Category Archives: Philosophy

Republic of Letters

Confessions of an AI Skeptic, Part 4 (of 5)

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(Part 1, Part 2, Part 3)

Last time out the discussion was focused on AI’s appetites for compute power and energy – appetites that are difficult to describe with any available superlative.  All that compute power costs money.  So does the electricity to run the computers and the infrastructure necessary to keep them cool.  And to run LLMs like ChatGPT, AI companies are such profligate spenders that they could embarrass a U.S. congressman during budget negotiations.  Also, like the spending of our own government (assuming you’re in the U.S.), the spending for AI is unsustainable.

Where Are the Profits?

Since generative AI burst onto the scene with the making of the commonly used AI platforms available to the public, the hype has become out of control.  Google CEO Sundar Pichai even went so far as to say that AI was a more profound technological development than fire or electricity (a smart-aleck yet astute commenter on YouTube asked about what happens to AI when we cut off the electricity – touché).  Part of me thinks he truly believes that ridiculous assertion, given his delivery.  But a more cynical side of me thinks he and others are engaging in the hype cycle to keep the investment dollars coming in – dollars that are desperately needed to keep the AI train rolling.

One recent financial example is instructive as to why they need the hype to keep investors interested.  Chipmaker NVIDIA, the premier maker of the processors used for AI workloads, recently just committed to invest $100 billion in OpenAI (through 2030), the creators of ChatGPT.  For the same period, OpenAI committed to buying $300 billion of cloud compute from Oracle.  Meanwhile, Oracle committed to buy $40 billion worth of chips from NVIDIA.  Now, I’m not exactly a business titan here, but by my back of the envelop math, Oracle is the only company making out in this deal – assuming this deal every completes.  Open AI, in the first half of 2025, had $4.3 billion in income, but still had a $13.5 billion loss.  That comes on the heels of a $5 billion loss in 2024.  Not exactly moving in the right direction.  How then, will Open AI come up with the $300 billion for the deal with Oracle?  Investors aren’t going to lose money forever.

The problem with AI companies is they cannot bring in the revenue to cover their costs.  Right now, OpenAI allows free access to ChatGPT, but with some fairly strict limits.  There are paid plans for $20/month, and while not as limited as the free version, it has limits nonetheless.  This revenue structure doesn’t even come close to covering their costs.  This video speculates that ChatGPT would have to raise prices to $500/month to cover their costs.  Who the heck is going to pay $500/month for ChatGPT??  That is not a path to profitability, and this cost structure is not exclusive to OpenAI.

Now one could point to a company like Amazon, which took a long time to report a profit (in large part because they were pumping revenues back into investments).  But all the while Amazon was losing money, they were making customers happy, selling products customers wanted and providing unparalleled convenience.  Is AI making customers happy?  According to this MIT report,  95% of companies – 95%!!! – are not seeing any returns on their AI investments. 

Fact is, AI is in a huge bubble right now.  Even Open AI boss Sam Altman – in a rare display of honesty rather than ridiculous hype – thinks AI is due for an implosion.  And another fact is that AI is a very long way from being profitable.  That’s hardly a surprise, with AI’s insatiable appetite for compute and the power and infrastructure to run it, a lot of unhappy customers, and little if any agreement on what makes a good business case for AI.  Read this for a good overview of where we’re at.  If you want a video explanation, watch this. 

Much of this bubble was preventable if the tech bros and others hadn’t hyped AI to the moon without knowing if they could deliver.  The first problem is the “I” in AI, as discussed in previous installments of this series.  What we call AI is most decidedly not intelligent, general or otherwise.  Further, it was hyped without full disclosure regarding its appetite for resources, and thus, money.  Maybe, if instead of calling it AI, they could have simply called their software by its true name – large language models (LLMs) – and said it mimics human intelligence although it isn’t truly intelligent.  They probably wouldn’t have had the ridiculous sums of money invested in it, and maybe “progress” would have been slower.  But they also wouldn’t have this bubble that is going to burst and cause a lot of people to take a bath, and not the kind that leaves them feeling clean and refreshed.  For a lot of people, it’s going to get ugly.

The financial status of AI and the bubble created around it reveals what might be the most critical barrier to all the hyped-up predictions, whether such predictions are doom and gloom or naïve utopianism – money.  AI is expensive.  Very expensive.  The money needed to provide the compute resources, energy, and other utilities is staggering, and because of the limits of computer technology I discussed previously, this picture is not going to improve.  For companies like OpenAI to become profitable, they would have to raise their prices an order of magnitude for their paid plans.  And that, of course, would lead to rapid decline in usage and worsen OpenAI’s already bleak financial picture.

Even worse (for AI companies) is that AI has, unlike most technologies, become more expensive as it has advanced.  OpenAI trained their GPT-3 model for about $4.6 million.  Their next big advancement, GPT-4, was trained for an estimated $80 million – $100 million.  GPT-5 (which was supposed to be a huge advancement over GPT-4 – it was anything but) was trained for a price estimated between $1.25 billion and $2.5 billion.  Thus, the training of GPT-4 was 1-2 orders of magnitude more costly than GPT-3, while training GPT-5 cost yet another order of magnitude over GPT-4 (and three orders of magnitude more than GPT-3). 

Even if a compute technology more suitable for AI was in existence, it would take a paradigm shift at the most fundamental level – away from silicon-based computing and the Von Neuman computer architecture that underlies virtually every computer from the room-sized ENIAC of yesteryear up to the smartphone you carry in your pocket today.  With trillions of dollars invested in that compute paradigm, nobody is going to be eager to suddenly abandon it and shift to a new one.  Practically speaking, it would be impossible.  But that’s a moot point anyway, because there is no such other paradigm.

AI is, quite simply, financially unsustainable.  And the prospects for that changing any time soon are virtually nil.

The AI Apocalypse Will Not Be Televised (Because it’s Not Going to Happen)

The discussion about the financial picture of AI leads me to another topic that is the product of AI hype – the doomsday scenarios where everybody loses their jobs and the world is ruled by a few evil megacorporations that have all the money while the rest of us live as feral beings looking for scraps while barely scraping by on universal basic income (UBI).  Scenarios that, if subject to any scrutiny, are quickly exposed as being beyond ridiculous.

Let’s put this to the test with some hypotheticals.  Let’s assume here in the U.S. that AI, in a relatively short time (maybe 5 years or so), was able to eliminate 125 million jobs – a little over 75% of the approximate number of people currently employed in this country.  Such a loss of jobs would result in a massive economic depression, one which would absolutely dwarf the Great Depression of the 1930’s (when U.S. unemployment peaked around 25%).  With so many people out of work, revenues to all businesses, evil megacorporations included, would plummet.  In such an economic shock, many businesses would close.  And recall from the above and previous installments of this series the insatiable appetite of AI for compute resources, power, and thus money.  With collapsing revenues, how are corporations (assuming they can even stay in business) going to pay for the huge costs associated with running all the AI necessary to replace those employees that were put out of work?  And even if all this AI could be kept running, where would the demand come from for the output when 75% of the people are jobless?  Why is any business going to pay for AI to produce all that output when they will never have enough consumers to pay for its staggering costs in the first place?

A lot of people when presented with the above scenario bring up the magical UBI that will suddenly appear without ever examining the underlying assumptions, so let’s do that now.  The U.S. is already $39 trillion – with a ‘t’ – in debt with spending levels that are far lower than those that would be required to finance UBI.  With 75% of the workforce out of work, tax revenues will collapse.  The federal government wouldn’t be able to finance many of its most basic functions, much less spending at its current levels, which is about $7 trillion for the most recent fiscal year.  The spending to support UBI would require the federal government to issue debt at rates that dwarf the present.  But who is going to buy that debt in a crushing economic depression, when corporate revenues are in the tank and when tax revenues reduce the prospects of repayment?

But can’t we increase taxes on the rich?  Oh sure, we can, but it still won’t be anywhere near enough to support UBI over the long term.  If you took all the wealth (not just income, but every last penny of wealth from the richest 100 Americans) you would net about $3.27 trillion, which is less than half our current annual spending.  Expanding that list to the Forbes 400, the amount of wealth adds up to about $6.6 trillion – still less than one year of annual spending.  And again, this is total wealth, not annual income that is significantly less. 

Conclusion?  There is no way to finance UBI.

Another conclusion?  There is no way to finance the attendant resources that must be consumed to support the widespread deployment of AI necessary to replace such a large number of jobs. A massive, short-term replacement of hundreds of millions of jobs with AI is doomed to collapse not only the economy and but also collapse its own prospects for ever being successful.  An AI employment apocalypse that eats all our jobs will end up eating itself, and in very short order.

