-minute coding problems from one that can manage a multi-hour engineering assignment. According to Big Matrix’s September 11, 2026 analysis, METR evaluated several frontier releases during 2026, including Claude Opus 4.6, GPT-5.4 and Gemini 3.1 Pro, within roughly 100 days. According to the same analysis, task-horizon testing measures the human-equivalent duration of work before model success falls to 50%. The measure is still incomplete. Long tasks can hide failures, and a model may produce convincing but incorrect work. Evaluators need to record correction time, tool use, supervision and the cost of repeated attempts. How does computing capacity reveal the labs’ pace? Training compute offers a physical measure of how much resources a laboratory is putting behind new systems. Researchers can compare processor hours, accelerator generations, energy use, data volume and inference capacity. Compute does not equal capability, but it helps explain why development can accelerate even when public releases appear similar. Training figures are often private, and laboratories disclose them unevenly. That makes public comparisons difficult. Independent analysts can still track data-center construction, chip purchases, cloud contracts and model-serving capacity, but those indicators require careful attribution because a facility may support several products. According to Stanford’s 2026 AI Index, more than 90% of notable frontier models were developed by industry, showing that the leading measurement data increasingly comes from companies rather than universities. According to the report’s benchmark findings, capability gains are arriving faster than the evaluation systems intended to measure them. For that reason, a credible pace indicator should pair disclosed compute with the resulting improvement per unit of compute. A laboratory that doubles its hardware but gains little on difficult, independent tests may be scaling inefficiently. A laboratory that achieves larger gains with similar resources may have improved data, algorithms or training methods. What do safety and reliability add? Safety results show whether capability growth is accompanied by control. A model that scores higher on coding or reasoning but becomes less truthful, more exploitable or harder to monitor has not delivered an unqualified improvement. Safety evaluations should therefore be published alongside capability results, not treated as a separate public-relations exercise. Stanford’s 2026 AI Index found a gap between the widespread reporting of capability benchmarks and the less consistent reporting of responsible-AI tests. That gap limits comparisons between laboratories. Public scorecards should include hallucination rates, cyber-abuse testing, privacy leakage, bias measures, refusal accuracy and performance under adversarial prompting. According to Stanford’s September 2026 report, responsible-AI benchmark reporting remains spotty among leading developers. According to Stanford’s incident count, 362 documented incidents were recorded in the report’s latest dataset, compared with 233 in 2024. Incident counts are not a direct measure of model capability. They do show how much harm is being observed and recorded around deployed systems. Researchers must separate model failures from failures caused by deployment, user behavior or weak safeguards. What should a practical frontier scorecard contain? A useful scorecard should track the same systems across time and publish enough detail for independent checking. Release frequency provides speed. Benchmarks provide task performance. Task horizons provide autonomy. Compute provides investment. Safety tests provide control. Cost and reliability show whether laboratory results survive contact with real users. Release interval: days between comparable model versions, with previews and production systems identified separately. Capability gain: change on difficult, contamination-resistant tests, reported with the evaluation date and test version. Task horizon: the longest human-equivalent assignment completed at a specified success rate. Efficiency: capability gained per unit of training compute and per dollar of inference. Reliability: error rates, factuality, tool failures and the amount of human correction required. Safety: harmful-capability tests, privacy results, cyber evaluations, refusal accuracy and documented incidents. Frontier development is not one race measured by one clock. The most defensible comparisons will combine public release records with independent evaluations and clearly dated laboratory disclosures. That approach can show whether a new system is truly more capable, merely more available or simply better packaged for deployment.

Nic Reeve·
Intel’s AI Surge Sparks Fierce Valuation Clash as Some Models Flag 80%+ Upside
AI & Tech

