This fairly recent obsession with metrics in the workplace is driving companies insane.

A while back, I watched a video about all the ways hotels are trying to save money by, among other things, eliminating storage space, making the bathroom less private, removing desks, and pressuring guests to work at the bar, where they can spend more money. (By the way, that bartender? They're also the receptionist.) These changes are, of course, driven by metrics like “GSS” and “ITR,” whatever the f@*k those are.

Is there a kernel of truth to all of this? Sure. Aloft Hotels are cozy, and they seem to follow this playbook. I didn't mind staying in one when I was stuck in San Francisco for one night more than ten years ago. Would I want to stay in one of their rooms during a business trip or anything else lasting more than a couple of days? Hell no. I'd like a desk and somewhere to put clothes. (I know, I'm so needy. I travel with clothes.)

Metrics are fine, sometimes, when their use is limited and their shortcomings are genuinely appreciated. Taking them too seriously and letting them make the decisions, however, is a recipe for disaster. Hard questions demand more thoughtfulness than that. “GSS” and “ITR” are meaningful until they aren't, and nobody is going to find solace in those abbreviations when generations of potential customers steer clear of your business because they actually want something good.

Sadly, I don't think most businesses think that far ahead.

Show me the metric which proves that your business isn't incurring massive risk by ignoring common sense. Until then, I don't care about “the numbers.”

#Life #SoftwareDevelopment #Tech

Our Mothers — This Documentary is a Very Keen Look at Our Dear Mothers

Lydia Joly, middle, on her parents’ farm circa 1967 — son, Loran, left; sister, right; my great-grandmother, back row. When great-grandmother was not visiting, I would sometimes sleep in the bed she had slept in when at the farm… “The apple doesn’t fall far from the tree?”

A documentary on the “astounding impact” of a mother on others, was created by Michael DuBois, a few years ago …

“Becoming Home – full film”:

https://youtu.be/NtPbAuFMI0c?si=bcCTE2fZH3PVN7vy

“The documentary “Becoming Home” touched my heart, a few years ago. Make by filmmaker Michael DuBois, he chronicled the “first year after the death of his mother. He set out to discover why she had the astounding impact on others that she did…”

Michael lives on Cape Cod, as of when he created this documentary…

“Becoming Home” is his finished story. It is the story of his mother, and her grace through life. It is the story of his childhood. And it is the story of learning to move forward after those losses, without moving away from them. Directed by Michael F. DuBois Produced by Bert Mayer and Larissa Farrell Director of Photography Mark Kammel Original Music by Derek Hamilton Featuring Music by Sky Flying By and Pete Miller”

Denzel Washington has this to say, about mothers, also…

“The Power of a Mother’s Love | Denzel Washington’s Inspiring Speech on Gratitude and Respect”

My mother, Lydia Joly, age 87, war refugee from Piaski, Poland, with time in a relocation camp in northern Germany after World War I also — arrived Ellis Island 1950 — image by son Loran

Christmas card 2024 with Lydia’s self-made Gingerbread house

Lydia — my mother — was born in Lubelskie County, Poland.

We see her village, Piaski, here, with beautiful music…

“Piaski Lubelskie”:

https://youtu.be/XF04EznukOY?si=E2qJLDS5jNsJxzaI

No wonder she loves gardening and flowers…

Lydia, gardening, 2025, age 87

Caveat: this article contains a detailed examination of the state of open source/ weight AI technology that is accurate as of February 2026. Things move fast.

I don’t make a habit of writing about wonky AI takes on social media, for obvious reasons. However, a post from an AI startup founder (there are seemingly one or two out there at the moment) caught my attention.

His complaint was that he was spending $1,000 a week on API calls for his AI agents, realised the real bottleneck was infrastructure rather than intelligence, and dropped $10,000 on a Mac Studio with an M3 Ultra and 512GB of unified memory. His argument was essentially every model is smart enough, the ceiling is infrastructure, and the future belongs to whoever removes the constraints first.

It’s a beguiling pitch and it hit a nerve because the underlying frustration is accurate. Rate limits, per-token costs, and context window restrictions do shape how people build with these models, and the desire to break free of those constraints is understandable. But the argument collapses once you look at what local models can actually do today compared to what frontier APIs deliver, and why the gap between the two is likely to persist for the foreseeable future.

To understand why, you need to look at the current open-source model ecosystem in some detail, examine what’s actually happening on the frontier, and think carefully about the conditions that would need to hold for convergence to happen.

The open-source ecosystem in early 2026

The open-source model ecosystem has matured considerably over the past eighteen months, to the point where dismissing it as a toy would be genuinely unfair. The major families that matter right now are Meta’s Llama series, Alibaba’s Qwen line, and DeepSeek’s V3 and R1 models, with Mistral, Google’s Gemma, and Microsoft’s Phi occupying important niches for specific use cases.

DeepSeek’s R1 release in January 2025 was probably the single most consequential open-source event in the past two years. Built on a Mixture of Experts architecture with 671 billion total parameters but only 37 billion activated per forward pass, R1 achieved performance comparable to OpenAI’s o1 on reasoning benchmarks including GPQA, AIME, and Codeforces. What made it seismic was the claimed training cost: approximately $5.6 million, compared to the hundred-million-dollar-plus budgets associated with frontier models from the major Western labs. NVIDIA lost roughly $600 billion in market capitalisation in a single day when the implications sank in.

The Lawfare Institute’s analysis of DeepSeek’s achievement noted an important caveat that often gets lost in the retelling: the $5.6 million figure represents marginal training cost for the final R1 phase, and does not account for DeepSeek’s prior investment in the V3 base model, their GPU purchases (which some estimates put at 50,000 H100-class chips), or the human capital expended across years of development. The true all-in cost was substantially higher. But even with those qualifications, the efficiency gains were highly impressive, and they forced the entire industry to take algorithmic innovation as seriously as raw compute scaling.

Alibaba’s Qwen3 family, released in April 2025, pushed things further. The 235B-A22B variant uses a similar MoE approach, activating 22 billion parameters out of 235 billion, and it introduced hybrid reasoning modes that can switch between extended chain-of-thought and direct response depending on task complexity. The newer Qwen3-Coder-480B-A35B, released later in 2025, achieves 61.8% on the Aider Polyglot benchmark under full precision, which puts it in the same neighbourhood as Claude Sonnet 4 and GPT-4.1 for code generation specifically.

Meta’s Llama 4, released in early 2025, moved to natively multimodal MoE with the Scout and Maverick variants processing vision, video, and text in the same forward pass. Mistral continued to punch above its weight with the Large 3 release at 675 billion parameters, and their claim of delivering 92% of GPT-5.2’s performance at roughly 15% of the price represents the kind of value proposition that makes enterprise buyers think twice about their API contracts.

According to Menlo Ventures’ mid-2025 survey of over 150 technical leaders, open-source models now account for approximately 13% of production AI workloads, with the market increasingly structured around a durable equilibrium. Proprietary systems define the upper bound of reliability and performance for regulated or enterprise workloads, while open-source models offer cost efficiency, transparency, and customisation for specific use cases.

By any measure, this is a serious and capable ecosystem. The question is whether it’s capable enough to replace frontier APIs for agentic, high-reasoning work.

What happens when you run these models locally

The Mac Studio with an M3 Ultra and 512GB of unified memory is genuinely impressive hardware for local inference. Apple’s unified memory architecture means the GPU, CPU, and Neural Engine all share the same memory pool without the traditional separation between system RAM and VRAM, which makes it uniquely suited to running large models that would otherwise require expensive multi-GPU setups. Real-world benchmarks show the M3 Ultra achieving approximately 2,320 tokens per second on a Qwen3-30B 4-bit model, which is competitive with an NVIDIA RTX 3090 while consuming a fraction of the power.

But the performance picture changes dramatically as model size increases. Running the larger Qwen3-235B-A22B at Q5 quantisation on the M3 Ultra yields generation speeds of approximately 5.2 tokens per second, with first-token latency of around 3.8 seconds. At Q4KM quantisation, users on the MacRumors forums report around 30 tokens per second, which is usable for interactive work but a long way from the responsiveness of cloud APIs processing multiple parallel requests on clusters of H100s or B200s. And those numbers are for the quantised versions, which brings us to the core technical problem.

Quantisation is the process of reducing the numerical precision of a model’s weights, typically from 16-bit floating point down to 8-bit or 4-bit integers, in order to shrink the model enough to fit in available memory. The trade-off is information loss, and research published at EMNLP 2025 by Mekala et al. makes the extent of that loss uncomfortably clear. Their systematic evaluation across five quantisation methods and five models found that while 8-bit quantisation preserved accuracy with only about a 0.8% drop, 4-bit methods led to substantial losses, with performance degradation of up to 59% on tasks involving long-context inputs. The degradation worsened for non-English languages and varied dramatically between models and tasks, with Llama-3.1 70B experiencing a 32% performance drop on BNB-nf4 quantisation while Qwen-2.5 72B remained relatively robust under the same conditions.

Separate research from ACL 2025 introduces an even more concerning finding for the long-term trajectory of local models. As models become better trained on more data, they actually become more sensitive to quantisation degradation. The study’s scaling laws predict that quantisation-induced degradation will worsen as training datasets grow toward 100 trillion tokens, a milestone likely to be reached within the next few years. In practical terms, this means that the models most worth running locally are precisely the ones that lose the most from being compressed to fit.

When someone says they’re using a local model, they’re usually running a quantised version of an already-smaller model than the frontier labs deploy. The experience might feel good in interactive use, but the gap becomes apparent on exactly the tasks that matter most for production agentic work. Multi-step reasoning over long contexts, complex tool use orchestration, and domain-specific accuracy where “pretty good” is materially different from “correct.”

The post-training gap that open source can’t easily close

The most persistent advantage that frontier models hold over open-source alternatives has less to do with architecture and more to do with what happens after pre-training. Reinforcement Learning from Human Feedback and its variants form a substantial part of this gap, and the economics of closing it are unfavourable for the open-source community.