This isn’t to say that AI will never replace any jobs.  And how many it will replace over the long haul remains to be seen.  It just means that simple economic reality, especially when paired with the economic realities of running AI, make the AI-will-take-your-job apocalypse scenario one that is so self-limiting that it’s a non-starter.  Think of it as an airplane that is so overloaded that it weighs too much to get off the ground.

More likely, AI will augment a lot of jobs.  But it’s simply too unreliable and too expensive to replace jobs en masse. 

This doesn’t mean we are out of the woods.  The coming AI apocalypse may be the bursting of the AI bubble and the collateral economic damage.  That one is far more plausible – and likely – than AI replacing everybody’s job.   

And returning to something hinted at above, AI isn’t always the most reliable thing in the world.  In fact, it can be wildly unreliable at times, too much so to risk replacing a human.  We’ll discuss that in the next installment.

Republic of Letters

Confessions of an AI Skeptic, Part 3 (of 5)

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(Part 2 can be found here)

(Part 1 can be found here)

One thing you never saw in any of the movies of the Terminator franchise (featuring some of the most menacing villains in all of sci-fi) was any of the various models having to stop for a recharge.  I can’t really blame James Cameron for that.  How cinematically compelling would it have been had the Cyberdyne Systems Model 101 portrayed by Arnold Scwharzenegger had to spend a significant amount of downtime recharging his battery?  Yet, if we were seeking a realistic portrayal of such a machine, it would have had to spend most of its time recharging.  And escaping the Terminator? Keep running, because his battery is going to be dead in very short order.

All of this is another way of saying that AI is a resource hog.  It is a voracious consumer of power and compute resources like the world has never seen.  The U.S. federal government looks positively judicious with taxpayer funds when compared to the way AI consumes resources.  However, what we call AI is bumping up against some hard physical limits, limits which present a Mt. Everest-sized obstacle to scaling.

A Compute Hog:

When a computer runs a program, it executes instructions, and in particular, machine level instructions, most often generated by a compiler that translates high-level language code into something it can understand.  The programs you run day-to-day, on your PC, your laptop, that computer you carry in your pocket called a “phone” can run programs that consume billions of processor cycles, where a cycle is the execution of an instruction.  But those software programs don’t even scratch the surface of what modern AI consumes.

Each of the tokens we mentioned in Part 2 places demands on a processor.  How much?  A prompt to an LLM that generates about 100 tokens in Open AI’s GPT-4 model (the latest model is GPT-5 now) can consume between 50-100 teraflops.  “Flops” in this context are floating-point operations per second, where floating-point is a type of data computer systems work with (basically a number that includes a mantissa and an exponent, digitally represented).  Tera means a trillion.  Trillion.  Also keep in mind that a prompt to an LLM includes two phases – a prefill phase (where the text you entered is broken down into tokens) and a decode phase (where the LLM generates tokens in response to your prompt).  So, for a relatively small prompt-and-answer, an LLM can consume between 50 and 100 trillion execution cycles.  Now consider longer conversations with an LLM.  These can easily run into the thousands of teraflops or more. 

Because of the astronomical amount of computing power AI workloads consume, the heavy lifting is done in data centers having the requisite amount.  Modern data centers include row upon row of servers, each with a number of GPUs.  As an aside, “GPU” stands for graphics processing unit, and while such processors were originally designed for graphics workloads, they are massively parallel and thus particularly well-suited for AI workloads. Some computers that process AI workloads also use a more specialized chip called a tensor processing units or TPU (which unlike a GPU, is specifically designed for AI workloads).  In addition to all the GPUs/TPUs, each server also includes a large amount of memory, the capacity of which is measured in terabytes.

In a sense, we’ve come full circle with computing.  Up until the 1970’s, we used to think of computers as room-sized behemoths, which they were.  That was the amount of space required to run the computing workloads of the time.  It was the advent of microprocessors and Moore’s Law (which is now deader than Francisco Franco) that started to shrink the size of computers down to something you can put on your desk or even carry in your pocket.  But now, with AI workloads, we are back up to gargantuan sizes again, with whole data centers that dwarf the large computers of yesteryear.  And we’re there because that’s the kind of space required to implement computing setups that can run compute-hogging AI. 

A Power Hog:

It doesn’t take a leap of imagination to realize that the requirement of that much computing power necessitates the consumption of a lot of electrical power.  But how much is a lot?  For this part, I turned to AI itself to tell me how much power it might use, and lacking any sense of modesty, it spit the answer right out.  It gave me the assumption of 750 giga-flops per token (750 billion instructions executed using floating-point data), with about 0.0001 kWh (kilowatt-hours) per token based on typical GPU/TPU energy usage (doesn’t sound like much, so far, does it? You just wait …).  The number of flops and the energy consumed scale linearly with token count.  Thus, a query that produces 1000 tokens would use, under this scenario, 0.01 kWh.  Moving the decimal place a couple spots to the right, that’s 10 Wh – i.e. enough energy to power a 10-watt LED bulb for an entire hour.  That’s for one very small conversation (compare that to what a human brain can do in an hour, running on about 14 Watts of power).

It’s not hard to see how some AI conversations use more power than Clark Griswold’s Christmas lights. 

And yet, we’re not done.  So far, we’ve only talked about the energy consumed by the computers themselves.  Thanks to the Laws of Why We Can’t Have Nice Things (sometimes referred to as “the Laws of Thermodynamics”), using that much compute power and thus that much electricity means a lot of excess heat is generated.  Something must be done about that heat, otherwise the computers in these data centers won’t run long before all the electronics are fried like a chicken in the kitchen of your local KFC (btw, Original Recipe >> Extra Krispy). 

We need to bring in cooling water, and lots of it.  That requires pumps to move the water in and then to move it out.  Some data centers also utilize large refrigerant systems to circulate cool air as well.  There has been some improvement on this front. Old data centers had about 30-40% energy overhead for cooling, while newer data centers have about 10-20% overhead.  Nevertheless, that’s still a lot of energy.

A recent story serves as an illustrative anecdote regarding AI energy consumption.  The story, linked here, refers to a planned AI data center for the state of Wyoming, one that will consume five times the amount of electricity as all the residents of Wyoming combined.  Not merely more energy, but five times more.  Not merely a few residents, but all residents of the state.

All that physical space, all that compute power, and all that energy, and yet this AI is still not intelligent, it still can’t think, and requires multiple orders of magnitude more energy to accomplish many of the same things humans can do.  Sure, it’s particularly well-suited for computational mathematics, more so than humans, but that’s not thinking, that’s just number crunching.  And of course, it took humans to design computers to be good at such things – humans that have, in their own skulls, a brain that can do amazing things running on about a mere 14 watts of power (or, in an hour, 14 Wh).  And with that 14 Wh, we have consciousness and true intelligence. 

The Wall:

Above, I wrote that AI faces a Mt. Everest-sized obstacle to scaling.  But more accurately, AI is racing head on into a wall, one that will kill scaling.

Let’s return to Moore’s Law, which was mentioned above.  The idea behind Moore’s Law was the product of Intel’s Gordon Moore, who postulated that the number of transistors on a given unit area of silicon would double every 18 months.  And for decades, that was true.  It’s because of Moore’s Law that you can carry in your pocket computing power, run off a battery, that is equivalent to a room-sized computer of the 1970’s.  But you can only get so small (sorry, Steve Martin). 

When transistor feature sizes were in the thousands, then the hundreds, and even the tens of nanometers, the progress of packing more functionality onto the same chip area marched onward, largely unabated.  But on the most advanced chips now – such as the GPUs/TPUs that run AI workloads – the smallest features sizes are in the single-digit nanometers.  You know what else has a size measured in single-digit nanometers?  Atoms.  Yes, atoms, the fundamental building block of all matter.  And you know what that means?  It means you have run into yet another wall. You are not going to build transistors smaller than atoms.  That is a hard, non-negotiable physical limitation.  And that means the end of Moore’s Law.

Furthermore, the top clock speeds for chips haven’t increased for about 15 years now.  The maximum speed at which an execution unit in one of these chips can execute instructions is therefor also facing a hard limit due to the material properties of the silicon upon which such chips are fabricated.

Now you will still get some denialists saying Moore’s Law is not dead, and they will point to chips where vertical stacking is conducted, but that’s not packing more transistors into a given area, that’s just using the vertical dimension to create more area.  Moore’s Law only works if individual transistors themselves can get smaller, and with the smallest feature sizes bumping up against atomic dimensions, that is no longer possible.  Moore’s Law has been dead for at least a decade. 