Intel’s AI Surge Sparks Fierce Valuation Clash as Some Models Flag 80%+ Upside

Intel’s rapid push into artificial intelligence chips and foundry services has ignited a sharp debate over what the stock is really worth, with one widely followed narrative now implying a fair value near $500 per share — more than four times the recent market price. While mainstream analysts still cluster between roughly $90 and $120 per share, user-driven valuation models and some sales-based frameworks argue that the market is deeply underestimating Intel’s long‑term AI earnings power, leaving the stock potentially more than 80% below fair value. Where the 82% Undervaluation Claim Comes From The headline figure that Intel could be about 82% below fair value stems from a narrative used on retail‑focused valuation platforms, which apply aggressive growth and margin assumptions to Intel’s emerging AI businesses. In several recent notes, that framework points to a fair value around $500.93 per share , compared with a share price near the $90–$100 range in late August 2026. On that basis, Intel is framed as roughly 80–82% undervalued, with the gap driven by bullish expectations for x86 server CPUs, AI accelerators and foundry contracts over the next decade. These narratives typically assume: Strong, sustained growth in Intel’s Data Center and AI (DCAI) segment. High adoption of Intel’s advanced manufacturing nodes, such as 18A, by external foundry customers. AI‑linked revenue eventually commanding premium valuation multiples similar to leading GPU and cloud infrastructure providers. Critically, this $500+ fair value is not a consensus Wall Street target but a specific, scenario‑driven model that extrapolates current AI momentum far into the future. Intel’s Latest AI and Earnings Momentum The bullish valuation arguments have gained traction as Intel’s reported numbers show AI demand increasingly driving the business. For the second quarter of 2026, Intel reported revenue of about $16.1 billion, up 25% year over year , and adjusted earnings per share of $0.42, beating analyst expectations. The company’s Data Center and AI Group stood out, delivering approximately 59% year‑over‑year growth , with management noting that AI‑linked businesses grew more than 70% and now account for roughly 70% of total revenue. Intel also guided third‑quarter revenue to a range of $15.8 billion to $16.8 billion and gross margins in the low‑40% band, signaling confidence that AI‑related demand will remain robust despite broader concerns about chip valuations. On the strategic side, Intel highlighted signed foundry and advanced packaging agreements with major technology players, including Google, Nvidia, Tesla and Apple, alongside partnerships tied to its 18A manufacturing node and High NA EUV lithography. Foundry revenue rose by more than 30% year over year, although external customers still represent a small share of the segment, keeping the long‑term foundry thesis partly unproven. Mainstream Fair Value Estimates: 90–120 Dollar Range Traditional analyst research paints a far more moderate picture of Intel’s intrinsic value. Morningstar, which has repeatedly updated its Intel model in response to the AI boom, lifted its fair value estimate multiple times in 2026. Earlier in the year, analysts raised Intel’s fair value to $90 per share from $60, citing a “stunning” rise in server CPU demand and a growing AI infrastructure build‑out. Following stronger results and upgraded expectations, Morningstar later increased its fair value estimate to around $105 per share , and some commentary mentions fair value figures just above $100 as AI‑related assumptions were refined further. Other analyst summaries show valuation targets and fair value estimates clustering between roughly $88 and $115 per share , with some firms setting price targets as high as $200 but many maintaining Neutral or Hold ratings due to execution and capital‑intensity concerns. On several discounted cash‑flow (DCF) models, Intel’s intrinsic value is calculated in the mid‑80s to low‑90s per share range, only slightly above or below the current market price, implying the stock is close to fairly valued on conservative cash‑flow assumptions. Sales‑Based Models Still See Undervaluation Separate from the more conservative DCF work, some valuation frameworks focused on price‑to‑sales (P/S) multiples argue that Intel’s AI‑driven mix and size justify a richer multiple than the market is currently assigning. One such model derives a “fair” P/S ratio of about 15.1x for Intel, compared with an observed multiple closer to 13.1x at the time of analysis, suggesting the stock trades at a discount to what its AI exposure and margin profile would warrant. Another narrative points to a fair P/S ratio nearer 17.9x , versus a contemporaneous multiple around 7.6x. Under that lens, Intel looks significantly undervalued on sales even if cash‑flow‑based intrinsic value appears only modestly above the share price. These sales‑centric approaches underpin much of the “still cheap” messaging, emphasizing Intel’s potential rerating as AI revenue becomes a larger and more stable component of the business. Not All Analysts Buy the Undervaluation Story Despite the enthusiasm around AI, some research houses remain skeptical that current valuations can be justified. Early in 2026, one widely cited report called Intel “overpriced” and warned that the shares were trading more than 30% above a fair value estimate of $32 per share , based on cautious assumptions about profitability and competitive risks. Although that figure has since been raised substantially by the same provider, the earlier stance illustrates how sensitive Intel’s perceived fair value is to underlying assumptions about AI demand durability, manufacturing execution and capital allocation. Even after upgrading their models to reflect the AI boom, some analysts argue that Intel’s stock has already priced in a great deal of optimism and may struggle if AI infrastructure spending normalizes or if rivals capture outsized share of accelerator and server CPU markets. AI Capital Raise Adds Another Layer to the Debate The valuation controversy has been sharpened by Intel’s recent decision to raise a large amount of equity capital to fund its AI ambitions. In mid‑August, the company launched a stock offering initially sized at $15 billion and then expanded it to $20 billion after strong investor demand. The sale briefly pressured the share price but was interpreted by some market watchers as a sign of management’s confidence in the scale of Intel’s AI opportunity and its foundry road map. For bullish valuation frameworks, the capital raise is seen as necessary fuel for growth; for skeptics, it reinforces concerns about dilution and the high cost of competing at the cutting edge of semiconductor manufacturing. A Wide Valuation Range, Driven by AI Assumptions As of late August 2026, Intel’s fair value estimates span a remarkably wide range — from the $80–$120 band common among traditional analysts to user‑driven narratives north of $500 per share. The claim that Intel could be roughly 82% below fair value relies on the most optimistic of these models, which assume sustained AI‑powered growth and premium valuation multiples over many years. For investors, the gap underscores how pivotal AI is to the Intel story: the more confidence markets place in Intel’s ability to convert its early AI momentum into durable, high‑margin earnings streams, the more plausible the higher end of that valuation spectrum becomes.