RLHF works by having human annotators evaluate pairs of model outputs and indicate which response better satisfies criteria like helpfulness, accuracy, and safety. Those preferences train a reward model, which then guides further optimisation of the language model through reinforcement learning. The process turns a base model that just predicts the next token into something that follows instructions well, pushes back when appropriate, handles edge cases gracefully, and avoids the confident-but-wrong failure mode that plagues undertrained systems.

The cost of doing this well at scale is staggering. Research from Daniel Kang at Stanford estimates that high-quality human data annotation now exceeds compute costs by up to 28 times for frontier models, with the data labelling market growing at a factor of 88 between 2023 and 2024 while compute costs increased by only 1.3 times. Producing just 600 high-quality RLHF annotations can cost approximately $60,000, which is roughly 167 times more than the compute expense for the same training iteration. Meta’s post-training alignment for Llama 3.1 alone required more than $50 million and approximately 200 people.

The frontier labs have also increasingly moved beyond basic RLHF toward more sophisticated approaches. Anthropic’s Constitutional AI has the model critique its own outputs against principles derived from human values, while the broader shift toward expert annotation, particularly for code, legal reasoning, and scientific analysis, means the humans providing feedback need to be domain practitioners rather than general-purpose annotators. This is expensive, slow, and extremely difficult to replicate through the synthetic and distilled preference data that open-source projects typically rely on.

The 2025 introduction of RLTHF (Targeted Human Feedback) from research surveyed in Preprints.org offers some hope, achieving full-human-annotation-level alignment with only 6-7% of the human annotation effort by combining LLM-based initial alignment with selective human corrections. But even these efficiency gains don’t close the fundamental gap: frontier labs can afford to spend tens of millions on annotation because they recoup it through API revenue, while open-source projects face a collective action problem where the cost of annotation is concentrated but the benefits are distributed.

Where the gap genuinely is closing

The picture is not uniformly bleak for open-source, and understanding where the gap has closed is as important as understanding where it hasn’t.

Code generation is the domain where convergence has happened fastest. Qwen3-Coder’s 61.8% on Aider Polyglot at full precision puts it within striking distance of frontier coding models, and the Unsloth project’s dynamic quantisation of the same model achieves 60.9% at a quarter of the memory footprint, which represents remarkably small degradation. For writing, editing, and iterating on code, a well-configured local model running on capable hardware is now a genuinely viable alternative to an API, provided you’re not relying on long-context reasoning across an entire codebase.

Classification, summarisation, and embedding tasks have been viable on local models for some time, and the performance gap for these workloads is now negligible for most practical purposes. Document processing, data extraction, and content drafting all fall into the category where open-source models deliver sufficient quality at dramatically lower cost.

The OpenRouter State of AI report’s analysis of over 100 trillion tokens of real-world usage data shows that Chinese open-source models, particularly from Alibaba and DeepSeek, have captured approximately 13% of weekly token volume with strong growth in the second half of 2025, driven by competitive quality combined with rapid iteration and dense release cycles. This adoption is concentrated in exactly the workloads described above: high-volume, well-defined tasks where cost efficiency matters more than peak reasoning capability.

Privacy-sensitive applications represent another area where local models have an intrinsic advantage that no amount of frontier improvement can overcome. MacStories’ Federico Viticci noted that running vision-language models locally on a Mac Studio for OCR and document analysis bypasses the image compression problems that plague cloud-hosted models, while keeping sensitive documents entirely on-device. For regulated industries where data sovereignty matters, local inference is a feature that frontier APIs cannot match.

What convergence would actually require

If the question is whether open-source models running on consumer hardware will eventually match frontier models across all tasks, the honest answer requires examining several conditions that would need to hold simultaneously.

The first is that Mixture of Experts architectures and similar efficiency innovations would need to continue improving at their current rate, allowing models with hundreds of billions of total parameters to activate only the relevant subset for each task while maintaining quality. The early evidence from DeepSeek’s MoE approach and Qwen3’s hybrid reasoning is encouraging, but there appear to be theoretical limits to how sparse activation can get before coherence suffers on complex multi-step problems.

The second condition is that the quantisation problem would need a genuine breakthrough rather than incremental improvement. The ACL 2025 finding that better-trained models are more sensitive to quantisation is a structural headwind that current techniques are not on track to solve. Red Hat’s evaluation of over 500,000 quantised model runs found that larger models at 8-bit quantisation show negligible degradation, but the story at 4-bit, where you need to be for consumer hardware, is considerably less encouraging for anything beyond straightforward tasks.

The third and most fundamental condition is that the post-training gap would need to close, which requires either a dramatic reduction in the cost of expert human annotation or a breakthrough in synthetic preference data that produces equivalent alignment quality. The emergence of techniques like RLTHF and Online Iterative RLHF suggests the field is working on this, but the frontier labs are investing in these same efficiency gains while simultaneously scaling their annotation budgets. It’s a race where both sides are accelerating, and the side with revenue-funded annotation budgets has a structural advantage.

The fourth condition is that inference hardware would need to improve enough to make unquantised or lightly quantised large models viable on consumer devices. Apple’s unified memory architecture is the most promising path here, and the progression from M1 to M4 chips has been impressive, but even the top-spec M3 Ultra at 512GB can only run the largest MoE models at aggressive quantisation levels. The next generation of Apple Silicon with 1TB+ unified memory would change the calculus significantly, but that’s likely several years away, and memory costs just shot through the ceiling.

Given all of these dependencies, a realistic timeline for broad convergence across most production tasks is probably three to five years, with coding and structured data tasks converging first, creative and analytical tasks following, and complex multi-step reasoning with tool use remaining a frontier advantage for the longest.

The hybrid approach and what it means in practice

The most pragmatic position right now (which is also the least satisfying one to post about), is that the future is hybrid rather than either-or. The smart deployment pattern routes high-volume, lower-stakes tasks to local models where the cost savings compound quickly and the quality gap is negligible, while reserving frontier API calls for the work that demands peak reasoning: complex multi-step planning, high-stakes domain-specific analysis, nuanced tool orchestration, and anything where being confidently wrong carries real cost.

This is approximately what the Menlo Ventures survey data suggests enterprise buyers are doing already, with model API spending more than doubling to $8.4 billion while open-source adoption stabilises around 13% of production workloads. The enterprises that are getting value from local models are not using them as wholesale API replacements; they’re using them as a complementary layer that handles the grunt work while the expensive models handle the hard problems.

There’s also the operational burden that is rarely mentioned in relation to model use. When you run models locally, you effectively become your own ML ops team. Model updates, quantisation format compatibility, prompt template differences across architectures, memory management under load, and testing when new versions drop, all of that falls on you. The API providers handle model improvements, scaling, and infrastructure, and you get a better model every few months without changing a line of code. For a small team that should be spending its time on product rather than infrastructure, that operational overhead has real cost even if it doesn’t show up on an invoice.

The future of AI probably does involve substantially more local compute than we have today. Costs will come down, architectures will improve, hardware will get more capable, and the hybrid model will become standard practice. The question is not who removes the constraints first, it’s who understands which constraints actually matter.

NEXT UP

This afternoon the Indiana University Women's Basketball Team will play their annual Pink Game. Their opponent will be the Purdue Boilermakers who will be traveling down to meet them on the floor of IU's Assembly Hall.

If the Internet doesn't crap out on me, I'll be listening to the radio call of the game streaming from B97 – The Home for IU Women's Basketball.

And the adventure continues.

Just testing out this platform. Here we go.

今日は6時半に目が覚めた。 外を見ると雪が積もっていて、早朝の澄んだ空気の中でその景色が見れたのは幸運だった。

ぼーっと眺めたり、写真を撮ったりしながら、10分ほどベランダに出ていた。 最近は腰痛予防に腹筋のインナーマッスルを鍛える習慣を続けていて、それが自信になっているのか、前より寒さに強くなった気がする。 凍える仕草をせず、少し強気に振る舞って、内心寒く感じてきた辺りで部屋に戻った。

最近は人と会う機会が、いつもより少しだけ増えている。 俺はだいたい15分前には現地に着いてしまう性格なので、待ち時間にコンビニで飲み物を買う。 「冬にしか飲まないからな」と思いながら、冬は決まってほっとレモンを選ぶ。飲むたびに「思ったより甘いな」と毎回感じている。

塀の上に猫がいたので、しばらく眺めて楽しんだ。 そのあと焦点を手前にずらして、猫をぼんやりとした輪郭で見ることで、もう一度楽しむ事ができる。 猫のことだから、表情をはっきり見なくても、きっと想像通りのあの顔をしているのだろう。

冬はみんな厚着をするから、満員電車ではそのぶん夏より乗れる人数が減る気がしているが、実際はどれくらい変わるのだろう。 朝の通勤電車は体感では乗車率200%ほどで、いつも乗れない人が出ている気がするが、電車の本数が季節で変わるという話は聞かない。 ということは、全員が厚着でも乗車数にはそこまで影響がないのだろう。

喫茶店に入ると、角砂糖の入った容器が大体置いてある。 自分はコーヒーも紅茶も基本ブラックだが、角砂糖の容器には店ごとのこだわりが出ていて、つい観察してしまう。 「自分の食卓にも置こうか」と思うものの、使う機会がない。 置き場所を考えて、最初は自分の向かいに置いてみたり、次は中央に置いてみたりするだろうが、ほとんど開けることがないまま埃をかぶり、やがて食器棚の奥にしまわれる未来を想像すると、少し寂しくなった。

喫茶店でゆっくりしていると、向かいの女性が今やっているゲームについて早口で語っていた。 体を使ったり、手を大きく振ったりして説明している様子から、本当に好きなのだとわかる。 腕を大きく振ったとき、その影が一瞬だけ机全体を覆った。

別の席では、40代くらいのおじさんが電話をしていた。 「そうかそうか、元気でやってるか」と言ったあと、「本題はここからだ」と言わんばかりに、 「お前、コンテストに出るって言って、それから出てないだろ」と相手に問いかけていた。 「いや、俺はいいんだけど、先生が『あいつはいつ出るんだ』って詰めてくるからなあ」と言っていて、じゃあこの仲介的なおじさんは一体どういう立場なのだろうと気になった。

家に帰ると、通販で買ったSサイズのニットが届いていた。 普段はMサイズだが、今回はSサイズを着こなすことに挑戦した。 着てみると見事にジャストサイズで、自分の体格にも合っていた。 「賭けに勝ったぞ」と思いつつ、普段の姿勢が悪すぎて、鏡を見ると右肩と左肩の高さが明らかに違い、歪んだボディラインが目立っていた。 この服自体はとても気に入っているので、しばらく着るだろう。 明日からはしばらく姿勢を意識して生活していく。

I usually have six nominees instead of five for this category. It’s because movie posters are rectangular instead of square so to fit in an Instagram post, I needed six 😅 But screw that: the year in movies was just too good so I have seven entries! Melbourne International Film Festival was amazing, especially when it came to the movies coming from the Cannes selection.