The denialists might also opine that there is some other technology on the horizon that will transcend the limitations placed on transistor sizes, while remaining vague about what those technologies are.  Some might cite different materials for chipmaking.  But most of these materials have some sort of fatal flaw.  Take for example graphene – the wonder material that is effectively a flat sheet of carbon atoms.  Graphene has been used to make transistors in laboratories, and those transistors can operate at significantly higher clock speeds (at least an order of magnitude more) while having much better properties than silicon regarding heat dissipation.  But there is a huge problem – graphene lacks something known as a bandgap.  Without getting into device physics, we’ll simplify thing by saying the lack of a bandgap means that such a transistor can never fully turn off, thereby making it useless for functioning as a switch, and therefore useless as the basis for a digital computer.

Analog computing is another technology championed by some.  And while it can be very useful in certain applications (as it can almost instantaneously do large matrix multiplications that hog computer cycles in the digital domain), it nevertheless suffers from the limitations from which all analog circuits suffer.  Analog circuits are more susceptible to noise, error cascading, and lack the necessary precision for many workloads.  Analog computing circuits are also much larger than the digital circuits of the GPUs/TPUs.

Quantum computers are the great hope for some, but we are a long way from a practical quantum computer.  Meanwhile, they are currently very error prone, of limited stability, and require cryogenic cooling (meaning hundreds of degrees below zero, and that’s true whether you are talking in Fahrenheit or Celsius).  There are questions as whether they could provide any advantage over the current computing paradigm for many workloads.  Most of the promise is in specialized workloads, but until we get practical, reliable quantum computers, we can do no more than speculate.

So the upshot of the above is that AI as we know it has, due to the various physical limits discussed above, has ran head-long into a wall.  However, that wall is imposed by physical limits.  We haven’t talked about financial limits yet.  If you think AI is a compute hog and a power hog, wait until you find out how much of a money hog it is.  The U.S. government has nothing on AI when it comes to burning through cash.

Republic of Letters

Interview: Rylee McDonald of ADVENT HORIZON

This morning, I had the great and grand pleasure of interviewing Rylee McDonald of Advent Horizon. We talked for about 35 minutes. You’ll see–though Rylee is a young guy–he is fully immersed in prog and new wave. And, he’s just as kind and insightful and brilliant as I expected after hearing the lyrics to his latest album, FALLING TOGETHER. Please support these guys! They’re the real deal.

Here’s the interview:

To order the new album, please go to Band Wagon USA!

Republic of Letters

Confessions of an AI Skeptic, Part 2 (of 5)

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(Part 1 can be found here)

Last time, the discussion focused largely on what happens at the circuit level of a computer system, and whether, starting from that, intelligence and consciousness could arise.  For this installment, I wanted to delve a little more into how we define intelligence.  Much of the hype surrounding AI is that we are soon going to see AGI – artificial general intelligence – as well as ASI – artificial super intelligence.  My skepticism remains solid that neither of these milestones will ever be achieved, certainly not with current computing architectures, if ever.

What is AI Doing?

Instead of focusing on the circuit-level, it’s instructive to go a few rungs up the abstraction ladder and discuss what happens when one sends a prompt to an LLM, or large language model (which encompasses the basis most of the well-known AI chatbots today – ChatGPT, Google Gemini, Grok, etc.).  This is a somewhat simplified explanation, but it’s enough to obtain a basic understanding.

When you send a prompt to say, ChatGPT, the words of that prompt are broken down into tokens.  These tokens can be full words, chunks of words (sub-words), or even symbols.  These tokens are then turned into numbers, in a process called embedding.  The numbers are then turned into numerical vectors that can have thousands of dimensions.  The numerical vectors are then fed into a transformer layer, where many, many matrix multiplications are performed.  Since a matrix multiplication comprises many individual (scalar) multiplications, the number of total multiplications becomes astronomical.  In other words, it’s doing a mountain of math under the hood.  

Each step involves multiplying huge grids of numbers together, and every one of those multiplications expands into millions or even billions of tiny arithmetic operations. Processing a single word can require hundreds of billions of multiplications and additions. To put that in perspective, if you sat with a calculator and did one multiplication every second, it would take you thousands of years to do what the model does in a fraction of a second for just one word.

In doing these kajillion multiplications, the AI model is predicting the next word, based on weights applied during said multiplications.  After all these multiplications are done, the resulting numbers are turned back into words for display on your computer screen.

While the operations described above may be algorithmically new, from the perspective of computers, the individual operations – namely, the multiplications – are nothing new at all.  Electronic computers of all kinds have been doing multiplications since they’ve existed.  This isn’t confined to your desktop or the room-sized behemoths of yesteryear, but also includes the pocket calculators that people like myself relied upon during engineering school before things like smartphones were as ubiquitous as they are today.

There are a couple of upshots to the above.  Thie first is, that while an LLM like ChatGPT may appear to understand language, in reality it does no such thing at all.  It just crunches numbers.  And not only that, the computer doesn’t even know it’s crunching numbers – refer back to the first installment – the number crunching is just the causing of basic switching circuits of the computer system to switch between logic 1 and logic 0 – high voltage and low voltage – really, really, really fast.

So if the computer doesn’t understand language, and doesn’t even know it’s crunching numbers to mimic the understanding of language, can it be considered intelligent?  If the answer is no, then how will computers become intelligent by simply making bigger, more computationally intensive models? 

How do you Define Intelligence?

This is a trickier question than it may appear.  We can recognize intelligence to be sure, which is exemplified by the fact that we can ponder and debate what exactly the term means.  But defining it with precision, drawing a hard line between intelligent and not intelligent?  That’s a much more difficult task.

We define humans in general as being intelligent (not to be confused with being wise).  And yet we still have a hard time drawing that line between what is intelligence and what is not, despite most of us being pretty sure that computers running AI haven’t yet reached intelligence.

And that’s the rub.  The people trying to create artificial general intelligence (AGI) – or any intelligence at all in computers/AI, are trying to solve it as an engineering problem.  But engineering problems require well-defined solutions.  If you want to put a satellite into an orbit with a perigee of 150 miles above the Earth’s surface and apogee of 160 miles, the solution is well-defined.  If you want to design an amplifier circuit that can take an input signal with an amplitude of 2 volts and output a corresponding signal having an amplitude of 12 volts, we know how to do that because, again, the solution is well defined.  There may be different ways to get to the same solution, but having a firm definition of the solution provides a framework and a guide for getting there. 

This is true even for some engineering problems that we haven’t solved, like nuclear fusion.  We know what man-made nuclear fusion it will look like, in terms of inputs and outputs, should we ever get there .  But that illustrates another point: even when we know what the solution looks like, it can be maddeningly difficult to achieve. 

With intelligence, general or otherwise, we can’t even agree on a definition. Not even AI’s biggest proponents can agree on a definition of intelligence, much less what would constitute true AGI.  What they all have in common is that they are trying to find an engineering solution to something that is essentially a philosophical problem.  And because the definition of intelligence is essentially a philosophical, it will continue to defy an engineering solution. 

So far, we’ve spent a lot of time talking about intelligence, the difficulty in defining what intelligence is, and stating why I believe computers running AI workloads are not even remotely intelligent.  What hasn’t been discussed so far will be the topic of the next installment – the rapacious appetite of AI in terms of resources.

Before I go, however, Apple has published a paper about AI entitled “The Illusion of Thinking.”  If you want to dig a little deeper, it can be found here. 

Republic of Letters

Confessions of an AI Skeptic, Part 1 (of 5)

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Artificial Intelligence, or AI, is all the rage these days.  “It’s going to eliminate all of our jobs!!”  “It’s going to become more intelligent than humans!!!” “It’s going to become sentient and turn into Skynet!!” 

Pffffft.

It’s not going to do any of those things.  Not even close.

Now don’t get me wrong – AI (and note – only the ‘A’ part of that is accurate) is here to stay.  And it’s going to lead to some very powerful tools, some of which can be very useful.  Of course, it will also lead to some tools that are not so useful.  And it will be misused and abused, which might be its most frightening prospect.

But if you are worried about Skynet, I’m here to tell you, don’t – The Terminator is a great action movie but not much more.  Nor should you worry about AI eliminating all the jobs, a notion that can be dispensed with in multiple ways, including with simple arithmetic.  We should once and for all dispense with the idea that AI will become conscious. Similarly, the notion that AI exhibits true intelligence should also be tossed in the wastebasket.  To understand why, we’ll start with the point that the rubber meets the road (or where software meets hardware) in computers.