Nic Reeve·
Harvard Adds AI Avatars to a $699 Founder Bootcamp
AI & Tech

Harvard Adds AI Avatars to a $699 Founder Bootcamp

Harvard Business School has put an artificial-intelligence twist on entrepreneurship training, rolling out a new eight-week startup bootcamp that pairs live instruction with AI-generated instructor avatars. The program, known as HBS Foundry, costs $699 and is designed to give aspiring founders more personalized feedback during pitch practice and simulated board meetings. According to reporting from TechCrunch, the course uses avatars built by AI video platform HeyGen to mimic instructors and respond during exercises such as practice pitches and boardroom scenarios. The weekly live sessions are still led by real teachers, but the AI avatars are intended to extend the feedback students receive between those classes. The idea reflects a broader push by business schools and online learning platforms to make startup education more scalable without losing the feel of individualized coaching. In this case, Harvard is trying to blend human-led teaching with digital replicas that can deliver critiques in a format similar to a live conversation. The bootcamp is part of Harvard Business School’s effort to reach founders beyond its traditional degree programs. HBS Foundry is aimed at entrepreneurs who want practical guidance on shaping and testing ideas, refining their pitches and preparing for investor-style questioning. By using AI avatars, the program attempts to offer more frequent and accessible feedback than a standard classroom model might allow. CryptoRank’s coverage highlighted the novelty of the setup, and the story quickly spread across tech and startup media because of the contrast between Harvard’s elite brand and the mass-market feel of a $699 online bootcamp. The price point is notably lower than the cost of many executive education offerings, which makes the course more accessible to early-stage founders and operators. The use of AI avatars also raises questions that are now common across education and corporate training: how well can synthetic instructors capture the nuance of a seasoned mentor, and where is the line between useful automation and imitation? In this program, the avatars are not replacing live faculty altogether, but they are taking on a role that traditionally depends on one-on-one human interaction. HeyGen, the company behind the avatars, has been positioning itself as a tool for AI-powered video creation and presentation workflows. In Harvard’s bootcamp, its technology is being used for a more specific purpose: simulating instructor feedback in a startup-training environment. That makes the course one of the more visible examples so far of generative AI moving from demo use cases into formal education. The broader significance lies in what Harvard is signaling about the future of teaching entrepreneurship. If AI avatars can reliably provide structured feedback on pitches, board meetings and founder communication, similar systems could be adopted by other schools, accelerators and corporate training programs. If they fall short, the experiment will still serve as a useful test of how far AI can go in roles that depend on judgment, tone and mentorship. For now, HBS Foundry stands out less as a replacement for professors than as a hybrid model that uses software to extend their reach. That combination of live instruction and AI-driven personalization is likely to draw attention from founders looking for practical training — and from educators watching how far the technology can be pushed.

Nic Reeve·