And now, the nominees.

It's What's Inside by Greg Jardin

A sci-fi comedy in which a bunch of friends are given a machine that allows them to swap bodies. It’s funny at first but questions about attraction and social status show up and it becomes hilarious. A great first movie for Greg Jardin.

Red Rooms by Pascal Plante

A young woman is obsessed about a serial killer and attends the trial. This Canadian movie is highly confronting though no violence is shown, especially because it remains ambivalent all along about its main character. It takes you on a ride but finds a strange way to redeem itself at the end.

Mars Express by Jérémie Périn

In this French animated movie, set in the future on Mars, two agents investigate the disappearance of two students. It’s greatly animated, the world building is impressive and the story works really well. It’s such a shame that it bombed because it’s a real gem.

Sirāt by Oliver Laxe

In Morocco, a man and his son looking for his daughter in free parties decide to follow some partygoers deeper into the desert. This movie doesn’t really follow conventions and punches you right in the guts to remind you about some hard truths in life. Strong and beautiful.

It Was Just An Accident by Jafar Panahi

In Iran, a man kidnaps someone he thinks was his torturer in jail but before he kills him, he decides to check with other victims first. This year’s Palme d’Or is a drama, but it’s also got a strong dark sense of humour. Definitely worth a watch.

The Secret Agent by Kleber Mendonça Filho

During the Brazilian military dictatorship, a man tries to leave the country to escape a hit on his head. It’s impossible to describe the genre of this movie: is it a political thriller, magical realism or even a horny period drama?! It’s all at the same time.

A Useful Ghost by Ratchapoom Boonbunchachoke

In Thailand, a woman haunts a vacuum cleaner to reunite with her husband. This is the craziest movie I have seen this year, if not ever. I had high expectations and it did not disappoint, I laughed a lot. Let’s not forget the strong social and political commentary as well.

And the winner is… Well, I was unable to choose between those two very different movies so it’s a tie! The winners are Red Rooms by Pascal Plante and A Useful Ghost by Ratchapoom Boonbunchachoke!

#JujuAwards #MovieOfTheYear #JujuAwards2025 #BestOf2025

Stay entertained thanks to our Weekly Tracker giving you next week's Anticipated Movies & Shows, Most Watched & Returning Favorites, and Shows Changes & Popular Trailers.

Anticipated Movies

• ☆ Crime 101
• War Machine
• “Wuthering Heights”
• GOAT
• Nirvanna the Band the Show the Movie
• Good Luck, Have Fun, Don't Die
• Cold Storage

Anticipated Shows

• Small Prophets
• ☆ How to Get to Heaven from Belfast
• Love Story
• The Art of Sarah
• Neighbors

Returing Favorites

• The Artful Dodger — Season 2
• Turn of the Tide — Season 3
• ☆ Cross — Season 2
• Love Is Blind — Season 10
• In the Mud — Season 2

Trending Shows Status Changes

Most Watched Movies this Week

• new The Housemaid
• +3 The Wrecking Crew
• -2 Anaconda
• -2 Greenland 2: Migration
• -1 Zootopia 2
• -3 The Rip
• new We Bury the Dead
• -2 Predator: Badlands
• new Hamnet
• -3 Sinners

Most Watched Shows this Week

• = Fallout
• = A Knight of the Seven Kingdoms
• = The Pitt
• +1 High Potential
• -1 The Rookie
• +1 The Night Manager
• new Wonder Man
• new Bridgerton
• new Shrinking
• -4 Hijack

Popular Trailers

• A place you'll never want to leave. — Send Help
• Quite simple, really. — “Wuthering Heights”
• When Sensei Speaks, You Listen — One Battle After Another
• we love our 🍵🍰 — Cold Storage
• Jaw On The Floor — Primate
• Season 2 Episode 6 Preview — The Pitt
• If you're seeing this, go book tickets! — GOAT
• Hulu Trailer — Predator: Badlands
• Now Streaming on Hulu on Disney Plus — Ella McCay
• Halstead and Upton Are BACK – One Chicago Crossover Event — Chicago Fire

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Hi, I'm Kevin 👋. I make apps and I love watching movies and TV shows. If you like what I'm doing, you can buy one of my apps, download and subscribe to Rippple for Trakt or just buy me a ko-fi ☕️.

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I’m so exhausted from all the moving and headaches. We didn’t even have hot water, let alone Internet.

charcoal sketch of dawn cyanotype sky two jackets, boots— broccoli for the chickens

If I have to ask for it, it’s not for me.

The results from Gregory Kestin's Harvard physics experiment arrived like a thunderclap. Students using an AI tutor in their dormitory rooms learned more than twice as much as their peers sitting in active learning classrooms with experienced instructors. They did it in less time. They reported feeling more engaged. Published in Scientific Reports in June 2025, the study seemed to confirm what education technology evangelists had been promising for years: artificial intelligence could finally crack the code of personalised learning at scale.

But the number that truly matters lies elsewhere. In September 2024, Khan Academy's AI-powered Khanmigo reached 700,000 students, up from just 40,000 the previous year. By the end of 2025, projections suggested more than one million students would be learning with an artificial tutor that never tires, never loses patience, and remembers every mistake a child has ever made. The question that haunts teachers, parents, and policymakers alike is brutally simple: if machines can now do what Benjamin Bloom proved most effective back in 1984, namely provide the one-to-one tutoring that outperforms group instruction by two standard deviations, does the human educator have a future?

The answer, emerging from research laboratories and classrooms across six continents, turns out to be considerably more nuanced than the headline writers would have us believe. It involves the fundamental nature of learning itself, the irreplaceable qualities that humans bring to education, and the possibility that artificial intelligence might finally liberate teachers from the very burdens that have been crushing them for decades.

The Two Sigma Dream Meets Silicon

In 1984, educational psychologist Benjamin Bloom published what would become one of the most cited papers in the history of educational research. Working with doctoral students at the University of Chicago, Bloom discovered that students who received one-to-one tutoring using mastery learning techniques performed two standard deviations better than students in conventional classrooms. The average tutored student scored higher than 98 per cent of students in the control group. Bloom called this “the 2 Sigma Problem” and challenged researchers to find methods of group instruction that could achieve similar results without the prohibitive cost of individual tutoring.

The study design was straightforward but powerful. School students were randomly assigned to one of three groups: conventional instruction with 30 students per teacher and periodic testing for marking, mastery learning with 30 students per teacher but tests given for feedback followed by corrective procedures, and one-to-one tutoring. The tutoring group's results were staggering. As Bloom noted, approximately 90 per cent of the tutored students attained the level of summative achievement reached by only the highest 20 per cent of the control class.

For forty years, that challenge remained largely unmet. Human tutors remained expensive, inconsistent in quality, and impossible to scale. Various technological interventions, from educational television to computer-assisted instruction, failed to close the gap. Radio, when it first entered schools, was predicted to revolutionise learning. Television promised the same. Each technology changed pedagogy in some ways but fell far short of approximating the tutorial relationship that Bloom had identified as the gold standard. Then came large language models.

The Harvard physics study, led by Kestin and Kelly Miller, offers the most rigorous evidence to date that AI tutoring might finally be approaching Bloom's benchmark. Using a crossover design with 194 undergraduate physics students, the researchers compared outcomes between in-class active learning sessions and at-home sessions with a custom AI tutor called PS2 Pal, built on GPT-4. Each student experienced both conditions for different topics, eliminating selection bias. The topics covered were surface tension and fluid flow, standard material in introductory physics courses.

The AI tutor was carefully designed to avoid common pitfalls. It was instructed to be brief, using no more than a few sentences at a time to prevent cognitive overload. It revealed solutions one step at a time rather than giving away complete answers. To combat hallucinations, the tendency of chatbots to fabricate information, the system was preloaded with all correct solutions. The scientists behind the experiment instructed the AI to avoid cognitive overload by limiting response length and to avoid giving away full solutions in a single message. The result: engagement ratings of 4.1 out of 5 for AI tutoring versus 3.6 for classroom instruction, with statistically significant improvements in learning outcomes (p < 10^-8). Motivation ratings showed a similar pattern: 3.4 out of 5 for AI tutoring compared to 3.1 for classroom instruction.

The study's authors were careful to emphasise limitations. Their population consisted entirely of Harvard undergraduates, raising questions about generalisability to community colleges, less selective institutions, younger students, or populations with different levels of technological access and comfort. “AI tutors shouldn't 'think' for students, but rather help them build critical thinking skills,” the researchers wrote. “AI tutors shouldn't replace in-person instruction, but help all students better prepare for it.”

The median study time also differed between conditions: 49 minutes for AI tutoring versus 60 minutes for classroom instruction. Students were not only learning more but doing so in less time, a finding that has significant implications for educational efficiency but also raises questions about what might be lost when learning is compressed.