The 1’s and 0’s of Artificial “Intelligence”

When I observe certain people hyping AI, namely those with a technical background, I notice they are mostly software engineers or programmers.  Many of these software engineers are extremely intelligent, and can make a computer do things – through programming – that I (also a technical person, but with a hardware/circuit orientation) could never dream of doing.  Nevertheless many of these AI-hypesters have a huge gap in their understanding of how computers actually work.  Their interactions with computers are through high-level programming languages, several layers of abstraction away from what is happening at the hardware/software interface.  Because of that they are only vaguely aware, at best, of the hard physical limits of computing.

For the non-technical, a little explanation is warranted here.  Almost all software programming – and AI is software – is done using what are called high-level languages – Python, Perl, C, … and for those of you who are old geezers (as am I), Fortran, Basic, Pascal, etc.  High-level programming languages are essential, as the practically infinite variety of software we use today would not be possible without them.  But the processor in your computer system cannot understand these languages directly – it needs what is known as a compiler that translates (“compiles”) the high-level language program into machine language that the computer understands.  And ultimately that means, in the digital computer systems we use, it gets converted into 1’s and 0’s. 

But even the 1’s and 0’s are somewhat of an abstraction – the processors used in computer systems are electronic circuits, and as such work with voltages and currents that represent these 1’s and 0’s, rather than working with the digits themselves.  Thus, in the chips used to implement computer systems, these 1’s and 0’s are represented by corresponding voltages – e.g., a “high” voltage for a logic 1, and a “low” (or no) voltage for a logic 0.  I’m not going to delve into the actual circuits as to how this is achieved (although they are relatively simple), other than to say you can think of these circuits as 2-position switches.  A single switch in this analogy can generate a logic 1, or high voltage in one position, and a logic 0, or low voltage in another position.  These switches, constructed using transistors, can be combined to form logic gates, and logic gates can be combined to form even more complex structures.  But at the heart of it all, at the lowest level, all you have are a bunch of switches that produce the voltage levels and corresponding binary logic levels.

Just about every computer system you own – from your smartphone, to your tablet, your laptop, your desktop – has billions of transistors, and thus billions of switches.  And they are nothing even remotely like neurons in the human brain.  Putting more of them together doesn’t turn them into neurons either.

Hey, I Came Here to Read About AI, not this Switch Stuff!

Ok, so you ask now, “if this essay is about AI, then where is he going with all this switch stuff?”  Where I’m going with this is to show you what AI – indeed what any software does – at the fundamental circuit level.   At the circuit level, it is, depending on the input voltage, making the output voltage change between a high voltage and a low voltage – between a logic 1 and a logic 0.  On circuits used in the chips of a computer system, this switching behavior can occur billions of times per second.  Multiply that by billions of these switching circuits, and you’ve a whole lotta switching going on.  And in AI computing workloads?  You have orders of magnitude more switching than the most processor-taxing game your kid runs on his gaming PC. 

But can true intelligence (much less consciousness) arise from this mere switching behavior, having billions of circuits switch between a logic 1 and logic 0 (a high voltage and a low voltage) billions of times per second?  Digital computers have been operating this way for decades now.  There is nothing remotely intelligent about the way they function.  Simply and adding more of switches and making them do it faster and faster doesn’t move ball even a nanometer closer to intelligent behavior, because the transistors used to create these switches are not neurons, and never will be neurons.  They’re just switches.  On or off.  Logic 1 or logic 0.  Putting more of these switches together into a more complex structure does not suddenly make them into neurons.  And because of this, computers will continue to understand language and human thought in the same way a radio understands music, i.e., not at all because they have no such capability of “understanding.”

If someone disagrees with me, and truly believes that AI can be truly intelligent and can truly become conscious, I’d love to hear their explanation as to how we are going to get there based on making more of these switching circuits and making them switch faster.  I’m all ears.  All I’ve ever heard from those that believe AI will become some sort of machine messiah (nod and wink to my progrock friends) are pure underpants gnomes-level leaps of logic.  As AI gets “better,” real intelligence and consciousness will just magically occur, they believe.

What an absolute load of bull-shinola.

The only surefire way I know to make electronic computers truly intelligent is this: convince God to “miracle” intelligence into computers.  If God wants computers to by intelligent, then by God (sorry) they will be.  But absent that, there is no other way.  Not with the computing systems we have now, not with CMOS switching circuits even in the billions of trillions, not with simply manipulating voltages to make 1’s and 0’s.  Ever more complex software programs – even what is called AI – isn’t going to suddenly cause intelligence, much less consciousness, to spring forth from silicon or some other substrate that may be used in the future.  If that’s all it took, we’d be there by now.

If you want to explore the topic of intelligence in man-made machines (or our inability to accomplish that), you can also explore Kurt Gödel’s incompleteness theorems.  I’m not going to get into the discussion about that here, other than to note that when Gödel came up with these theorems, it freaked him out a little bit as he thought he might have proven the existence of God.  But that’s pushing the limits of this discussion, so you’re on your own here.

Intelligent, sentient computers of the electronic variety make for great science fiction.  HAL from Arthur C. Clarke’s 2001: A Space Odyssey is one of Sci-Fi’s most memorable characters.  My personal favorite – Mike, from Robert A. Heinlein’s The Moon is a Harsh Mistress – is another one that seeped into the consciousness of many Sci-Fi aficionados.  But those computers are fiction, and intelligent electronic computers like them will remain so, absent divine intervention.

Notice I said “electronic computers.”  Biological computers are also a thing, and they can be very intelligent.  And better yet, there is a way to make intelligent biological computers – it’s very old tech, a time-tested technique known as “having babies.”  But that’s also another discussion.

“But hey, you didn’t address it taking all our jobs and all the other things AI is going to do, good and bad!”  This piece is getting kind of longish, but I will return with more confessions of my AI skepticism, and soon.  Or, as another AI character once said, “I’ll be back.”

Republic of Letters

The Pineapple Thief on Insideout

InsideOutMusic announces signing of progressive art-rock group The Pineapple Thief

 

North American tour dates revealed for Nov/Dec 2026

Photo credit: Martin Bostock

InsideOutMusic is pleased to announce the signing of progressive art-rock group The Pineapple Thief.  Founded in 1999 by Bruce Soord, the band has long been one of the genre’s most successful and accomplished outfits, releasing 16 studio albums and touring worldwide. The band consisting of Soord (vocals, guitars), Jon Sykes (bass), Steve Kitch (keyboards), and Gavin Harrison (drums), is working on a new album for release in late 2026.Bruce Soord comments: “Joining Inside Out is a definitive milestone for The Pineapple Thief. Having spent the past year developing new material, it became clear that Inside Out is the perfect home for our next musical journey. We are energised by this new partnership and can’t wait to reveal what we’ve been working on!”Thomas Waber, head of InsideOutMusic, adds: “We are extremely excited to welcome The Pineapple Thief to the InsideOutMusic family. As longtime followers of the band, it feels like the right time to be working together, and we can’t wait to help bring their new material into the world.” 

The Pineapple Thief recently announced the following festival shows in Europe:June 25th  Istanbul TK – Zorlu PSM – with The GatheringJune 27th  Cornwall GB – Morvala Festival of ArtsJuly 3rd  Joensuu FIN – Ilovaari FestivalJuly 4th  Helsinki FIN – CoolHead LiveJuly 16th  Bronnoysund NO – RootsfestivalenAug. 2nd  Manchester GB – Radar Festival Now, the band is revealing headline tour dates across North America.  Tickets go on sale Friday, April 17th at 10am local time.Nov. 17th  Washington DC – Howard TheaterNov. 19th  Philadelphia PA – Theatre of Living ArtsNov. 20th  New York City NY – Gramercy TheatreNov. 21st  Somerville MA – Somerville TheaterNov. 22nd  Quebec City QC – CapitoleNov. 24th  Montreal QC – Beanfield TheatreNov. 25th  Toronto ON – Danforth Music HallNov. 27th  Chicago IL – House of BluesNov. 28th  Cleveland OH – House of BluesNov. 29th  St. Louis MO – Delmar HallDec. 1st  Dallas TX – The Bomb FactoryDec. 3rd  Denver CO – SummitDec. 5th  Phoenix AZ – Crescent BallroomDec. 6th  San Diego CA – The Observatory North ParkDec. 8th  Los Angeles CA – The BellwetherDec. 9th  San Francisco CA – August HallDec. 11th   Seattle WA – Neptune TheaterDec. 12th  Portland OR – Revolution HallDec. 13th  Vancouver BC – Hollywood TheaterMore news to come…

Republic of Letters

Happy 25th Birthday, Burning Shed!

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Burning Shed Logo

Thank You


This month, (almost) unbelievably, marks 25 years of Burning Shed.
 
We’d like to issue a heartfelt thank you for your support over the many years and provide some insight into the company and what our plans are for this anniversary year.
 
‘The Shed’ emerged out of an idea Tim Bowness had for an idealistic online / on-demand label. Peter Chilvers – one of Tim’s musical partners – was experienced in the then mysterious world of e-commerce. Pete Morgan, the final piece of the Burning Shed jigsaw, was running Noisebox, a record label and duplication company (that dealt with releases by Tim and Steven Wilson’s band No-Man).
 
Over several intense gatherings (fuelled by eggs, chips, beans and the milkiest of coffees), a plan was hatched. After six months of trying to convince a bank that the notion of selling CDs from a website wasn’t witchcraft, that plan was in motion.
 
Tim brought in the music, Peter created the coding and Pete ‘The Morganiser’ took charge of logistics.
 
Initially, the idea was to issue elegantly packaged, cost-effective CDR releases – designed by Carl Glover – that allowed artists to experiment and, crucially, generate a little income from their endeavours. 



 
Luckily, the first releases – including albums by No-Man, Bass Communion, Roger Eno and Hugh Hopper – proved to be more successful than anticipated and Burning Shed rapidly evolved. Soon the CDRs became CDs and via word of mouth the company was hosting official stores for artists and labels including Robert Fripp / King Crimson, Stewart & Gaskin / Hatfield & The North, Jethro Tull, XTC, Kscope Records and many others (including, of course, No-Man and Porcupine Tree).
 
Peter Chilvers left in 2008 to work with Brian Eno, but Tim and Pete persisted, building the company up. 25 years on, the Shed is driven by the same instincts as it was at the very beginning.
 
As a “run by artists for artists” company, we try and ensure that the musicians and labels we deal with receive as much money as they can and that deals and accounting are transparent. There are no hidden costs or binding contracts. The idea has always been to release and help globally distribute great music at reasonable prices in the best way possible (to make sure it arrives in perfect condition and on time).
 
To celebrate our 25th anniversary, from April until next March we’ll be bringing you special releases, merchandise and giveaways including more raffle winners each month.
 
We’re also putting on a number of events throughout the year, starting with three co-headline gigs by Tim Bowness with Butterfly Mind plus Bruce Soord & Jon Sykes (The Pineapple Thief):
 
Sun 24 May – Liverpool, Philharmonic Music Room
Fri 29 May – Bath Fringe Festival at Rondo Theatre
Sat 20 June – London, The 100 Club
 
Ticket links are available via https://timbowness.co.uk/live/
 
Looking forward, we’re in a much more complicated world. When we started, it was relatively easy. Shipping involved a jiffy bag, a label and a stamp. Selling online is now more complex, with electronic customs declarations, tariffs, Brexit, GPSR, GDPR, etc etc. From operating out of the corner of Pete’s office at Noisebox, we now have a warehouse and a truly superb team of people making sure everything runs smoothly.
 
None of this would have been possible without the support of all the artists and labels we have worked with over the years. Most importantly, it would not have worked without you, our customers.
 
We know there are many other places to buy music from, so that makes it all the more special that you continue to order from us. Some of you have been with us since the very beginning, some of you have just found us. We are extremely grateful to every one of you, old and new.
 
Thank you for supporting what we do.
 
Here’s to the next 25 years!
 
Tim and Pete

Republic of Letters

Best Albums, Counting Back from 2026

A former student, Chuck, just posted his favorite albums from 2001. I must admit, of his list, I only knew one of the albums, Dave Matthews Band’s EVERYDAY. Inspired by Chuck, though, I decided to look back over the years from this year. Here’s what I found. Let me know what I’m missing.

Best Albums, 2021–Five Years Ago

Frost, Day and Age

Transatlantic, The Absolute Universe

Big Big Train, Common Ground

Steve Hackett, Surrender of Silence

Best Albums, 2016–Ten Years Ago

Pineapple Thief, Your Wilderness

Steven Wilson, 4 ½

Iamthemorning, Lighthouse

Marillion, FEAR

Kansas, The Prelude Implicit

Riverside, Eye of the Soundscape

Frost, Falling Satellites

Best Albums, 2011–Fifteen Years Ago

Steven Wilson, Grace for Drowning

Yes, Fly From Here

Airbag, All Rights Removed

Porcupine Tree, Anesthetize

Wobbler, Rites at Dawn

Riverside, Memories in My Head

Cosmograf, When Age has done Its Duty

Neal Morse, Testimony Two

Best Albums, 2006–Twenty Years Ago

David Gilmour, On an Island

Frost, Milliontown

Pure Reason Revolution, The Dark Third

Porcupine Tree, Arriving Somewhere

The Tangent, A Place in the Queue

Tool, 10,000 Days

Best Albums 2001–Twenty-five Years Ago

Tool, Lateralus

Muse, Origin of Symmetry

Dave Matthews Band, Everyday

Yes, Magnification

Radiohead, Amnesiac

Transatlantic, Bridge Across Forever

Marillion, Anoraknophobia

Best Albums, 1996–Thirty Years Ago

Flower Kings, Retropolis

Rush, Test for Echo

Porcupine Tree, Signify

Yes, Keys to Ascension

Spock’s Beard, Beware of Darkness

Stone Temple Pilots, Tiny Music

Dave Matthews Band, Crash

Best Albums, 1991–Thirty-five Years Ago

Talk Talk, Laughing Stock

Marillion, Holidays in Eden

Live, Mental Jewelry

My Bloody Valentine, Loveless

Pearl Jam, 10

Matthew Sweet, Girlfriend

U2, Actung Baby

Best Albums, 1986–Forty Years Ago

XTC, Skylarking

Talk Talk, The Colour of Spring

Peter Gabriel, So

The Smiths, The Queen is Dead

Republic of Letters

The Spawton Files, Part II: The Music Business

[Dear Spirit of Cecilia Reader, greetings! This is the second part of an interview I conducted with the mighty Greg Spawton. Yes, I love the man–as a friend, as an inspiration, and as an artist. Most of this part of the conversation revolves around Greg’s role in the band, the future of the band, and the relationship with InsideOut/Sony. Part III will deal with Woodcut. As you can see, Greg is a man of impeccable integrity, always trying to better himself and those around him. Please enjoy. By the way, if you’re looking for The Spawton Files, Part I, click here. Yours, Brad]

Brad: So let’s switch topics for a moment.  I’d like to look at a broad topic, for a moment, a meta topic.  You know, as I look back over the long history of Big Big Train, in a lot of ways, I mean, you are obviously the steady character of the band.  You’re the still point, to use a T.S. Eliot image.  Everything revolves around you.  You’re the monastery and everything—all of time—is passing around it in some way [a reference to Walter Miller’s Canticle for Leibowitz].  So, I’m curious, do you still see Big Big Train as a band or do you see it as a project?   You and I are both getting up there in age.  Do you see BBT continuing some day when maybe you’re not involved?

Greg:  That’s really a good question.  It’s interesting because it started as a band and then it became a project really especially when it was me and Andy and bringing in who we could to help us finish off what we were doing.  So in the kind of mid-period, we were a band.  And then when David joined and Nick DVG and Rickard and we got the sort of steady lineup from 2009 from the Underfall Yard album and then started playing live again.  It went back to being a band, you know.  So it’s been a sort of circular process, really.  If anything it’s got even more that vibe now because, of course, we become a proper touring band on a tour bus and all those things.  So it is very much the rock and roll lifestyle that I used to read about in books.  It’s, you know, the whole Spinal Tap thing; it’s absolutely like a documentary, but it is so much real life, too.  So we’ve experienced all these things.  So it’s very much a band now.

And of course, I lost, as you know, David a few years ago, and he was my brother in music, really.  So that was an incredibly sad and destructive moment to lose him. When I think back on it, if we hadn’t chosen Alberto as our new lead singer, I don’t think anything else would have worked.  I think we just happened to get the right guy to actually help me carry this forward because I’m 60 now. And I’ve been doing this a very long time.   I remember when we were we were recording Woodcut in the US. With his energy—he’s 20 years younger than me—and his energy to be able to produce and run those sessions, whereas I was sort of watching, thinking I used to be that guy, you know.  I used to be that guy, and I think as you get older, the level of your ability to stay on these things becomes slightly diminished, I think.  So I need more help.  Alberto is the right guy in the right place at the right time.  So, we’re already thinking about the album after Woodcut, and that’s how it has to be in the music business.

Brad: You’re already thinking quite a long way ahead.

Greg: I see myself on a tour bus in 10 years time?  I don’t know.  I mean, I think it depends on some commercial realities here.  So if the band continues to grow and is commercially successful enough to warrant touring, yeah, I think I probably could if I’m well enough.  I enjoy the lifestyle, so I think I would want to continue to do that.