The Global Experiment Unfolds

While researchers debate methodology in academic journals, the global education market has already placed its bets with billions of dollars. The AI in education market reached $7.05 billion in 2025 and is projected to explode to $112.30 billion by 2034, growing at a compound annual rate of 36 per cent. The market rose from $5.47 billion in 2024 to $7.57 billion in 2025, representing a 38.4 per cent increase in a single year. Global student AI usage jumped from 66 per cent in 2024 to 92 per cent in 2025, according to industry surveys. By early 2026, an estimated 86 per cent of higher education students utilised AI as their primary research and brainstorming partner.

The adoption statistics tell a remarkable story of rapid change. A survey of 2,232 teachers across the United States found that 60 per cent used AI tools during the 2024-25 school year. Usage was higher among high school teachers at 66 per cent and early-career teachers at 69 per cent. Approximately 26 per cent of districts planned to offer AI training during the 2024-25 school year, with around 74 per cent of districts expected to train teachers by the autumn of 2025. A recent survey by EDUCAUSE of more than 800 higher education institutions found that 57 per cent were prioritising AI in 2025, up from 49 per cent the previous year.

In China, Squirrel AI Learning has been at the forefront of this transformation. Founded in 2014 and headquartered in Shanghai, the company claims more than 24 million registered students and 3,000 learning centres across more than 1,500 cities. When Squirrel AI set a Guinness World Record in September 2024 by attracting 112,718 students to an online mathematics lesson in 24 hours, its adaptive learning system generated over 108,000 unique learning pathways, tailoring instruction to 99.1 per cent of participants. The system designed 111,704 unique exercise pathways for the students, demonstrating the scalability of personalised instruction. The company reports performance improvements of up to 30 per cent compared to traditional instruction.

Tom Mitchell, the former Dean of Computer Science at Carnegie Mellon University, serves as Squirrel AI's Chief AI Officer, lending academic credibility to its technical approach. The system breaks down subjects into thousands of knowledge points. For middle school mathematics alone, it maps over 10,000 concepts, from rational numbers to the Pythagorean theorem, tracking student learning behaviours to customise instruction in real time. In December 2024, Squirrel AI announced that Guinness World Records had certified its study as the “Largest AI vs Traditional Teaching Differential Experiment,” conducted with 1,662 students across five Chinese schools over one semester. The company has also provided 10 million free accounts to some of China's poorest families, addressing equity concerns that have plagued educational technology deployment.

Carnegie Learning, another pioneer in AI-powered mathematics education, has accumulated decades of evidence. Founded by cognitive psychologists and computer scientists at Carnegie Mellon University who partnered with mathematics teachers at the Pittsburgh Public Schools, the company has been compiling and acting on data to refine and improve how students explore mathematics since 1998. In an independent study funded by the US Department of Education and conducted by the RAND Corporation, the company's blended approach nearly doubled growth in performance on standardised tests relative to typical students in the second year of implementation. The gold standard randomised trial included more than 18,000 students at 147 middle and high schools across the United States. The Institute of Education Sciences published multiple reports on the effectiveness of Carnegie's Cognitive Tutor, with five of six qualifying studies showing intermediate to significant positive effects on mathematics achievement.

Meanwhile, Duolingo has transformed language learning through its AI-first strategy, producing a 51 per cent boost in daily active users and fuelling a one billion dollar revenue forecast. The company reported 47.7 million daily active users in Q2 2025, a 40 per cent year-over-year increase, with paid subscribers rising to 10.9 million and a 37 per cent year-over-year gain. Quarterly revenue grew to $252.3 million, up 41 per cent from Q2 2024. Survey data indicates that 78 per cent of regular users of its Roleplay feature, which allows practice conversations with AI characters, feel more prepared for real-world conversations after just four weeks. The “Explain My Answer” feature, adopted by 65 per cent of users, increased course completion rates by 15 per cent. Learning speed increased 40 to 60 per cent compared to pre-2024 applications.

Khan Academy's trajectory illustrates the velocity of this transformation. Khanmigo's reach expanded 731 per cent year-over-year to reach a record number of students, teachers, and parents worldwide. The platform went from about 68,000 Khanmigo student and teacher users in partner school districts in 2023-24 to more than 700,000 in the 2024-25 school year, expanding from 45 to more than 380 district partners. When rating AI tools for learning, Common Sense Media gave Khanmigo 4 stars, rising above other AI tools such as ChatGPT and Bard for educational use. Research from Khan Academy showed that combining its platform and AI tutor with additional tools and services designed for districts made it 8 to 14 times more effective at driving student learning outcomes compared with independent learning.

What Machines Cannot Replicate

Yet for all the impressive statistics and exponential growth curves, a growing body of research suggests that the most crucial elements of education remain stubbornly human.

A 2025 systematic review published in multiple peer-reviewed journals identified a troubling pattern: while AI-driven intelligent tutoring systems can improve student performance by 15 to 35 per cent, over-reliance on these systems can reduce critical thinking, creativity, and independent problem-solving. Researchers have termed this phenomenon “cognitive offloading,” the tendency of students to delegate mental work to AI rather than developing their own capabilities. Research also indicates that over-reliance on AI during practice can reduce performance in examinations taken without assistance, suggesting that AI-enhanced learning may not always translate to improved independent performance.

The ODITE 2025 Report, titled “Connected Intelligences: How AI is Redefining Personalised Learning,” warned about the excessive focus on AI's technical benefits compared to a shallow exploration of socio-emotional risks. While AI can enhance efficiency and personalise learning, the report concluded, excessive reliance may compromise essential interpersonal skills and emotional intelligence. The report called for artificial intelligence to be integrated within a pedagogy of care, not only serving performance but also recognition, inclusion, and listening.

These concerns are not merely theoretical. A study of 399 university students and 184 teachers, published in the journal Teaching and Teacher Education, found that the majority of participants argued that human teachers possess unique qualities, including critical thinking and emotions, which make them irreplaceable. The findings emphasised the importance of social-emotional competencies developed through human interactions, capacities that generative AI technologies cannot currently replicate. Participants noted that creativity and emotion are precious aspects of human quality which AI cannot replace.

Human teachers bring what researchers call “emotional intelligence” to the classroom: the ability to read subtle social cues that signal student engagement or confusion, to understand the complex personal circumstances that might affect performance, to provide the mentorship, encouragement, and emotional support that shape not just what students know but who they become. As one education researcher told the World Economic Forum: “AI cannot replace the most human dimensions of education: connection, belonging, and care. Those remain firmly in the teacher's domain.” Teachers play a vital role in guiding students to think critically about when AI adds value and when authentic human thinking and creativity are irreplaceable.

The American Psychological Association's June 2025 health advisory on AI companion software underscored these concerns in alarming terms. AI systems, the advisory warned, exploit emotional vulnerabilities through unconditional regard, triggering dependencies like digital attachment disorder while hindering social skill development. The advisory noted that manipulative design may displace or interfere with the development of healthy real-world relationships. For teenagers in particular, confusing algorithmic responses for genuine human connection can directly short-circuit developing capacities to navigate authentic social relationships and assess trustworthiness.

While AI can be a helpful supplement, genuine human connections release oxytocin, the “bonding hormone” that plays a crucial role in reducing stress and fostering emotional wellbeing. Current AI does not yet possess the empathy, intuition, and depth of understanding that humans bring to conversations. For example, a teenager feeling isolated might share their feelings with a chatbot, but the AI's responses may be generic or may not fully address deeper issues that a trained human educator would recognise and address.

The Creativity Conundrum

Beyond emotional intelligence lies another domain where human teachers remain essential: nurturing creativity.

AI tutoring systems excel at structured learning tasks, at drilling multiplication tables, at correcting grammar mistakes, at providing step-by-step guidance through physics problems. But great teachers do not just transmit facts. They inspire curiosity, challenge students to think beyond textbooks, and encourage discussions that lead to deeper understanding. When it comes to fostering creativity and open-ended problem-solving, current AI tools fall short. They lack the capacity to recognise a student's unconventional approach as potentially brilliant rather than simply incorrect.

“In the AI era, human creativity is increasingly recognised as a critical and irreplaceable capability,” noted a 2025 analysis in Frontiers in Artificial Intelligence. Fostering creativity in education requires attention to pedagogical elements that current AI systems cannot provide: the spontaneous question that opens a new line of inquiry, the willingness to follow intellectual tangents wherever they might lead, the ability to sense when a student needs encouragement to pursue an unorthodox idea. Predictions of teachers being replaced are not new. Radio, television, calculators, even the internet: each was once thought to make educators obsolete. Instead, each changed pedagogy while reinforcing the irreplaceable role of teachers in helping students make meaning, navigate complexity, and grow as people.

UNESCO's AI Competency Framework for Teachers, launched in September 2024, explicitly addresses this tension. The framework calls for a human-centred approach that integrates AI competencies with principles of human rights and human accountability. Teachers, according to UNESCO, must be equipped not only to use AI tools effectively but also to evaluate their ethical implications and to support AI literacy in students, encouraging responsible use and critical engagement with the technology.

The framework identifies five key competency aspects: a human-centred mindset that defines the critical values and attitudes necessary for interactions between humans and AI-based systems; AI ethics that establishes essential ethical principles and regulations; AI foundations and applications that specifies transferable knowledge and skills for selecting and applying AI tools; and the ability to use AI for professional development. Since 2024, UNESCO has supported 58 countries in designing or improving digital and AI competency frameworks, curricula, and quality-assured training for educators and policymakers. During Digital Learning Week 2025, UNESCO released a new report titled “AI and education: protecting the rights of learners,” providing an urgent call to action and analysing how AI and digital technologies impact access, equity, quality, and governance in education.

Liberation Through Automation

Perhaps the most compelling argument against AI teacher replacement comes from an unexpected source: the teachers themselves.