Would the band continue without me?  Weirdly enough, I could see it happening now.  In one way, I’m just a bass player and one of the songwriters.  So, you know, in one way it’s easy for me to be replaced, but in other ways, as you said, Brad, I’ve been kind of the guy, the old man in the band, and the guy that’s been there from the start.  So I don’t know the answer.   I think we’d have to see.  But I hope to be continuing to do this.  The fire hasn’t diminished.  I’m still burning with this and I still think we’ve got a lot to offer, andI’m very conscious that bands in the later stages of their career often start writing some albums that are less strong.  There are some exceptions to this, like Marillion, for example.  But there are many others where the bands and the albums are not quite as good.  The fire that drove them early on is gone.

And I don’t feel that way for us.  I think Woodcut’s a strong album and the album we’re going to work on after that, I think, has also got the potential to be very strong.

As long as I feel that we’re making good stuff, I want to carry on doing it.  But that’s only if we can keep offering good music.  I want to keep doing it.

Brad: That’s excellent Greg.  That’s exactly the answer I was hoping you would give and, frankly, it’s one of the reasons I’ve liked you for so long.  I know that you’re always trying to improve your art, and I just think that’s so critical.  I just turned 58, and I understand completely how these things work.  In some weird way, I actually feel I’m at the top of my game right now, even though I don’t have quite the energy I did 20 years ago.  I just finally feel like I know what I’m doing.  You know, when I go into the classroom or when I’m writing, there’s a certain confidence that I have.  I had energy 20 years ago, but I didn’t have quite the confidence I have now.

Greg: that’s a really good point that actually.  I feel exactly the same.  I feel it when I go on stage now. I know the ropes, and I’m pretty much in command of things.  When things go wrong, as they do, we just get through it.

You know, the band has a very settled lineup now.  We get on well on the tour bus, all those things.  That’s really important as the bands I read about where there’s a sort of simmering dislike or hatred amongst some band members.  Having to spend 24 hours a day with that person that you fallen out with would be horrible.  I’m too old for that.  On the tour bus, we’ve got a very good idea if someone’s not feeling great today.  If someone’s tired, give them some space, you know, just all the things that make sort of family.  Maturity, you’re absolutely right, Brad. Maturity, I think, brings those things to you.

Brad: Yeah, yeah [I say in awe and humility!].

On another topic.  I don’t know how much you can talk about this because I’m sure part of this is confidential. But what did InsideOut do for you guys?  How does it change the band now that you are with a major label.  How much autonomy do you still have for English Electric?

Greg: it turned out have been a bit of a revelation for me, really.  I was kind of reluctant to sign to a major label. Especially after having done things myself and with my bandmates for so long.  But they’ve [InsideOut] been brilliant.  The A&R people there are wonderful. They’re entirely supportive.  They’re full of great ideas.

We just launched a new sort of website for Woodcut today and that was entirely their idea.  I have no question that it’s in their business interest for us to do well.  If I look at it through their eyes, I can see that the older guard, say Steve Hackett, etc.—the generation on from us—may not be making music forever.  It will not be making music forever.  Obviously they’re looking to see if they can develop even an older band like us to develop us to get to the next level.  So I can see it in business terms.  I get that.  But their personal relationships and the way that they are clearly interested in us, in what we do, has been wonderful as well.  I genuinely cannot speak highly enough of them.  They’ve been great.  With my strange life, you know, where I cam to being a properly professional musician quite late in life.

It’s been quite an eye opener for me to walk through the doors at Sony in London and to kind of see this.  I mean, it’s very different.  Guys walking around with laptops and, you know, they’ve got an amazing cafe there.  It’s a restaurant rather.

Sony is a big, big organization and involved in all areas of the entertainment world.  So it’s not just a music thing that’s there.  It’s a lot of stuff.

So it’s been really interesting. As for Woodcut, I said to Nick, our manager, we’re going to do a concept album.  I was thinking Lamb Lies Down on Broadway and Topographic Oceans.  Sony was like, “if that’s what they want to do, that’s fine.  They’re a prog rock band.”  So, they were brilliant about it, and I think after we delivered the music, they knew it was a good album.  It’s been a very interesting experience for me.

And so it’s been an interesting eye-opening experience for me in terms of learning how the business works.

Brad: Greg, let’s come back to that one second.  A logistical question.  Have you noticed that sales are much different since you’ve been with InsideOut rather than when you were with just English Electric?

Greg: So no, I haven’t.  What I have noticed is that they’ve kept us at a high-ish level.  The problem is we’re fighting a rearguard action because, of course, the music business is no longer set up on [physical] sales anymore.  It’s set up for streaming.  And, of course, the streaming sites are partly owned by the record labels.  On the one hand, what we used to rely on is diminishing or could be diminishing.  And on the other hand, streaming is sort of doing that. Because when we were releasing our albums by ourselves, we were reliant wholly upon this element of the business, the album sales that we were making, the physical product.

Now we get an advance and things like that, so it’s a slightly different set up.  And of course, we’re touring and, therefore, there’s income coming from that.  All these things.  So it’s different for us now.  Our income isn’t based on one thing only.  It’s based on a whole raft of different things.  It’s a fight, and our sales have held up because we’re on InsideOut

If we were not on InsideOut, I think our sales would’ve declined dramatically.  InsideOut markets effectively and have opened up markets for us.

We’re all very dead keen to play in Japan, and they’ve been supportive of The Likes of Us going out as a Japanese special edition and similarly with Woodcut.  That’s going on with a bonus track in Japanese, which has been fun for Alberto to sing and to get translated.

So, you know, they’ve opened up the world to us a bit more, I think, than the little cottage industry that we were before, and our sales are holding up because of that.  Yeah, yeah, bless them.

Brad: I actually have a brother who lives in Tokyo, and he always sends me the Japanese version of your albums.  So, I have those as well and very proudly own them!

Republic of Letters

The Finest Cut: Big Big Train’s Latest

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Dear Spirit of Cecilia Readers, whenever Big Big Train offers up a new album, it’s not just yet another release, it’s a major–and not unoften life changing–event.  In early February, the band will be releasing a concept album, the sixteen-track Woodcut.  Thanks to the good folks at Big Big Train and Insideout Music, we were graced with an advanced review copy of the new album.  To say it’s brilliant would be the grand understatement of both 2025 and 2026.  Here’s what Tad, Rick, and Brad think about it.

Brad: Hey guys!  So great to be reviewing this with you both.  As I start to type this, it’s a Sunday morning (I went to Mass last night), and the snow is ever so gently but steadily falling.  My cats are prowling around, and I’m sitting in my precious work chair, and I’m also enjoying a great cup of coffee.  And, of course, I’m listening to Woodcut.  Life is good.  Really good.

My journey with the band goes back to 2009 when our own Carl E. Olson the Grand sent me a track from Big Big Train’s The Underfall Yard.  I’d never heard of the band, but I was immediately taken with the track Carl shared, “Evening Star.”  So taken, in fact, that I immediately bought the album and, much to my surprise, I reached out to Greg Spawton through Facebook.  It was potentially presumptuous and obnoxious to do so, but I just had to let this man know what I thought of his music.  My first listen to The Underfall Yard wasn’t just a listen to yet another new album, another new band.  This was a major moment in my life–akin to hearing Selling England by the Pound or Moving Pictures or Hounds of Love or The Colour of Spring for the first time.  My soul was rocked by the very depth and majesty of the art.  To this day, The Underfall Yard remains one of my two or three all-time favorite albums, and I never tire of hearing it.  Indeed, every new listen is a rewarding one.

And, amazingly enough, rather than chastizing me for invading his privacy, Greg very graciously wrote back to me, and we began a correspondence and friendship that very much lasts through this day.  In our correspondence, we’ve shared not only tidbits about life, but books.  And, my kids–when younger–colored pictures for him!  We even sent him some tree nuts and various seeds from Michigan, hoping he could replant them in English soil.

Amazingly enough, I interviewed Greg last week via Zoom.  It was the first time we had actually ever spoken to one another, face to face!  God bless, modern technology.

This is a long way of saying, I can’t even imagine the last sixteen years of my life without Greg’s friendship or without Big Big Train as the soundtrack to my life and my writing.

What about you guys?  How did you first encounter Big Big Train?

Tad: Well, Brad, I first encountered BBT because of you! We had just connected online through a mutual love of Talk Talk’s Spirit of Eden, and you messaged me that I had to check out this band, Big Big Train. Their website had downloads of most of The Difference Machine and The Underfall Yard (this was before any streaming services), and I was hooked. It wasn’t until I bought hard copies of their albums that I realized Difference Machine and Underfall Yard had different singers!

Like you, The Underfall Yard remains a favorite album of mine, regardless of artist or genre. It is a timeless work of art, suffused with gratitude and grace. That said, they’ve come a long way since then, haven’t they? With the exception of Greg Spawton and Nick D’Virgilio, they are an entirely different group now. The one constant has been the consistently high quality of their music. The only other artist I can compare them to in that respect is Glass Hammer.

Rick: Brad & Tad, thanks for inviting me to join the celebration! As I’ve said elsewhere, despite Prog Magazine’s consistently championing Big Big Train over the years, I didn’t connect with them seriously until 2016. I was searching for a musical mood enhancer one afternoon at work, and I came across From Stone and Steel on Spotify.

Any number of things about that BluRay soundtrack appealed to me: the band was so tight, David Longdon’s singing was so adventurous, the scenarios and soundscapes were so involving. But it was actually the brass that got me.  When they slammed into the choruses of “The Underfall Yard” and the lead trumpet soared heavenward at the end of “Victorian Brickwork”, I was hooked! (In fact, I cried during that “Victorian Brickwork” playout that afternoon – and I still do, every time!)  

I had to hear more. Folklore was just out, so I bought it ASAP and loved it. Ditto for the back catalog, including my favorite to this day, English Electric: Full Power. And to cap it all off, I ordered the Stone and Steel Blu-Ray via BBT’s website. Only when I got it, the thing wouldn’t play – due to technical issues with my Blu-Ray player that had already caused American customers plenty of headaches. What was I supposed to do?  

That’s where Big Big Train’s amazing fans, the Passengers, came in. With an enthusiastic welcome to BBT’s Facebook group and all the kindness in the world, they steered me toward both a downloadable version of the video and a Blu-Ray player that would play S&S. I was so moved, I figured out how to burn the download version to DVD and shared instructions for doing so with the group – I even got attaboys from band members about that!  

It struck me that this was a band and a fandom where you could feel at home, and I started proclaiming the wonders of BBT to anyone who would listen. When my friend Rob Olson saw Sarah Ewing’s Folklore-era band portrait as my laptop’s background screen, he said, “I need to introduce you to another friend of mine . . . “  And that’s how I got to know Brad. So I’ve always considered discovering Big Big Train an event with exponential benefits; when music connects people and builds friendships, it’s an amazing gift.

Brad: I love both stories, guys.  Very nice.  And, I’m so glad that I served as a BBT evangelist!  I’ve been doing everything I can to promote the band since first hearing them in 2009.  They’re worthy of being shouted about, to be sure!  And, let me note here, Tad, I’m so glad to be reviewing with you, my friend.  And, Rick, likewise–I’m so glad to be reviewing with you.  Honestly, I think we should make this a permanent arrangement.  As much as you guys have time for.

Ok, let’s talk Woodcut.  We’ve been–by the grace of Insideout Music, Roie Avin, and the band–given an early look at the album.  Wow, just wow.  I’m still at that stage where my jaw is on the floor, and I’m just gobsmacked.  I’ve now listened to the album, 10-12 times, and it has not in the least grown old.  Indeed, each listen has only made me love this album more and more.

Let me admit–and it’s hard to admit–my reluctance to dive into this version of Big Big Train.  I was so in love with the David Longdon/Greg Spawton combination that I didn’t want to love a new iteration of the band.  I was, sadly and to my shame, very reluctant to allow the new singer to replace the old.  For that, I publicly apologize.  Frankly, I’m better than such pettiness, and I’m truly sorry I was so hesitant to embrace change.  So, Alberto Bravin, I owe you a huge apology.  For what it’s worth–coming from a long-time die-hard fan–a belated welcome to the band and all it stands for.  After hearing Woodcut, I think you’re absolutely brilliant and so very worthy of the band, and the band worthy of you.  Again, who am I to say all of this?  Just a hard-headed goofball from the U.S. who should practice what he preaches–love and charity and welcoming–more than he does.

Ok, bless me Father, for I have sinned!

Now, in the moments after absolution . . .  I can declare that I think Bravin is simply genius.  From what Greg said in an interview with me (which was awesome), the concept really did come from the two men, with Bravin providing the needed inspiration and energy to get the thing written.  Musically, the album reflects the whole band, but with Bravin doing the hard work of making it a concept album in terms of intermixing musical themes, and with Greg and Claire Lindley writing most of the lyrics.  So, definitely a team effort but moved as a project by Bravin.

If this is what Bravin is capable of doing, then, by all means, keep running.  I think the band is in very safe hands.

Tad: Brad, I was the same as you regarding BBT getting a new vocalist, but I am 100% on board the Big Big Bravin Train! He was definitely an inspired choice. 

Okay, Brad and Rick, I’m interested to hear your thoughts on Woodcut. I love the way it begins with a short, stately instrumental and then immediately plunges into the first single, The Artist. There is an interplay among the musicians here that is amazing. Greg Spawton’s bass is simply outstanding as it reinforces the staccato riff that underpins the melody. (Rick forgive me, I’m not a trained musician, so I don’t know if I’m getting my terms right!) Bravin’s vocals are terrific here – full of passion while avoiding histrionics. I also love the background vocal harmonies – I don’t think BBT has had any like these before – they are very rich and complex. 

The production overall is really nice: multilayered without sounding cluttered. I’m a big fan of the bass, and it’s so nice to hear Spawton’s playing featured prominently in the mix. For comparison’s sake (I know, comparison is the thief of joy), I listened to East Coast Racer immediately after The Artist, and the latter has incredible power. Woodcut’s production is the best of any BBT album to date; it just sounds amazing.

Brad: Thanks so much, Tad.  I’m in complete agreement with you.  I love Greg’s bass–so utterly driving and mesmerizing–Bravin sounds amazing, the story and the lyrics are just brilliant.  During my interview with Greg, he mentioned that there was some concern about the band doing a concept album.  I will admit, I’m the very last person to criticize a concept album.  From the Moody Blues (Days of Future Passed), Genesis (Lamb Lies Down on Broadway), Pink Floyd (Animals), XTC (Skylarking), Riverside (original trilogy), Rush (Clockwork Angels), Steven Wilson (HAND.CANNOT.ERASE), Coheed and Cambria (the norm, rather than the exception) etc., I’m a huge fan.  If I could, I would rather listen to concept albums any day or non-concept albums.  

Yet, I probably define concept rather loosely, as, to me, BBT has already written concept albums.  The Underfall Yard, English Electric, and Grand Tour are all so tied together as to be concept albums.  Am I being too loose in my definition, what do you think?

Any, I love everything about this album.  It really soars.  The bass, the guitars, the drums, the keyboards (absolutely love the keyboards) and Bravin’s plaintive vocals.  It really does all come together rather brilliantly.

Rick: Brad, to tackle one point you raised: there really isn’t a hard and fast definition of a concept album.  I tend to think of albums with a start-to-finish, narrative story line – The Who’s Tommy & Quadrophenia, Pink Floyd’s The Wall, Genesis’ The Lamb Lies Down on Broadway, Neal Morse’s Testimony & Testimony 2  – as what that genre was called when it emerged back in the late 1960s: rock operas!  On the other hand, albums like the Floyd’s Dark Side of the Moon and Wish You Were Here, Yes’ Tales from Topographic Oceans, the BBT albums you mentioned would be concept albums in my thinking – they focus on common lyrical and musical ideas throughout, but any story they tell is more implicit.  Of those two types, I’d call Woodcut a rock opera, just to be a little retro!

I understand the reluctance you both felt to embrace a post-David Longdon Big Big Train; I remember how hollow it seemed to consider the band continuing in the wake of his untimely death.  But when I got to interview Alberto Bravin early in 2024, I thought, “this guy has obviously bonded with Spawton; he genuinely loves and respects the band’s legacy; and he’s very much his own man. This could be interesting.”  Then The Likes of Us proved to be another first-rate album, one I couldn’t stop listening to, with great music and a lyrical throughline that felt very personal, but also very relatable.  And I had the privilege of seeing BBT live twice in the last two years: at their 2024 kick-off North American gig in Fort Wayne, and at a hole-in-the-wall rock club on the west side of Detroit last year.  Looking back at those shows, I realized that every single person in the band could hold the stage all by themselves – but also that live, they played off each other constantly and made each other better!