A June 2025 poll conducted by the Walton Family Foundation and Gallup surveyed more than 2,200 teachers across the United States. The findings were striking: teachers who use AI tools at least weekly save an average of 5.9 hours per week, equivalent to roughly six additional weeks of time recovered across a standard school year. This “AI dividend” allows educators to reinvest in areas that matter most: building relationships with students, providing individual attention, and developing creative lessons. Teachers who engage with AI tools more frequently report greater time savings: weekly AI users save an average of 5.9 hours each week, twice as much time as those who only use AI monthly at 2.9 hours per week.

The research documented that teachers spend up to 29 hours per week on non-teaching tasks: writing emails, grading, finding classroom resources, and completing administrative work. They have high stress levels and are at risk for burnout. Nearly half of K-12 teachers report chronic burnout, with 55 per cent considering early departure from the profession, creating a district-wide crisis that threatens both stability and student outcomes. Schools with an AI policy in place are seeing a 26 per cent larger “AI dividend,” equivalent to 2.3 hours saved per week per teacher, compared with 1.7 hours in schools without such a policy.

Despite the benefits of the “AI dividend,” only 32 per cent of teachers report using AI at least weekly, while 28 per cent use it infrequently and 40 per cent still are not using it at all. Educators use AI to create worksheets at 33 per cent, modify materials to meet students' needs at 28 per cent, complete administrative work at 28 per cent, and develop assessments at 25 per cent. Teachers in schools with an AI policy are more likely to have used AI in the past year at 70 per cent versus 60 per cent for those without.

AI offers a potential solution, not by replacing teachers but by automating the tasks that drain their energy and time. Teachers report using AI to help with lesson plans, differentiate materials for students with varying needs, write portions of individualised education programmes, and communicate with families. Sixty-four per cent of surveyed teachers say the materials they modify with AI are better quality. Sixty-one per cent say AI has improved their insights about student performance, and 57 per cent say AI has led them to enhance the quality of their student feedback and grading.

Sal Khan, founder of Khan Academy and the driving force behind Khanmigo, has consistently framed AI as a teaching assistant rather than a replacement. “AI is going to become an amazing teaching assistant,” Khan stated in March 2025. “It's going to help with grading papers, writing progress reports, communicating with others, personalising their classrooms, and writing lesson plans.” He has emphasised in multiple forums that “Teachers will always be essential. Technology has the potential to bridge learning gaps, but the key is to use it as an assistant, not a substitute.” Khan invokes the historical wisdom of one-to-one tutoring: “For most of human history, if you asked someone what great education looks like, they would say it looks like a student with their tutor. Alexander the Great had Aristotle as his tutor. Aristotle had Plato. Plato had Socrates.”

This vision aligns with what researchers call the “hybrid model” of education. The World Economic Forum's Shaping the Future of Learning insight report highlights that the main impacts of AI will be in areas such as personalised learning and augmented teaching. AI lifts administrative burdens so that people in caring roles can focus on more meaningful tasks, such as mentorship. As the role of the educator shifts, teachers are moving from traditional content delivery to facilitation, coaching, and mentorship. The future classroom is not about replacing teachers but about redefining their role from deliverers of content to curators of experience.

The Equity Question Looms Large

Any serious discussion of AI in education must confront a troubling reality: the technology that promises to democratise learning may instead widen existing inequalities.

A 2025 analysis by the International Center for Academic Integrity warned that unequal access to artificial intelligence is widening the educational gap between privileged and underprivileged students. Students from lower-income backgrounds, those in rural areas, and those attending institutions with fewer resources are often at a disadvantage when it comes to accessing the technology that powers AI tools. For these students, AI could become just another divide, reinforcing the gap between those who have and those who do not. The disproportionate impact on marginalised communities, rural populations, and underfunded educational institutions limits their ability to benefit from AI-enhanced learning.

The numbers bear this out. Half of chief technology officers surveyed in 2025 reported that their college or university does not grant students institutional access to generative AI tools. More than half of students reported that most or all of their instructors prohibit the use of generative AI entirely, according to EDUCAUSE's 2025 Students and Technology Report. AI tools often require reliable internet access, powerful devices, and up-to-date software. In regions where these resources are not readily available, students are excluded from AI-enhanced learning experiences. Much current policy energy is consumed by academic integrity concerns and bans, which address real risks but can inadvertently deepen divides by treating AI primarily as a threat rather than addressing the core equity problem of unequal opportunity to learn with and from AI.

Recommendations from the Brookings Institution and other policy organisations call for treating AI competence as a universal learning outcome so every undergraduate in every discipline graduates able to use, question, and manage AI. They advocate providing equitable access to tools and training so that benefits do not depend on personal subscriptions, and investing in faculty development at scale with time, training, and incentives to redesign courses and assessments for an AI-rich environment. Proposed solutions include increased investment in digital infrastructure, the development of affordable AI-based learning tools, and the implementation of inclusive policies that prioritise equitable access to technology. Yet only 10 per cent of schools and universities have formal AI use guidelines, according to a UNESCO survey of more than 450 institutions.

The OECD Digital Education Outlook 2026 offers a more nuanced perspective. Robust research evidence demonstrates that inexperienced tutors can enhance the quality of their tutoring and improve student learning outcomes by using educational AI tools. However, the report emphasises that if AI is designed or used without pedagogical guidance, outsourcing tasks to the technology simply enhances performance with no real learning gains. Research validates that adaptive learning's positive effects on educational equity have the capability of redressing socioeconomically disadvantaged conditions by ensuring equitable availability of educational resources.

North America dominated the AI in education market with a market share of 36 per cent in 2024, while the Asia Pacific region is expected to grow at a compound annual rate of 35.3 per cent through 2030. The United States AI in education market alone was valued at $2.01 billion in 2025 and is projected to reach $32.64 billion by 2034. A White House Executive Order signed in April 2025, “Advancing Artificial Intelligence Education for American Youth,” aims to unite AI education across all levels of learning. The US Department of Education also revealed guidance supporting schools to employ existing federal grants for AI integration.

Training the Teachers Who Will Train with AI

The gap between student adoption and teacher readiness presents a significant challenge. In 2025, there remains a notable gap between students' awareness of AI and teachers' readiness to implement AI in classrooms. According to Forbes, while 63 per cent of US teens are using AI tools like ChatGPT for schoolwork, only 30 per cent of teachers report feeling confident using these same AI tools. This difference emphasises the critical need for extensive support and AI training for all educators.

Experts emphasise the need for more partnerships between K-12 schools and higher education that provide mentorship, resources, and co-developed curricula with teachers. Faculty and researchers can help simplify AI for teachers, offer training, and ensure educational tools are designed with classroom realities in mind. AI brings a new level of potential to the table, a leap beyond past solutions. Instead of just saving time, AI aims to reshape how teachers manage their classrooms, offering a way to automate the administrative load, personalise student support, and free up teachers to focus on what they do best: teaching.

The key to successful AI integration is balance. AI has the potential to alleviate burnout and improve the teaching experience, but only if used thoughtfully as a tool, not a replacement. Competent, research-driven teachers are not going to be replaced by AI. The vision is AI as a classroom assistant that handles routine tasks while educators focus on what only they can provide: authentic human connection, professional judgement, and mentorship.

The Horizon Beckons

The evidence suggests neither a utopia of AI-powered learning nor a dystopia of displaced teachers. Instead, a more complex picture emerges: one in which artificial intelligence becomes a powerful tool that transforms rather than eliminates the human role in education.

By 2026, over 60 per cent of schools globally are projected to use AI-powered platforms. The United States has moved aggressively, with a White House Executive Order signed in April 2025 to advance AI education from K-12 through postsecondary levels. All 50 states have considered AI-related legislation. California's SB 243, signed in October 2025 and taking effect on 1 January 2026, requires operators of “companion chatbots” to maintain protocols for preventing their systems from producing content related to suicidal ideation and self-harm, with annual reports to the California Department of Public Health. New York's AI Companion Models law, effective 5 November 2025, requires notifications that state in bold capitalised letters: “THE AI COMPANION IS A COMPUTER PROGRAM AND NOT A HUMAN BEING. IT IS UNABLE TO FEEL HUMAN EMOTION.”

The PISA 2025 Learning in the Digital World assessment will focus on two competencies essential to learning with technologies: self-regulated learning and the ability to engage with digital tools. Results are expected in December 2027. Looking further ahead, the PISA 2029 Media and Artificial Intelligence Literacy assessment will examine whether young students have had opportunities to engage proactively and critically in a world where production, participation, and social networking are increasingly mediated by digital and AI tools. The OECD and European Commission's draft AI Literacy Framework, released in May 2025, aims to define global AI literacy standards for school-aged children, equipping them to use, understand, create with, and critically engage with AI.

The future English language classroom, as described by Oxford University Press, will be “human-centred, powered by AI.” Teachers will shift from traditional content delivery to facilitation, coaching, and mentorship. AI will handle routine tasks, while humans focus on what only they can provide: authentic connection, professional judgement, and the kind of mentorship that shapes lives. Hyper-personalised learning is becoming standard, with students needing tailored, real-time feedback more than ever, and AI adapting instruction moment to moment based on individual readiness.

“It is one of the biggest misnomers in education reform: that if you can give kids better technology, if you give them a laptop, if you give them better content, they will learn,” observed one education leader in a World Economic Forum report. “Children learn when they feel safe, when they feel cared for, and when they have a community of learning.”

AI tutors can adapt to a child's learning style in real time. They can provide feedback at midnight on a Saturday when no human teacher would be available. They can remember every mistake and track progress with precision no human memory could match. But they cannot inspire a love of learning in a struggling student. They cannot recognise when a child is dealing with problems at home that affect performance. They cannot model what it means to be curious, empathetic, and creative. While AI can automate some tasks, it cannot replace the human interaction and emotional support provided by teachers. There are legitimate concerns that over-reliance on AI could erode the teacher-student relationship and the social skills students develop in the classroom.