I think that that band chemistry is a big part of why, as an album, Woodcut is so strong; it’s engrossing in a way that feels natural and organic.  Tad, you’re right about “The Artist”: those precise, tough band riffs and Spawton’s distinctive bass licks – plus the chiming 12-string guitar – have a  powerful impact and really pull you in.  And there’s more where that came from in the second single, “The Sharpest Blade”: folk and metal elements that share a harmonic vocabulary, Clare Lindley and Bravin working as lyrical and vocal foils, each urging the other forward.  Neither of these songs are clever for the sake of just being clever or showing off musical chops; it’s thoroughly eclectic, heartfelt, slamming stuff!

Something about the album as a whole: I saw that, both in the promo material and on his Substack, Greg mentioned The Lamb and Topographic Oceans as prototypical concept albums.  And both of those albums have always had mixed reviews, from the general public, from prog fans, even from the band members themselves.  I tend to feel that, lyrically,The Lamb doesn’t quite stick the landing of Gabriel’s surreal storyline, and that Topographic starts great and ends great, but kind of sags in the middle.  The measure of Woodcut’s achievement is that it doesn’t have either of those problems.  The whole thing jells and builds, through all of its twists and turns and ebbs and flows, from start to finish – and the final destination is well worth the journey, with multiple genuine goosebump moments, in the tradition of “she fliiiiies!”.  (Another thing that struck me: The Lamb’s 50th anniversary super deluxe box came out last fall – with a great remaster that clarifies how strongly the music carries the plot – and there’s a Topographic box set coming out the same week as this album.  Speaking as one hardcore BBT fan to two others, I can’t help but wish we had a Woodcut super-deluxe box right now!!) 

Tad: Rick, thank you for your wonderfully perceptive thoughts! I’m so glad you mentioned “The Sharpest Blade”, as that is my favorite track (at the time of this writing)! I love Clare’s vocals, and the largely acoustic instrumentation is perfect. The melody has a distinct Celtic feel, with a hint of menace to it. It’s a fantastic track!

As for the concept, correct me if I’m wrong, but it seems to concern an artist whose medium is woodcuts. He somehow winds up in a scene he has created with his “sharpest blade”. While this artificial world is beautiful, he is desperate to get back to reality:

I can’t find a way back to the path that leads me home

No place for a man under the shroud

I’m out of bounds
(From “Dreams In Black and White”)

The ending is ambiguous, which I like. There are several possible ways to interpret it.

Brad: A huge thank you to you both.  Excellent thoughts from both of you.  The kind of thoughts that really make me think and really make me question my own perspective.  

Rick, it’s quite possible that I’m simply too much of a fanboy here, but I feel like The Lamb delivered everything just perfectly.  I say this, however, as someone who has obsessed over this album since I first encountered it.  Every time I listen to it, I feel that full immersion.  In fact, I so desperately want to immerse myself completely in it.  It’s a part of the joy of the album.  And, I can just imagine my youth–so there’s a lot of nostalgia involved in this–turnng off the bedroom lights and putting the headphones on, and just losing myself for the duration of the full album.  Rael!

As much as I wanted to love Tales from Topographic Oceans, I just never could.  I could never immerse myself in the same way.  Too many things drew me out–the goofy random bangs and noises, the nonsensical lyrics.  I guess I respect what Yes was trying to do, but I can’t embrace it.

As of this writing, I’ve probably listened to Woodcut 15 times, the full-way through.  To me, it perfectly captures everything that The Lamb tried to do and completely avoids the “noise” and excess of Tales.  The story has me completely hooked, and I very much want to know every nuance of it.  From what I can tell, it’s a fairy tale, complete and whole and without apology.  It strikes me very much like Tolkien’s Smith of Wooton Major.  An artist–one truly gifted by nature and grace–enters into the woods (Fairy, or something akin to Dante’s Divine Comedy), where he encounters truly beautiful and truly perilous things (like Frodo at Galadriel’s mirror).  At this point, the artist can choose either the darkness or the light.  Not surprisingly, given everything BBT stands for, the story has a happy ending, with the artist choosing the light.  

Tolkien said that all good Fairy Stories must end with a “euchatrastrophe”–the surprising ending of pure joy.  With the end of Woodcut, we find ourselves with Sam replanting the Shire with the soil of Lothlorien and with Dante seeing the Most Blessed Trinity.  In BBT terms, we find ourselves having entered the “Meadowland” of Grimspound.  We encountered the truest beauty and the greatest good imaginable, and we didn’t turn away, even in the realization that everything we are is miniscule compared to the majesties of the universe and of the divine.  We could’ve run in shame, or embraced the darkness in wrath, but we choose the Good, the True, and the Beautiful.  Or, like Wiglaf in Beowulf, we return to the “Meadhall in Winter.”

My sense is that even though Bravin has brilliantly become the front man (and he’s genius, in and of himself), the shadow of David Longdon hangs over this whole album.  Just as The Lord of the Rings is the fulfilment of The Hobbit, Woodcut is the fulfillment of Grimspound and The Underfall Yard and English Electric and the Grand Tour.  That is, Bravin doesn’t take us out of BBT, he takes us further in.  There is harmony and continuity, not discord and revolution.  Simply put, we end in euchatastrophe.

Tad: Brad, I can’t add anything to your brilliant analysis, which makes a lot of sense, by the way. They should have had you write the liner notes! 

The last thing I want to mention is how well the entire album flows. Even though there are 16 tracks, they segue into each other so well that it’s really one seamless suite of music. This is definitely an album to listen to in its entirety. I’m really impressed with it, and I am so excited for this new chapter in the long story of Big Big Train.

Rick: Brad, as always, you find marvelously apt comparisons! I hadn’t thought of Smith of Wooton Major in this context, but it certainly resonates; Tolkien knew the English legendarium of Faerie well, though he didn’t write fiction based on it till his latter years, and he called it the Perilous Realm for good reason. (Robert Holdstock’s Ryhope Wood series and Susanna Clarke’s Jonathan Strange & Mister Norrell are other outstanding takes on that literary tradition, by the way – though both are inherently darker.) There’s this uneasy, major/minor ambivalence in the music of “The Lie of the Land”, the point in the story where the Artist finds the mystical heartwood, that captures that feel. Beauty and danger, grace and temptation impinge on a life that’s felt stagnant until that point.

And it also resonates that “Meadowlands” is this album’s landing point in the BBT cosmos. If the Spawton/Longdon band had a mission statement, that gorgeous song was it. But I’d argue that, at the end of the album, the landing point is also a launching point. The Artist embraces all the good in what he’s desired, but with everything he’s experienced, he can’t stay at that still point. Like in Leonard Cohen’s classic song “Night Comes On”, he has to “go back, go back to the world” and witness to the joy and the pain he’s lived through. (Didn’t know I was gonna synthesize both of your POVs, guys, but there it is!  Eagerly waiting on Andy Stuart’s book on the album to explore his take.) 

And Tad, while you’re right about how seamless and organic the album is as a whole, there are so many standout moments in individual tracks, too: more hard-hitting band riffs on “Albion Press”; the breathtaking moment Spawton’s bass pedals kick in on “Arcadia”; how Nick D’Virgilio’s fingerprints are all over “Warp and Weft”, with herky-jerky guitar licks that feel like XTC, a cappella singing a la Gentle Giant or Spock’s Beard, and a lead vocal that remind me of his great solo album Invisible. If your attention ever wanders while you listen, there’s something that pulls you back in right quick.

But again, the entire album flows – especially from “Light Without Heat” through “Last Stand”, an album finale that stands alongside any favorite prog epic any of us could name. That last section especially has it all – expansive musical themes (including callbacks to earlier in the album), inspired solo work (especially from Oskar Holldorff and Rikard Sjoblom) and gripping development in “Cut and Run” that sets up the final, cathartic anthem, with Bravin riding above it all. His vocal on “Counting Stars” is right up there with Longdon’s best moments (ultimately backed by bass pedals and Paul Mitchell’s trumpet, no less – “Victorian Brickwork” re-envisioned?). And then a spiraling, shattering ending that has to be heard to be believed! If I had to sum up my reaction to Woodcut, the first time I heard it, I was definitely impressed; now, on repeat listens, it genuinely moves me. In other words, it does what Big Big Train’s music has consistently done for me for nearly ten years now.

Brad: Tad and Rick, thank you so much!  What an amazing discussion.  I’ve had the chance to say this elsewhere, but I think that one of the highest compliments we can ever give to art is that art, properly conceived and properly made real, always leads to the formation of friendships and communities.  No one in the music world better inspires the creation of friendship and communities than does Greg Spawton and Big Big Train.  Their very art makes us better.  It makes us more human and, dare I say in this world of shadows, more humane.

For those interested in Woodcut, you can purchase at either Burning Shed or Band Wagon USA.  Both sites are linked here.

Most of all, enjoy!