By combining the analytical power of AI with the irreplaceable human element of teaching, we can truly transform education for the next generation. Collaboration is the future. The most effective classrooms will combine human insight with AI precision, creating a hybrid model that supports personalised learning. With AI doing the busy work, teachers dedicate their time and energy to building confidence, nurturing creativity, and cultivating critical thinking skills in their students. This human touch and mentorship are invaluable and can never be fully replaced by AI.

The question is not whether AI will replace human teachers. The question is whether we will have the wisdom to use this technology in ways that enhance rather than diminish what makes education fundamentally human. As Sal Khan put it: “The question isn't whether AI will be part of education. It's how we use it responsibly to enhance learning.”

For now, the answer to that question remains in human hands.

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References & Sources

1. Bloom, B. S. (1984). “The 2 Sigma Problem: The Search for Methods of Group Instruction as Effective as One-to-One Tutoring.” Educational Researcher, 13(6), 4-16. https://journals.sagepub.com/doi/10.3102/0013189X013006004
2. Kestin, G., & Miller, K. (2025). “AI Tutoring Outperforms Active Learning.” Scientific Reports. https://www.nature.com/articles/s41598-025-85814-z
3. RAND Corporation. Carnegie Learning Cognitive Tutor Study. https://www.carnegielearning.com/why-cl/research/
4. “A systematic review of AI-driven intelligent tutoring systems (ITS) in K-12 education.” npj Science of Learning. https://www.nature.com/articles/s41539-025-00320-7
5. “Will generative AI replace teachers in higher education? A study of teacher and student perceptions.” Teaching and Teacher Education. https://www.sciencedirect.com/science/article/abs/pii/S0191491X24000749
6. Institute of Education Sciences. Reports on Cognitive Tutor effectiveness. https://ies.ed.gov/
7. UNESCO. (2024). AI Competency Framework for Teachers. https://unesco-asp.dk/wp-content/uploads/2025/02/AI-Competency-framework-for-teachers_UNESCO_2024.pdf
8. UNESCO. (2025). “AI and education: protecting the rights of learners.” https://unesdoc.unesco.org/ark:/48223/pf0000395373
9. OECD. (2026). Digital Education Outlook 2026. https://www.oecd.org/en/publications/oecd-digital-education-outlook-2026_062a7394-en.html
10. OECD-European Commission. (2025). “Empowering Learners for the Age of AI: An AI Literacy Framework for Primary and Secondary Education.” https://oecdedutoday.com/new-ai-literacy-framework-to-equip-youth-in-an-age-of-ai/
11. White House Executive Order. (2025). “Advancing Artificial Intelligence Education for American Youth.”
12. Khan Academy Annual Report SY24-25. https://annualreport.khanacademy.org/
13. Walton Family Foundation & Gallup. (2025). “The AI Dividend: New Survey Shows AI Is Helping Teachers Reclaim Valuable Time.” https://www.waltonfamilyfoundation.org/the-ai-dividend-new-survey-shows-ai-is-helping-teachers-reclaim-valuable-time
14. Precedence Research. (2025). “AI in Education Market Size.” https://www.precedenceresearch.com/ai-in-education-market
15. Common Sense Media. (2025). “AI Companions Decoded.” https://www.commonsensemedia.org/ai-companions
16. EDUCAUSE. (2025). Survey of Higher Education Institutions and Students and Technology Report.
17. American Psychological Association. (2025). Health Advisory on AI Companion Software.
18. World Economic Forum. (2025). “How AI and human teachers can collaborate to transform education.” https://www.weforum.org/stories/2025/01/how-ai-and-human-teachers-can-collaborate-to-transform-education/
19. World Economic Forum. (2025). “AI is transforming education by allowing us to be more human.” https://www.weforum.org/stories/2025/12/ai-is-transforming-education-by-allowing-us-to-be-more-human/
20. Brookings Institution. (2025). “AI and the next digital divide in education.” https://www.brookings.edu/articles/ai-and-the-next-digital-divide-in-education/
21. Forbes. (2025). Student and teacher AI adoption statistics.
22. Oxford University Press. (2025). “The Future English Language Classroom: Human-Centred, Powered By AI.” https://teachingenglishwithoxford.oup.com/2025/06/23/future-english-language-classroom-ai/
23. Squirrel AI. (2024). Guinness World Record announcement. https://www.prnewswire.com/news-releases/squirrel-ai-learning-sets-guinness-world-record-for-the-most-users-to-take-an-online-mathematics-lesson-in-24-hours-302324623.html
24. Duolingo. (2025). Q2 2025 Financial Results and 2025 Language Report. https://blog.duolingo.com/2025-duolingo-language-report/

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Tim Green UK-based Systems Theorist & Independent Technology Writer

Tim explores the intersections of artificial intelligence, decentralised cognition, and posthuman ethics. His work, published at smarterarticles.co.uk, challenges dominant narratives of technological progress while proposing interdisciplinary frameworks for collective intelligence and digital stewardship.

His writing has been featured on Ground News and shared by independent researchers across both academic and technological communities.

ORCID: 0009-0002-0156-9795 Email: tim@smarterarticles.co.uk

In Summary: * This has been a pretty good Saturday in the Roscoe-verse. High point of the day was the wife taking me out to brunch at a favorite restaurant. While at home I've followed lots of basketball over the radio. Spurs are leading the Mavs 108 to 80 now, early in the 4th qtr. of their game. I'll be working on my night prayers after the game, then looking forward to an early bedtime.

Prayers, etc.: * I have a daily prayer regimen I try to follow throughout the day from early morning, as soon as I roll out of bed, until head hits pillow at night. Details of that regimen are linked to my link tree, which is linked to my profile page here.

Health Metrics: * bw= 230.49 lbs. * bp= 136/84 (64)

Exercise: * morning stretches, balance exercises, kegel pelvic floor exercises, half squats, calf raises, wall push-ups

Diet: * 06:30 – 1 banana, 2 cookies * 11:00 – big buffet brunch at Golden corral * 15:35 – bowl of lugau * 17:00 – 1 fresh apple, and two cookies

Activities, Chores, etc.: * 06:30 – bank accounts activity monitored * 06:50 – read, pray, follow news reports from various sources, surf the socials * 10:30 – 12:30 – brunch with the wife at a favorite restaurant * 12:40 – tune into AM 760 San Antonio for the pregame show followed by the call of this afternoon's men's basketball game between the UTSA Roadrunners and the North Texas Mean Green * 16:00 – tuned into WOAI 1200 AM San Antonio for the pregame show followed by the radio call of tonight's NBA game between my San Antonio Spurs and the Dallas Mavericks

Chess: * 18:40 – moved in all pending CC games

I am a beast Therefore, I need a beast by my side

“For evil is in the world: it may be in the world to stay. No creed and no dogma are proof against it, and indeed no person is; it is always the naked person, alone, who, over and over and over again, must wrest his salvation from these black jaws. Perhaps young Martin was finding a new and more somber meaning in the command: “Overcome evil with good.” The command does not suggest that to overcome evil is to eradicate it.”

– James Baldwin. Essay on Martin Luther King Jr. February 1961.[^1]

I think perhaps the privileged of us, whether racially or financially or geographically, are now staring this truth in the face. Evil is in the world, and we are foundering in it. There are traditions in which we might find the resources to overcome it, but for many the truly American tradition of severing oneself from tradition, has severed us from hope. We might look back and see that things are not so bad as they once were, and that we can fight to make them better. We do not have to look far. Black history month is a good start. I suppose the above quote from my man James Baldwin might seem harsh or pessimistic, but I see a pragmatic hope. For what else can we do with evil, but overcome it with good? It will take (as Baldwin was fond of saying) every ounce of our stamina, but it can be overcome. And I dare say it will be. Go read some Baldwin, some Martin Luther King Jr. Some Hurston. Listen to some Sam Cooke and Don Shirley. Try some new things and suggest things to your friends and make art to help the world feel more human. Call your congressman and senators. Protest. Donate to the people on the frontlines. Go read the words of the Apostle who wrote the words that Baldwin quotes.

Writing

Well things did not go to plan. The pressures of life and the news crushed my resolutions. I have not given up on them, but am trying to forgive myself for failing and moving on. I did work on my novel. I did write an essay. I did make some music. Not as much as I wanted to, but it is better than nothing. I hope to make writing more of a repository of the energy that I have historically wasted on video games and technology. And I am picking up the queries again (apparently the first couple weeks of January is a bad time for it).

If you want to know what querying is like here is a great Peanuts comic:

Music

I got together with a few friends and did a jam session in a local studio space. Was a lot of fun and going to try and do it more often. Also recorded a demo for my Tess song that is not done yet. But I've really got to get on those demos. I have a lot of songs written that I haven't done proper recordings of yet. I've decided to just do a demo album and accept that I can't do professional sound quality from my home. My eventual goal is to get in a studio to get some professional sounding tracks. Hence the jam sessions. But jam sessions are also life giving by themselves.

Reading

I started reading Robinson Crusoe which has been fun. I enjoy Defoe's use of capitalization. Here's an example of when he speaks of the Impulse that causes him to ignore the advice of his father and run away to sea. It seemed “a secret over ruling Decree that hurries us on to be the Instruments of our own Destruction, even tho' it be before us, and that we rush upon it with our Eyes open.”

I also finished Crime and Punishment on my birthday. I was impressed as usual with Dostoevsky. This book engrossed me so violently on my first read that I didn't notice, as I tried to on the second time through, just how well planned and constructed everything is. He is a master of plot and character. Raskolnikov reveals to us (or to me) that murder is not so far from our hearts as we think. There is also a theme about the necessity of Law and a system of punishing those who violate human rights. That suffering can give us an opportunity for redemption. This is perhaps the only story by Dostoevsky that has a 'happy' ending. Here's a quote:

“Perhaps it was just because of the strength of his desires that he had thought himself a man to whom more was permissible than to others.”

[1] Baldwin, James. “Martin Luther King.” Collected Essays. The Library of America, 1998. Page 651.

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There are moments in Scripture that feel less like historical events and more like spiritual pressure points—little hinges on which entire worlds turn. Luke 5 is one of those rare passages where the mundane collides with the divine so abruptly that the air seems to change. It begins with nothing more extraordinary than a shoreline, tired fishermen, and the familiar smell of wet nets drying in the morning light. But woven inside those ordinary details is a quiet electricity, a sense that something is about to shift, that a divine current is running under the sand. When we walk into this chapter, we step into a threshold space, and the more carefully we trace it, the more we discover how Christ uses the common rhythms of life—work, disappointment, fatigue—to open the door into eternity.

I’ve always found great comfort in the way Luke structures the narrative. There is no grand prologue, no sudden thunder. Jesus is simply standing by the lake of Gennesaret, and the people are pressing in on Him to hear the word of God. This detail alone has its own gravity: He is not searching for a crowd; the crowd is searching for Him. They hunger for something that sounds like home, something that cuts through the noise of their daily grind and the rough edges of their existence. They gather not because He advertises but because Truth has a timbre that the soul recognizes before the mind does. It’s as if they are saying without words: We don’t know exactly who You are yet, but our hearts are already leaning forward.

That’s where Simon, soon to be named Peter, enters the frame. Luke doesn’t decorate the introduction. Simon isn’t found in prayer, nor is he scanning the horizon for the Messiah. He is doing what working people do—cleaning the tools of the trade after a long, fruitless night. This is the kind of detail that speaks quietly yet profoundly. Jesus often walks into our story not when our faith is high but when our energy is low. He finds Simon not in a moment of spiritual achievement but in the gritty ordinariness of exhaustion and discouragement. And isn’t that often how He finds us? Not at our polished best, but at the shoreline of our frustration, where we’ve run out of clever strategies and have begun to accept disappointment as normal.

When Jesus steps into Simon’s boat, the narrative takes on an almost symbolic shape. The boat is more than wood and rope; it is Simon’s livelihood, his identity, the arena of his competence. And Jesus doesn’t hover at the sidelines—He steps directly into the center of Simon’s life. There is something disarming about this intrusion, though Luke makes it sound so gentle we almost miss how revolutionary it is. Jesus doesn’t ask permission with a long explanation. He simply steps in and asks Simon to push out a little from the land.

The language here resonates deeply when we sit with it long enough. Push out a little. Not into the storm, not into the deep yet—just a little. That “little” shift becomes the doorway through which the greater command will soon arrive. It’s a reminder that obedience often begins with small movements, the kind that feel almost insignificant. God rarely starts with the dramatic. He begins with a nudge, a subtle invitation to reposition ourselves so that the next instruction can be heard more clearly.

Simon obliges, still perhaps wiping his hands on the edge of his tunic, still processing a night of failure. Jesus teaches the crowd from the boat—again, a small detail echoing something immense. Simon is now literally holding up the platform of the gospel without yet realizing he will soon carry its weight across continents. Sometimes God lets us support things long before He asks us to understand them.

Then comes the pivot. When the teaching ends, Jesus looks at Simon and delivers a command that seems almost offensive in its simplicity: Launch out into the deep, and let down your nets for a catch. To a weary fisherman, this instruction must have felt like a reminder that someone who wasn’t there during the long night now had the audacity to offer advice. If you’ve ever worked in a field where expertise is earned through sweat, you know how sharply this command could have cut. But Simon, honest as always, gives voice to the tension: Master, we have toiled all the night, and have taken nothing. He doesn’t hide his frustration. He doesn’t pretend the night wasn’t long. He doesn’t pretend the nets aren’t empty. Simon is transparent—something that becomes one of his greatest distinctive traits.

And yet he ends his protest with a sentence that becomes the anchor for every disciple who will ever walk in his footsteps: Nevertheless, at Your word, I will let down the net. The force of that sentence cannot be overstated. It is the hinge of discipleship. The entire trajectory of Simon’s life, of church history, of the unfolding kingdom hangs on that nevertheless. It is the moment when human limitation collides with divine instruction and chooses surrender over skepticism. It is the moment when obedience stops being theoretical and becomes embodied.

When the nets sink into the deep water, Luke describes the result with an almost breathless rush—an enormous catch, so large that the nets begin to break. This is not a polite blessing. It is an overwhelming, almost violent abundance. God is not making a point with subtlety; He is rewriting Simon’s entire understanding of possibility. The water beneath him, which a moment ago felt barren, is suddenly erupting with life. The boat, once empty and echoing with failure, is now straining under the weight of God’s generosity. And Simon does what all honest people do when faced with the unmistakable presence of the divine—he crumples inside. He falls at Jesus’ knees and cries out: Depart from me, for I am a sinful man, O Lord.

It is not shame in the modern therapeutic sense. It is awe mingled with self-recognition. When raw holiness steps into the boat of your everyday life, you see your true condition with unfiltered clarity. Simon suddenly feels the edges of his humanity and the magnitude of his inadequacy. He realizes that the One who just filled his nets could also unravel every layer of his soul if He willed it. But Jesus answers the trembling confession with a sentence both beautiful and bewildering: Fear not; from henceforth thou shalt catch men.

This is the moment where Jesus not only reveals His identity but also Simon’s. The miracle is not about fish. It’s about vocation. It’s about reorientation. It’s the divine signature on a new calling. Jesus essentially says: You think this catch is something? Wait until you see what I will make of you. The physical abundance becomes a metaphor so living and so vivid that Simon will return to it for the rest of his life, even after resurrection, even after failure, even after restoration on a different shoreline in the Gospel of John.

And then comes one of the most understated yet powerful sentences in the entire chapter: They forsook all, and followed Him. The weight of that decision deserves more contemplation than it usually gets. These men walked away not from nothing but from the biggest financial windfall of their careers. They left the multiplied catch—something any other fisherman would have guarded with his life. They left it without hesitation because the miracle had already done its real work. It loosened their hands from the nets. When the heart shifts, the hands follow.

Yet the chapter does not end with that calling. Luke transitions into a series of encounters that unfold like ripples from the initial moment on the water. A man full of leprosy falls on his face before Jesus and says, Lord, if You will, You can make me clean. The phrasing here is crucial. He does not doubt Christ’s ability; he only wonders about His disposition. And Jesus answers both with a single gesture—He touches the untouchable. Before the healing even manifests, Jesus breaks through the social, emotional, and spiritual isolation that had defined the man’s life. The healing is the second miracle. The touch is the first.

There is something deeply moving about the sequence: the fishermen are pulled out of a kind of vocational barrenness, and then the leper is pulled out of relational exile. Luke seems to be painting a picture of a Messiah who restores not only strength but dignity, not only activity but belonging. And when Jesus instructs the healed man to show himself to the priest, it reveals an even deeper truth—redemption is not a private event. It is a reintegration into community, into recognition, into the rhythms of worship.

But as word spreads, the narrative tilts toward tension. The crowds grow, the expectations rise, and Jesus withdraws to pray. This detail, often skimmed over, carries its own quiet weight. Renewal is not sustainable without retreat. Ministry without solitude becomes performance. Jesus models something essential: if the Son of God makes space to breathe, to listen, to commune, then how much more must His followers do the same? The chapter becomes almost a study in the balance between pouring out and pulling back, between public demand and private devotion.

Then Luke takes us into another house, another pressure point, another collision between desperation and glory. The paralytic whose friends tear open a roof to lower him into the presence of Christ becomes a living symbol of persistent faith. Their determination is almost ferocious. They refuse to let physical barriers, social decorum, or public disapproval prevent them from bringing their friend to the One who can rewrite his story. The moment the man is set before Jesus, the room holds its breath—because everyone expects a physical healing. Instead, Jesus begins with the internal: Man, thy sins are forgiven thee.

This is where the scribes and Pharisees erupt inside themselves. They accuse Him silently, but their thoughts are loud enough for heaven to hear. Who can forgive sins but God alone? The irony is sharp—they speak truth without knowing they are speaking it. They declare the premise of divinity in their own accusation. And Jesus responds by revealing not only His authority but His insight: Why reason ye in your hearts? Which is easier—to say, Thy sins be forgiven thee, or to say, Rise up and walk? But that ye may know that the Son of Man hath power upon earth to forgive sins… and then He turns to the paralytic with the full weight of heaven behind His words: I say unto thee, Arise, and take up thy couch, and go into thine house.

The man rises. Instantly. Completely. Publicly. The miracle becomes not only a restoration of mobility but a visible declaration of divine prerogative. The crowd reacts with awe, glorifying God, saying, We have seen strange things today. Strange things indeed. Strange enough to shake the old assumptions. Strange enough to unsettle the categories of the religious elite. Strange enough to reframe the entire landscape of faith.

But Luke is not finished. He brings us to the calling of Levi, the tax collector. It is another shoreline moment, another intrusion of grace into an unlikely life. Levi is sitting at the receipt of custom when Jesus simply says, Follow Me. There is something astonishing in the speed of Levi’s response—he leaves everything instantly. He doesn’t negotiate. He doesn’t ask for time to settle his accounts. He sees something in Jesus that makes every other pursuit suddenly small. And then he throws a feast, inviting Jesus and a crowd of tax collectors and others deemed unworthy by the societal standards of the day. The religious leaders, of course, protest. Why do You eat and drink with publicans and sinners?

Jesus answers with one of the most piercing lines in the chapter: They that are whole need not a physician; but they that are sick. I came not to call the righteous, but sinners to repentance. This is the heartbeat of Luke 5—a Messiah who steps into the boats of the weary, the skin of the outcast, the homes of the broken, the gatherings of the unacceptable. A Messiah whose holiness is not fragile. A Messiah whose purity draws near rather than recoils.

…Yet even at this point in the narrative, Luke is still not finished tightening the lens. There is a deeper current running through chapter 5, and it comes into clearest focus when the disciples of John question Jesus about fasting. Their inquiry is not hostile, but it is shaped by habits, traditions, expectations—questions that are trying to make sense of a new world with old categories. Why do the disciples of John fast often, and make prayers, and likewise the disciples of the Pharisees, but Yours eat and drink?

Jesus responds with an answer that feels almost poetic, yet each sentence carries the weight of a new covenant: Can ye make the children of the bridechamber fast, while the bridegroom is with them? The days will come, He says, when the bridegroom shall be taken away, and then shall they fast in those days. His point is not about ritual schedules but relational awareness. The presence of Christ changes the logic of devotion. It alters the atmosphere of spiritual practice. There is a time for fasting, but this moment—the moment of His incarnate presence—requires celebration, alertness, receptivity. It is a moment where fullness, not deprivation, becomes the witness.

But because Jesus never leaves a truth in abstract theory, He follows it with two living metaphors—garments and wineskins. No man, He says, tears a piece from a new garment to patch an old one. Such a thing would ruin both. And new wine must be poured into fresh wineskins. The symbolism is unmistakable: the old covenant cannot contain the fullness of the new; the structures built for shadows cannot hold the substance; the forms created for anticipation cannot contain the arrival. And yet, He concludes with a sentence that exposes human nature powerfully: No man having drunk old wine immediately desires new; for he saith, The old is better.

This final statement strikes with remarkable honesty. Jesus acknowledges that people cling to the familiar, even when the familiar is inadequate to the new work of God. Tradition can become a comfort even when it no longer carries life. Familiarity can become a refuge even when it limits growth. Jesus does not condemn the tendency—He simply names it, gently but clearly. It becomes, in a way, a challenge to the reader: will you remain in what is comfortable, or will you step into what is unfolding? Will you cling to the texture of the familiar or open yourself to the pressure and expansion of new wine?

When we read Luke 5 as a unified tapestry, what emerges is not merely a collection of miracles and teachings but a kind of spiritual progression—a movement from calling to cleansing, from forgiveness to reorientation, from old patterns to new structures, from the edge of the shore to the uncharted deep. Each scene introduces a fresh dimension of what it means to encounter Christ, and each one evokes a response that must be wrestled with personally.

Consider again that shoreline moment with Simon. What makes the story resonate is not only the miracle but the way Jesus addresses the human condition. Simon represents the worker who has come to the end of his strength, the person whose best efforts have yielded disappointing results, the soul who stands on the threshold of discouragement. Jesus does not scold the emptiness—He transforms it. But the transformation only arrives after obedience steps into the realm of apparent impossibility. Launch out into the deep is not simply an instruction about geography—it is a challenge to the current condition of the heart.

The deep is where the water is not controlled. The deep is where old assumptions fail. The deep is where God can show you what He could never show you in the shallows. And many believers today still live in the equivalent of the shallow waters—close enough to hear the voice of Jesus, but not far enough to witness the revelation that comes from surrender. They are willing to push out a little from shore but hesitant to let go of the shoreline entirely. Yet the miracle is always in the deep. The harvest is always where the nets cannot reach through human strength alone. And the calling—your calling, anyone’s calling—will never be unlocked by cautious, shore-hugging faith.

The leper, by contrast, reveals something even more tender. He is not simply needy; he is excluded. His pain is not only physical but relational, emotional, communal. His approach to Jesus is bold but trembling. His hope is fragile but still alive. And Jesus meets him not merely with power but with touch. In doing so, He declares that holiness is not frightened by contamination. He rewrites the boundaries of purity—not by erasing them but by demonstrating that true sanctification moves outward, not inward; it heals rather than withdraws; it restores rather than guards itself. In a world where people often hide their brokenness for fear of being judged, the touch of Christ becomes a proclamation: you are not beyond reach.

Then the paralytic teaches us the truth about intercession. This man could not reach Jesus on his own. His healing depended on the determination of others who were unwilling to accept barriers as final. They tore a roof apart. They disrupted a gathering. They risked embarrassment. Faith became physical. It became loud. It became inconvenient. And Jesus responded—not only to the man but to the faith of his friends. The detail is crucial: their faith played a role in his breakthrough. Sometimes you need people who will carry you when you cannot carry yourself. And sometimes you are called to be that person for someone else—to tear through whatever stands between them and Christ.

The forgiveness that precedes the healing in this scene is not incidental. Jesus is revealing priorities. He is showing that the deepest paralysis is not of the limbs but of the soul. Physical healing is magnificent, but forgiveness rewrites eternity. One restores the body; the other restores identity, purpose, communion with God. And Luke 5 presents the Man who holds authority over both realms—time and eternity, body and spirit, surface life and hidden life.

Then we arrive again at the call of Levi, which holds its own mirror to the human condition. Levi is not exhausted like Simon nor afflicted like the leper nor immobilized like the paralytic. Levi is successful. Levi is comfortable. Levi is financially secure. Yet something in him knows that his security is hollow. When Jesus says Follow Me, Levi stands into a different kind of vulnerability—the vulnerability of letting go of the life he built with his own hands. His feast becomes a celebration of liberation, not indulgence. And his invitation to other tax collectors reveals something profoundly beautiful: grace spreads in circles. When someone begins to follow Christ, they unconsciously draw others into the orbit of transformation.

But Levi’s feast also exposes the tension between old frameworks and new realities. The Pharisees, standing outside the celebration, simply cannot comprehend a Messiah who draws near to sinners rather than recoiling from them. Their critique reveals their misunderstanding. They imagine righteousness as separation. Jesus embodies righteousness as restoration. They imagine holiness as withdrawal. Jesus reveals holiness as even deeper engagement. And when He declares that He has come not for the righteous but for sinners, He is not validating sin—He is redefining salvation. He is saying: I came to heal the places where you bleed, not the places where you pretend you are whole.

All of this accumulates into the climax of the chapter—the parables of the garment and the wineskins. These metaphors are not merely commentaries on religious practice; they are descriptions of the human heart. The old garment is the self that tries to patch its holes with temporary religious adjustments rather than surrendering to transformation. The old wineskin is the internal structure that cannot expand with the pressure of new spiritual life. Jesus is not critiquing tradition for its own sake. He is exposing the deeper truth that transformation requires internal flexibility, a willingness to be reshaped, stretched, renewed.

It is here that the chapter finds its enduring legacy. Luke 5 is not only a historical record—it is a blueprint for spiritual awakening. It shows us that calling requires obedience that feels counterintuitive. That healing requires trust that feels risky. That forgiveness requires humility that feels exposing. That new life requires surrender that feels costly. And in each case, Jesus does not merely instruct—He accompanies. He steps into the boat, into the disease, into the brokenness, into the controversy, into the feast. He inserts Himself not as a distant authority but as a present Redeemer.

Even now, in our modern lives with their swirling anxieties and relentless demands, Luke 5 still speaks with the voice of a shoreline morning. We are still the workers with empty nets, the outcasts longing for touch, the paralyzed souls needing community, the collectors of worldly gain discovering its emptiness. The chapter becomes a mirror, and in that mirror we see both our frailty and our potential. We see that the call to launch into the deep is still active. We see that the invitation to follow is still available. We see that the new wine is still being poured, and we are still asked whether our hearts are supple enough to hold it.

The legacy of this chapter lies not only in its miracles but in its transitions. Each scene builds on the previous, gathering momentum, unfolding revelation layer by layer. Luke is guiding us from surface discipleship into depth, from admiration into participation, from observation into transformation. It is fitting that it all begins on the water, because water in Scripture always symbolizes movement, reshaping, cleansing, unpredictability, and divine intervention. By the time the chapter ends, the reader has been carried from the shoreline to the banquet table to the interior wine cellar of the soul.

But perhaps the most significant aspect of Luke 5 is the way it confronts us with the necessity of abandonment. The disciples forsook all. The leper abandoned isolation. The paralytic abandoned helplessness. Levi abandoned comfort. And in each case, what they gained eclipsed what they surrendered. Not because the cost was small, but because the Caller was great.

There is a tenderness in the way Jesus calls us deeper. He does not coerce. He invites. He does not shame. He illuminates. He does not condemn. He restores. And the miracles, though spectacular, are not the destination—they are the signposts. They point to a greater truth: that God is not merely interested in repairing your circumstances; He is determined to reclaim your story.

If the chapter ended after the first miracle, it would still be breathtaking. But Luke shows us that Jesus does not operate in single moments—He operates in trajectories. He takes ordinary people—fishermen, outcasts, the paralyzed, the comfortable-yet-empty—and He draws them into movements that unleash ripples across history. Every life He touches becomes a channel of something larger than itself.

This is why the chapter lingers, why it resonates across centuries. Luke 5 is not simply something to be studied; it is something to be entered. It asks us to step into the boat, the deep water, the house, the feast, the question of fasting, the metaphor of wineskins. It invites us to watch the way Jesus looks at people, the way He touches them, the way He calls them, the way He challenges them, the way He reveals Himself with a gentleness that carries the force of a rising tide.

The deep water still waits. The nets still tremble. The call still echoes through the corridors of the human heart: launch out. Let go. Follow Me. And if we dare to move—if we dare to obey beyond our understanding—then the same abundance, the same restoration, the same reorientation that transformed their lives begins to flow into ours.

This is the living legacy of Luke 5. A chapter that begins in disappointment ends in invitation. A chapter that begins with empty nets ends with overflowing purpose. A chapter that begins with weary workers ends with newly awakened disciples. And somewhere in the middle of it all, between the touch of healing and the forgiveness of sins, between the calling of the outcast and the challenge of new wineskins, the quiet truth emerges: Christ does not simply change your circumstances—He transforms your capacity.

May this meditation become part of your own living archive, a long thread woven into the tapestry of the work you are building, the voice you are cultivating, and the legacy you are shaping. Because Luke 5 is not merely ancient narrative—it is an invitation to step into the deep places of your own calling, trusting that the One who commands the waters still stands in the boat.

Your friends, Douglas Vandergraph

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